Guide to Statistics Volume 1

Guide to Statistics Volume 1
TIBCO Spotfire S+ 8.2
®
Guide to Statistics, Volume 1
November 2010
TIBCO Software Inc.
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Reference
The correct bibliographic reference for this document is as follows:
TIBCO Spotfire S+® 8.2 Guide to Stats Volume 1 TIBCO Software Inc.
Technical
Support
For technical support, please visit http://spotfire.tibco.com/support
and register for a support account.
iii
TIBCO SPOTFIRE S+ BOOKS
Note about Naming
Throughout the documentation, we have attempted to distinguish between the language
(S-PLUS) and the product (Spotfire S+).
•
“S-PLUS” refers to the engine, the language, and its constituents (that is objects,
functions, expressions, and so forth).
•
“Spotfire S+” refers to all and any parts of the product beyond the language, including
the product user interfaces, libraries, and documentation, as well as general product and
language behavior.
®
The TIBCO Spotfire S+ documentation includes books to address
your focus and knowledge level. Review the following table to help
you choose the Spotfire S+ book that meets your needs. These books
are available in PDF format in the following locations:
•
In your Spotfire S+ installation directory (SHOME\help on
Windows, SHOME/doc on UNIX/Linux).
•
In the Spotfire S+ Workbench, from the Help Spotfire S+
Manuals menu item.
•
In Microsoft Windows , in the Spotfire S+ GUI, from the
Help Online Manuals menu item.
®
®
Spotfire S+ documentation.
iv
Information you need if you...
See the...
Must install or configure your current installation
of Spotfire S+; review system requirements.
Installtion and
Administration Guide
Want to review the third-party products included
in Spotfire S+, along with their legal notices and
licenses.
Licenses
Spotfire S+ documentation. (Continued)
Information you need if you...
See the...
Are new to the S language and the Spotfire S+
GUI, and you want an introduction to importing
data, producing simple graphs, applying statistical
Getting Started
Guide
®
models, and viewing data in Microsoft Excel .
Are a new Spotfire S+ user and need how to use
Spotfire S+, primarily through the GUI.
User’s Guide
Are familiar with the S language and Spotfire S+,
and you want to use the Spotfire S+ plug-in, or
customization, of the Eclipse Integrated
Development Environment (IDE).
Spotfire S+ Workbench
User’s Guide
Have used the S language and Spotfire S+, and
you want to know how to write, debug, and
program functions from the Commands window.
Programmer’s Guide
Are familiar with the S language and Spotfire S+,
and you want to extend its functionality in your
own application or within Spotfire S+.
Application
Developer’s Guide
Are familiar with the S language and Spotfire S+,
and you are looking for information about creating
or editing graphics, either from a Commands
window or the Windows GUI, or using Spotfire
S+ supported graphics devices.
Guide to Graphics
Are familiar with the S language and Spotfire S+,
and you want to use the Big Data library to import
and manipulate very large data sets.
Big Data
User’s Guide
Want to download or create Spotfire S+ packages
for submission to the Comprehensive S-PLUS
Archive Network (CSAN) site, and need to know
the steps.
Guide to Packages
v
Spotfire S+ documentation. (Continued)
vi
Information you need if you...
See the...
Are looking for categorized information about
individual S-PLUS functions.
Function Guide
If you are familiar with the S language and
Spotfire S+, and you need a reference for the
range of statistical modelling and analysis
techniques in Spotfire S+. Volume 1 includes
information on specifying models in Spotfire S+,
on probability, on estimation and inference, on
regression and smoothing, and on analysis of
variance.
Guide to Statistics,
Vol. 1
If you are familiar with the S language and
Spotfire S+, and you need a reference for the
range of statistical modelling and analysis
techniques in Spotfire S+. Volume 2 includes
information on multivariate techniques, time series
analysis, survival analysis, resampling techniques,
and mathematical computing in Spotfire S+.
Guide to Statistics,
Vol. 2
GUIDE TO STATISTICS CONTENTS OVERVIEW
Volume 1
Chapter 1
Introduction
Introduction to Statistical Analysis
in Spotfire S+
1
Chapter 2 Specifying Models in Spotfire S+
27
Chapter 3
Probability
49
Chapter 4
Descriptive Statistics
93
Estimation and Chapter 5 Statistical Inference for One- and
Two-Sample Problems
Inference
Chapter 6
Chapter 7
Chapter 8
Goodness of Fit Tests
Statistical Inference for Counts and
Proportions
Cross-Classified Data and Contingency
Tables
Chapter 9 Power and Sample Size
Regression and Chapter 10 Regression and Smoothing for
Continuous Response Data
Smoothing
117
159
181
203
221
235
Chapter 11 Robust Regression
331
Chapter 12
379
Generalizing the Linear Model
Chapter 13 Local Regression Models
433
Chapter 14
Models
Linear and Nonlinear Mixed-Effects
461
Chapter 15
Nonlinear Models
525
vii
Contents Overview
Analysis of
Variance
Chapter 16 Designed Experiments and Analysis
of Variance
567
Chapter 17
Further Topics in Analysis of Variance
617
Chapter 18
Multiple Comparisons
673
Index, Volume 1
Volume 2
Chapter 19
Classification and Regression Trees
Multivariate
Techniques
Chapter 20
Principal Components Analysis
Survival
Analysis
viii
699
1
37
Chapter 21 Factor Analysis
65
Chapter 22
Discriminant Analysis
83
Chapter 23
Cluster Analysis
107
Chapter 24
Hexagonal Binning
153
Chapter 25
Analyzing Time Series and Signals
163
Chapter 26
Overview of Survival Analysis
235
Chapter 27 Estimating Survival
249
Chapter 28
The Cox Proportional Hazards Model
271
Chapter 29
Parametric Regression in Survival
Models
347
Chapter 30
Life Testing
377
Chapter 31
Expected Survival
415
Contents Overview
Other Topics
Chapter 32
Quality Control Charts
443
Chapter 33
Resampling Techniques: Bootstrap
and Jackknife
475
Chapter 34 Mathematical Computing in
Spotfire S+
501
Index, Volume 2
543
ix
Contents Overview
x
CONTENTS
Important Information
ii
TIBCO Spotfire S+ Books
iv
Guide to Statistics Contents Overview
vii
Preface
xix
Chapter 1 Introduction to Statistical Analysis in Spotfire
S+
1
Introduction
2
Developing Statistical Models
3
Data Used for Models
4
Statistical Models in S-PLUS
8
Example of Data Analysis
Chapter 2 Specifying Models in Spotfire S+
14
27
Introduction
28
Basic Formulas
29
Interactions
32
The Period Operator
36
Combining Formulas with Fitting Procedures
37
Contrasts: The Coding of Factors
39
Useful Functions for Model Fitting
44
Optional Arguments to Model-Fitting Functions
46
xi
Contents
References
Chapter 3
Probability
48
49
Introduction
51
Important Concepts
52
S-PLUS Probability Functions
56
Common Probability Distributions for Continuous
Variables
60
Common Probability Distributions for Discrete Variables69
Other Continuous Distribution Functions in S-PLUS
76
Other Discrete Distribution Functions in S-PLUS
84
Examples: Random Number Generation
86
References
91
Chapter 4
Descriptive Statistics
93
Introduction
94
Summary Statistics
95
Measuring Error in Summary Statistics
106
Robust Measures of Location and Scale
110
References
115
Chapter 5 Statistical Inference for One- and Two-Sample
Problems
117
Introduction
118
Background
123
One Sample: Distribution Shape, Location, and Scale 129
Two Samples: Distribution Shapes, Locations, and Scales
136
xii
Two Paired Samples
143
Correlation
149
References
158
Contents
Chapter 6
Goodness of Fit Tests
159
Introduction
160
Cumulative Distribution Functions
161
The Chi-Square Goodness-of-Fit Test
165
The Kolmogorov-Smirnov Goodness-of-Fit Test
168
The Shapiro-Wilk Test for Normality
172
One-Sample Tests
174
Two-Sample Tests
178
References
180
Chapter 7 Statistical Inference for Counts and
Proportions
181
Introduction
182
Proportion Parameter for One Sample
184
Proportion Parameters for Two Samples
186
Proportion Parameters for Three or More Samples
189
Contingency Tables and Tests for Independence
192
References
201
Chapter 8 Cross-Classified Data and Contingency Tables
203
Introduction
204
Choosing Suitable Data Sets
209
Cross-Tabulating Continuous Data
213
Cross-Classifying Subsets of Data Frames
216
Manipulating and Analyzing Cross-Classified Data
219
Chapter 9 Power and Sample Size
221
Introduction
222
Power and Sample Size Theory
223
xiii
Contents
Normally Distributed Data
224
Binomial Data
229
References
234
Chapter 10 Regression and Smoothing for Continuous
Response Data
235
Introduction
237
Simple Least-Squares Regression
239
Multiple Regression
247
Adding and Dropping Terms from a Linear Model
251
Choosing the Best Model—Stepwise Selection
257
Updating Models
260
Weighted Regression
261
Prediction with the Model
270
Confidence Intervals
272
Polynomial Regression
275
Generalized Least Squares Regression
280
Smoothing
290
Additive Models
301
More on Nonparametric Regression
307
References
328
Chapter 11 Robust Regression
xiv
331
Introduction
333
Overview of the Robust MM Regression Method
334
Computing Robust Fits
337
Visualizing and Summarizing Robust Fits
341
Comparing Least Squares and Robust Fits
345
Robust Model Selection
349
Controlling Options for Robust Regression
353
Contents
Theoretical Details
359
Other Robust Regression Techniques
367
References
378
Chapter 12
Generalizing the Linear Model
379
Introduction
380
Generalized Linear Models
381
Generalized Additive Models
385
Logistic Regression
387
Probit Regression
404
Poisson Regression
407
Quasi-Likelihood Estimation
415
Residuals
418
Prediction from the Model
420
Advanced Topics
424
References
432
Chapter 13 Local Regression Models
433
Introduction
434
Fitting a Simple Model
435
Diagnostics: Evaluating the Fit
436
Exploring Data with Multiple Predictors
439
Fitting a Multivariate Loess Model
446
Looking at the Fitted Model
452
Improving the Model
455
Chapter 14 Linear and Nonlinear Mixed-Effects Models
461
Introduction
463
Representing Grouped Data Sets
465
xv
Contents
Fitting Models Using the lme Function
479
Manipulating lme Objects
483
Fitting Models Using the nlme Function
493
Manipulating nlme Objects
497
Advanced Model Fitting
505
References
523
Chapter 15
Nonlinear Models
Introduction
526
Optimization Functions
527
Examples of Nonlinear Models
539
Inference for Nonlinear Models
544
References
565
Chapter 16
Variance
Designed Experiments and Analysis of
567
Introduction
568
Experiments with One Factor
570
The Unreplicated Two-Way Layout
578
The Two-Way Layout with Replicates
591
k
Many Factors at Two Levels: 2 Designs
602
References
615
Chapter 17
xvi
525
Further Topics in Analysis of Variance
617
Introduction
618
Model Coefficients and Contrasts
619
Summarizing ANOVA Results
626
Multivariate Analysis of Variance
654
Split-Plot Designs
656
Repeated-Measures Designs
658
Contents
Rank Tests for One-Way and Two-Way Layouts
662
Variance Components Models
664
Appendix: Type I Estimable Functions
668
References
670
Chapter 18
Multiple Comparisons
673
Overview
674
Advanced Applications
684
Capabilities and Limits
694
References
696
Index
699
xvii
Contents
xviii
Preface
PREFACE
Introduction
Welcome to the Spotfire S+ Guide to Statistics, Volume 1.
This book is designed as a reference tool for TIBCO Spotfire S+ users
who want to use the powerful statistical techniques in Spotfire S+.
The Guide to Statistics, Volume 1 covers a wide range of statistical and
mathematical modeling. No single user is likely to tap all of these
resources, since advanced topics such as survival analysis and time
series are complete fields of study in themselves.
All examples in this guide are run using input through the
Commands window, which is the traditional method of accessing the
power of Spotfire S+. Many of the functions can also be run through
the Statistics dialogs available in the graphical user interface. We
hope that you find this book a valuable aid for exploring both the
theory and practice of statistical modeling.
Online Version
The Guide to Statistics, Volume 1 is also available online:
•
In Windows, through the Online Manuals entry of the main
Help menu, or in the /help/statman1.pdf file of your
Spotfire S+ home directory.
•
In Solaris or Linux, in the /doc/statman1.pdf file of your
home directory.
You can view it using an Adobe Acrobat Reader, which is required
for reading any of the Spotfire S+manuals.
The online version of the Guide to Statistics, Volume 1 has particular
advantages over print. For example, you can copy and paste example
S-PLUS code into the Commands window and run it without having
to type the function calls explicitly. (When doing this, be careful not
to paste the greater-than “>” prompt character, and note that distinct
colors differentiate between input and output in the online manual.)
A second advantage to the online guide is that you can perform fulltext searches. To find information on a certain function, first search,
and then browse through all occurrences of the function’s name in the
guide. A third advantage is in the contents and index entries: all
entries are links; click an entry to go to the selected page.
xix
Chapter
Evolution of
Spotfire S+
Spotfire S+ has evolved from its beginnings as a research tool. The
contents of this guide have grown, and will continue to grow, as the SPLUS language is improved and expanded. This means that some
examples in the text might not exactly match the formatting of the
output you obtain; however, the underlying theory and computations
are as described here.
In addition to the range of functionality covered in this guide, there
are additional modules, libraries, and user-written functions available
from a number of sources. Refer to the User’s Guide for more details.
Companion
Guides
The Guide to Statistics, Volume 2, together with Guide to Statistics,
Volume 1, is a companion volume to the User’s Guide , the Programmer’s
Guide, and the Application Developer’s Guide. These manuals, as well as
the rest of the manual set, are available in electronic form. For a
complete list of manuals, see the section Spotfire S+
introductory material.
®
Books in the
This volume covers the following topics:
•
Overview of statistical modeling in Spotfire S+
•
The Spotfire S+ statistical modeling framework
•
Review of probability and descriptive statistics
•
Statistical inference for one, two, and many sample problems,
both continuous and discrete
•
Cross-classified data and contingency tables
•
Power and sample size calculations
•
Regression models
•
Analysis of variance and multiple comparisons
The Guide to Statistics, Volume 2 covers tree models, multivariate
analysis techniques, cluster analysis, survival analysis, quality control
charts, resampling techniques, and mathematical computing.
xx
INTRODUCTION TO
STATISTICAL ANALYSIS IN
SPOTFIRE S+
1
Introduction
2
Developing Statistical Models
3
Data Used for Models
Data Frame Objects
Continuous and Discrete Data
Summaries and Plots for Examining Data
4
4
4
5
Statistical Models in S-PLUS
The Unity of Models in Data Analysis
8
9
Example of Data Analysis
The Iterative Process of Model Building
Exploring the Data
Fitting the Model
Fitting an Alternative Model
Conclusions
14
14
15
18
24
25
1
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
INTRODUCTION
All statistical analysis has, at its heart, a model which attempts to
describe the structure or relationships in some objects or phenomena
on which measurements (the data) are taken. Estimation, hypothesis
testing, and inference, in general, are based on the data at hand and a
conjectured model which you may define implicitly or explicitly. You
specify many types of models in S-PLUS using formulas, which
express the conjectured relationships between observed variables in a
natural way. The power of S-PLUS as a statistical modeling language
lies in its convenient and useful way of organizing data, its wide
variety of classical and modern modeling techniques, and its way of
specifying models.
The goal of this chapter is to give you a feel for data analysis in
Spotfire S+: examining the data, selecting a model, and displaying
and summarizing the fitted model.
2
Developing Statistical Models
DEVELOPING STATISTICAL MODELS
The process of developing a statistical model varies depending on
whether you follow a classical, hypothesis-driven approach
(confirmatory data analysis) or a more modern, data-driven approach
(exploratory data analysis). In many data analysis projects, both
approaches are frequently used. For example, in classical regression
analysis, you usually examine residuals using exploratory data
analytic methods for verifying whether underlying assumptions of the
model hold. The goal of either approach is a model which imitates, as
closely as possible, in as simple a way as possible, the properties of
the objects or phenomena being modeled. Creating a model usually
involves the following steps:
1. Determine the variables to observe. In a study involving a
classical modeling approach, these variables correspond to
the hypothesis being tested. For data-driven modeling, these
variables are the link to the phenomena being modeled.
2. Collect and record the data observations.
3. Study graphics and summaries of the collected data to
discover and remove mistakes and to reveal low-dimensional
relationships between variables.
4. Choose a model describing the important relationships seen
or hypothesized in the data.
5. Fit the model using the appropriate modeling technique.
6. Examine the fit using model summaries and diagnostic plots.
7.
Repeat steps 4–6 until you are satisfied with the model.
There are a wide range of possible modeling techniques to choose
from when developing statistical models in Spotfire S+. Among these
are linear models (lm), analysis of variance models (aov), generalized
linear models (glm), generalized additive models (gam), local
regression models (loess), and tree-based models (tree).
3
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
DATA USED FOR MODELS
This section provides descriptions of the most common types of data
objects used when developing models in Spotfire S+. There are also
brief descriptions and examples of common S-PLUS functions used
for developing and displaying models.
Data Frame
Objects
Statistical models allow inferences to be made about objects by
modeling associated observational or experimental data, organized
by variables. A data frame is an object that represents a sequence of
observations on some chosen set of variables. Data frames are like
matrices, with variables as columns and observations as rows. They
allow computations where variables can act as separate objects and can
be referenced simply by naming them. This makes data frames very
useful in modeling.
Variables in data frames are generally of three forms:
Continuous
and Discrete
Data
•
Numeric vectors
•
Factors and ordered factors
•
Numeric matrices
The type of data you have when developing a model is important for
deciding which modeling technique best suits your data. Continuous
data represent quantitative data having a continuous range of values.
Categorical data, by contrast, represent qualitative data and are
discrete, meaning they can assume only certain fixed numeric or
nonnumeric values.
In S-PLUS, you represent categorical data with factors, which keep
track of the levels or different values contained in the data and the
level each data point corresponds to. For example, you might have a
factor gender in which every element assumed one of the two values
"male" and "female". You represent continuous data with numeric
objects. Numeric objects are vectors, matrices, or arrays of numbers.
Numbers can take the form of decimal numbers (such as 11, -2.32, or
14.955) and exponential numbers expressed in scientific notation
(such as .002 expressed as 2e-3).
4
Data Used for Models
A statistical model expresses a response variable as some function of a
set of one or more predictor variables. The type of model you select
depends on whether the response and predictor variables are
continuous (numeric) or categorical (factor). For example, the
classical regression problem has a continuous response and
continuous predictors, but the classical ANOVA problem has a
continuous response and categorical predictors.
Summaries
and Plots for
Examining
Data
Before you fit a model, you should examine the data. Plots provide
important information on mistakes, outliers, distributions, and
relationships between variables. Numerical summaries provide a
statistical synopsis of the data in a tabular format.
Among the most common functions to use for generating plots and
summaries are the following:
•
summary: provides a synopsis of an object. The following
example displays a summary of the kyphosis data frame:
> summary(kyphosis)
Kyphosis
absent:64
present:17
•
plot:
•
hist:
•
qqnorm:
•
pairs:
Age
Min.: 1.00
1st Qu.: 26.00
Median: 87.00
Mean: 83.65
3rd Qu.:130.00
Max.:206.00
Number
Min.: 2.000
1st Qu.: 3.000
Median: 4.000
Mean: 4.049
3rd Qu.: 5.000
Max.:10.000
Start
Min.: 1.00
1st Qu.: 9.00
Median:13.00
Mean:11.49
3rd Qu.:16.00
Max.:18.00
a generic plotting function, plot produces different
kinds of plots depending on the data passed to it. In its most
common use, it produces a scatter plot of two numeric
objects.
creates histograms.
creates quantile-quantile plots.
creates, for multivariate data, a matrix of scatter plots
showing each variable plotted against each of the other
variables. To create the pairwise scatter plots for the data in
the matrix longley.x, use pairs as follows:
> pairs(longley.x)
The resulting plot appears as in Figure 1.1.
5
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
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Data Used for Models
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coplot: provides a graphical look at cross-sectional
relationships, which enable you to assess potential interaction
effects. The following example shows the effect of the
interaction between C and E on values of NOx. The resulting
plots appear as in Figure 1.2.
>
>
>
+
+
attach(ethanol)
E.intervals <- co.intervals(E, 9, 0.25)
coplot(NOx ~ C | E, given.values = E.intervals,
data = ethanol, panel = function(x,y) {
panel.smooth(x, y, span = 1, degree = 1)) }
Given : E
0.6
0.8
1.0
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Figure 1.2: Coplot of response and predictors.
7
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
STATISTICAL MODELS IN S-PLUS
The development of statistical models is, in many ways, data
dependent. The choice of the modeling technique you use depends on
the type and structure of your data and what you want the model to
test or explain. A model may predict new responses, show general
trends, or uncover underlying phenomena. This section gives general
selection criteria to help you develop a statistical model.
The fitting procedure for each model is based on a unified modeling
paradigm in which:
•
A data frame contains the data for the model.
•
A formula object specifies the relationship between the
response and predictor variables.
•
The formula and data frame are passed to the fitting function.
•
The fitting function returns a fit object.
There is a relatively small number of functions to help you fit and
analyze statistical models in S-PLUS.
•
•
8
Fitting models:
•
lm:
linear (regression) models.
•
aov
and varcomp: analysis of variance models.
•
glm:
generalized linear models.
•
gam:
generalized additive models.
•
loess:
•
tree:
local regression models.
tree models.
Extracting information from a fitted object:
•
fitted:
•
coefficients
•
residuals
returns fitted values.
or coef: returns the coefficients (if present).
or resid: returns the residuals.
Statistical Models in S-PLUS
•
The Unity of
Models in Data
Analysis
•
summary:
•
anova:
provides a synopsis of the fit.
for a single fit object, produces a table with rows
corresponding to each of the terms in the object, plus a
row for residuals. If two or more fit objects are used as
arguments, anova returns a table showing the tests for
differences between the models, sequentially, from first to
last.
Plotting the fitted object:
•
plot:
•
qqnorm: produces a normal probability plot, frequently
used in analysis of residuals.
•
coplot: provides a graphical look at cross-sectional
relationships for examining interaction effects.
plot a fitted object.
•
For minor modifications in a model, use the update function
(adding and deleting variables, transforming the response,
etc.).
•
To compute the predicted response from the model, use the
predict function.
Because there is usually more than one way to model your data, you
should learn which type(s) of model are best suited to various types of
response and predictor data. When deciding on a modeling
technique, it helps to ask: “What do I want the data to explain? What
hypothesis do I want to test? What am I trying to show?”
Some methods should or should not be used depending on whether
the response and predictors are continuous, factors, or a combination
of both. Table 1.1 organizes the methods by the type of data they can
handle.
9
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
Table 1.1: Criteria for developing models.
Model
Response
Predictors
lm
Continuous
Both
aov
Continuous
Factors
glm
Both
Both
gam
Both
Both
loess
Continuous
Both
tree
Both
Both
Linear regression models a continuous response variable, y, as a
linear combination of predictor variables xj, for j = 1,...,p. For a single
predictor, the data fit by a linear model scatter about a straight line or
curve. A linear regression model has the mathematical form
p
yi = β0 +
 βj xij + εi ,
j=1
where εi, referred to, generally, as the error, is the difference between
the ith observation and the model. On average, for given values of the
predictors, you predict the response best with the following equation:
p
y = β0 +
 βj xj .
j=1
Analysis of variance models are also linear models, but all predictors
are categorical, which contrasts with the typically continuous
predictors of regression. For designed experiments, use analysis of
variance to estimate and test for effects due to the factor predictors.
For example, consider the catalyst data frame, which contains the
data below.
10
Statistical Models in S-PLUS
> catalyst
1
2
3
4
5
6
7
8
Temp Conc Cat Yield
160
20
A
60
180
20
A
72
160
40
A
54
180
40
A
68
160
20
B
52
180
20
B
83
160
40
B
45
180
40
B
80
Each of the predictor terms, Temp, Conc, and Cat, is a factor with two
possible levels, and the response term, Yield, contains numeric data.
Use analysis of variance to estimate and test for the effect of the
predictors on the response.
Linear models produce estimates with good statistical properties
when the relationships are, in fact, linear, and the errors are normally
distributed. In some cases, when the distribution of the response is
skewed, you can transform the response, using, for example, square
root, logarithm, or reciprocal transformations, and produce a better
fit. In other cases, you may need to include polynomial terms of the
predictors in the model. However, if linearity or normality does not
hold, or if the variance of the observations is not constant, and
transformations of the response and predictors do not help, you
should explore other techniques such as generalized linear models,
generalized additive models, or classification and regression trees.
Generalized linear models assume a transformation of the expected (or
average) response is a linear function of the predictors, and the
variance of the response is a function of the mean response:
p
η ( E ( y ) ) = β0 +
 βj xj
j=1
VAR ( y ) = φV ( μ ) .
Generalized linear models, fitted using the glm function, allow you to
model data with distributions including normal, binomial, Poisson,
gamma, and inverse normal, but still require a linear relationship in
the parameters.
11
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
When the linear fit provided by glm does not produce a good fit, an
alternative is the generalized additive model, fit with the gam function.
In contrast to glm, gam allows you to fit nonlinear data-dependent
functions of the predictors. The mathematical form of a generalized
additive model is:
p
η(E(y )) =
 fj ( xj )
j=1
where the fj term represents functions to be estimated from the data.
The form of the model assumes a low-dimensional additive structure.
That is, the pieces represented by functions, fi, of each predictor
added together predict the response without interaction.
In the presence of interactions, if the response is continuous and the
errors about the fit are normally distributed, local regression (or loess)
models, allow you to fit a multivariate function which include
interaction relationships. The form of the model is:
y i = g(x i1, x i2, …, x ip) + ε i
where g represents the regression surface.
Tree-based models have gained in popularity because of their
flexibility in fitting all types of data. Tree models are generally used
for exploratory analysis. They allow you to study the structure of
data, creating nodes or clusters of data with similar characteristics.
The variance of the data within each node is relatively small, since the
characteristics of the contained data is similar. The following example
displays a tree-based model using the data frame car.test.frame:
>
>
>
>
car.tree <- tree(Mileage ~ Weight, car.test.frame)
plot(car.tree, type = "u")
text(car.tree)
title("Tree-based Model")
The resulting plot appears as in Figure 1.3.
12
Statistical Models in S-PLUS
Tree-based Model
Weight<2567.5
|
Weight<2280
Weight<3087.5
Weight<2747.5
34.00
Weight<3637.5
28.89
Weight<2882.5
Weight<3322.5
25.62
18.67
Weight<3197.5
23.33
24.11
22.00
20.60
20.40
Figure 1.3: A tree-based model for Mileage versus Weight.
13
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
EXAMPLE OF DATA ANALYSIS
The example that follows describes only one way of analyzing data
through the use of statistical modeling. There is no perfect cookbook
approach to building models, as different techniques do different
things, and not all of them use the same arguments when doing the
actual fitting.
The Iterative
Process of
Model Building
As discussed at the beginning of this chapter, there are some general
steps you can take when building a model:
1. Determine the variables to observe. In a study involving a
classical modeling approach, these variables correspond
directly to the hypothesis being tested. For data-driven
modeling, these variables are the link to the phenomena
being modeled.
2. Collect and record the data observations.
3. Study graphics and summaries of the collected data to
discover and remove mistakes and to reveal low-dimensional
relationships between variables.
4. Choose a model describing the important relationships seen
or hypothesized in the data.
5. Fit the model using the appropriate modeling technique.
6. Examine the fit through model summaries and diagnostic
plots.
7.
Repeat steps 4–6 until you are satisfied with the model.
At any point in the modeling process, you may find that your choice
of model does not appropriately fit the data. In some cases, diagnostic
plots may give you clues to improve the fit. Sometimes you may need
to try transformed variables or entirely different variables. You may
need to try a different modeling technique that will, for example,
allow you to fit nonlinear relationships, interactions, or different error
structures. At times, all you need to do is remove outlying, influential
data, or fit the model robustly. A point to remember is that there is no
one answer on how to build good statistical models. By iteratively
fitting, plotting, testing, changing, and then refitting, you arrive at the
best model for your data.
14
Example of Data Analysis
The following analysis uses the built-in data set auto.stats, which
contains a variety of data for car models between the years 19701982, including price, miles per gallon, weight, and more. Suppose
we want to model the effect that Weight has on the gas mileage of a
car. The object, auto.stats, is not a data frame, so we start by
coercing it into a data frame object:
> auto.dat <- data.frame(auto.stats)
Attach the data frame to treat each variable as a separate object:
> attach(auto.dat)
Look at the distribution of the data by plotting a histogram of the two
variables, Weight and Miles.per.gallon. First, split the graphics
screen into two portions to display both graphs:
> par(mfrow = c(1, 2))
Plot the histograms:
> hist(Weight)
> hist(Miles.per.gallon)
0
5
5
10
10
20
15
The resulting histograms appear in Figure 1.4.
0
Exploring the
Data
2000
3000
4000
5000
Weight
10
20
30
40
Miles.per.gallon
Figure 1.4: Histograms of Weight and Miles.per.gallon.
Subsetting (or subscripting) gives you the ability to look at only a
portion of the data. For example, type the command below to look at
only those cars with mileage greater than 34 miles per gallon.
> auto.dat[Miles.per.gallon > 34,]
15
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
Datsun 210
Subaru
Volk Rabbit(d)
Price Miles.per.gallon Repair (1978)
4589
35
5
3798
35
5
5397
41
5
Repair (1977) Headroom Rear.Seat Trunk Weight
5
2.0
23.5
8
2020
4
2.5
25.5
11
2050
4
3.0
25.5
15
2040
Length Turning.Circle Displacement Gear.Ratio
Datsun 210
165
32
85
3.70
Subaru
164
36
97
3.81
Volk Rabbit(d)
155
35
90
3.78
Datsun 210
Subaru
Volk Rabbit(d)
Suppose you want to predict the gas mileage of a particular auto
based upon its weight. Create a scatter plot of Weight versus
Miles.per.gallon to examine the relationship between the variables.
First, reset the graphics window to display only one graph, and then
create the scatter plot:
> par(mfrow = c(1,1))
> plot(Weight, Miles.per.gallon)
The plot appears in Figure 1.5. The figure displays a curved scattering
of the data, which might suggest a nonlinear relationship. Create a
plot from a different perspective, giving gallons per mile (1/
Miles.per.gallon) as the vertical axis:
> plot(Weight, 1/Miles.per.gallon)
The resulting scatter plot appears in Figure 1.6.
16
Example of Data Analysis
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Figure 1.5: Scatter plot of Weight versus Miles.per.gallon.
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Weight
Figure 1.6: Scatter plot of Weight versus 1/Miles.per.gallon.
17
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
Fitting the
Model
Gallons per mile is more linear with respect to weight, suggesting that
you can fit a linear model to Weight and 1/Miles.per.gallon. The
formula 1/Miles.per.gallon ~ Weight describes this model. Fit the
model by using the lm function, and name the fitted object fit1:
> fit1 <- lm(1/Miles.per.gallon ~ Weight)
As with any S-PLUS object, when you type the name, fit1, Spotfire
S+ prints the object. In this case, S-PLUS uses the specific print
method for lm objects:
> fit1
Call:
lm(formula = 1/Miles.per.gallon ~ Weight)
Coefficients:
(Intercept)
Weight
0.007447302 1.419734e-05
Degrees of freedom: 74 total; 72 residual
Residual standard error: 0.006363808
Plot the regression line to see how well it fits the data. The resulting
line appears in Figure 1.7.
> abline(fit1)
18
Example of Data Analysis
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2500
3000
3500
4000
4500
Weight
Figure 1.7: Regression line of fit1.
Judging from Figure 1.7, the regression line does not fit well in the
upper range of Weight. Plot the residuals versus the fitted values to see
more clearly where the model does not fit well.
> plot(fitted(fit1), residuals(fit1))
The plot appears as in Figure 1.8.
19
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
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fitted(fit1)
Figure 1.8: Plot of residuals for fit1.
Note that with the exception of two outliers in the lower right corner,
the residuals become more positive as the fitted values increase. You
can identify the outliers by typing the following command, then
interactively clicking on the outliers with your mouse:
> outliers <- identify(fitted(fit1), residuals(fit1),
+ n=2, labels = names(Weight))
To stop the interactive process, click on either the middle or right
mouse button. The resulting plot with the identified outliers appears
in Figure 1.9. The identify function allows you to interactively select
points on a plot. The labels argument and names function label the
points with their names in the fitted object. For more information on
the identify function, see the chapter Traditional Graphics in the
Guide to Graphics.
20
Example of Data Analysis
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Olds 98
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Cad. Seville
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0.01
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0.07
fitted(fit1)
Figure 1.9: Plot with labeled outliers.
The outliers in Figure 1.9 correspond to cars with better gas mileage
than other cars in the study with similar weights. You can remove the
outliers using the subset argument to lm.
> fit2 <- lm(1/Miles.per.gallon ~ Weight,
+ subset = -outliers)
Plot Weight versus 1/Miles.per.gallon with two regression lines:
one for the fit1 object and one for the fit2 object. Use the lty
graphics parameter to differentiate between the regression lines:
> plot(Weight, 1/Miles.per.gallon)
> abline(fit1, lty=2)
> abline(fit2)
The two lines appear with the data in Figure 1.10.
A plot of the residuals versus the fitted values shows a better fit. The
plot appears in Figure 1.11.
> plot(fitted(fit2), residuals(fit2))
21
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
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2500
3000
3500
4000
4500
Weight
0.015
Figure 1.10: Regression lines of fit1 versus fit2.
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Figure 1.11: Plot of residuals for fit2.
22
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•
0.07
0.08
Example of Data Analysis
To see a synopsis of the fit contained in fit2, use summary as follows:
> summary(fit2)
Call: lm(formula = 1/Miles.per.gallon ~ Weight,
subset = - outliers)
Residuals:
Min
1Q
Median
3Q
Max
-0.01152 -0.004257 -0.0008586 0.003686 0.01441
Coefficients:
(Intercept)
Weight
Value Std. Error t value Pr(>|t|)
0.0047
0.0026 1.8103
0.0745
0.0000
0.0000 18.0625
0.0000
Residual standard error: 0.00549 on 70 degrees of freedom
Multiple R-squared: 0.8233
F-statistic: 326.3 on 1 and 70 degrees of freedom, the
p-value is 0
Correlation of Coefficients:
(Intercept)
Weight -0.9686
The summary displays information on the spread of the residuals,
coefficients, standard errors, and tests of significance for each of the
variables in the model (which includes an intercept by default). In
addition, the summary displays overall regression statistics for the fit.
As expected, Weight is a very significant predictor of 1/
Miles.per.gallon. The amount of the variability of 1/
Miles.per.gallon explained by Weight is about 82%, and the
residual standard error is .0055, down about 14% from that of fit1.
To see the individual coefficients for fit2, use coef as follows:
> coef(fit2)
(Intercept)
Weight
0.004713079 1.529348e-05
23
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
Fitting an
Alternative
Model
Now consider an alternative approach. Recall the plot in Figure 1.5,
which showed curvature in the scatter plot of Weight versus
Miles.per.gallon. This indicates that a straight line fit may be an
inappropriate model. You can fit a nonparametric nonlinear model to
the data using gam with a cubic spline smoother:
> fit3 <- gam(Miles.per.gallon ~ s(Weight))
> fit3
Call:
gam(formula = Miles.per.gallon ~ s(Weight))
Degrees of Freedom: 74 total; 69.00244 Residual
Residual Deviance: 704.7922
The plot of fit3 in Figure 1.12 is created as follows:
20
> plot(fit3, residuals = T, scale =
+ diff(range(Miles.per.gallon)))
15
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2500
3000
3500
4000
Weight
Figure 1.12: Plot of additive model with smoothed spline term.
24
4500
Example of Data Analysis
The cubic spline smoother in the plot appears to give a good fit to the
data. You can check the fit with diagnostic plots of the residuals as we
did for the linear models. You should also compare the gam model
with a linear model using aov to produce a statistical test.
Use the predict function to make predictions from models. The
newdata argument to predict specifies a data frame containing the
values at which the predictions are required. If newdata is not
supplied, the predict function makes predictions at the data
originally supplied to fit the gam model, as in the following example:
> predict.fit3 <- predict(fit3)
Create a new object predict.high and print it to display cars with
predicted miles per gallon greater than 30:
> predict.high <- predict.fit3[predict.fit3 > 30]
> predict.high
Ford Fiesta Honda Civic Plym Champ
30.17946
30.49947
30.17946
Conclusions
The previous example shows a few simple methods for taking data
and iteratively fitting models until the desired results are achieved.
The chapters that follow discuss in far greater detail the modeling
techniques mentioned in this section. Before proceeding further, it is
good to remember that:
•
General formulas define the structure of models.
•
Data used in model-fitting are generally in the form of data
frames.
•
Different methods can be used on the same data.
•
A variety of functions are available for diagnostic study of the
fitted models.
•
The S-PLUS functions, like model-fitting in general, are
designed to be very flexible for users. Handling different
preferences and procedures in model-fitting are what make
Spotfire S+ very effective for data analysis.
25
Chapter 1 Introduction to Statistical Analysis in Spotfire S+
26
SPECIFYING MODELS IN
SPOTFIRE S+
2
Introduction
28
Basic Formulas
Continuous Data
Categorical Data
General Formula Syntax
29
30
30
31
Interactions
Continuous Data
Categorical Data
Nesting
Interactions Between Continuous and Categorical
Variables
32
33
33
33
The Period Operator
36
Combining Formulas with Fitting Procedures
The data Argument
Composite Terms in Formulas
37
37
38
Contrasts: The Coding of Factors
Built-In Contrasts
Specifying Contrasts
39
39
41
Useful Functions for Model Fitting
44
Optional Arguments to Model-Fitting Functions
46
References
48
34
27
Chapter 2 Specifying Models in Spotfire S+
INTRODUCTION
Models are specified in TIBCO Spotfire S+ using formulas, which
express the conjectured relationships between observed variables in a
natural way. Formulas specify models for the wide variety of
modeling techniques available in Spotfire S+. You can use the same
formula to specify a model for linear regression (lm ), analysis of
variance (aov), generalized linear modeling (glm ), generalized
additive modeling (gam ), local regression (loess), and tree-based
regression (tree).
For example, consider the following formula:
mpg ~ weight + displ
This formula can specify a least squares regression with mpg regressed
on two predictors, weight and displ, or a generalized additive model
with purely linear effects. You can also specify smoothed fits for
weight and displ in the generalized additive model as follows:
mpg ~ s(weight) + s(displ)
You can then compare the resulting fit with the purely linear fit to see
if some nonlinear structure must be built into the model.
Formulas provide the means for you to specify models for all
modeling techniques: parametric or nonparametric, classical or
modern. This chapter provides you with an introduction to the syntax
used for specifying statistical models. The chapters that follow make
use of this syntax in a wide variety of specific examples.
28
Basic Formulas
BASIC FORMULAS
A formula is an S-PLUS expression that specifies the form of a model
in terms of the variables involved. For example, to specify that mpg is
modeled as a linear model of the two predictors weight and displ,
use the following formula:
mpg ~ weight + displ
The tilde (~) character separates the response variable from the
explanatory variables. For something to be interpreted as a variable,
it must be one of the following:
•
Numeric vector, for continuous data
•
Factor or ordered factor, for categorical data
•
Matrix
For each numeric vector in a model, S-PLUS fits one coefficient. For
each matrix, S-PLUS fits one coefficient for each column. For factors,
the equivalent of one coefficient is fit for each level of the factor; see
the section Contrasts: The Coding of Factors on page 39 for more
details.
If your data set includes a character variable, you should convert it to
a factor before including it in a model formula. You can do this with
the factor function, as follows:
> test.char <- c(rep("Green",2), rep("Blue",2),
+ rep("Red",2))
> test.char
[1] "Green" "Green" "Blue" "Blue" "Red"
"Red"
> data.class(test.char)
[1] "character"
> test.fac <- factor(test.char)
> test.fac
[1] Green Green Blue Blue Red
Red
29
Chapter 2 Specifying Models in Spotfire S+
> data.class(test.fac)
[1] "factor"
> levels(test.fac)
[1] "Blue" "Green" "Red"
You can use any acceptable S-PLUS expression in place of a variable,
provided the expression evaluates to something interpretable as one
or more variables. Thus, the formula
log(mpg) ~ weight + poly(displ, 2)
specifies that the natural logarithm of mpg is modeled as a linear
function of weight and a quadratic polynomial of displ.
Continuous
Data
Each continuous variable you provide in a formula generates one
coefficient in the fitted model. Thus, the formula
mpg ~ weight + displ
fits the model
mpg
= β0 + β1 weight + β2 displ + ε
Implicitly, a S-PLUS formula always includes an intercept term,
which is β0 in the above formula. You can, however, remove the
intercept by specifying the model with -1 as an explicit predictor:
mpg ~ -1 + weight + displ
Similarly, you can include an intercept by including +1 as an
explicitly predictor.
When you provide a numeric matrix as one term in a formula, SPLUS interprets each column of the matrix as a separate variable in
the model. Any names associated with the columns are carried along
as labels in the subsequent fits.
Categorical
Data
When you specify categorical variables (factors or ordered factors) as
predictors in formulas, the modeling functions fit the equivalent of a
coefficient for each level of the variable. For example, to model
salary as a linear function of age (continuous) and gender (factor),
specify the following formula:
salary ~ age + gender
30
Basic Formulas
Different parameters are computed for the two levels of gender. This
is equivalent to fitting two dummy variables: one for males and one for
females. Thus, you need not create and specify dummy variables in
the model.
Although multiple dummy variables are returned, only one
additional parameter is computed for each factor variable in a
formula. This because the parameters are not independent of the
intercept term; more details are provided in the section Contrasts:
The Coding of Factors.
General
Formula
Syntax
Table 2.1, based on page 29 of Chambers and Hastie (1992),
summarizes the syntax of S-PLUS formulas. You can create and save
formulas as objects using the formula function:
> form.eg.1 <- formula(Fuel ~ poly(Weight, 2) + Disp. +
+ Type)
> form.eg.1
Fuel ~ poly(Weight, 2) + Disp. + Type
Table 2.1: A summary of formula syntax.
Expression
Meaning
T~F
T is modeled as a function of F
Fa + Fb
Include both Fa and Fb in the model
Fa - Fb
Include all of Fa in the model, except what is in Fb
Fa : Fb
The interaction between Fa and Fb
Fa * Fb
Shorthand notation for Fa + Fb + Fa : Fb
Fb %in% Fa
Fb is nested within Fa
Fa / Fb
Shorthand notation for Fa + Fb %in% Fa
F^m
All terms in F crossed to order m
31
Chapter 2 Specifying Models in Spotfire S+
INTERACTIONS
You can specify interactions for categorical data (factors), continuous
data, or a mixture of the two. In each case, additional parameters are
computed that are appropriate for the different types of variables
specified in the model. The syntax for specifying an interaction is the
same in each case, but the interpretation varies depending on the data
types.
To specify a particular interaction between two or more variables, use
a colon (:) between the variable names. Thus, to specify the
interaction between gender and race, use the following term:
gender:race
You can use an asterisk (*) to specify all terms in the model created by
subsets of the named variables. Thus,
salary ~ age * gender
is equivalent to
salary ~ age + gender + age:gender
You can remove terms with a minus or hyphen (-). For example, the
formula
salary ~ gender*race*education - gender:race:education
is equivalent to
salary ~ gender + race + education + gender:race +
gender:education + race:education
This is a model consisting of all terms in the full model except the
three-way interaction. Another way to specify this model is by using
the power notation. The following formula includes all terms of order
two or less:
salary ~ (gender + race + education) ^ 2
32
Interactions
Continuous
Data
By specifying interactions between continuous variables in a formula,
you include multiplicative terms in the corresponding model. Thus,
the formula
mpg ~ weight * displ
fits the model
mpg
Categorical
Data
= β0 + β1 weight + β2 displ + β3(weight)(displ) + ε
For categorical data, interactions add coefficients for each
combination of the levels in the named factors. For example, consider
two factors, Opening and Mask, with three and five levels, respectively.
The Opening:Mask term in a formula adds 15 additional parameters to
the model. For example, you can specify a two-way analysis of
variance with the following notation:
skips ~ Opening + Mask + Opening:Mask
Using the asterisk operator *, this simplifies to:
skips ~ Opening*Mask
Either formula fits the following model:
skips
= μ + Openingi + Maskj + (Opening : Mask)ij + ε
In practice, because of dependencies among the parameters, only
some of the total number of parameters specified by a model are
computed.
Nesting
Nesting arises in models when the levels of one or more factors make
sense only within the levels of other factors. For example, in sampling
the U.S. population, a sample of states is drawn, from which a sample
of counties is drawn, from which a sample of cities is drawn, from
which a sample of families or households is drawn. Counties are
nested within states, cities are nested within counties, and households
are nested within cities.
33
Chapter 2 Specifying Models in Spotfire S+
In S-PLUS formulas, there is special syntax to specify the nesting of
factors within other factors. For example, you can write the countywithin-state model using the term
county %in% state
You can state the model more succinctly with
state / county
This syntax means “state and county within state,” and is thus
equivalent to the following formula terms:
state + county %in% state
The slash operator (/) in nested models is the counterpart of the
asterisk (*), which is used for factorial models; see the previous section
for examples of formulas for factorial models.
The syntax for nested models can be extended to included multiple
levels of nesting. For example, you can specify the full state-countycity-household model as follows:
state / county / city / household
Interactions
Between
Continuous
and
Categorical
Variables
For continuous data combined with categorical data, interactions add
one coefficient for the continuous variable for each level of the
categorical variable. This arises, for example, in models that have
different slope estimates for different groups, where the categorical
variables specify the groups.
When you combine continuous and categorical data using the nesting
syntax, it is possible to specify analysis of covariance models. For
example, suppose gender (categorical) and age (continuous) are
predictors in a model. You can fit separate slopes for each gender
using the following nesting syntax:
salary ~ gender / age
This fits an analysis of covariance model equivalent to:
μ + genderi + βi age
Note that this is also equivalent to a model with the term gender*age.
However, the parametrization for the two models is different. When
you fit the nested model, Spotfire S+ computes estimates of the
34
Interactions
individual slopes for each group. When you fit the factorial model,
you obtain an overall slope estimate plus the deviations in the slope
for the different group contrasts.
For example, with the term gender/age, the formula expands into
main effects for gender followed by age within each level of gender.
One coefficient is computed for age from each level of gender, and
another coefficient estimates the contrast between the two levels of
gender. Thus, the nested formula fits the following type of model:
Salary M = μ + α g + β 1 × age
Salary F = μ – α g + β 2 × age
The intercept is μ, the contrast is α g , and the model has coefficients βi
for age within each level of gender. Thus, you obtain separate slope
estimates for each group.
Conversely, the formula with the term gender*age fits the following
model:
Salary M = μ – α g + β × age – γ × age
Salary F = μ + α g + β × age + γ × age
You obtain the overall slope estimate β , plus the deviations in the
slope for the different group contrasts.
You can fit the equal slope, separate intercept model by specifying:
salary ~ gender + age
This fits a model equivalent to:
μ + gender i + β × age
35
Chapter 2 Specifying Models in Spotfire S+
THE PERIOD OPERATOR
The single period (.) operator can act as a default left or right side of a
formula. There are numerous ways you can use periods in formulas.
For example, consider the function update, which allows you to
modify existing models. The following example uses the data frame
fuel.frame to display the usage of the single “.” in formulas. First, we
define a model that includes only an intercept term:
> fuel.null <- lm(Fuel ~ 1, data = fuel.frame)
Next, we use update to add the Weight variable to the model:
> fuel.wt <- update(fuel.null, . ~ . + Weight)
> fuel.wt
Call:
lm(formula = Fuel ~ Weight, data = fuel.frame)
Coefficients:
(Intercept)
Weight
0.3914324 0.00131638
Degrees of freedom: 60 total; 58 residual
Residual standard error: 0.3877015
The periods on either side of the tilde (~) in the above example are
replaced by the left and right sides of the formula used to fit the object
fuel.null.
Another use of the period operator arises when referencing data
frame objects in formulas. In the following example, we fit a linear
model for the data frame fuel.frame:
> lm(Fuel ~ ., data = fuel.frame)
Here, the new model includes all columns in fuel.frame as
predictors, with the exception of the response variable Fuel. In the
example
> lm(skips ~ .^2, data = solder.balance)
all columns in solder.balance enter the model as both main effects
and second-order interactions.
36
Combining Formulas with Fitting Procedures
COMBINING FORMULAS WITH FITTING PROCEDURES
The data
Argument
Once you specify a model with its associated formula, you can fit it to
a given data set by passing the formula and the data to the
appropriate fitting procedure. For the following example, create the
data frame auto.dat from the data set auto.stats by typing
> auto.dat <- data.frame(auto.stats)
The auto.dat data frame contains numeric columns named
Miles.per.gallon, Weight, and Displacement, among others. You
can fit a linear model using these three columns as follows:
> lm(Miles.per.gallon ~ Weight + Displacement,
+ data = auto.dat)
You can fit a smoothed model to the same data with the call:
> loess(Miles.per.gallon ~ s(Weight) + s(Displacement),
+ data = auto.dat)
All S-PLUS fitting procedures accept a formula and an optional data
frame as the first two arguments. If the individual variables are in
your search path, you can omit the data specification:
> lm(Miles.per.gallon ~ Weight + Displacement)
> loess(Miles.per.gallon ~ s(Weight) + s(Displacement))
This occurs, for example, when you create the variables explicitly in
your working directory, or when you attach a data frame to your
search path using the attach function.
Warning
If you attach a data frame for fitting models and have objects in your .Data directory with names
that match those in the data frame, the data frame variables are masked and are not used in the
actual model fitting. For more details, see the help file for the masked function.
37
Chapter 2 Specifying Models in Spotfire S+
Composite
Terms in
Formulas
As we previously mention, certain operators such as +, -, *, and /
have special meanings when used in formula expressions. Because of
this, the operators must appear at the top level in a formula and only
on the right side of the tilde (~). However, if the operators appear
within arguments to functions in the formula, they work as they
normally do in S-PLUS. For example:
Kyphosis ~ poly(Age, 2) + I((Start > 12) * (Start - 12))
Here, the * and - operators appear within arguments to the I
function, and thus evaluate as normal arithmetic operators. The sole
purpose of the I function is, in fact, to protect special operators on the
right sides of formulas.
You can use any acceptable S-PLUS expression in place of any
variable within a formula, provided the expression evaluates to
something interpretable as one or more variables. The expression
must evaluate to one of the following:
•
Numeric vector
•
Factor or ordered factor
•
Matrix
Thus, certain composite terms, including poly, I, and bs, can be used
as formula variables. For details, see the help files for these functions.
38
Contrasts: The Coding of Factors
CONTRASTS: THE CODING OF FACTORS
A coefficient for each level of a factor cannot usually be estimated
because of dependencies among the coefficients in the overall model.
An example of this is the sum of all dummy variables for a factor, which
is a vector of all ones that has length equal to the number of levels in
the factor. Overparameterization induced by dummy variables is
removed prior to fitting, by replacing the dummy variables with a set
of linear combinations of the dummy variables, which are
1. functionally independent of each other, and
2. functionally independent of the sum of the dummy variables.
A factor with k levels has k – 1 possible independent linear
combinations. A particular choice of linear combinations of the
dummy variables is called a set of contrasts. Any choice of contrasts for
a factor alters the specific individual coefficients in the model, but
does not change the overall contribution of the factor to the fit.
Contrasts are represented in S-PLUS as matrices in which the
columns sum to zero, and the columns are linearly independent of
both each other and a vector of all ones.
Built-In
Contrasts
S-PLUS provides four different kinds of contrasts as built-in functions
1. Treatment contrasts
The default setting in Spotfire S+ options. The function
contr.treatment implements treatment contrasts. Note that
these are not true contrasts, but simply include each level of a
factor as a dummy variable, excluding the first one. This
generates statistically dependent coefficients, even in
balanced experiments.
> contr.treatment(4)
1
2
3
4
2
0
1
0
0
3
0
0
1
0
4
0
0
0
1
2. Helmert contrasts
39
Chapter 2 Specifying Models in Spotfire S+
The function contr.helmert implements Helmert contrasts.
The j th linear combination is the difference between the
j + 1 st level and the average of the first j levels. The
following example returns a Helmert parametrization based
upon four levels:
> contr.helmert(4)
1
2
3
4
[,1] [,2] [,3]
-1
-1
-1
1
-1
-1
0
2
-1
0
0
3
3. Orthogonal polynomials
The function contr.poly implements polynomial contrasts.
Individual coefficients represent orthogonal polynomials if
the levels of the factor are equally spaced numeric values. In
general, contr.poly produces k – 1 orthogonal contrasts for a
factor with k levels, representing polynomials of degree 1 to
k – 1 . The following example uses four levels:
> contr.poly(4)
L
[1,] -0.6708204
[2,] -0.2236068
[3,] 0.2236068
[4,] 0.6708204
Q
0.5
-0.5
-0.5
0.5
C
-0.2236068
0.6708204
-0.6708204
0.2236068
4. Sum contrasts
The function contr.sum implements sum contrasts. This
produces contrasts between the k th level and each of the first
k – 1 levels:
> contr.sum(4)
1
2
3
4
40
[,1] [,2] [,3]
1
0
0
0
1
0
0
0
1
-1
-1
-1
Contrasts: The Coding of Factors
Specifying
Contrasts
Use the functions C, contrasts, and options to specify contrasts. Use
C to specify a contrast as you type a formula; it is the simplest way to
alter the choice of contrasts. Use contrasts to specify a contrast
attribute for a factor variable. Use options to specify the default
choice of contrasts for all factor variables. We discuss each of these
three approaches below.
Many fitting functions also include a contrast argument, which
allows you to fit a model using a particular set of contrasts, without
altering the factor variables involved or your session options. See the
help files for individual fitting functions such as lm for more details.
The C Function
As previously stated, the C function is the simplest way to alter the
choice of contrasts. A typical call to the function is C(object, contr),
where object is a factor or ordered factor and contr is the contrast to
alter. An optional argument, how.many, specifies the number of
contrasts to assign to the factor. The value returned by C is the factor
with a "contrasts" attribute equal to the specified contrast matrix.
For example, in the solder.balance data set, you can specify sum
contrasts for the Mask column with the call C(Mask, sum). You can
also use a custom contrast function, special.contrast, that returns a
matrix
of
the
desired
dimension
with
the
call
C(Mask, special.contrast).
Note
If you create your own contrast function, it must return a matrix with the following properties:
•
The number of rows must be equal to the number of levels specified, and the number of
columns must be one less than the number of rows.
•
The columns must be linearly independent of each other and of a vector of all ones.
You can also specify contrasts by supplying the contrast matrix
directly. For example, consider a factor vector quality that has four
levels:
> quality <- factor(
+ c("tested-low", "low", "high", "tested-high"),
+ levels = c("tested-low", "low", "high", "tested-high"))
> levels(quality)
41
Chapter 2 Specifying Models in Spotfire S+
[1] "tested-low"
"low"
"high"
"tested-high"
You can contrast levels 1 and 4 with levels 2 and 3 by including
quality in a model formula as C(quality, c(1,-1,-1,1)). Two
additional contrasts are generated, orthogonal to the one supplied.
To contrast the “low” values in quality versus the “high” values,
provide the following contrast matrix:
> contrast.mat <- matrix(c(1,-1,-1,1,1,1,-1,-1), ncol=2)
> contrast.mat
[1,]
[2,]
[3,]
[4,]
The contrasts
Function
[,1] [,2]
1
1
-1
1
-1
-1
1
-1
Use the contrasts function to define the contrasts for a particular
factor whenever it appears. The contrasts function extracts contrasts
from a factor and returns them as a matrix. The following sets the
contrasts for the quality factor:
> contrasts(quality) <- contrast.mat
> contrasts(quality)
tested-low
low
high
tested-high
[,1] [,2] [,3]
1
1 -0.5
-1
1 0.5
-1
-1 -0.5
1
-1 0.5
The quality vector now has the contrast.mat parametrization by
default any time it appears in a formula. To override this new setting,
supply a contrast specification with the C function.
42
Contrasts: The Coding of Factors
Setting the
Use the options function to change the default choice of contrasts for
contrasts Option all factors, as in the following example:
> options()$contrasts
factor
ordered
"contr.treatment" "contr.poly"
> options(contrasts = c(factor = "contr.helmert",
+ ordered = "contr.poly"))
> options()$contrasts
[1] "contr.helmert" "contr.poly"
43
Chapter 2 Specifying Models in Spotfire S+
USEFUL FUNCTIONS FOR MODEL FITTING
As model building proceeds, you’ll find several functions useful for
adding and deleting terms in formulas. The update function starts
with an existing fit and adds or removes terms as you specify. For
example, create a linear model object as follows:
> fuel.lm <- lm(Mileage ~ Weight + Disp., data = fuel.frame)
You can use update to change the response to Fuel, using a period on
the right side of the tilde (~)to represent the current state of the model
in fuel.lm:
> update(fuel.lm, Fuel ~ . )
The period operator in this call includes every predictor in fuel.lm in
the new model. Only the response variable changes.
You can drop the Disp. term, keeping the response as Mileage with
the command:
> update(fuel.lm, . ~ . - Disp.)
Another useful function is drop1, which computes statistics obtained
by dropping each term from the model one at a time. For example:
> drop1(fuel.lm)
Single term deletions
Model: Mileage ~ Weight +
Df Sum of Sq
RSS
<none>
380.3
Weight 1
323.4 703.7
Disp.
1
0.6 380.8
Disp.
Cp
420.3
730.4
407.5
Each line presents model summary statistics that correspond to
dropping the term indicated in the first column. The first line in the
table corresponds to the original model; no terms (<none>) are
deleted.
44
Useful Functions for Model Fitting
There is also an add1 function which adds one term at a time. The
second argument to add1 provides the scope for added terms. The
scope argument can be a formula or a character vector indicating the
terms to be added. The resulting table prints a line for each term
indicated by the scope argument:
> add1(fuel.lm, c("Type", "Fuel"))
Single term additions
Model: Mileage ~ Weight + Disp.
Df Sum of Sq
RSS
Cp
<none>
380.271 420.299
Type 5
119.722 260.549 367.292
Fuel 1
326.097 54.173 107.545
45
Chapter 2 Specifying Models in Spotfire S+
OPTIONAL ARGUMENTS TO MODEL-FITTING FUNCTIONS
In most model-building calls, you’ll need to specify the data frame to
use. You may need arguments that check for missing values in the
data frame, or select only particular portions of the data frame to use
in the fit. The following list summarizes the standard optional
arguments available for most model-fitting functions.
•
data:
specifies a data frame in which to interpret the variables
named in the formula, subset and weights arguments. The
following example fits a linear model to data in the
fuel.frame data frame:
> fuel.lm <- lm(Fuel ~ Weight + Disp.,
+ data = fuel.frame)
•
weights: specifies a vector of observation of weights. If
weights is supplied, the fitting algorithm minimizes the
sum
of the squared residuals multiplied by the weights:
 wi ri .
2
Negative weights generate a S-PLUS error. We recommend
that the weights be strictly positive, since zero weights give no
residuals; to exclude observations from your model, use the
subset argument instead. The following example fits a linear
model to the claims data frame, and passes number to the
weights argument:
> claims.lm <- lm(cost ~ age + type + car.age,
+ data = claims, weights = number,
+ na.action = na.exclude)
•
subset:
indicates a subset of the rows of the data to be used in
the fit. The subset expression should evaluate to a logical or
numeric vector, or a character vector with appropriate row
names. The following example fits a linear model to data in
the auto.dat data frame, excluding those observations for
which Miles.per.gallon is greater than 35:
> auto.lm <- lm(1/Miles.per.gallon ~ Weight,
+ data = auto.dat, subset = Miles.per.gallon < 35)
46
Optional Arguments to Model-Fitting Functions
•
na.action: specifies a missing-data filter function. This is
applied to the model frame after any subset argument has
been used. The following example passes na.exclude to the
na.action argument, which drops any row of the data frame
that contains a missing value:
> ozone.lm <- lm(ozone ~ temperature + wind,
+ data = air, subset = wind > 8,
+ na.action = na.exclude)
Each model fitting function has nonstandard optional arguments, not
listed above, which you can use to fit the appropriate model. The
following chapters describe the available arguments for each model
type.
47
Chapter 2 Specifying Models in Spotfire S+
REFERENCES
Chambers, J.M., Hastie T.J. (Eds.) (1992). Statistical Models in S.
London: Chapman & Hall.
48
PROBABILITY
3
Introduction
51
Important Concepts
Random Variables
Probability Density and Cumulative Distribution
Functions
Mean
Variance and Deviation
Quantiles
Moments
52
52
52
54
54
55
55
S-PLUS Probability Functions
Random Number Generator r
Probability Function p
Density Function d
Quantile Function q
56
56
56
57
57
Common Probability Distributions for Continuous
Variables
Uniform Distribution
Normal Distribution
Chi-Square Distribution
t Distribution
F Distribution
60
60
61
64
65
67
Common Probability Distributions for Discrete
Variables
Binomial Distribution
Poisson Distribution
Hypergeometric Distribution
69
69
71
74
Other Continuous Distribution Functions in S-PLUS
Beta Distribution
76
76
49
Chapter 3 Probability
50
Exponential Distribution
Gamma Distribution
Weibull Distribution
Logistic Distribution
Cauchy Distribution
Lognormal Distribution
Distribution of the Range of Standard Normals
Multivariate Normal Distribution
Stable Family of Distributions
76
77
77
78
79
80
81
82
82
Other Discrete Distribution Functions in S-PLUS
Geometric Distribution
Negative Binomial Distribution
Distribution of Wilcoxon Rank Sum Statistic
84
84
84
85
Examples: Random Number Generation
Inverse Distribution Functions
The Polar Method
86
86
88
References
91
Introduction
INTRODUCTION
Probability theory is the branch of mathematics that is concerned
with random, or chance, phenomena. With random phenomena,
repeated observations under a specified set of conditions do not
always lead to the same outcome. However, many random
phenomena exhibit a statistical regularity. Because of this, a solid
understanding of probability theory is fundamental to most statistical
analyses.
A probability is a number between 0 and 1 that tells how often a
particular event is likely to occur if an experiment is repeated many
times. A probability distribution is used to calculate the theoretical
probability of different events. Many statistical methods are based on
the assumption that the observed data are a sample from a population
with a known theoretical distribution. This assumption is crucial. If
we proceed with an analysis under the assumption that a particular
sample is from a known distribution when it is not, our results will be
misleading and invalid.
In this chapter, we review the basic definitions and terminology that
provide the foundation for statistical models in TIBCO Spotfire S+.
This chapter is not meant to encompass all aspects of probability
theory. Rather, we present the facts as concise statements and relate
them to the functions and distributions that are built into Spotfire S+.
We begin with formal definitions and important concepts, including
mathematical descriptions of a random variable and a probability
density. We then introduce the four basic probability functions in SPLUS, and illustrate how they are used in conjunction with particular
distributions. As a final example, we show how to transform uniform
random numbers to ones from other distributions.
51
Chapter 3 Probability
IMPORTANT CONCEPTS
Random
Variables
A random variable is a function that maps a set of events, or outcomes
of an experiment, onto a set of values. For example, if we consider the
experiment of tossing a coin, a random variable might be the number
of times the coin shows heads after ten tosses. The random variable in
this experiment can only assume a finite number of values 0, 1, ..., 10,
and so it is called a discrete random variable. Likewise, if we observe the
failure rates of machine components, a random variable might be
lifetime of a particular component. The random variable in this
experiment can assume infinitely many real values, and so it is called
a continuous random variable.
Probability
Density and
Cumulative
Distribution
Functions
The probability density function (pdf) for a random variable provides a
complete description of the variable’s probability characteristics. If X
is a discrete random variable, then its density function f X ( x ) is
defined as
fX ( x ) = P ( X = x ) .
In words, the density gives the probability that X assumes a particular
finite value x. Because of this definition, f X ( x ) is sometimes referred
to as the frequency function for a discrete random variable. For f X ( x ) to
be valid, it must be nonnegative and the sum of all possible
probabilities must equal 1:
n
 fX ( xi )
= 1,
i=1
where X can assume the values x 1, x 2, …, x n .
For a continuous random variable Y , the density f Y ( y ) is used to find
the probability that Y assumes a range of values, a < Y < b :
P(a < Y < b) =
52
b
a f Y ( y ) .
Important Concepts
Since a continuous random variable can assume infinitely many real
values, the probability that Y is equal to any single value is zero:
P( Y = a ) =
a
a f Y ( y )
= 0.
As with discrete variables, the probabilities for all possible values of a
continuous variable must be nonnegative and sum to 1:
∞
–∞ f Y ( y ) dy
= 1.
It is sometimes convenient to consider the cumulative distribution
function (cdf), which also describes the probability characteristics of a
random variable. For a discrete random variable X , the distribution
F X ( x ) is the probability that X is less than some value x. The
cumulative distribution is found by summing probabilities for all real
values less than x:
FX ( x ) = P ( X ≤ x ) =
 fX ( t ) .
t≤x
If Y is a continuous random variable, the cumulative distribution
function F Y ( y ) takes the following form:
FY ( y ) = P ( Y ≤ y ) =
y
– ∞ f Y ( y ) .
These equations illustrate a relationship between the density and
distribution functions for a random variable. If one function is known,
the other can be easily calculated. Because of this relationship, the
terms distribution and density are often used interchangeably when
describing the overall probability characteristics of a random
variable.
53
Chapter 3 Probability
Mean
The mean or expected value of a random variable describes the center of
the variable’s density function. If X is a discrete random variable and
x 1, x 2 , … , x n
assumes
the
values
with
probabilities
f X ( x 1 ), f X ( x 2 ) , …, f X ( x n ) , then the mean μ X is given by the weighted
sum
n
 xi fX ( xi ) .
μX =
i=1
If Y is a continuous random variable with a probability density
function f Y ( y ) , the mean μ Y is given by
μY =
Variance and
Deviation
∞
–∞ yf Y ( y ) dy .
The variance and standard deviation of a random variable are measures
of dispersion. The variance is the average value of the squared
deviation from the variable’s mean, and the standard deviation is the
square root of the variance. If X is a discrete random variable with
2
density function f X ( x ) and mean μ X , the variance σ X is given by the
weighted sum
n
2
σX
=
 ( xi – μX )
2
fX ( xi ) .
i=1
The standard deviation of X , σ X , provides an indication of how
dispersed the values x 1, x 2, …, x n are about μ X . In practice, it is
sometimes desirable to compute the mean absolute deviation of a
random variable instead of its variance. For a discrete variable X , the
mean deviation is
 xi – μX fX ( xi ) .
i
Likewise, if Y is a continuous random variable with density function
2
f Y ( y ) and mean μ Y , the variance σ Y is defined to be:
2
σY =
54
∞
– ∞ ( y – μ Y )
2
f Y ( y ) dy .
Important Concepts
The standard deviation of Y is σ Y , and the mean absolute deviation
is
Quantiles
∞
– ∞
y – μ Y f Y ( y ) dy .
The pth quantile of a probability distribution F is defined to be the
value t such that F ( t ) = p , where p is a probability between 0 and 1.
For a random variable X , this definition is equivalent to the statement
P ( X ≤ t ) = p . Special cases include those quantiles corresponding to
p = 1 ⁄ 2, p = 3 ⁄ 4 , and p = 1 ⁄ 4 . When p = 1 ⁄ 2 , the quantile is
called the median of the probability distribution. When p = 3 ⁄ 4 and
p = 1 ⁄ 4 , the quantiles are called the upper quartile and lower quartile,
respectively. The difference between the upper and lower quartiles of
a distribution is often referred to as the interquartile range, or IQR.
The mode of a probability distribution function is a quantile for which
the function reaches a local maximum. If a distribution has only one
local maximum across its range of values, then it is said to be
unimodal. Likewise, if a distribution has exactly two local maximums,
then it is said to be bimodal. This statistical property is not related to
the S-PLUS function mode, which returns the data class of an S-PLUS
object.
Moments
The moments of a random variable provide a convenient way of
summarizing a few of the quantities discussed in this section. The rth
moment of a random variable X is defined to be the expected value
r
of the quantity X . In practice, central moments are often used in place
of ordinary moments. If a random variable X has mean μ X , the rth
central moment is defined to be the expected value of the quantity
r
( X – μ X ) . The first central moment is similar to the mean absolute
deviation, and the second central moment is the variance of a
distribution. The third central moment is called the skewness, and is a
measure of asymmetry in a probability density function. The fourth
central moment is called the kurtosis, and is a measure of peakedness
in a density function.
55
Chapter 3 Probability
S-PLUS PROBABILITY FUNCTIONS
For each of the most common distributions, S-PLUS contains four
functions that perform probability calculations. These four functions
generate random numbers, calculate cumulative probabilities,
compute densities, and return quantiles for the specified distributions.
Each of the functions has a name beginning with a one-letter code
indicating the type of function: rdist, pdist, ddist, and qdist,
respectively, where dist is the S-PLUS distribution function. The
four functions are described briefly below. Table 3.1 lists the
distributions currently supported in Spotfire S+, along with the codes
used to identify them. For a complete description of the pseudorandom number generator implemented in Spotfire S+, see Chapter
34, Mathematical Computing in Spotfire S+.
Random
Number
Generator r
The random number generator function, rdist, requires an
argument specifying sample size. Some distributions may require
additional arguments to define specific parameters (see Table 3.1).
The rdist function returns a vector of values that are sampled from
the appropriate probability distribution function. For example, to
generate 25 random numbers from a uniform distribution on the
interval [– 5,5] , use the following expression:
> runif(25,-5,5)
[1] 2.36424 -1.20289 1.68902 -3.67466 -3.90192
[6] 0.45929 0.46681 1.06433 -4.78024 1.80795
[11] 2.45844 -3.48800 2.54451 -1.32685 1.49172
[16] -2.40302 3.76792 -4.99800 1.70095 2.66173
[21] -1.26277 -4.94573 -0.89837 1.98377 -2.61245
Probability
Function p
56
The probability function, pdist, requires an argument specifying a
vector of quantiles (possibly of length 1). Some distributions may
require additional arguments to define specific parameters (see Table
3.1). The pdist function returns a vector of cumulative probabilities
that correspond to the quantiles. For example, to determine the
probability that a Wilcoxon rank sum statistic is less than or equal to
24, given that the first sample has 4 observations and the second
sample has 6 observations, use the command below.
S-PLUS Probability Functions
> pwilcox(24, 4, 6)
[1] 0.6952381
Density
Function d
The density function, ddist, requires an argument specifying a
vector of quantiles (possibly of length 1). Some distributions may
require additional arguments to define specific parameters (see Table
3.1). The ddist function returns a vector of corresponding values
from the appropriate probability density function. For example, to
determine the probability that a Wilcoxon rank sum statistic is equal
to 24, given that the first sample has 4 observations and the second
sample has 6 observations, use the following command:
> dwilcox(24,4,6)
[1] 0.07619048
Quantile
Function q
The quantile function, qdist, requires an argument specifying a
vector of probabilities (possibly of length 1). Some distributions may
require additional arguments to define specific parameters (see Table
3.1). The qdist function returns a vector of quantiles corresponding
to the probabilities for the appropriate distribution function. For
example, to compute the 0.95 quantile of a chi-square distribution
that has 5 degrees of freedom, use the following expression:
> qchisq(.95, 5)
[1] 11.0705
The result says that 95% of numbers drawn from the given chi-square
distribution will have values less than 11.0705.
57
Chapter 3 Probability
Table 3.1: Probability distributions in S-PLUS.
Code
Distribution
Required
Parameters
beta
beta
shape1, shape2
binom
binomial
size, prob
cauchy
Cauchy
chisq
chi-square
exp
exponential
f
F
df1, df2
gamma
Gamma
shape
geom
geometric
prob
hyper
hypergeometric
m, n, k
lnorm
Optional
Parameters
Defaults
location, scale
location=0,
scale=1
rate
1
rate
rate=1
lognormal
meanlog, sdlog
meanlog=0,
sdlog=1
logis
logistic
location, scale
location=0,
scale=1
mvnorm
multivariate normal
mean, cov, sd, rho
mean=rep(0,d),
cov=diag(d),
sd=1
nbinom
negative binomial
norm
normal
mean, sd
mean=0, sd=1
nrange
range of standard
normals
58
df
size, prob
size
S-PLUS Probability Functions
Table 3.1: Probability distributions in S-PLUS. (Continued)
Code
Distribution
Required
Parameters
pois
Poisson
lambda
stab
stable
index
t
Student’s t
df
unif
uniform
weibull
Weibull
shape
wilcox
Wilcoxon rank sum
statistic
m, n
Optional
Parameters
Defaults
skewness
skewness=0
min, max
min=0, max=1
scale
scale=1
59
Chapter 3 Probability
COMMON PROBABILITY DISTRIBUTIONS FOR
CONTINUOUS VARIABLES
A continuous random variable is one that can assume any value
within a given range. Examples of continuous variables include
height, weight, personal income, distance, and dollar amount. This
section describes five of the most common continuous distributions:
uniform, normal, chi-square, t, and F. See the section Other
Continuous Distribution Functions in S-PLUS for descriptions of
additional distributions.
Uniform
Distribution
The uniform distribution describes variables that can assume any
value in a particular range with equal probability. That is, all possible
values of a uniform random variable have the same relative
frequency, and all have an equal chance of appearing. Given the
endpoints of the interval [ a, b ] as parameters, the probability density
function for a uniform random variable is defined as:
1
f a, b ( x ) = ------------, a ≤ x ≤ b .
b–a
Outside of the interval [ a, b ] , the density is equal to zero. Plots of this
density function for various values of a and b all have the same
rectangular shape, with a constant maximum of 1 ⁄ ( b – a ) in the
interval [ a, b ] .
S-PLUS functions
dunif, punif, qunif, runif
Each of these functions has optional parameters for the min ( a ) and
max ( b ) of the defined density interval. By default, the values for these
parameters are a = 0 and b = 1 .
There is a S-PLUS function sample that also produces a vector of
values uniformly chosen from a given population. For an example of
this function, see the section Common Probability Distributions for
Discrete Variables.
60
Common Probability Distributions for Continuous Variables
Command line example
A common application of continuous uniform random variables is in
queueing theory. For example, suppose a bus arrives every 15
minutes at a certain bus stop, on the quarter hour. If passengers arrive
randomly at the bus stop between 7:00 and 7:15 a.m., what is the
probability that a particular person will wait more than 12 minutes for
a bus? This will occur if the passenger arrives between 7:00 and 7:03.
> punif(3,0,15)-punif(0,0,15)
[1] 0.2
Therefore, a passenger has a 20% chance of waiting more than 12
minutes for the bus.
Windows GUI Example
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Type the values 0 and 3 in the first column.
3. Highlight the column and select Data Distribution
Functions.
4. By default, S-PLUS generates cumulative probability values.
Select uniform in the Distribution field, and change the
Minimum and Maximum parameters to 0 and 15.
5. Click OK.
6. The values 0.00 and 0.20 appear in the second column of the
data window, which is named Probability. This means that
the probability of arriving between 7:00 and 7:03 is
0.20 – 0.00 , or 20%.
Normal
Distribution
The normal, or Gaussian, distribution is unimodal and symmetric
about its mean. Given the mean μ and the standard deviation σ > 0
as parameters, the probability density function for a normal random
variable is defined as:
1
1 x–μ 2
f μ, σ ( x ) = ----------------- exp – ---  ------------ .
2 σ 
2
2πσ
61
Chapter 3 Probability
Plots of this density function for various values of μ and σ all have
the same “bell” shape, with a global maximum at μ and tails that
approach zero as x becomes large or small.
In theory, the normal distribution ranges from negative to positive
infinity, implying that normal random variables can assume any real
value. However, the bulk of the values that a normal variable assumes
are within two standard deviations of its mean. For example, consider
the standard normal distribution, where μ = 0 and σ = 1 . Sixty-eight
percent of the values that a standard normal variable assumes will fall
in the range from -1.00 to +1.00. In addition, ninety-five percent of
the values will fall in the range from -1.96 to +1.96.
S-PLUS functions
dnorm, pnorm, qnorm, rnorm
Each of these functions has optional parameters for mean ( μ ) and sd
( σ ) . By default, the values for these parameters are μ = 0 and
σ = 1.
Command line example 1
The following command shows how to plot histograms of multiple
25-observation samples, each having mean 0 and standard deviation
1.
> hist(rnorm(25,0,1))
Repeat this many times and observe the variation in the distributions.
Windows GUI Example 1
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Select Data Random Numbers.
3. In the dialog that appears, the name of the new data window
is filled for the Data Set, and Sample is filled for the Target
Column. Specify a Sample Size of 25, and leave the defaults
for Distribution, Mean, and Standard Deviation.
4. Click Apply.
62
Common Probability Distributions for Continuous Variables
5. Highlight the Sample column in the data window, open the
Plots 2D palette, and select Histogram.
6. Put the Random Numbers dialog and the graph sheet side
by side, and click Apply to create a new sample and plot.
Repeat this many times and observe the variation in the
distributions.
Command line example 2
Suppose pulmonary function is standardized on a normal distribution
with mean 0 and standard deviation 1. If a score of -1.5 is considered
to be poor pulmonary health for young people, what percentage of
children are in poor pulmonary health?
> pnorm(-1.5,0,1)
[1] 0.0668072
Thus, about 7% of children are classified as having poor pulmonary
health.
Windows GUI Example 2
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Type -1.5 in the first cell.
3. Highlight the column and select Data Distribution
Functions. By default, S-PLUS uses a normal distribution
with mean 0 and standard deviation 1.
4. Click OK.
5. The value 0.07 appears in the second column of the data
window, which is named Probability. To see more decimal
places in the display, highlight the columns and click the
Increase Precision button on the DataSet toolbar.
The Central Limit The normal distribution is very important in statistical analyses, and
arises often in nearly every field of study. Generally speaking, any
Theorem
variable that is a sum of numerous independent random variables can
be approximated by a normal distribution. Consequently, the normal
distribution offers a reasonable approximation for many variables
that may not strictly follow a normal distribution. The Central Limit
Theorem formalizes this idea. In practice, the normal approximation
63
Chapter 3 Probability
is usually a good one for relatively small sample sizes if the actual
distribution of the sample is fairly symmetric. If the actual distribution
is very skewed, then the sample size must be large for the normal
approximation to be accurate.
Chi-Square
Distribution
The chi-square distribution is derived from a standard normal
distribution and is primarily used in hypothesis testing of parameter
estimates. If Z 1, Z 2, …, Z n are standard normal variables, each having
mean μ = 0 and standard deviation σ = 1 , then a chi-square
2
variable χ with n degrees of freedom is defined as the sum of their
squares:
n
2
χ =
 Zi
2
.
i=1
A chi-square random variable with n degrees of freedom has the
following probability density function:
1
–x ⁄ 2 ( n ⁄ 2 ) – 1
,
f n ( x ) = ---------------------------x
-e
n⁄2
2 Γ(n ⁄ 2)
where Γ is the gamma function,
Γ(y) =
∞
0 u
y – 1 –u
e du , y > 0 .
Since a chi-square random variable is a sum of squares, the density
function f n ( x ) is only defined for positive x and n . For small values
of n , plots of the chi-square distribution are skewed and asymmetric.
As the number of degrees of freedom increases, the distribution
becomes more symmetric and approaches the shape of a regular
Gaussian curve.
S-PLUS functions
dchisq, pchisq, qchisq, rchisq
Each of these functions requires you to specify a value for the df ( n ) .
64
Common Probability Distributions for Continuous Variables
Command line example
Find the upper and lower 2.5th percentile of a chi-square distribution
with 12 degrees of freedom.
> qchisq(0.975,12)
[1] 23.3366
> qchisq(0.025,12)
[1] 4.403789
Windows GUI Example
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Type the values 0.975, and 0.025 in the first column. Highlight
the column and click the Increase Precision button on the
DataSet toolbar to increase the precision of the display.
3. Highlight the first column and select Data Distribution
Functions.
4. In the Result Type field, select Quantile. From the
Distribution dropdown list, select chisquare. In the Degrees
of Freedom field, type 12.
5. Click OK.
6. The values 23.34 and 4.40 appear in the second column of the
data window, which is named Quantile.
t Distribution
The t distribution is derived from both a standard normal
distribution and a chi-square distribution. If Z is a standard normal
2
variable and χ is a chi-square random variable with n degrees of
freedom, then a t variable with n degrees of freedom is defined to be
the ratio
Z
t = ---------------- .
2
χ ⁄n
65
Chapter 3 Probability
A t random variable with n degrees of freedom has the following
probability density function:
n+1
–( n + 1 )
Γ  ------------
2 ------------------2
 2 
x


f n ( x ) = ------------------------ 1 + ----
n
n
Γ  --- nπ
 2
Plots of this density function are similar in shape to plots of the
normal distribution. Although the t distribution is unimodal and
symmetric about its mean, t values are less concentrated and the
density function tends to zero more slowly than the normal
distribution. In practice, the t distribution represents the mean of a
Gaussian sample with unknown variance. Chapter 5, Statistical
Inference for One- and Two-Sample Problems, discusses the
t distribution in the context of estimation and hypothesis testing for
means of samples.
S-PLUS functions
dt, pt, qt, rt
Each of these functions requires you to specify a value for the df ( n ) .
Command line example
What is the 95th percentile of the t distribution that has 20 degrees of
freedom?
> qt(0.95,20)
[1] 1.724718
Windows GUI Example
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Type 0.95 in the first cell.
3. Highlight the first column and select Data Distribution
Functions.
66
Common Probability Distributions for Continuous Variables
4. In the Result Type field, select Quantile. From the
Distribution dropdown list, select t. In the Degrees of
Freedom field, type 20.
5. Click OK.
6. The value 1.72 appears in the second column of the data
window, which is named Quantile. To see more decimal
places in the display, click the Increase Precision button on
the DataSet toolbar.
F Distribution
The F distribution is the ratio of two independent chi-square
variables, each divided by its own degrees of freedom. If χ m and χ n
are chi-square random variables with m and n degrees of freedom,
respectively, then an F random variable is defined to be
χm ⁄ m
F = -------------.
χn ⁄ n
An F variable with m and n degrees of freedom has the following
probability density function:
m+n
–( m + n )
Γ  -------------
-------------------- 2  m⁄2–1 m m⁄2
mx 2



---f m, n ( x ) = --------------------------- x
1 + ------ n

n
m  n

Γ ---- Γ -- 2   2
Like the chi-square distribution, the density function f m· , n ( x ) is
defined for positive x , m , and n only.
The F distribution is used in the analysis of variance to test the
equality of sample means. In cases where two means are
independently estimated, we expect the ratio of the two sample
variances to have a F distribution.
S-PLUS functions
df, pf, qf, rf
These functions require you to specify two values for the number of
degrees of freedom, one for each underlying chi-square variable.
67
Chapter 3 Probability
Command line example
Find the upper 5th percentile of an F distribution with 4 and 10
degrees of freedom.
> qf(0.95,4,10)
[1] 3.47805
Windows GUI Example
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Type 0.95 in the first cell.
3. Highlight the first column and select Data Distribution
Functions.
4. In the Result Type field, select Quantile. From the
Distribution dropdown list, select f. In the Degrees of
Freedom 1 field, type 4, and in the Degrees of Freedom 2
field, type 10.
5. Click OK.
6. The value 3.48 appears in the second column of the data
window, which is named Quantile. To see more decimal
places in the display, click the Increase Precision button on
the DataSet toolbar.
68
Common Probability Distributions for Discrete Variables
COMMON PROBABILITY DISTRIBUTIONS FOR DISCRETE
VARIABLES
A discrete random variable is one that can assume only a finite
number of values. Examples of discrete variables include the outcome
of rolling a die, the outcome of flipping a coin, and the gender of a
newborn child. Many discrete probability distributions are based on
the Bernoulli trial, an experiment in which there is only two possible
outcomes. The outcomes are often denoted as “head” and “tail”, or
“success” and “failure”. Mathematically, it is convenient to designate
the two outcomes as 1 and 0. A variable X is a Bernoulli random
variable with parameter p if X assumes the values 1 and 0 with the
probabilities P ( X = 1 ) = p and P ( X = 0 ) = 1 – p , where 0 ≤ p ≤ 1 .
In Spotfire S+ you can generate a series of Bernoulli trials using the
sample function. The following command returns a Bernoulli sample
of size 20 with replacement, using probabilities of 0.35 and 0.65 for 0
and 1, respectively:
> sample(0:1, 20, T, c(0.35, 0.65))
[1] 0 0 0 1 1 0 0 1 1 1 1 0 0 0 1 1 1 1 0 1
This section describes three of the most common discrete
distributions: binomial, Poisson, and hypergeometric. See the section
Other Discrete Distribution Functions in S-PLUS for descriptions of
additional distributions.
Binomial
Distribution
The binomial distribution describes the probability that one of two
events occurs a certain number of times in n trials. If X 1, X 2, …, X n
are independent Bernoulli random variables, each having a
probability parameter p and possible values of 0 or 1, then a
binomial random variable X is defined as their sum:
n
X =
 Xi .
i=1
69
Chapter 3 Probability
A binomial random variable with parameters n and p has the
following probability density function:
n k
n–k
,
f n, p ( k ) =   p ( 1 – p )
 k
n
n!
where   = ----------------------- . This density gives the probability that
 k
k! ( n – k )!
exactly k successes occur in n Bernoulli trials.
S-PLUS functions
dbinom, pbinom, qbinom, rbinom
Each of these functions require you to specify values for the size ( n )
and prob ( p ) parameters.
Command line example
A classic illustration for the binomial distribution is the coin toss. The
following examples compute the probability of getting 6 heads with
10 throws of a fair coin.
What is the probability of getting 6 heads with 10 throws of a fair
( p = 0.5 ) coin?
> dbinom(6,10,0.5)
[1] 0.2050781
What is the probability of getting at most 6 heads with 10 throws of a
fair coin?
> pbinom(6,10,0.5)
[1] 0.828125
Suppose someone is tossing a coin, and you are not sure whether the
coin is fair. In 10 throws, what is the largest number of heads you
would expect in order to be 95% confident that the coin is fair?
> qbinom(0.95,10,0.5)
[1] 8
70
Common Probability Distributions for Discrete Variables
Thus, if 9 or 10 tosses showed heads, you would suspect that the coin
might not be fair.
Windows GUI Example
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Type 6 in the first cell.
3. Highlight the first column and choose Data Distribution
Functions.
4. In the Result Type field, select Density. From the
Distribution dropdown list, select binomial. Type 0.5 in the
Probability field and type 10 in the Sample Size field.
5. Click Apply.
6. The value 0.21 appears in the second column of the data
window, which is named Density.
7.
To find the probability of throwing at most 6 heads with 10
throws of the coin, change the Result Type field to
Probability in the Distribution Functions dialog.
8. Click Apply.
9. The value 0.83 appears in a Probability column of the data
window.
10. To find the maximum number of heads that you would
expect from 10 throws to be 95% confident that the coin is
fair, type 0.95 in the first cell of a new column in the data
window. Name the new column V4.
11. In the Distribution Functions dialog, type V4 in the Source
Column field, and change the Result Type to Quantile.
12. Click OK.
13. The value 8 appears in a Quantile column of the data
window.
Poisson
Distribution
The Poisson distribution is the limit of a binomial distribution, as the
number of Bernoulli trials n gets large and the probability of a
success p gets small. Formally, a binomial distribution approaches a
71
Chapter 3 Probability
Poisson distribution if n → ∞ and p → 0 in a way such that their
product remains constant, np = λ . A Poisson random variable with a
parameter λ has the following probability density function:
k –λ
λ e
f λ ( k ) = ------------- , k = 0, 1, 2, …
k!
In practice, computing exact binomial probabilities is convenient for
small sample sizes only, which suggests when Poisson approximations
can arise. Suppose X is a binomial random variable that describes the
number of times an event occurs in a given interval of time. Assume
that we can divide the time interval into a large number of equal
subintervals, so that the probability of an event in each subinterval is
very small. Three conditions must hold for a Poisson approximation
to be valid in this situation. First, the number of events that occur in
any two subintervals must be independent of one another. Second,
the probability that an event occurs is the same in each subinterval of
time. Third, the probability of two or more events occurring in a
particular subinterval is negligible in comparison to the probability of
a single event. A process that meets these three conditions is called a
Poisson process, and arises in fields as diverse as queueing theory and
insurance analysis.
A Poisson random variable with parameter λ has a mean value of λ .
Consequently, the number of events that occur in a Poisson process
over t subintervals of time has a mean value of λt .
S-PLUS functions
dpois, ppois, qpois, rpois
Each of these functions requires you to specify a value for lambda.
Command line example
The following example is taken from Rosner (1995). The number of
deaths attributed to typhoid fever over a 1-year period is a Poisson
random variable with λ = 4.6 . What is the probability distribution
for the number of deaths over a 6-month period? To find this, we use
a parameter of 2.3, since the time interval in question is half of 1 year.
72
Common Probability Distributions for Discrete Variables
To find the probability of 0, 1, 2, 3, 4, or 5 deaths in a 6-month
period, use the following command:
> dpois(0:5,2.3)
[1] 0.10025884 0.23059534 0.26518464 0.20330823 0.11690223
[6] 0.05377503
To find the probability of more than 5 deaths, use the following
command:
> 1-ppois(5,2.3)
[1] 0.03
Windows GUI Example
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Highlight the first column and choose Data Fill. Select
<END> from the dropdown list for Columns, type 6 in the
Length field, and type 0 in the Start field.
3. Click OK.
4. A sequence of integers from 0.00 to 5.00 appear in the first
column, which is named V1.
5. Highlight the column and choose Data Distribution
Functions.
6. In the Result Type field, select Density. From the
Distribution dropdown list, select poisson. Type 2.3 in the
field for Mean.
7.
Click Apply.
8. The values 0.10, 0.23, 0.27, 0.20, 0.12, and 0.05 appear in the
second column of the data window, which is named Density.
To see more decimal places in the display, click the Increase
Precision button on the DataSet toolbar.
9. To find the probability that more than 5 deaths occur in a 6month period, type 5 in the first cell of a new column and
name the column V3.
73
Chapter 3 Probability
10. In the Distribution Functions dialog, type V3 in the Source
Column field, and change the Result Type to Quantile.
11. Click OK.
12. The value 0.97 appears in a Probability column of the data
window. This means that the probability that more than five
deaths occur is 1 – 0.97 , or 0.3.
Hypergeometric The hypergeometric distribution is used in the analysis of two
categorical variables, and is best described by the classic Urn Model.
Distribution
Suppose an urn contains b balls, of which m are red and n = b – m
are black. A hypergeometric random variable denotes the number of
red balls drawn when k balls are taken from the urn without
replacement. Given the parameters m , n , and k , the hypergeometric
probability density function is:
 m  n 
 r   k – r
f m, n, k ( r ) = -------------------------- .
 m + n
 k 
This density gives the probability that exactly r red balls are drawn
from the urn.
The hypergeometric distribution is similar to the binomial
distribution: where a binomial variable is sampled from a finite
population with replacement, a hypergeometric variable is sampled
without replacement. In fact, as b → ∞ and the proportion of red
balls in the urn approaches p , the hypergeometric distribution
converges to a corresponding binomial distribution.
Hypergeometric random variables arise primarily in acceptance
sampling in manufacturing. That is, the number of sample products
that should be tested for quality in a particular batch follows a
hypergeometric distribution. Such information can be used to
determine an acceptable limit for the number of defective products.
74
Common Probability Distributions for Discrete Variables
S-PLUS functions
dhyper, phyper, qhyper, rhyper
These functions require you to specify values for the number of red
balls in the urn ( m ) , the number of black balls in the urn ( n ) , and the
number of balls drawn without replacement ( k ) .
Command line example
A box contains 100 balls, of which 50 are red and 50 are black. Ten
balls are drawn from the box at random without replacement. What is
the probability that all of the balls chosen will be red?
> dhyper(10, 50, 50, 10)
[1] 0.000593
Thus, the probability of choosing ten out of ten red balls from the box
is quite low.
Windows GUI Example
1. Open an empty data set by clicking the New Data Set button
on the standard toolbar.
2. Type 10 in the first cell.
3. Highlight the first column and choose Data Distribution
Functions.
4. In the Results Type field, select Density. From the
Distribution dropdown list, choose hypergeometric. Type
10 for the Sample Size, and type 50 for both the Total
Successes and Total Failures.
5. Click OK.
6. The values 0.00 appears in the second column of the data
window, which is named Density. To see more decimal places
in the display, click the Increase Precision button on the
DataSet toolbar.
75
Chapter 3 Probability
OTHER CONTINUOUS DISTRIBUTION FUNCTIONS IN
S-PLUS
Beta
Distribution
The beta distribution is very versatile, and plots of the distribution
function can assume a wide variety of shapes. This flexibility allows
many uncertainties to be described by beta random variables.
Example applications include statistical likelihood ratio tests, random
walks, and Bayesian inference in decision theory.
The standard form of the beta probability density function is:
1
a–1
b–1
f a, b ( x ) = -----------------x
(1 – x)
,
B ( a, b )
where 0 ≤ x ≤ 1 , a and b are positive shape parameters, and B is the
beta function,
B ( a, b ) =
1 a–1
0 u
(1 – u)
b–1
du .
S-PLUS functions
dbeta, pbeta, qbeta, rbeta
Each of these functions requires you to specify values for the two
shape parameters.
Exponential
Distribution
The exponential distribution is one-sided and is characterized by a
memoryless property. It is often used to model the lifetimes of machine
components and the wait times in Poisson processes. For example,
suppose that the random variable X denotes the lifetime of a
particular electronic component. Given that the component survives
for t months, the probability that it survives for s more is not
dependent on t . Formally, the memoryless property is stated in the
following conditional probability:
P( X > t + s X > t) = P( X > s) .
In Poisson processes, exponential random variables describe the wait
times between events.
76
Other Continuous Distribution Functions in S-PLUS
The exponential probability density function is defined as follows:
f λ ( x ) = λe
– λx
,
where x > 0 and λ is a positive parameter.
S-PLUS functions
dexp, pexp, qexp, rexp
Each of these functions has an optional argument for the rate ( λ )
parameter. By default, λ = 1 .
Gamma
Distribution
The gamma distribution is a generalization of the exponential
distribution. Where an exponential variable models the wait time
until the next event in a Poisson process, a gamma random variable
models the wait time until the nth event. In applied work, gamma
distributions provide models for many physical situations, including
meteorological precipitation processes and personal income data in
the United States.
The probability density function for a gamma random variable is
defined as:
α
λ
α – 1 – λx
f α, λ ( x ) = ------------ x
e ,
Γ(α)
where x > 0 , α > 0 is a shape parameter, and β (the inverse of λ ) is a
scale parameter, and Γ is the gamma function.
S-PLUS functions
dgamma, pgamma, qgamma, rgamma
Each of these functions requires you to specify a value for the shape
( α ) parameter. They also have optional arguments for the rate ( λ )
parameter, which is defined to be 1 by default.
Weibull
Distribution
The Weibull distribution is closely related to the exponential
distribution, and is commonly used in manufacturing to test the
breaking strength of materials. In this context, Weibull random
variables can model the lifetimes of machine components more
realistically than exponential random variables. This is because the
77
Chapter 3 Probability
Weibull distribution has a failure rate (or hazard function) that varies
with time, whereas the exponential has a constant failure rate due to
the memoryless property. In some contexts, the lifetime of particular
components may increase or decrease with time, making the Weibull
distribution more appropriate.
The probability density function for Weibull random variables is:
α α–1
x α
f α, β ( x ) = -----α- x
exp  –  ---  ,
  β 
β
where x > 0 , α is a positive shape parameter, and β is a positive
scale parameter. When α = 1 , this distribution corresponds to an
exponential distribution with a hazard rate of 1 ⁄ β . The failure rate of
the Weibull distribution decreases with time when 0 < β < 1 , is
constant when β = 1 , and increases when β > 1 . In Spotfire S+, the
Weibull distribution is the default for Parametric Survival and Life
Testing.
S-PLUS functions
dweibull, pweibull, qweibull, rweibull
Each of these functions requires you to specify a value for the shape
( α ) parameter. They also have an optional argument for the scale
( β ) parameter, which is defined to be 1 by default.
Logistic
Distribution
The logistic distribution is similar in shape to a Gaussian distribution,
though it has longer tails. Logistic random variables are used heavily
to model growth curves, but they have also been used in bioassay
studies and other applications.
The probability density function for a logistic random variable is
defined to be:
λ–x
exp  -----------
 θ 
f λ, θ ( x ) = ------------------------------------------------2- ,
λ–x
θ  1 + exp  ----------- 

 θ 
where λ is a location parameter and θ is a positive scale parameter.
78
Other Continuous Distribution Functions in S-PLUS
With respect to growth curves, the logistic distribution function F
satisfies the following: the derivative of F with respect to x is
proportional to [ F ( x ) – A ] [ B – F ( x ) ] with A < B . The interpretation
of this statement is that the rate of growth is proportional to the
amount already grown, multiplied by the amount of growth that is
still expected.
S-PLUS functions
dlogis, plogis, qlogis, rlogis
Each of these functions has optional arguments for the location ( λ )
and scale ( θ ) parameters. By default, the values of these arguments
are λ = 0 and θ = 1 .
Cauchy
Distribution
Like the Gaussian distribution, the Cauchy distribution is unimodal
and symmetric. Like the t distribution, however, plots of the Cauchy
distribution have tails that tend to zero much more slowly than a
normal distribution. Given two independent standard normal
variables Z 1 and Z 2 , each having mean 0 and standard deviation 1, a
standard Cauchy random variable Z is defined as their quotient:
Z
Z = ----1- .
Z2
Thus, a standard Cauchy random variable follows a t distribution
with one degree of freedom. A general Cauchy variable is defined by
multiplying Z by a positive scale parameter θ , and then adding a
location parameter λ .
Given λ and θ , the probability density function for a general Cauchy
random variable is:
x–λ
f λ, θ ( x ) =  πθ 1 +  ----------- 
 θ  

2
–1
.
The density function for a standard Cauchy variable corresponds to
the case when λ = 0 and θ = 1 .
79
Chapter 3 Probability
The Cauchy density has a few peculiar properties that provide
counterexamples to some accepted statistical results. For example, the
tails of the density are long enough so that its mean and variance do
not exist. In other words, the density decreases so slowly that a wide
range of values can occur with significant probability, and so the
integral expressions for the mean and variance diverge.
S-PLUS functions
dcauchy, pcauchy, qcauchy, rcauchy
Each of these functions has optional arguments for the location ( λ )
and scale ( θ ) parameters. By default, the values of these parameters
are λ = 0 and θ = 1 .
Lognormal
Distribution
The lognormal distribution is a logarithmic transformation of the
normal distribution. Given a normal random variable Y with
parameters μ and σ , a lognormal random variable X is defined to be
its exponential:
Y
X = e .
Thus, the natural logarithm of data that follows a lognormal
distribution should be approximately Gaussian.
The probability density function for a lognormal random variable is:
1
1
2
f μ, σ ( x ) = – ----------------- exp  – --------2- ( log x – μ )  ,


σx 2π
2σ
where x > 0 , and μ and σ > 0 are the mean and standard deviation,
respectively, of the logarithm of the random variable. With this
μ
definition, e is a scale parameter for the distribution, and σ is a
shape parameter.
The lognormal distribution is sometimes referred to as the
antilognormal distribution, since it is the distribution of an exponential
(or antilogarithm) of a normal variable. When applied to economic
data, particularly production functions, it is sometimes called the
Cobb-Douglas distribution. In some cases, lognormal random variables
can represent characteristics like weight, height, and density more
realistically than a normal distribution. Such variables cannot assume
80
Other Continuous Distribution Functions in S-PLUS
negative values, and so they are naturally described by a lognormal
distribution. Additionally, with a small enough σ , it is possible to
construct a lognormal distribution that closely resembles a normal
distribution. Thus, even if a normal distribution is felt to be
appropriate, it might be replaced by a suitable lognormal distribution.
S-PLUS functions
dlnorm, plnorm, qlnorm, rlnorm
Each of these functions has optional arguments for the meanlog ( μ )
and sdlog ( σ ) parameters. By default, the values of these arguments
are μ = 0 and σ = 1 .
Distribution of
the Range of
Standard
Normals
The distribution of the range of standard normal random variables is
primarily used for the construction of R-charts in quality control
work. Given n standard normal variables Z 1, Z 2, …, Z n , each with
mean 0 and standard deviation 1, the range is defined as the difference
between the minimum and maximum of the variables.
S-PLUS functions
dnrange, pnrange, qnrange, rnrange
Each of these functions requires you to specify a value for the size
( n ) of the sample. They also have an optional nevals argument that
defines the number of iterations in the density, probability, and
quantile computations. The probability density function for the range
of standard normals is a complicated integral equation, and can
therefore require significant computation resources. A higher value of
nevals will result in better accuracy, but will consume more machine
time. By default, nevals is set to 200.
81
Chapter 3 Probability
Multivariate
Normal
Distribution
The multivariate normal distribution is the extension of the Gaussian
distribution to more than one dimension. Let d be the number of
dimensions in the multivariate distribution, let μ be a vector of length
d specifying the mean in each dimension, and let Σ be a d × d
variance-covariance matrix. The probability density function for a
multivariate normal random variable is given by:
f μ, Σ ( x ) = ( 2π )
–d ⁄ 2
Σ
–1 ⁄ 2
1
–1
exp  – --- ( x – μ )′Σ ( x – μ ) ,
 2

where x is the vector ( x 1, x 2, …, x d ) , and Σ is the determinant of Σ .
S-PLUS functions
dmvnorm, pmvnorm, rmvnorm
Each of these functions has an optional argument for the mean vector
( μ ) . In addition, you can specify the variance-covariance matrix ( Σ )
through the cov and sd arguments. If supplied, the variancecovariance matrix is the product of the cov matrix and the sd
argument, which contains the standard deviations for each
dimension. By default, mean is a vector of zeros, cov is an identity
matrix, and sd is a vector of ones.
Stable Family
of
Distributions
Stable distributions are of considerable mathematical interest. A
family is considered stable if the convolution of two distributions from
the family also belongs to the family. Each stable distribution is the
limit distribution of a suitably scaled sum of independent and
identically distributed random variables. Statistically, they are used
when an example of a very long-tailed distribution is required.
S-PLUS functions
rstab
The rstab function requires a value from the interval ( 0, 2 ] for an
index argument. For small values of the index, the distribution
degenerates to point mass at 0. An index of 2 corresponds to the
normal distribution, and an index of 1 corresponds to the Cauchy
distribution. Smaller index values produce random numbers from
stable distributions with longer tails. The rstab function also has an
optional skewness argument that indicates the modified skewness of
82
Other Continuous Distribution Functions in S-PLUS
the distribution. Negative values correspond to left-skewed random
numbers, where the median is smaller than the mean (if it exists).
Positive values of skewness correspond to right-skewed random
numbers, where the median is larger than the mean. By default, the
skewness is set to 0.
S-PLUS contains only the rstab probability function for the stable
family of distributions. The efficient computation of density,
probability, and quantile values is currently an open problem.
83
Chapter 3 Probability
OTHER DISCRETE DISTRIBUTION FUNCTIONS IN S-PLUS
Geometric
Distribution
The geometric distribution describes the number of failures before
the first success in a sequence of Bernoulli trials. In binomial
distributions, we think of the number of trials n and the probability of
a success p as fixed parameters, so that the number of successes k is
the random variable. Reversing the problem, we could ask how many
trials would be required to achieve the first success. In this
formulation, the number of failures is the random variable, and p and
k = 1 are fixed.
A geometric random variable with a parameter p has the following
probability density function:
n
f p ( n ) = p ( 1 – p ) , n = 0, 1, 2, …
This density gives the probability that exactly n failures occur before
a success is achieved.
S-PLUS functions
dgeom, pgeom, qgeom, rgeom
Each of these functions require you to specify a value for the prob ( p )
parameter.
Negative
Binomial
Distribution
The negative binomial distribution is a generalization of the
geometric distribution. It models the number of failures before
exactly r successes occur in a sequence of Bernoulli trials. When
r = 1 , a negative binomial random variable follows a geometric
distribution, and in general, a negative binomial variable is a sum of r
independent geometric variables.
Given the probability of a success p and the number of successes r as
parameters, the negative binomial probability density function is:
r + k – 1 r
k
f p, r ( k ) = 
p ( 1 – p ) , k = 0, 1, 2, …


k
84
Other Discrete Distribution Functions in S-PLUS
This density gives the probability that exactly k failures occur before
r successes are achieved.
S-PLUS functions
dnbinom, pnbinom, qnbinom, rnbinom
Each of these functions require you to specify values for the size ( r )
and prob ( p ) parameters.
Distribution of
Wilcoxon Rank
Sum Statistic
The Wilcoxon rank sum statistic, also known as the Mann-Whitney test
statistic, is a nonparametric method for comparing two independent
samples. The test itself is best described in terms of treatment and
control groups. Given a set of m + n experimental units, we randomly
select n and assign them to a control group, leaving m units for a
treatment group. After measuring the effect of the treatment on all
units, we group the m + n observations together and rank them in
order of size. If the sum of the ranks in the control group is too small
or too large, then it’s possible that the treatment had an effect.
The distribution of the Wilcoxon rank sum statistic describes the
probability characteristics of the test values. Given m and n as
m(m + 1)
parameters, the rank sum statistic takes on values between ----------------------2
m ( m + 2n + 1 )
and ------------------------------------ .
2
S-PLUS functions
dwilcox, pwilcox, qwilcox, rwilcox
Each of these functions require you to specify sizes ( m and n ) for the
two independent samples.
The wilcox functions are available in S-PLUS via the command line
only.
85
Chapter 3 Probability
EXAMPLES: RANDOM NUMBER GENERATION
In this section, we illustrate two of the common algorithms for
random number generation: the inverse cdf method and the polar
method. The algorithms we discuss are both standard techniques from
introductory statistics textbooks, and involve transformations of
uniform random variables. The techniques can thus be applied to
develop random number generators for distributions that are not
implemented in S-PLUS. The algorithms we present do not
encompass all random number generators for all distributions, and
due to efficiency considerations, they are not the algorithms
implemented in S-PLUS. Nevertheless, they are solid examples of
how the S-PLUS probability functions can be modified to serve
different analytical needs.
For details on the pseudo-random number generator implemented in
S-PLUS, see Chapter 34, Mathematical Computing in Spotfire S+.
Inverse
Distribution
Functions
A fundamental result from probability theory states that if U is a
uniform random variable on the interval [0,1] , and another variable
–1
X = F ( U ) for some function F , then the cumulative distribution
function for X is F . This leads to the inverse cdf method for generating
random numbers from a uniform distribution:
1. Given a distribution function u = F ( x ) , find an expression
–1
for the inverse function x = F ( u ) .
2. Generate uniform random variables on the interval [0,1] , and
–1
substitute them into F . The resulting values are randomly
sampled from the distribution F .
This method is practical for those distribution functions with inverses
that can be easily calculated.
The Exponential
Distribution
Exponential random variables have a probability density function
f λ ( x ) = λe
– λx
, where x > 0 and λ is a positive parameter. The
exponential distribution function F λ is the integral of f λ over positive
x values, which gives F λ ( x ) = 1 – e
86
– λx
. Solving F λ for x , we find the
Examples: Random Number Generation
–1
inverse function F λ ( x ) = – ln ( 1 – x ) ⁄ λ . We can therefore generate
–1
uniform random variables and substitute them into F λ to calculate
exponential variables. The code below packages this process into a SPLUS function exp.rng.
>
+
+
+
exp.rng <- function(n,lambda=1) {
unif.variables <- runif(n,0,1)
return((-1/lambda)*log(1-unif.variables))
}
To generate 15 exponential random variables with the default
parameter λ = 1 , use the following command:
> exp.rng(15)
[1] 0.5529780 3.0265630 0.5664921 1.2665062 0.1150221
[6] 0.1091290 2.4797445 2.7851495 1.0714771 0.1501076
[11] 1.5948872 1.4719187 0.4208105 0.8323065 0.6344408
The Double
Exponential
Distribution
The double exponential or Laplace distribution is not explicitly
implemented in S-PLUS. However, it is straightforward to develop a
random number generator for this distribution based on a
transformation of exponential variables. To do this, we use the
method
outlined
Law
and
Kelton’s
text
<referenceyear>(1991)<reference-year>.
The probability density function for a double exponential random
variable is defined as:
λ –λ x
,
f λ ( x ) = --- e
2
where λ is a positive parameter. Whereas the regular exponential
density is defined for positive x only, the Laplace density is defined
for all x . In fact, plots of the Laplace density function show that it is
two exponential densities placed back-to-back. In other words, it is
symmetric about x = 0 and includes both the exponential density
and its mirror image across the y axis. This gives the process below
for generating Laplace random variables.
87
Chapter 3 Probability
1. Calculate an exponential random variable X .
2. Calculate a uniform random variable U on the interval [0,1] .
3. If U ≤ 0.5 , return – X . This step ensures that we sample
negative values from the Laplace distribution approximately
half of the time.
4. If U > 0.5 , return X . This step ensures that we sample
positive values from the Laplace distribution approximately
half of the time.
The code below packages this process into the function laplace.rng.
> laplace.rng <- function(n,lambda=1) {
+ return(rexp(n,rate=lambda) * ifelse(runif(n)<=.5, -1, 1))
+ }
To generate 12 Laplace random variables with the default parameter
λ = 1 , use the following command:
> laplace.rng(12)
[1] -0.40098376 -0.37866455 -0.97648670 3.31844284
[5] 0.03778431 -0.11506231 -0.45228857 -1.66733404
[9] -0.97993096 -3.84597617 3.31298104 -0.04314876
The Polar
Method
The polar method, or Box-Muller method for generating random
variables is most often seen in the context of the normal or
multivariate normal distributions. The justification behind the
method relies on a few theoretical details which we only briefly
mention here. For a rigorous justification of the method, we refer the
interested user to a general statistics text such as Rice (1995).
A fundamental transformation law of probabilities states that if X is a
vector of jointly distributed continuous random variables that is
mapped into U , then the density functions of X and U are related via
the determinant of the Jacobian of the transformation. We can use this
result to relate the probability characteristics of normally distributed
cartesian coordinates (X 1,X 2) and their corresponding polar
coordinates (r,θ) .
88
Examples: Random Number Generation
The Normal
Distribution
The two-dimensional polar method for generating normal random
variables is:
1. Generate two uniform random variables U 1 and U 2 on the
interval [0,1] .
2. Calculate the values
X1 =
– 2σ ln ( U 1 ) cos ( 2πU 2 )
X2 =
– 2σ ln ( U 1 ) sin ( 2πU 2 ) .
3. It can be shown with the fundamental transformation law that
X 1 and X 2 are independent Gaussian random variables with
mean 0 and standard deviation σ . Graphically,
– 2σ ln ( U 1 )
is the radius r of the point (X 1,X 2) in polar coordinates, and
2πU 2 is the angle θ .
4. To calculate normal random variables with arbitrary mean μ ,
return the values X 1 + μ and X 2 + μ .
The code below packages this process into the S-PLUS function
gaussian.rng.
>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
gaussian.rng <- function(n,mu=0,sigma=1) {
x <- vector(mode="numeric")
# Check whether n is even or odd.
if(abs(n/2-floor(n/2))<.Machine$double.eps) {
odd.indices <- seq(from=1,to=n,by=2)
even.indices <- seq(from=2,to=n,by=2)
unif.variables <- runif(n,0,1) }
else { odd.indices <- seq(from=1,to=n,by=2)
even.indices <- seq(from=2,to=n+1,by=2)
unif.variables <- runif(n+1,0,1) }
u1 <- unif.variables[odd.indices]
u2 <- unif.variables[even.indices]
x[odd.indices] <- sqrt(-2*sigma*log(u1))*cos(2*pi*u2)
x[even.indices] <- sqrt(-2*sigma*log(u1))*sin(2*pi*u2)
x <- x+mu
return(x[1:n])
}
89
Chapter 3 Probability
To generate 12 Gaussian random variables with the default
parameters μ = 0 and σ = 1 , use the following command:
> gaussian.rng(12)
[1] -1.54634074 -0.37344362 -0.10249664 0.24225650
[5] 1.02383498 0.80662589 0.40487670 -2.15404022
[9] -1.22147040 0.02814069 0.17593919 -1.33878256
90
References
REFERENCES
Altman, D.G. (1991). Practical Statistics for Medical Research. London:
Chapman & Hall.
Chambers, J.M. & Hastie, T.J. (1993). Statistical Models in S. London:
Chapman & Hall.
Chambers, J.M., Mallows, C.L., & Stuck, B.W. (1976). A method for
simulating random variables. Journal of the American Statistical
Association, 71(354):340-344.
DeGroot, M.H. (1975). Probability and Statistics.
Massachusetts: Addison-Wesley Publishing Company.
Reading,
Evans, M., Hastings, N., and Peacock, B. (1993). Statistical
Distributions (2nd ed.). New York: John Wiley & Sons, Inc.
Freedman, D., Pisani, R., & Purves, R. (1978). Statistics. New York:
W.W Norton and Company.
Hanushek, E.A. & Jackson, J.E. (1977). Statistical Methods for Social
Scientists. Orlando, FL: Academic Press, Inc.
Hartley, H.O. (1942). The range in random samples. Biometrika,
32:334-348.
Hoel, P.G., Port, S.C., & Stone, C.J. (1971). Introduction to Probability
Theory. Boston: Houghton Mifflin Company.
Iversen, G.R. & Gergen, M. (1997). Statistics: The Conceptual Approach.
New York: Springer-Verlag Inc.
Johnson, N.L., Kotz, S., & Balakrishnan, N. (1994). Continuous
Univariate Distributions, Vol.1 (2nd ed.). New York: John Wiley & Sons,
Inc.
Johnson, N.L., Kotz, S., & Balakrishnan, N. (1995). Continuous
Univariate Distributions, Vol.2 (2nd ed.). New York: John Wiley & Sons,
Inc.
Larsen, R.J. & Marx, M.L. (1981). An Introduction to Mathematical
Statistics and Its Applications. Englewood Cliffs, NJ: Prentice-Hall, Inc.
Law, A.M., & Kelton, W.D. (1991). Simulation Modeling and Analysis.
New York: McGraw-Hill, Inc.
91
Chapter 3 Probability
Miller, I. & Freund, J.E. (1977). Probability and Statistics for Engineers
(2nd ed.). Englewood Cliffs, NJ: Prentice-Hall, Inc.
Rice, J.A. (1995). Mathematical Statistics and Data Analysis (2nd ed.).
Belmont, CA: Duxbury Press.
Rosner, B. (1995). Fundamentals of Biostatistics (4th ed.). Belmont, CA:
Duxbury Press.
Venables, W.N. & Ripley B.D. (1997). Modern Applied Statistics with
S-PLUS (2nd ed.). New York: Springer-Verlag.
92
DESCRIPTIVE STATISTICS
Introduction
4
94
Summary Statistics
Measures of Central Tendency
Measures of Dispersion
Measures of Shape
The summary Function
95
95
98
102
105
Measuring Error in Summary Statistics
Standard Error of the Mean
Confidence Intervals
106
106
107
Robust Measures of Location and Scale
M Estimators of Location
Measures of Scale Based on M Estimators
110
110
112
References
115
93
Chapter 4 Descriptive Statistics
INTRODUCTION
When collecting data from a particular population, a researcher often
knows a few defining characteristics about the population. For
example, the researcher may know that the data is from a nearly
normal population, in the sense that its theoretical distribution is close
to Gaussian. It is sometimes tempting to jump directly into complex
data analyses and assume that a known theoretical distribution fully
describes the data. However, it is usually wise to assume little, and
instead examine the data in a rigorous manner.
There are two complementary approaches when initially examining a
data set: exploratory data analysis and descriptive statistics. Exploratory
data analysis involves various graphs that illustrate relationships in
the data set. An example of this technique is provided in Chapter 1,
Introduction to Statistical Analysis in Spotfire S+. In this chapter, we
discuss common descriptive statistics that are used to numerically
examine the characteristics of a data set. Given a set of n
observations X 1, X 2, …, X n , we think of them as random samples
from a population with a particular distribution. In this context,
descriptive statistics are estimates of the location, scale, and shape of
the distribution. We begin by discussing common measures such as
the sample mean and variance. We then present a few of the more
robust measures, such as M estimators, Huber estimates, and bisquare
functions.
Throughout this chapter, we include examples in which descriptive
statistics are used and computed in TIBCO Spotfire S+. Wherever
possible, we provide menu examples for the Spotfire S+ graphical
user interface (GUI). At this time, however, there are some
computations that are available only through the command line
functions.
94
Summary Statistics
SUMMARY STATISTICS
Measures of
Central
Tendency
Measures of central tendency provide an indication of the center of a
population. Because of this, they are sometimes referred to as measures
of location. Estimates of population centers are useful in determining
the expected value of a sample, or where (on average) an observation
from the population tends to lie.
Mean
The mean is by far the most common measure of central tendency.
Given a sample X 1, X 2, …, X n , the mean X is simply the arithmetic
average of the observations:
1
X = --n
n
 Xi .
i=1
It can be shown that X is an unbiased estimate of the true mean of the
population. Suppose the theoretical distribution from which the
observations are sampled has a mean of μ . Then the expected value
of X is equal to μ , and the sample mean provides an unbiased
estimate of the true mean. In other words, X is equal to the true mean
of the population on average.
Command line example
The S-PLUS function mean requires you to specify a numeric vector,
and it returns the arithmetic average of the vector.
> mean(lottery.payoff)
[1] 290.3583
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Mean.
95
Chapter 4 Descriptive Statistics
4. Click OK.
5. The value 290.3583 appears in a Report window.
The sample mean is attractive as a measure of location because it is a
conceptually straightforward estimate. However, X is very sensitive
to outlying observations. By changing a single observation in a
sample, the arithmetic mean can be made arbitrarily large or
arbitrarily small. As a result, it is often used in conjunction with robust
measures of location, which are insensitive to outlying data points.
We discuss a few of the simpler robust measures here. For additional
statistics, see the section Robust Measures of Location and Scale.
Trimmed Mean
The first robust measure of location that we discuss is the trimmed
mean. Given a sample, we first sort the observations in ascending
order. If we know that a certain percentage of the observations are
prone to extreme values, we discard them from either end of the
sorted data before computing the mean. As a result, the trimmed
mean estimates the population center more closely than the
arithmetic mean, especially in the presence of outliers.
Example
The S-PLUS function mean has an optional trim argument for
computing the trimmed mean of a vector. A value between 0 and 0.5,
representing the percentage of observations to be discarded from
either extreme of the data vector, can be specified for trim. The
arithmetic average of the trimmed vector is returned. This example
computes the 20% trimmed mean of the lottery.payoff vector.
> mean(lottery.payoff, trim=0.2)
[1] 274.1558
Median
96
The second robust measure of location that we discuss is the median.
Given a sample of size n , we first sort the observations in ascending
order. If n is odd, the median M is defined to be the middle value. If
n is even, then M is equal to the average of the two middle values.
The median is not affected by extreme values in a sample, and is
therefore quite robust against outlying observations.
Summary Statistics
Command line example
The S-PLUS function median requires you to specify a numeric
vector, and it returns the median of the vector.
> median(lottery.payoff)
[1] 270.25
Note that the median of the lottery.payoff vector is lower than the
arithmetic mean. This indicates that the data vector has a few large
values that influence the mean.
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Median.
4. Click OK.
5. The value 270.25 appears in a Report window.
Mode
The third robust measure of location that we discuss is the mode. The
mode of a sample is defined to be the most frequently occurring value
in it. Graphically, the mode is the value at which a histogram of the
data reaches a maximum. For fairly symmetric distributions of data,
the mode is a good indicator of the population center. For skewed
distributions, the mode can indicate whether the bulk of the values
occur in the higher or lower ranges.
Example
You can use the S-PLUS function table to compute the mode of a
sample. The following two commands define and test a function that
returns the mode of a numeric vector. Note that this statistical
property is not related to the S-PLUS function mode, which returns the
data class of a S-PLUS object.
>
+
+
+
Mode <- function(x) {
tab <- table(x)
Mode <- as.numeric(names(tab)[table(x) == max(tab)])
return(c(mode=Mode, count=max(tab))) }
97
Chapter 4 Descriptive Statistics
> Mode(lottery.payoff)
mode count
127
4
This result says that the value 127 occurs most often (4 times) in the
lottery.payoff vector. This value is considerably less than either the
mean or the median, which may indicate that a large number of the
lottery.payoff observations are in the lower range of values.
Measures of
Dispersion
Measures of dispersion provide an indication of the variability, or
“scatteredness,” in a collection of data points. Because of this,
dispersion statistics are sometimes referred to as measures of scale.
Many of these statistics are based on averaging the distance of each
observation from the center of the data, and therefore involve
measures of location.
Range
As a first measure of scale in a data set, it is often natural to examine
the range, which is the difference between the maximum and
minimum values.
Command line example
The S-PLUS function range requires you to specify a numeric object,
and it returns the minimum and maximum values in the object.
> range(lottery.payoff)
[1]
83.0 869.5
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Minimum and Maximum.
4. Click OK.
5. The values 83.0 and 869.5 appear in a Report window.
98
Summary Statistics
Variance and
Standard
Deviation
The variance of a sample is the average value of the squared deviation
from the sample mean, and the standard deviation is the square root of
the variance. Given a sample X 1, X 2, …, X n and the arithmetic mean
2
of the sample X , the variance s is defined as:
1
2
s = -----------n–1
n
 ( Xi – X )
2
.
i=1
The standard deviation of the sample is therefore equal to s . The sum
of squares for the sample is equal to
 ( Xi – X )
2
.
i
If s
2
is the average of the squared deviation, one might expect a
2
divisor of n instead of n – 1 . However, it can be shown that s is an
unbiased estimate of the population variance, whereas a divisor of n
produces a biased estimate. Suppose the theoretical distribution from
2
which the observations are sampled has a variance of σ . Then the
2
2
expected value of s is equal to σ , and the sample variance provides
2
an unbiased estimate of the true variance. In other words, s is equal
to the true variance of the population on average.
Command line example
The S-PLUS functions var and stdev require you to specify a
numeric vector, and they return the sample variance and standard
deviation of the vector, respectively.
> var(lottery.payoff)
[1] 16612.21
> stdev(lottery.payoff)
[1] 128.8884
We can also compute the biased estimate of variance with an optional
argument to var:
> var(lottery.payoff, unbiased=F)
99
Chapter 4 Descriptive Statistics
[1] 16546.81
The standard deviation using the biased estimate is the square root of
this value, or 128.6344. By default, the unbiased argument is set to
TRUE, giving an estimate of the variance that uses the n – 1 divisor.
With the SumSquares argument, we can compute the unnormalized
sum of squares for lottery.payoff:
> var(lottery.payoff, SumSquares=T)
[1] 4202890
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Variance and Std. Deviation.
4. Click OK.
5. The unbiased variance 16612.21 and corresponding standard
deviation 128.8884 appear in a Report window.
Like the sample mean, the range and sample variance are both very
sensitive to outliers. As a result, they are often used in conjunction
with robust measures of scale, which are insensitive to outlying
observations. We discuss a few of the simpler robust measures here.
For additional statistics, see the section Robust Measures of Location
and Scale.
Median Absolute
Deviation
The first robust measure of scale that we discuss is the median absolute
deviation, or MAD. Given a collection of data points X 1, X 2, …, X n
and a measure of the population center, the MAD is the median
distance from the X i to the center. For example, if the population
center is the mean X , the MAD is defined as the median of the values
X i – X . If the population center is the median M , the MAD is
defined as the median of the values X i – M .
100
Summary Statistics
Example
The S-PLUS function mad requires you to specify a numeric vector,
and it returns the median absolute deviation of the vector. The mad
function includes an optional center argument, which defines the
measure of location to use in the computation. By default, center is
equal to the median of the sample.
> mad(lottery.payoff)
[1] 122.3145
With the following syntax, we compute the median absolute
deviation using the 20% trimmed mean as the population center:
> mad(lottery.payoff,
+ center = mean(lottery.payoff, trim=0.2))
[1] 123.2869
Interquartile
Range
The second robust measure of scale that we discuss is the interquartile
range, or IQR. Given a collection of data points X 1, X 2, …, X n , the
IQR is the difference between the upper and lower (or third and first)
quartiles of the sample. The IQR is the visual tool used in boxplots to
display the spread of a sample around its median.
Command line example
You can use the S-PLUS function quantile to compute the
interquartile range of a sample. The following two commands define
and test a function that returns the IQR of a numeric vector.
> iqr <- function (x) diff(quantile(x, c(0.25, 0.75)))
> iqr(lottery.payoff)
75%
169.75
Note that the quantile function interpolates between data points to
find the specified quantiles. For integer samples, it is sometimes
desirable to compute the quartiles without interpolation. In this
situation, the boxplot function can be used with the plot=F argument.
The boxplot function defines quantiles to be exactly equal to a data
point, or halfway between two points. This was the method first
introduced by Tukey for computing quantiles, presumably because it
101
Chapter 4 Descriptive Statistics
made the computations by hand easier. The following commands
define a function for returning the IQR of a numeric vector without
interpolation:
>
+
+
+
+
+
iqr.data <- function(x) {
temp.boxplot <- boxplot(x, plot=F)
upper.quart <- temp.boxplot$stats[2,1]
lower.quart <- temp.boxplot$stats[4,1]
return(upper.quart-lower.quart)
}
> iqr.data(lottery.payoff)
[1] 171
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
First Quartile and Third Quartile.
4. Click OK.
5. The values 194.25 and 364.00 appear in a Report window.
The interquartile range is 364.00 – 194.25 , or 169.75.
Measures of
Shape
102
Measures of shape describe the overall pattern in the distribution of data
values. For example, generate a histogram of a collection of data
points. Measures of shape might describe how symmetric or
asymmetric the distribution in the histogram is, whether it has a
unique center or multiple centers, or if the distribution is relatively
flat. The most popular measures of shape compare a particular data
set to a normal distribution. The normal distribution provides a
reference point, and the measures of shape indicate how similar or
different the data is to a Gaussian density function.
Summary Statistics
The measures of shape that S-PLUS computes are based on the rth
central moment of a sample. Given a sample X 1, X 2, …, X n with
arithmetic mean X , the rth central moment m r is defined as:
1
m r = --n
n
 ( Xi – X )
r
.
i=1
Skewness
Skewness is a signed measure that describes the degree of symmetry,
or departure from symmetry, in a distribution. For a sample with
second and third central moments of m 2 and m 3 , respectively, the
coefficient of skewness b 1 is defined to be:
m3
b 1 = ----------.
3⁄2
m2
Positive values of b 1 indicate skewness (or long-tailedness) to the
right, negative values indicate skewness to the left, and values close to
zero indicate a nearly-symmetric distribution. S-PLUS implements a
variation of b 1 called Fisher’s G1 measure to calculate skewness. If the
size of a sample is n , Fisher’s G1 measure of skewness is:
b1 n ( n – 1 )
-.
g 1 = ----------------------------n–2
Command line example
> skewness(lottery.payoff)
[1] 1.021289
This value is positive, which indicates a long tail to the right of the
distribution’s center. The result matches our conclusions from the
robust measures of location: both the median and mode of
lottery.payoff are considerably less than the mean, which imply
that a few large values skew the distribution.
103
Chapter 4 Descriptive Statistics
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Skewness.
4. Click OK.
5. The value 1.021289 appears in a Report window.
Kurtosis
Kurtosis is a measure that describes the degree of peakedness in a
distribution. For a sample with second and fourth central moments of
m 2 and m 4 , respectively, the coefficient of kurtosis b 2 is defined to be:
m
b 2 = -----42- .
m2
Large values of b 2 usually imply a high peak at the center of the data,
and small values of b 2 imply a broad peak at the center. S-PLUS
implements a variation of b 2 called Fisher’s G2 measure to calculate
kurtosis. If the size of a sample is n , Fisher’s G2 measure of kurtosis
is:
(n + 1)(n – 1)
3(n – 1)
g 2 = ---------------------------------- b 2 – -------------------- .
(n – 2)(n – 3 )
n+1
Command line example
> kurtosis(lottery.payoff)
[1] 1.554491
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Kurtosis.
104
Summary Statistics
4. Click OK.
5. The value 1.554491 appears in a Report window.
The summary
Function
The S-PLUS function summary can operate on numeric objects to
return basic descriptive statistics in a tabular format. The output of the
summary function includes the minimum, maximum, quartiles, mean,
and median of numeric data. It is useful for printing purposes, and for
viewing a group of descriptive statistics together in one table.
Command line example
> summary(lottery.payoff)
Min. 1st Qu. Median
83 194.25 270.25
Mean 3rd Qu. Max.
290.36
364 869.5
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Mean and the Quantiles group: Minimum, First Quartile,
Median, Third Quartile, Maximum.
4. Click OK.
5. The values 83.0, 194.25, 270.25, 290.36, 364.0, and 869.5
appear in a Report window.
105
Chapter 4 Descriptive Statistics
MEASURING ERROR IN SUMMARY STATISTICS
Once we compute summary statistics for a particular collection of
data points, we are interested in measuring the amount of variation in
the estimates. This informs us how much emphasis we should give the
estimates when proceeding with statistical analyses of the data. Two
common measures of the variability in descriptive statistics are called
standard error and confidence intervals. In this section, we discuss these
measures for the sample mean only, as they are both based on largesample asymptotics. Their justifications rely on normal
approximations, which are not necessarily meaningful in the context
of the sample variance and other measures.
Standard Error
of the Mean
The standard error of the mean (or SEM) is a measure of the variation in
the location estimate X . Suppose that a sample X 1, X 2, …, X n is from
2
a population with a true mean and variance of μ and σ ,
respectively. We compute the sample mean X and the sample
2
variance s , and we wish to find a measure of the potential error in
X . Since X is an unbiased estimate, its expected value is equal to the
true mean μ . Moreover, it can be shown that the standard deviation
of X is equal to σ ⁄ n . The following estimate S X is therefore defined
as the standard error of the mean:
s
S X = ------- .
n
In practice, the SEM is useful in the context of repeated sampling. For
instance, suppose multiple samples of size n are taken from the same
population. In this situation, we think of the arithmetic mean X as a
random variable with a particular distribution. The Central Limit
Theorem tells us that, after enough samples, the distribution of X is
2
approximately normal with parameters μ and σ . Since the bulk of
106
Measuring Error in Summary Statistics
the values in a normal distribution occur within two standard
deviations of the mean, we expect the arithmetic mean of a sample to
be within twice the SEM of X .
Command line example
You can use the S-PLUS function stdev to compute the standard
error of the mean for a sample. The following two commands define
and test a function that returns the SEM of a numeric vector.
> sem <- function(x) c(mean = mean(x),
+ SEM = stdev(x)/sqrt(length(x)))
> sem(lottery.payoff)
mean
SEM
290.3583 8.087176
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Mean and Std. Error of Mean.
4. Click OK.
5. The values 290.358268 and 8.087176 appear in a Report
window.
Confidence
Intervals
A confidence interval is a range of values that contains an estimate with
some specified probability, or confidence. If a confidence interval spans
a relatively small range, we can be reasonably sure that an estimate is
accurate. Conversely, if an interval is large, then the estimate can vary
widely from sample to sample. In most analyses, 95% confidence
levels are used to understand the variability and uncertainty in an
estimate.
S-PLUS computes upper and lower confidence levels for the sample
mean X by using multiples of the SEM. Suppose that a sample
X 1, X 2, …, X n is from a population with a true mean of μ . We first
107
Chapter 4 Descriptive Statistics
calculate the sample mean X and the standard error of the mean S X .
For point estimates such as X , S-PLUS implements confidence
intervals based on quantiles of a t distribution. This is because the
standardized quantity ( X – μ ) ⁄ S X follows a t distribution with n – 1
degrees of freedom. The upper and lower ( 1 – α ) % confidence levels
are therefore defined as:
α
X ± S X q n – 1  --- ,
 2
where q n – 1 is a function that returns quantiles of the t distribution
with n – 1 degrees of freedom. To compute 95% confidence levels,
we set α = 0.05 .
Command line example
You can use the S-PLUS function t.test to compute confidence
levels for the mean of numeric vector. The t.test function has an
optional conf.level argument, which is set to 0.95 by default.
> t.test(lottery.payoff)
One-sample t-Test
data: lottery.payoff
t = 35.9035, df = 253, p-value = 0
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
274.4315 306.2850
sample estimates:
mean of x
290.3583
This result says that the 95% lower confidence level for the mean is
274.4315, and the upper confidence level is 306.285. If we take
multiple samples similar to the lottery.payoff vector, we can expect
about 95% of the sample means to lie between 274.4315 and 306.285.
108
Measuring Error in Summary Statistics
GUI example
1. Choose Statistics Data Summaries Summary
Statistics.
2. Type lottery.payoff in the field for Data Set.
3. Click on the Statistics tab, and deselect all options except for
Mean and Conf. Limits for Mean. Leave the Conf. Level
option at 0.95.
4. Click OK.
5. The values 290.358268, 274.431506, and 306.285029 appear
in a Report window.
109
Chapter 4 Descriptive Statistics
ROBUST MEASURES OF LOCATION AND SCALE
M Estimators
of Location
M estimators are a class of robust location measures that seek to find a
compromise between the sample mean and median. Given a sample
X 1, X 2, …, X n from a population with a true standard deviation of σ ,
it can be shown that the sample mean minimizes the function
n
2

h 1 ( μ̂ ) =
i=1
i – μ̂
X
------------- .
 σ 
Likewise, the median of the sample minimizes the function
n

h 2 ( μ̂ ) =
i=1
X i – μ̂
-------------- .
σ
M estimators minimize the general function
n
h ( μ̂ ) =
 X i – μ̂
- ,
 Ψ  ------------σ 
i=1
where Ψ is some weight function and the solution μ̂ is the robust
measure of location.
A wide variety of weight functions have been proposed for
M estimators. S-PLUS implements two choices for Ψ : Huber functions
and Tukey’s bisquare functions. A Huber Ψ function is defined as:
x

ΨH ( x ) = 
 sign ( x )c
x <c
x ≥c
,
where sign ( x ) is equal to -1, 0, or 1 depending on the sign of x , and
c is a tuning constant. This function is linear from – c to c and is
constant outside of this interval. Thus, Ψ H assigns the constant weight
sign ( x )c to outlying observations. Tukey’s bisquare Ψ function is
defined as:
110
Robust Measures of Location and Scale
 2 2 2
ΨT ( x ) =  x ( c – x )
0

x ≤c,
x >c
where c is a tuning constant. This function is a fifth degree
polynomial from – c to c and is zero outside of this interval. Unlike
Huber functions, bisquare functions completely ignore extreme
outliers.
In practice, the true standard deviation of a population is not known,
and σ must be approximated to compute M estimators of location.
Therefore, a robust measure of scale σ̂ (such as the MAD) is needed
in calculations of Ψ functions.
Example
You can use the S-PLUS function location.m to compute a robust
M estimator for the center of a numeric vector. The location.m
function includes optional scale, psi.fun, and parameters
arguments, which respectively define the measure of scale ( σ̂ ), Ψ
function, and tuning constant ( c ) to use in the computation. By
default, scale is the median absolute deviation from the median of
the sample, psi.fun is equal to Tukey’s bisquare function, and
parameters is set to 5.
> location.m(lottery.payoff)
[1] 279.2969
attr(, "convergence"):
sum
width evals
1.584635e-013 1.752494e-008
5
attr(, "call"):
location.m(x = lottery.payoff)
With the following syntax, we compute an M estimator of location
using a Huber Ψ function. In this case, the default value of
parameters is equal to 1.45.
> location.m(lottery.payoff, psi.fun="huber")
[1] 279.8903
attr(, "convergence"):
111
Chapter 4 Descriptive Statistics
sum
width evals
8.326673e-016 8.677228e-007
5
attr(, "call"):
location.m(x = lottery.payoff, psi.fun = "huber")
Measures of
Scale Based on
M Estimators
S-PLUS implements two robust measures of scale that are based on
M estimators of location: bisquare A estimates and Huber τ estimates.
A estimates use the asymptotic variance of M estimators as a
computationally straightforward way to approximate scale. Suppose
that a sample of size n has an M estimator of location μ M that we
compute using a function Ψ and a scale estimate s M . To simplify
notation, let X = ( X 1, X 2, …, X n ) be the vector of sample values and
let Y = ( X – μ M ) ⁄ s M . It can be shown that the asymptotic variance
2
A of μ M takes the form:
2 2
2
k sM E [ Ψ ( Y ) ]
A = -----------------------------------,
2
( E [ Ψ' ( Y ) ] )
2
where k is a constant, Ψ' is the derivative of Ψ with respect to μ M ,
and E denotes expected value. Replacing the expected value signs
with summations and taking the square root of the result, we obtain
the following A estimate of scale:
2
ks M n  Ψ ( Y i )
i
A = ---------------------------------------.
 Ψ' ( Y i )
i
S-PLUS implements A estimates that use the median absolute
deviation for s M and Tukey’s bisquare function for Ψ . The value for
k is chosen so that A is a consistent estimate for Gaussian models; it
is set to 0.9471 in S-PLUS.
112
Robust Measures of Location and Scale
The class of τ estimates was first introduced in the context of
regression by Yohai and Zamar in 1986. Suppose that a sample of size
n has an M estimator of location μ M that we compute using a scale
estimate s M . To simplify notation, let X = ( X 1, X 2, …, X n ) be the
vector of sample values and let Y = ( X – μ M ) ⁄ s M . A τ estimate of
scale is defined to be:
1
τ = ks M ---  ρ ( Y i ) ,
n
i
where k is a constant and ρ is a weight function. The value for k is
chosen so that τ is a consistent estimate for Gaussian models; it is set
to 1.048 in S-PLUS. The τ estimates implemented in S-PLUS use the
median absolute deviation for s M and Huber’s function ρ H for the
weight function:
2
x
ρH ( x ) = 
 c2
x ≤c
x >c
.
The constant c is a tuning parameter that can be adjusted to obtain
desired asymptotic properties from τ .
Example
You can use the S-PLUS functions scale.a and scale.tau to
compute robust measures of scale based on M estimators of location.
The scale.a function computes bisquare A estimates, and the
scale.tau function computes Huber τ estimates. Both functions
include optional center and tuning arguments, which define the
measure of location in the MAD calculations and the tuning constants
( c ) for Ψ and ρ , respectively. By default, center is the median of the
sample in both functions, tuning is set to 3.85 in scale.a, and tuning
is equal to 1.95 in scale.tau.
113
Chapter 4 Descriptive Statistics
The following two commands compute A estimates of scale for the
lottery.payoff vector. The first command uses the median of
lottery.payoff as the estimate of location, and the second command
uses an M estimator.
> scale.a(lottery.payoff)
[1] 118.2306
> scale.a(lottery.payoff,
+ center = location.m(lottery.payoff))
[1] 119.2025
The next two commands compute τ estimates of scale for
lottery.payoff. The first command uses the median as the estimate
of location, and the second command uses an M estimator.
> scale.tau(lottery.payoff)
[1] 120.8589
> scale.tau(lottery.payoff,
+ center = location.m(lottery.payoff))
[1] 122.1694
114
References
REFERENCES
Altman, D.G. (1991). Practical Statistics for Medical Research. London:
Chapman & Hall.
Freedman, D., Pisani, R., & Purves, R. (1978). Statistics. New York:
W.W Norton and Company.
Hoaglin, D.C., Mosteller, F., & Tukey, J.W. (1983). Understanding
Robust and Exploratory Data Analysis. New York: John Wiley & Sons,
Inc.
Iversen, G.R. & Gergen, M. (1997). Statistics: The Conceptual Approach.
New York: Springer-Verlag, Inc.
Miller, I. & Freund, J.E. (1977). Probability and Statistics for Engineers
(2nd ed.). Englewood Cliffs, NJ: Prentice-Hall, Inc.
Rice, J.A. (1995). Mathematical Statistics and Data Analysis (2nd ed.).
Belmont, CA: Duxbury Press.
Rosner, B. (1995). Fundamentals of Biostatistics (4th ed.). Belmont, CA:
Duxbury Press.
Tukey, J.W. (1977). Exploratory Data Analysis. Reading, Massachusetts:
Addison-Wesley Publishing Company.
Velleman, P.F. & Hoaglin, D.C. (1981). Applications, Basics, and
Computing of Exploratory Data Analysis. Boston: Duxbury Press.
Wilcox, R.R. (1997). Introduction to Robust Estimation and Hypothesis
Testing. San Diego: Academic Press.
Yohai, V.J. & Zamar, R. (1986). High breakdown-point estimates of
regression by means of the minimization of an efficient scale. Technical
Report No. 84, Department of Statistics, University of Washington,
Seattle.
Yohai, V.J. & Zamar, R. (1988). High breakdown-point estimates of
regression by means of the minimization of an efficient scale. Journal
of the American Statistical Association, 83:406-413.
115
Chapter 4 Descriptive Statistics
116
STATISTICAL INFERENCE FOR
ONE- AND TWO-SAMPLE
PROBLEMS
5
Introduction
118
Background
Exploratory Data Analysis
Statistical Inference
Robust and Nonparametric Methods
123
123
125
127
One Sample: Distribution Shape, Location, and
Scale
Setting Up the Data
Exploratory Data Analysis
Statistical Inference
129
130
130
133
Two Samples: Distribution Shapes, Locations, and
Scales
Setting Up the Data
Exploratory Data Analysis
Statistical Inference
136
137
137
138
Two Paired Samples
Setting Up the Data
Exploratory Data Analysis
Statistical Inference
143
145
145
147
Correlation
Setting Up the Data
Exploratory Data Analysis
Statistical Inference
149
151
151
153
References
158
117
Chapter 5 Statistical Inference for One- and Two-Sample Problems
INTRODUCTION
Suppose you have one or two samples of data that are continuous in
the sense that the individual observations can take on any possible
value in an interval. You often want to draw conclusions from your
data concerning underlying “population” or distribution model
parameters that determine the character of the observed data. The
parameters that are most often of interest are the mean and variance
in the case of one sample, and the relative means and variances and
the correlation coefficient in the case of two samples. This chapter
shows you how to use TIBCO Spotfire S+ to carry out statistical
inference for these parameters.
Often, your samples of data are assumed to come from a distribution
that is normal, or Gaussian. A normal distribution has the familiar bellshaped population “frequency” curve (or probability density) shown by
the solid line in Figure 5.1. Another common assumption is that the
observations within a sample are serially uncorrelated with one another.
In fact, the data seldom come from an exactly normal distribution.
Usually, a more accurate assumption is that the samples are drawn
from a nearly normal distribution—that is, a nearly bell-shaped curve
whose tails do not go to zero in quite the same way as those of the true
normal distribution, as shown by the dotted line in Figure 5.1.
It is important that you be aware that nearly normal distributions,
which have “heavier tails” than a normal distribution, give rise to
outliers, that is, unusually aberrant or deviant data values. For
example, in Figure 5.1 the left-hand tail of the nearly normal
distribution is heavier than the tail of the normal distribution, but the
right hand tail is not, and so this nearly normal distribution generates
outliers which fall to the left (smaller values than) the bulk of the data.
Even though your data have only a nearly normal distribution, rather
than a normal distribution, you can use a normal distribution as a
good “nominal” model, as indicated by Figure 5.1. Thus, you are
interested in knowing the values of the parameters of a normal
distribution (or of two normal distributions in the case of two samples)
that provide a good nominal distribution model for your data.
118
0.10
0.12
0.14
Introduction
0.0
0.02
0.04
0.06
0.08
Normal
Nearly normal
0
5
10
15
20
25
x
Figure 5.1: Normal and nearly normal densities.
A normal distribution is characterized by two parameters: the mean μ
2
and the variance σ , or, equivalently, the mean and the standard
deviation σ (the square root of the variance). The mean locates the
center of symmetry of the normal distribution, and so the parameter μ
is sometimes referred to as the location. Similarly, the standard
deviation provides a measure of the spread of the distribution, and
thus can be thought of as a scale parameter.
In the case of two samples, X 1, X 2, …, X n and Y 1, Y 2, …, Y n , for two
variables X and Y , you may also be interested in the value of the
correlation coefficient ρ . The parameter ρ measures the correlation (or
linear dependency) between the variables X and Y . The value of ρ is
reflected in the scatter plot obtained by plotting Y i versus X i for
i = 1, 2, …, n . A scatterplot of Y i versus X i , which has a roughly
elliptical shape, with the values of Y i increasing with increasing
119
Chapter 5 Statistical Inference for One- and Two-Sample Problems
values of X i , corresponds to positive correlation ρ (see, for example,
Figure 5.7). An elliptically-shaped scatter plot with the values of Y i
decreasing with increasing values of X i corresponds to negative
correlation ρ . A circular shape to the scatter plot corresponds to a
zero value for the correlation coefficient ρ .
Keep in mind that the correlation between two variables X and Y , as
just described, is quite distinct from serial correlation between the
observations within one or both of the samples when the samples are
collected over time. Whereas the former reveals itself in a scatterplot
of the Y i versus the X i , the latter reveals itself in scatter plots of the
observations versus lagged values of the observations; for example, a
scatter plot of Y i versus Y i + 1 or a scatter plot of X i versus X i + 1 . If
these scatter plots have a circular shape, the data are serially
uncorrelated. Otherwise, the data have some serial correlation.
Generally, you must be careful not to assume that data collected over
time are serially uncorrelated. You need to check this assumption
carefully, because the presence of serial correlation invalidates most
of the methods of this chapter.
To summarize: You want to draw conclusions from your data
2
concerning the population mean and variance parameters μ and σ
for one sample of data, and you want to draw conclusions from your
data concerning the population means μ 1 , μ 2 , the population
variances σ 12 , σ 22 and the population correlation coefficient ρ for two
samples of data. You frame your conclusions about the above
parameters in one of the following two types of statistical inference
statements, illustrated for the case of the population mean μ in a onesample problem:
•
A CONFIDENCE INTERVAL. With probability 1 – α , the
mean μ lies within the confidence interval (L,U).
•
A HYPOTHESIS TEST. The computed statistic T compares
the null hypothesis that the mean μ has the specified value μ0
with the alternative hypothesis that μ ≠ μ 0 . At any level of
significance greater than the reported p-value for T, we reject
the null hypothesis in favor of the alternative hypothesis.
120
Introduction
A more complete description of confidence intervals and hypothesis
tests is provided in the section Statistical Inference on page 125.
Classical methods of statistical inference, such as Student’s t methods,
rely on the assumptions that the data come from a normal distribution
and the observations within a sample are serially uncorrelated. If your
data contain outliers, or are strongly nonnormal, or if the
observations within a sample are serially correlated, the classical
methods of statistical inference can give you very misleading results.
Fortunately, there are robust and nonparametric methods which give
reliable statistical inference for data that contain outliers or are
strongly nonnormal. Special methods are needed for dealing with
data that are serially correlated. See, for example, Heidelberger and
Welch (1981).
In this chapter, you learn to use S-PLUS functions for making both
classical and robust or nonparametric statistical inference statements
for the population means and variances for one and two samples, and
for the population correlation coefficient for two samples. The basic
steps in using S-PLUS functions are essentially the same no matter
which of the above parameters you are interested in. They are as
follows:
1. Setting up your data.
Before Spotfire S+ can be used to analyze the data, you must
put the data in a form that Spotfire S+ recognizes.
2. Exploratory data analysis (EDA).
EDA is a graphically-oriented method of data analysis which
helps you determine whether the data support the
assumptions required for the classical methods of statistical
inference: an outlier-free nearly normal distribution and
serially uncorrelated observations.
3. Statistical inference.
Once you’ve verified that your sample or samples are nearly
normal, outlier-free, and uncorrelated, you can use classical
methods of statistical inference that assume a normal
distribution and uncorrelated observations, to draw
conclusions from your data.
121
Chapter 5 Statistical Inference for One- and Two-Sample Problems
If your data are not nearly normal and outlier-free, the results
of the classical methods of statistical inference may be
misleading. Hence, you often need robust or nonparametric
methods, as described in the section Robust and
Nonparametric Methods on page 127.
122
Background
BACKGROUND
This section prepares you for using the S-PLUS functions in the
remainder of the chapter by providing brief background information
on the following three topics: exploratory data analysis, statistical
inference, and robust and nonparametric methods.
Exploratory
Data Analysis
The classical methods of statistical inference depend heavily on the
assumption that your data are outlier-free and nearly normal, and that
your data are serially uncorrelated. Exploratory data analysis (EDA)
uses graphical displays to help you obtain an understanding of
whether or not such assumptions hold. Thus, you should always carry
out some graphical exploratory data analysis to answer the following
questions:
•
Do the data come from a nearly normal distribution?
•
Do the data contain outliers?
•
If the data were collected over time, is there any evidence of
serial correlation (correlation between successive values of the
data)?
You can get a pretty good picture of the shape of the distribution
generating your data, and also detect the presence of outliers, by
looking at the following collection of four plots: a histogram, a boxplot,
a density plot, and a normal qq-plot. Examples of these four plots are
provided in Figure 5.2.
Density plots are essentially smooth versions of histograms, which
provide smooth estimates of population frequency, or probability density
curves; for example, the normal and nearly normal curves of Figure
5.1. Since the latter are smooth curves, it is both appropriate and
more pleasant to look at density plots than at histograms.
A normal qq-plot (or quantile-quantile plot) consists of a plot of the
ordered values of your data versus the corresponding quantiles of a
standard normal distribution; that is, a normal distribution with mean
zero and variance one. If the qq-plot is fairly linear, your data are
reasonably Gaussian; otherwise, they are not.
123
Chapter 5 Statistical Inference for One- and Two-Sample Problems
Of these four plots, the histogram and density plot give you the best
picture of the distribution shape, while the boxplot and normal
qq-plot give the clearest display of outliers. The boxplot also gives a
clear indication of the median (the solid dot inside the box), and the
upper and lower quartiles (the upper and lower ends of the box).
A simple S-PLUS function can create all four suggested distributional
shape EDA plots, and displays them all on a single screen or a single
hard copy plot. Define the function as follows:
> eda.shape <- function(x) {
+
par(mfrow = c(2, 2))
+
hist(x)
+
boxplot(x)
+
iqd <- summary(x)[5] - summary(x)[2]
+
plot(density(x, width = 2 * iqd),
+
xlab = "x", ylab = "", type = "l")
+
qqnorm(x, pch = 1)
+
qqline(x)
+
invisible()
+ }
This function is used to make the EDA plots you see in the remainder
of this chapter. The argument width = 2*iqd to density sets the
degree of smoothness of the density plot in a good way. For more
details on writing functions, see the Programmer’s Guide.
If you have collected your data over time, the data may contain serial
correlation. That is, the observations may be correlated with one
another at different times. The assessment of whether or not there is
any time series correlation in the context of confirmatory data
analysis for location and scale parameters is an often-neglected task.
You can check for obvious time series features, such as trends and
cycles, by looking at a plot of your data against time, using the
function ts.plot. You can check for the presence of less obvious
serial correlation by looking at a plot of the autocorrelation function
for the data, using the acf function. These plots can be created, and
displayed one above the other, with the following S-PLUS function.
124
Background
> eda.ts <- function(x) {
+
par(mfrow = c(2, 1))
+
ts.plot(as.ts(x), type = "b", pch = 1)
+
acf(x)
+
invisible()
+ }
This function is used to make the time series EDA plots you find in
the remainder of this chapter. See, for example, Figure 5.3. The
discussion of Figure 5.3 includes a guideline for interpreting the acf
plot.
Warning
If either the time series plot or the acf plot suggests the presence of serial correlation, you can
place little credence in the results computed in this chapter, using either the Student’s t statistic
approach or using the nonparametric Wilcoxon approach. A method for estimating the
population mean in the presence of serial correlation is described by Heidelberger and Welch
(1981). Seek expert assistance, as needed.
Statistical
Inference
Formal methods of statistical inference provide probability-based
statements about population parameters such as the mean, variance,
and correlation coefficient for your data. You may be interested in a
simple point estimate of a population parameter. For example, the
sample mean is a point estimate of the population mean. However, a
point estimate neither conveys any uncertainty about the value of the
estimate, nor indicates whether a hypothesis about the population
parameter is to be rejected. To address these two issues, you will
usually use one or both of the following methods of statistical
inference: confidence intervals and hypothesis tests.
We define these two methods for you, letting θ represent any one of
the parameters you may be interested in; for example, θ may be the
mean μ , or the difference between two means μ 1 – μ 2 , or the
correlation coefficient ρ .
CONFIDENCE INTERVALS. A ( 1 – α )100% confidence interval
for the true but unknown parameter θ is any interval of the form
(L,U), such that the probability is 1 – α that (L,U) contains θ . The
probability α with which the interval (L,U) fails to cover q is
125
Chapter 5 Statistical Inference for One- and Two-Sample Problems
sometimes called the error rate of the interval. The quantity
( 1 – α ) × 100% is called the confidence level of the confidence interval.
Common values of α are α = 0.01, 0.05, 0.1 , which yield 99 %,
95 %, and 90 % confidence intervals, respectively.
HYPOTHESIS TESTS. A hypothesis test is a probability-based
method for making a decision concerning the value of a population
parameter θ (for example, the population mean μ or standard
deviation σ in a one-sample problem), or the relative values of two
population parameters θ 1 and θ 2 (for example, the difference
between the population means μ 1 – μ 2 in a two-sample problem).
You begin by forming a null hypothesis and an alternative hypothesis. For
example, in the two-sample problem your null hypothesis is often the
hypothesis that θ 1 = θ 2 , and your alternative hypothesis is one of the
following:
•
The two-sided alternative θ 1 ≠ θ 2
•
The greater-than alternative θ 1 > θ 2
•
The less-than alternative θ 1 < θ 2
Your decision to accept the null hypothesis, or to reject the null
hypothesis in favor of your alternative hypothesis is based on the
observed value T = t obs of a suitably chosen test statistic T . The
probability that the statistic T exceeds the observed value t obs when
your null hypothesis is in fact true, is called the p-value.
For example, suppose you are testing the null hypothesis that θ = θ 0
against the alternative hypothesis that θ ≠ θ 0 in a one-sample
problem. The p-value is the probability that the absolute value of T
exceeds the absolute value of t obs for your data, when the null
hypothesis is true.
In formal hypothesis testing, you proceed by choosing a “good”
statistic T and specifying a level of significance, which is the probability
of rejecting a null hypothesis when the null hypothesis is in fact true.
126
Background
In terms of formal hypothesis testing, your p-value has the following
interpretation: the p-value is the level of significance for which your
observed test statistic value t obs lies on the boundary between
acceptance and rejection of the null hypothesis. At any significance
level greater than the p-value, you reject the null hypothesis, and at
any significance level less than the p-value you accept the null
hypothesis. For example, if your p-value is 0.03, you reject the null
hypothesis at a significance level of 0.05, and accept the null
hypothesis at a significance level of 0.01.
Robust and
Nonparametric
Methods
Two problems frequently complicate your statistical analysis. For
example, Student’s t test, which is the basis for most statistical
inference on the mean-value locations of normal distributions, relies
on two critical assumptions:
1. The observations have a common normal (or Gaussian)
2
distribution with mean μ and variance σ .
2. The observations are independent.
However, one or both of these assumptions often fail to hold in
practice.
For example, if the actual distribution for the observations is an
outlier-generating, heavy-tailed deviation from an assumed Gaussian
distribution, the confidence level remains quite close to ( 1 – α )100% ,
but the average confidence interval length is considerably larger than
under normality. The p values based on the Student’s t test are also
heavily influenced by outliers.
In this example, and more generally, you would like to have statistical
methods with the property that the conclusions you draw are not
much affected if the distribution for the data deviates somewhat from
the assumed model; for example, if the assumed model is a normal,
or Gaussian distribution, and the actual model for the data is a nearly
normal distribution. Such methods are called robust. In this chapter
you will learn how to use an S-PLUS function to obtain robust point
estimates and robust confidence intervals for the population
correlation coefficient.
For one and two-sample location parameter problems (among
others), there exist strongly robust alternatives to classical methods, in
the form of nonparametric statistics. The term nonparametric means that
127
Chapter 5 Statistical Inference for One- and Two-Sample Problems
the methods work even when the actual distribution for the data is far
from normal; that is, when the data do not have to have even a nearly
normal distribution. In this chapter, you will learn to use one of the
best of the nonparametric methods for constructing a hypothesis test
p-value, namely the Wilcoxon rank method, as implemented in the SPLUS function wilcox.test.
It is important to keep in mind that serial correlation in the data can
quickly invalidate the use of both classical methods (such as Student’s
t) and nonparametric methods (such as the Wilcoxon rank method)
for computing confidence intervals and p values. For example, a 95%
Student’s t confidence interval can have a much higher error rate
than 5% when there is a small amount of positive correlation in the
data. Also, most modern robust methods are oriented toward
obtaining insensitivity toward outliers generated by heavy-tailed
nearly normal distributions, and are not designed to cope with serial
correlation. For information on how to construct confidence intervals
for the population mean when your data are serially correlated and
free of outliers, see Heidelberger and Welch (1981).
128
One Sample: Distribution Shape, Location, and Scale
ONE SAMPLE: DISTRIBUTION SHAPE, LOCATION, AND
SCALE
In 1876, the French physicist Cornu reported a value of 299,990 km/
sec for c, the speed of light. In 1879, the American physicist A.A.
Michelson carried out several experiments to verify and improve on
Cornu’s value.
Michelson obtained the following 20 measurements of the speed of
light:
850
1000
740
980
900
930
1070
650
930
760
850 950 980
810 1000 1000
980
960
880
960
To obtain Michelson’s actual measurements in km/sec, add 299,000
km/sec to each of the above values.
The twenty observations can be thought of as observed values of
twenty random variables with a common but unknown mean-value
location μ. If the experimental setup for measuring the speed of light
is free of bias, then it is reasonable to assume that μ is the true speed
of light.
In evaluating this data, we seek answers to at least five questions:
1. What is the speed of light μ ?
2. Has the speed of light changed relative to our best previous
value μ 0 ?
3. What is the uncertainty associated with our answers to (1) and
(2)?
4. What is the shape of the distribution of the data?
5. The measurements were taken over time. Is there any
evidence of serial correlation?
The first three questions were probably in Michelson’s mind when he
gathered his data. The last two must be answered to determine which
techniques can be used to obtain valid statistical inferences from the
data. For example, if the shape of the distribution indicates a nearly
normal distribution without outliers, we can use the Student’s t tests in
attempting to answer question (2). If the data contain outliers or are
far from normal, we should use a robust method or a nonparametric
129
Chapter 5 Statistical Inference for One- and Two-Sample Problems
method such as the Wilcoxon signed-rank test. On the other hand, if
serial correlation exists, neither the Student’s t nor the Wilcoxon test
offers valid conclusions.
In this section, we use S-PLUS to analyze the Michelson data.
Identical techniques can be used to explore and analyze any set of
one-sample data.
Setting Up the
Data
The data form a single, ordered set of observations, so they are
appropriately described in S-PLUS as a vector. Use the scan function
to create the vector mich:
> mich <- scan()
1: 850 740 900 1070 930
6: 850 950 980 980 880
11: 1000 980 930 650 760
16: 810 1000 1000 960 960
21:
Exploratory
Data Analysis
To start, we can evaluate the shape of the distribution, by making a set
of four EDA plots, using the eda.shape function described in the
section Exploratory Data Analysis on page 123:
> eda.shape(mich)
The plots, shown in Figure 5.2, reveal a distinctly skewed distribution,
skewed toward the left (that is, toward smaller values), but rather
normal in the middle region. The distribution is thus not normal, and
probably not even "nearly" normal.
The solid horizontal line in the box plot is located at the median of the
data, and the upper and lower ends of the box are located at the upper
quartile and lower quartile of the data, respectively. To get precise
values for the median and quartiles, use the summary function:
> summary(mich)
Min. 1st Qu. Median Mean 3rd Qu. Max.
650
850
940 909
980 1070
The summary shows, from left to right, the smallest observation, the
first quartile, the median, the mean, the third quartile, and the largest
observation. From this summary you can compute the interquartile
130
One Sample: Distribution Shape, Location, and Scale
0
700
2
800
4
900
6
1000
8
range, IQR = 3Q – 1Q . The interquartile range provides a useful
criterion for identifying outliers—any observation which is more than
1.5 × IQR above the third quartile or below the first quartile is a
suspected outlier.
700
800 900
1100
0.0
700
0.001
800
x
0.002
900
0.003
1000
x
600
800
1000
x
-2
-1
0
1
2
Quantiles of Standard Normal
Figure 5.2: Exploratory data analysis plots.
To examine possible serial correlation, or dependency, make two
plots using the eda.ts function defined in the section Exploratory
Data Analysis on page 123.
131
Chapter 5 Statistical Inference for One- and Two-Sample Problems
> eda.ts(mich)
700
800
900
1000
The top plot in Figure 5.3 reveals a somewhat unusual excursion at
observations 14, 15, 16, and perhaps a slightly unusual oscillation in
the first 6 observations. However, the autocorrelation function plot in
the lower part of Figure 5.3 reveals no significant serial
correlations—all values lie within the horizontal dashed lines for lags
greater than 0.
5
10
15
20
Time
-0.4
0.0
ACF
0.4
0.8
Series : x
0
2
4
6
8
Lag
Figure 5.3: Time series plots.
132
10
12
One Sample: Distribution Shape, Location, and Scale
Statistical
Inference
Because the Michelson data are not normal, you should probably use
the Wilcoxon signed-rank test rather than the Student’s t test for your
statistical inference. For illustrative purposes, we’ll use both.
To compute Student’s t confidence intervals for the population meanvalue location parameter μ, and to compute Student’s t significance
test p values for the parameter μ 0 , use the function t.test.
To perform the test, you specify the confidence level, the
hypothesized mean-value location μ , and the hypothesis being tested,
as follows:
•
conf.level specifies the confidence level of the confidence
interval. Usual values are 0.90, 0.95, or 0.99. The default is
0.95.
•
specifies the null hypothesis value μ 0 of μ . The default is
μ0 = 0, which is often inappropriate for one-sample problems.
mu
You should choose μ carefully, using either a previously
accepted value or a value suggested by the data before
sampling.
•
specifies the specific hypothesis being tested.
There are three options:
alternative
•
"two.sided"
•
tests the hypothesis that the true mean is
greater than μ 0 .
•
"less"
tests the hypothesis that the true mean is not
equal to μ 0 . This is the default alternative.
"greater"
tests the hypothesis that the true mean is less than
μ0 .
For Michelson’s data, suppose you want to test the null hypothesis
value μ 0 = 990 (plus 299,000) against a two-sided alternative. To do
this, use t.test with the argument mu=990, as in the command below:
> t.test(mich, mu = 990)
One-sample t-Test
data: mich
t = -3.4524, df = 19, p-value = 0.0027
133
Chapter 5 Statistical Inference for One- and Two-Sample Problems
alternative hypothesis: true mean is not equal to 990
95 percent confidence interval:
859.8931 958.1069
sample estimates:
mean of x
909
The p value is 0.0027, which is highly significant. S-PLUS returns
other useful information besides the p value, including the t statistic
value, the degrees of freedom (df ), the sample mean, and the
confidence interval.
134
One Sample: Distribution Shape, Location, and Scale
Our example used the default confidence level of 0.95. If you specify
a different confidence level, as in the following command:
> t.test(mich, conf.level = .90, mu = 990)
You obtain a new confidence interval of (868,950), which is shorter
than before, but nothing else changes in the output from t.test.
Wilcoxon Signed
Rank Test
p Values
To perform the Wilcoxon signed rank nonparametric test, use the
function wilcox.test. As with t.test, the test is completely
determined by the confidence level, the hypothesized mean μ0, and
the hypothesis to be tested. These options are specified for
wilcox.test exactly as for t.test.
For example, to test the hypothesis that μ = 990 (plus 299,000), use
as follows:
wilcox.test
> wilcox.test(mich, mu = 990)
Wilcoxon signed-rank test
data: mich
signed-rank normal statistic with correction Z = -3.0715,
p-value = 0.0021
alternative hypothesis: true mu is not equal to 990
Warning messages:
cannot compute exact p-value with ties in:
wil.sign.rank(dff, alternative, exact, correct)
The p value of 0.0021 compares with the t test p value of 0.0027 for
testing the same null hypothesis with a two-sided alternative.
Michelson’s data have several tied values. Because exact p values
cannot be computed if there are tied values (or if the null hypothesis
mean is equal to one of the data values), a normal approximation is
used and the associated Z statistic value is reported.
135
Chapter 5 Statistical Inference for One- and Two-Sample Problems
TWO SAMPLES: DISTRIBUTION SHAPES, LOCATIONS, AND
SCALES
Suppose you are a nutritionist interested in the relative merits of two
diets, one featuring high protein, the other low protein. Do the two
diets lead to differences in mean weight gain? Consider the data in
Table 5.1, which shows the weight gains (in grams) for two lots of
female rats, under the two diets.
Table 5.1: Weight gain data.
High Protein
Low Protein
134
70
146
118
104
101
119
85
124
107
161
132
107
94
83
113
129
97
123
136
Two Samples: Distribution Shapes, Locations, and Scales
The first lot, consisting of 12 rats, was given the high protein diet, and
the second lot, consisting of 7 rats, was given the low protein diet.
These data appear in section 6.9 of Snedecor and Cochran (1980).
The high protein and low protein samples are presumed to have
mean-value location parameters μ H and μ L , and standard deviation
scale parameters σ H and σ L , respectively. While you are primarily
interested in whether there is any difference in the μ ’s, you may also
be interested in whether or not the two diets result in different
variabilities, as measured by the standard deviations (or their squared
values, the variances). This section shows you how to use S-PLUS
functions to answer such questions.
Setting Up the
Data
In the two-sample case, each sample forms a set of data. Thus, you
begin by creating two data vectors, gain.high and gain.low,
containing the first and second columns of data from Table 5.1:
> gain.high <- scan()
1: 134 146 104 119 124 161 107 83 113 129 97 123
13:
> gain.low <- scan()
1: 70 118 101 85 107 132 94
8:
Exploratory
Data Analysis
For each sample, make a set of EDA plots, consisting of a histogram,
a boxplot, a density plot and a normal qq-plot, all displayed in a twoby-two plot layout, using the eda.shape function defined in the
section Exploratory Data Analysis on page 123.
> eda.shape(gain.high)
> eda.shape(gain.low)
The resulting plots for the high-protein group are shown in Figure 5.4.
They indicate that the data come from a nearly normal distribution,
and there is no indication of outliers. The plots for the low-protein
group, which we do not show, support the same conclusions.
137
80
0
1
100
2
120
3
140
4
160
Chapter 5 Statistical Inference for One- and Two-Sample Problems
80
100
140
180
120
80
0.0
100
0.005
x
0.010
140
0.015
160
x
50
100
150
200
x
-1
0
1
Quantiles of Standard Normal
Figure 5.4: EDA plots for high-protein group.
Since the data were not collected in any specific time order, you need
not make any exploratory time series plots to check for serial
correlation.
Statistical
Inference
138
Is the mean weight gain the same for the two groups of rats?
Specifically, does the high-protein group show a higher average
weight gain? From our exploratory data analysis, we have good
reason to believe that Student’s t test will provide a valid test of our
Two Samples: Distribution Shapes, Locations, and Scales
hypotheses. As in the one-sample case, you can get confidence
intervals and hypothesis test p values for the difference μ1 - μ2
between the two mean-value location parameters μ 1 and μ 2 using
the functions t.test and wilcox.test.
As before, each test is specified by a confidence level, a hypothesized
μ0 (which now refers to the difference of the two sample means), and
the hypothesis to be tested. However, because of the possibility that
the two samples may be from different distributions, you may also
specify whether the two samples have equal variances.
You define the test to be performed using the following arguments to
t.test:
•
conf.level specifies the confidence level of the confidence
interval. Usual values are 0.90, 0.95, or 0.99. The default is
0.95.
•
mu
specifies the null hypothesis value μ0 of μ diff = μ H – μ L .
The default is μ 0 = 0 .
•
•
specifies the hypothesis being tested. There are
three options:
alternative
•
"two.sided"
•
tests the hypothesis that the difference of
means is greater than μ 0 .
•
tests the hypothesis that the difference of means is
less than μ 0 .
tests the hypothesis that the difference of
means is not equal to μ 0 . This is the default alternative.
"greater"
"less"
specifies whether equal variances are assumed for
the two samples. The default is var.equal=TRUE.
var.equal
To determine the correct setting for the option var.equal, you can
either use informal inspection of the EDA boxplots or use the
function var.test for a more formal test. If the heights of the boxes in
the two boxplots are approximately the same, then so are the
variances of the two outlier-free samples. The var.test function
performs the F test for variance equality on the vectors representing
the two samples.
139
Chapter 5 Statistical Inference for One- and Two-Sample Problems
For the weight gain data, the var.test function returns:
> var.test(gain.high, gain.low)
F test for variance equality
data: gain.high and gain.low
F = 1.0755, num df = 11, denom df = 6, p-value = 0.9788
alternative hypothesis: true ratio of variances is not
equal to 1
95 percent confidence interval:
0.198811 4.173718
sample estimates:
variance of x variance of y
457.4545
425.3333
The evidence supports the assumption that the variances are the
same, so var.equal=T is a valid choice.
We are interested in two alternative hypotheses: the two-sided
alternative that μ H – μ L = 0 and the one-sided alternative that
μ H – μ L > 0 . To test these, we run the standard two-sample t test
twice, once with the default two-sided alternative and a second time
with the one-sided alternative alt="g".
You get both a confidence interval for μ H – μ L , and a two-sided test
of the null hypothesis that μ H – μ L = 0 , by the following simple use
of t.test:
> t.test(gain.high, gain.low)
Standard Two-Sample t-Test
data: gain.high and gain.low
t = 1.8914, df = 17, p-value = 0.0757
alternative hypothesis: true difference in means is
not equal to 0
95 percent confidence interval:
-2.193679 40.193679
sample estimates:
mean of x mean of y
120
101
The p value is 0.0757, so the null hypothesis is rejected at the 0.10
level, but not at the 0.05 level. The confidence interval is (-2.2, 40.2).
140
Two Samples: Distribution Shapes, Locations, and Scales
To test the one-sided alternative that μ H – μ L > 0 , use t.test again
with the argument alternative="greater" (abbreviated below for
ease of typing):
> t.test(gain.high, gain.low, alt = "g")
Standard Two-Sample t-Test
data: gain.high and gain.low
t = 1.8914, df = 17, p-value = 0.0379
alternative hypothesis: true difference in means
is greater than 0
95 percent confidence interval:
1.525171
NA
sample estimates:
mean of x mean of y
120
101
In this case, the p value is just half of the p value for the two-sided
alternative. This relationship between the p values of the one-sided
and two-sided alternatives holds in general. You also see that when
you use the alt="g" argument, you get a lower confidence bound.
This is the natural one-sided confidence interval corresponding to the
“greater than” alternative.
Hypothesis Test
p-Values Using
wilcox.test
To get a two-sided hypothesis test p value for the “two-sided”
alternative, based on the Wilcoxon rank sum test statistic, use
wilcox.test, which takes the same arguments as t.test:
> wilcox.test(gain.high, gain.low)
Wilcoxon rank-sum test
data: gain.high and gain.low
rank-sum normal statistic with correction Z = 1.6911,
p-value = 0.0908
alternative hypothesis: true mu is not equal to 0
Warning messages:
cannot compute exact p-value with ties in:
wil.rank.sum(x, y, alternative, exact, correct)
141
Chapter 5 Statistical Inference for One- and Two-Sample Problems
The above p value of 0.0908, based on the normal approximation
(used because of ties in the data), is rather close to the t statistic
p value of 0.0757.
142
Two Paired Samples
TWO PAIRED SAMPLES
Often two samples of data are collected in the context of a comparative
study. A comparative study is designed to determine the difference
between effects, rather than the individual effects. For example,
consider the data in Table 5.2, which give values of wear for two kinds
of shoe sole material, A and B, along with the differences in values.
Table 5.2: Comparing shoe sole material
Boy
wear.A
wear.B
wear.A-wear.B
1
14.0(R)
13.2(L)
0.8
2
8.8(R)
8.2(L)
0.6
3
11.2(L)
10.9(R)
0.3
4
14,2(R)
14.3(L)
-0.1
5
11.8(L)
10.7(R)
1.1
6
6.4(R)
6.6(L)
-0.2
7
9.8(R)
9.5(L)
0.3
8
11.3(R)
10.8(L)
0.5
9
9.3(L)
8.8(R)
0.5
10
13.6(R)
13.3(L)
0.3
In the table, (L) indicates the material was used on the left sole and
(R) indicates it was used on the right sole.
The experiment leading to this data, described in Box, Hunter, and
Hunter (1978), was carried out by taking 10 pairs of shoes and putting
a sole of material A on one shoe and a sole of material B on the other
shoe in each pair. Which material type went on each shoe was
143
Chapter 5 Statistical Inference for One- and Two-Sample Problems
determined by randomizing, with equal probability that material A
was on the right shoe or left shoe. A group of 10 boys then wore the
shoes for a period of time, after which the amount of wear was
measured. The problem is to determine whether shoe material A or B
is longer wearing.
You could treat this problem as a two-sample location problem and
use either t.test or wilcox.test, as described in the section Two
Samples: Distribution Shapes, Locations, and Scales on page 136, to
test for a difference in the means of wear for material A and material
B. However, you will not be very successful with this approach
because there is considerable variability in wear of both materials
types A and B from individual to individual, and this variability tends
to mask the difference in wear of material A and B when you use an
ordinary two-sample test.
However, the above experiment uses paired comparisons. Each boy
wears one shoe with material A and one shoe with material B. In
general, pairing involves selecting similar individuals or things. One
often uses self-pairing as in the above experiment, in which two
procedures, often called treatments, are applied to the same individual
(either simultaneously or at two closely spaced time intervals) or to
similar material. The goal of pairing is to make a comparison more
sensitive by measuring experimental outcome differences on each
pair, and combining the differences to form a statistical test or
confidence interval. When you have paired data, you use t.test and
wilcox.test with the optional argument paired = T.
The use of paired versions of t.test and wilcox.test leads to
improved sensitivity over the usual versions when the variability of
differences is smaller than the variability of each sample; for example,
when the variability of differences of material wear between materials
A and B is smaller than the variability in wear of material A and
material B.
144
Two Paired Samples
Setting Up the
Data
In paired comparisons you start with two samples of data, just as in
the case of ordinary two-sample comparisons. You begin by creating
two data vectors, wear.A and wear.B, containing the first and second
columns of Table 5.2. The commands below illustrate one way of
creating the data vectors.
> wear.A <- scan()
1: 14.0 8.8 11.2 14.2 11.8 6.4 9.8 11.3 9.3 13.6
11:
> wear.B <- scan()
1: 13.2 8.2 10.9 14.3 10.7 6.6 9.5 10.8 8.8 13.3
11:
Exploratory
Data Analysis
You can carry out exploratory data analysis on each of the two paired
samples x 1, …, x n and y 1, …, y n , as for an ordinary two-sample
problem, as described in the section Exploratory Data Analysis on
page 137. However, since your analysis is based on differences, it is
appropriate to carry out EDA based on a single sample of differences
d i = x i – y i , i = 1, … , n .
In the shoe material wear experiment, you use eda.shape on the
difference wear.A-wear.B:
> eda.shape(wear.A - wear.B)
The results are displayed in Figure 5.5. The histogram and density
indicate some deviation from normality that is difficult to judge
because of the small sample size.
145
0.0
-0.2
0.2
1.0
0.6
2.0
1.0
3.0
Chapter 5 Statistical Inference for One- and Two-Sample Problems
-0.2
0.2
0.6
1.0
0.0
-0.2
0.2
0.4
x
0.6
0.8
1.0
1.2
x
-0.5
0.5
1.0
x
1.5
-1
0
1
Quantiles of Standard Normal
Figure 5.5: EDA plots for differences in shoe sole material wear.
You might also want to make a scatter plot of wear.B versus wear.A,
using plot(wear.A,wear.B), as a visual check on correlation between
the two variables. Strong correlation is an indication that withinsample variability is considerably larger than the difference in means,
and hence that the use of pairing will lead to greater test sensitivity. To
obtain the scatter plot of Figure 5.6, use the following S-PLUS
expression:
> plot(wear.A, wear.B)
146
Two Paired Samples
14
•
••
•
10
wear.B
12
• •
•
•
8
•
•
8
10
12
14
wear.A
Figure 5.6: Scatter plot of wear.A versus wear.B.
Statistical
Inference
To perform a paired t test on the shoe material wear data, with the
default two-sided alternative use t.test with the paired argument, as
follows:
> t.test(wear.A, wear.B, paired = T)
Paired t-Test
data: wear.A and wear.B
t = 3.3489, df = 9, p-value = 0.0085
alternative hypothesis: true mean of differences is not
equal to 0
95 percent confidence interval:
0.1330461 0.6869539
sample estimates:
mean of x - y
0.41
147
Chapter 5 Statistical Inference for One- and Two-Sample Problems
The p value of .0085 is highly significant for testing the difference in
mean wear of materials A and B. You also get the 95% confidence
interval (0.13, 0.67) for the difference in mean values. You can control
the type of alternative hypothesis with the alt optional argument, and
you can control the confidence level with the conf.level optional
argument, as usual. To perform a paired Wilcoxon test (often called
the Wilcoxon signed rank test) on the shoe material data, with the
default two-sided alternative use wilcox.test with the paired
argument, as follows:
> wilcox.test(wear.A, wear.B, paired = T)
Wilcoxon signed-rank test
data: wear.A and wear.B
signed-rank normal statistic with correction Z = 2.4495,
p-value = 0.0143
alternative hypothesis: true mu is not equal to 0
Warning messages:
cannot compute exact p-value with ties in:
wil.sign.rank(dff, alternative, exact, correct)
The p value of 0.0143 is highly significant for testing the null
hypothesis of equal centers of symmetry for the distributions of
wear.A and wear.B. You can control the type of alternative hypothesis
by using the optional argument alt as usual.
148
Correlation
CORRELATION
What effect, if any, do housing starts have on the demand for
residential telephone service? If there is some useful association, or
correlation, between the two, you may be able to use housing start data
as a predictor of growth in demand for residential phone lines.
Consider the data displayed in Table 5.3 (in coded form), which
relates to residence telephones in one area of New York City.
The first column of data, labeled “Diff. HS,” shows annual first
differences in new housing starts over a period of fourteen years. The
first differences are calculated as the number of new housing starts in
a given year, minus the number of new housing starts in the previous
year. The second column of data, labeled “Phone Increase,” shows
the annual increase in the number of “main” residence telephone
services (excluding extensions), for the same fourteen-year period.
Table 5.3: The phone increase data.
Diff. HS
Phone Increase
0.06
1.135
0.13
1.075
0.14
1.496
-0.07
1.611
-0.05
1.654
-0.31
1.573
0.12
1.689
0.23
1.850
-0.05
1.587
149
Chapter 5 Statistical Inference for One- and Two-Sample Problems
Table 5.3: The phone increase data. (Continued)
Diff. HS
Phone Increase
-0.03
1.493
0.62
2.049
0.29
1.943
-0.32
1.482
-0.71
1.382
The general setup for analyzing the association between two samples
of data such as those above is as follows. You have two samples of
observations, of equal sizes n, of the random variables X 1, X 2, …, X n
and Y 1, Y 2, …, Y n . Let’s assume that each of the two-dimensional
vector random variables ( X i, Y i ) , i = 1, 2, …, n , have the same joint
distribution.
The most important, and commonly used measure of association
between two such random variables is the (population) correlation
coefficient parameter ρ , defined as
E ( x – μ1 ) ( Y – μ2 )
ρ = -------------------------------------------- ,
σ1σ2
where μ 1 , μ 2 and σ 1 , σ 2 are the means and standard deviations,
respectively, of the random variables X and Y . The E appearing in
the numerator denotes the statistical expected value, or expectation
operator, and the quantity E ( X – μ 1 ) ( Y – μ 2 ) is the covariance between
the random variables X and Y . The value of ρ is always between 1
and -1.
Your main goal is to use the two samples of observed data to
determine the value of the correlation coefficient ρ . In the process
you want to do sufficient graphical EDA to feel confident that your
determination of ρ is reliable.
150
Correlation
Setting Up the
Data
The data form two distinct data sets, so we create two vectors with the
suggestive names diff.hs and phone.gain:
> diff.hs <- scan()
1: .06 .13 .14 -.07 -.05 -.31 .12
8: .23 -.05 -.03 .62 .29 -.32 -.71
15:
> phone.gain <- scan()
1: 1.135 1.075 1.496 1.611 1.654 1.573 1.689
8: 1.850 1.587 1.493 2.049 1.943 1.482 1.382
15:
Exploratory
Data Analysis
If two variables are strongly correlated, that correlation may appear
in a scatter plot of one variable against the other. For example, plot
phone.gain versus diff.hs using the following command:
> plot(diff.hs, phone.gain)
The results are shown in Figure 5.7. The plot reveals a strong positive
correlation, except for two obvious outliers. To identify the
observation numbers associated with the outliers in the scatter plot,
along with that of a third suspicious point, we used identify as
follows:
> identify(diff.hs, phone.gain, n = 3)
See the online help for a complete discussion of identify.
151
Chapter 5 Statistical Inference for One- and Two-Sample Problems
2.0
•
•
1.8
•
1.6
•
•
•
•3
•
•
•
1.2
1.4
phone.gain
•
•
•1
•2
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
diff.hs
Figure 5.7: Scatter plot of phone.gain versus diff.hs.
The obvious outliers occur at the first and second observations. In
addition, the suspicious point (labeled “3” in the scatter plot) occurs at
the third observation time.
Since you have now identified the observation times of the outliers,
you can gain further insight by making a time series plot of each
series:
> plot(diff.hs, type = "b")
> plot(phone.gain, type = "b")
You should also make an autocorrelation plot for each series:
> acf(diff.hs)
> acf(phone.gain)
The results are shown in Figure 5.8. Except for the first three
observations of the two series phone.gain and diff.hs, there is a
strong similarity of shape exhibited in the two time series plots. This
accounts for the strong positive correlation between the two variables
152
Correlation
diff.hs and phone.gain shown in Figure 5.7. The dissimilar behavior
of the two time series plots for the first three observations produces
the two obvious outliers, and the suspicious point, in the scatter plot
of phone.gain versus diff.hs.
0.2
•
•
•
•
•
•
•
•
•
•
-0.6
diff.hs
•
•
•
•
2
4
6
8
10
12
14
1.8
•
•
•
1.2
phone.gain
Index
•
•
•
•
•
•
•
•
•
•
•
2
4
6
8
10
12
14
Index
-0.5
0.5
Series : diff.hs
0
2
4
6
8
10
8
10
Lag
-0.5
0.5
Series : phone.gain
0
2
4
6
Lag
Figure 5.8: Time series and ACF plots of phone increase data.
The ACF plots show little evidence of serial correlation within each of
the individual series.
Statistical
Inference
From your exploratory data analysis, two types of questions present
themselves for more formal analysis. If the evidence for correlation is
inconclusive, you may want to test whether there is correlation
between the two variables of interest by testing the null hypothesis
that ρ = 0 . On the other hand, if your EDA convinces you that
correlation exists, you might prefer a point estimate ρ̂ of the
correlation coefficient ρ , or a confidence interval for ρ .
153
Chapter 5 Statistical Inference for One- and Two-Sample Problems
Hypothesis Test
p-Values
You can get p values for the null hypothesis that ρ = 0 by using the
function cor.test. To perform this test, you specify the alternative
hypothesis to be tested and the test method to use, as follows:
•
specifies the alternative hypothesis to be tested.
There are three options:
alternative
•
"two.sided"
•
"greater"
•
"less"
(the default alternative) tests the alternative
hypothesis that ρ ≠ 0.
tests the alternative hypothesis that ρ > 0.
tests the alternative hypothesis that ρ < 0.
You can also use the abbreviated forms alt="g" and alt="l".
•
method
specifies which of the following methods is used:
•
"pearson" (the default) uses the standard Pearson sample
correlation coefficient.
•
uses the rank-based Kendall’s τ measure of
correlation.
•
"kendall"
"spearman" uses the rank-based Spearman’s
ρ measure of
correlation.
You can abbreviate these methods by using enough of the character
string to determine a unique match; here "p", "k", and "s" work.
Because both Kendall’s τ and Spearman’s ρ methods are based on
ranks, they are not so sensitive to outliers and nonnormality as the
standard Pearson estimate.
Below is a simple use of cor.test to test the alternative hypothesis
that there is a positive correlation in the phone gain data. We use the
default choice of the classical Pearson estimate with the one-sided
alternative alt="g".
154
Correlation
> cor.test(diff.hs, phone.gain, alt = "g")
Pearson product-moment correlation
data: diff.hs and phone.gain
t = 1.9155, df = 12, p-value = 0.0398
alternative hypothesis: true coef is greater than 0
sample estimates:
cor
0.4839001
You get a normal theory t-statistic having the modest value of 1.9155,
and a p value of 0.0398. The estimate of ρ is 0.48, to two decimal
places. There are 14 bivariate observations, and since the mean is
estimated for each sample under the null hypothesis that ρ > 0 , the
number of degrees of freedom (df) is 12.
Since your EDA plots reveal two obvious bivariate outliers in the
phone gain data, the nonparametric alternatives, either Kendall’s τ or
Spearman’s ρ , are preferable in determining p values for this case.
Using Kendall’s method, we obtain the following results:
> cor.test(diff.hs, phone.gain, alt = "g",method = "k")
Kendall’s rank correlation tau
data: diff.hs and phone.gain
normal-z = 2.0834, p-value = 0.0186
alternative hypothesis: true tau is greater than 0
sample estimates:
tau
0.4175824
The p-value obtained from Kendall’s method is smaller than that
obtained from the Pearson method. The null hypothesis is rejected at
a level of 0.05. Spearman’s ρ , by contrast, yields a p value similar to
that of the standard Pearson method.
155
Chapter 5 Statistical Inference for One- and Two-Sample Problems
Warning
The values returned for tau and rho (0.407 and 0.504, respectively, for the phone gain data) do
not provide unbiased estimates of the true correlation ρ. Transformations of tau and rho are
required to obtain unbiased estimates of ρ.
Point Estimates
and Confidence
Intervals for ρ
You may want an estimate ρ̂ of ρ , or a confidence interval for ρ .
The function cor.test gives you the classical sample correlation
coefficient estimate r of ρ , when you use the default Pearson’s
method. However, cor.test does not provide you with a robust
estimate of ρ , (since neither Kendall’s τ nor Spearman’s ρ provide
an unbiased estimate of ρ ). Furthermore, cor.test does not provide
any kind of confidence interval for ρ .
To obtain a robust point estimate of ρ , use the function cor with a
nonzero value for the optional argument trim. You should specify a
fraction α between 0 and 0.5 for the value of this argument. This
results in a robust estimate which consists of the ordinary sample
correlation coefficient based on the fraction ( 1 – α ) of the data
remaining after “trimming” away a fraction α . In most cases, set
trim=0.2. If you think your data contain more than 20% outliers, you
should use a larger fraction in place of 0.2. The default value is
trim=0, which yields the standard Pearson sample correlation
coefficient.
Applying cor to the phone gain data, you get:
> cor(diff.hs, phone.gain, trim = 0.2)
[1] 0.7145078
Comparing this robust estimate to our earlier value for ρ obtained
using cor.test, we see the robust estimate yields a larger estimate of
ρ . This is what you expect, since the two outliers cause the standard
sample correlation coefficient to have a value smaller than that of the
“bulk” of the data.
156
Correlation
To obtain a confidence interval for ρ , we’ll use the following
procedure (as in Snedecor and Cochran (1980)). First, transform ρ
using Fisher’s z transform, which consists of taking the inverse
hyperbolic tangent transform x = atanh ( ρ ) . Then, construct a
confidence interval for the correspondingly transformed true
correlation coefficient ρ = atanh ( ρ ) . Finally, transform this interval
back to the original scale by transforming each endpoint of this
interval with the hyperbolic tangent transformation tanh.
To implement the procedure just described as an S-PLUS function,
create the function cor.confint as follows:
> cor.confint <- function(x, y, conf.level = .95, trim = 0)
+ {
+
z <- atanh(cor(x, y, trim))
+
b <- qnorm((1 - conf.level)/2)/(length(x) - 3)^.5
+
ci.z <- c(z - b, z + b)
+
conf.int <- tanh(ci.z)
+
conf.int
+ }
You can now use your new function cor.confint on the phone gain
data:
> cor.confint(diff.hs, phone.gain)
[1]
0.80722628 -0.06280425
> cor.confint(diff.hs, phone.gain, trim = .2)
[1] 0.9028239 0.2962300
When you use the optional argument trim=0.2, you are basing the
confidence interval on a robust estimate of ρ, and consequently you
get a robust confidence interval. Since the robust estimate has the
value 0.72, which is larger than the standard (nonrobust) estimate
value of 0.48, you should be reassured to get an interval which is
shifted upward, and is also shorter, than the nonrobust interval you
got by using cor.confint with the default option trim=0.
157
Chapter 5 Statistical Inference for One- and Two-Sample Problems
REFERENCES
Bishop, Y.M.M., Fienberg, S.J., & Holland, P.W. (1980). Discrete
Multivariate Analysis: Theory and Practice. Cambridge, MA: The MIT
Press .
Box, G.E.P., Hunter, W.G., & Hunter, J.S. (1978). Statistics for
Experimenters: An Introduction to Design, Data Analysis and Model
Building. New York: John Wiley & Sons, Inc.
Conover, W.J. (1980). Practical Nonparametric Statistics (2nd ed.). New
York: John Wiley & Sons, Inc.
Heidelberger, P. & Welch, P.D. (1981). A spectral method for
confidence interval generation and run-length control in simulations.
Communications of the ACM 24:233-245.
Hogg, R.V. & Craig, A.T. (1970). Introduction to Mathematical Statistics
(3rd ed.). Toronto, Canada: Macmillan.
Mood, A.M., Graybill, F.A., & Boes, D.C. (1974). Introduction to the
Theory of Statistics (3rd ed.). New York: McGraw-Hill.
Snedecor, G.W. & Cochran, W.G. (1980). Statistical Methods (7th ed.).
Ames, IA: Iowa State University Press.
158
GOODNESS OF FIT TESTS
6
Introduction
160
Cumulative Distribution Functions
161
The Chi-Square Goodness-of-Fit Test
165
The Kolmogorov-Smirnov Goodness-of-Fit Test
168
The Shapiro-Wilk Test for Normality
172
One-Sample Tests
Comparison of Tests
Composite Tests for a Family of Distributions
174
174
174
Two-Sample Tests
178
References
180
159
Chapter 6 Goodness of Fit Tests
INTRODUCTION
Most S-PLUS functions for hypothesis testing assume a certain
distributional form (often normal) and then use data to make
conclusions about certain parameters of the distribution, often the
mean or variance. In Chapter 5, Statistical Inference for One- and
Two-Sample Problems, we describe EDA techniques to informally
test the assumptions of these procedures. Goodness of fit (GOF) tests
are another, more formal tool to assess the evidence for assuming a
certain distribution.
There are two types of GOF problems, corresponding to the number
of samples. They ask the following questions:
1. One sample. Does the sample arise from a hypothesized
distribution?
2. Two sample. Do two independent samples arise from the same
distribution?
S-PLUS implements the two best-known GOF tests:
•
Chi-square, in the chisq.gof function.
•
Kolmogorov-Smirnov, in the ks.gof function.
The chi-square test applies only in the one-sample case; the
Kolmogorov- Smirnov test can be used in both the one-sample and
two-sample cases.
Both the chi-square and Kolmogorov-Smirnov GOF tests work for
many different distributions. In addition, S-PLUS includes the
function shapiro.test, which computes the Shapiro-Wilk W-statistic
for departures from normality. This statistic can be more powerful
than the other two tests for determining whether a particular data set
arises from the normal (Gaussian) distribution.
This chapter describes all three tests, together with a graphical
function, cdf.compare, that can be used as an exploratory tool for
evaluating goodness of fit.
160
Cumulative Distribution Functions
CUMULATIVE DISTRIBUTION FUNCTIONS
For a random variable X , a cumulative distribution function (cdf),
F ( x ) = P [ X ≤ x ] , assigns a measure between 0 and 1 of the
probability that X < x . If X 1, …, X n form a random sample from a
continuous distribution with observed values x 1, …, x n , an empirical
distribution function Fn can be defined for all x , – ∞ < x < ∞ , so that
F n ( x ) is the proportion of observed values less than or equal to x .
The empirical distribution function estimates the unknown cdf.
To decide whether two samples arise from the same unknown
distribution, a natural procedure is to compare their empirical
distribution functions. Likewise, for one sample, you can compare its
empirical distribution function with a hypothesized cdf. For more
information on cumulative distribution functions, see Chapter 3,
Probability.
A graphical comparison of either one empirical distribution function
with a known cdf, or of two empirical distribution functions, can be
obtained easily in S-PLUS using the function cdf.compare. For
example, consider the plot shown in Figure 6.1. In this example, the
empirical distribution function and a hypothetical cdf are quite close.
This plot is produced using the cdf.compare function as follows:
#
>
>
>
Set the seed for reproducibility.
set.seed(222)
z <- rnorm(100)
cdf.compare(z, distribution = "normal")
161
Chapter 6 Goodness of Fit Tests
0.0
0.2
0.4
0.6
0.8
1.0
Empirical and Hypothesized normal CDFs
-3
-2
-1
0
1
2
3
solid line is the empirical d.f.
Figure 6.1: The empirical distribution function of a sample of size 100 generated
from a N(0,1) distribution. The dotted line is the smoothed theoretical N(0,1)
distribution evaluated at the sample points.
You may also compare distributions using quantile-quantile plots
(qqplots) generated by either of the following functions:
•
qqnorm, which compares one sample with a normal
distribution.
•
qqplot,
which compares two samples.
For our normal sample z, the command qqnorm(z) produces the plot
shown in Figure 6.2.
162
0
-3
-2
-1
z
1
2
Cumulative Distribution Functions
-2
-1
0
1
2
Quantiles of Standard Normal
Figure 6.2: A qqnorm plot of a sample from a normal distribution.
Departures from linearity show how the sample differs from the
normal, or how the two sample distributions differ. To compare
samples with distributions other than the normal, you may produce
qqplots using the function ppoints. For more details, see the chapter
Traditional Graphics in the Guide to Graphics.
In many cases, the graphical conclusions drawn from either
cdf.compare or the qqplots make more formal tests such as the
chi-square or Kolmogorov-Smirnov unnecessary. For example,
consider the two empirical distributions compared in Figure 6.3.
They clearly have different distribution functions:
> x <- rnorm(30)
> y <- runif(30)
> cdf.compare(x, y)
163
Chapter 6 Goodness of Fit Tests
0.0
0.2
0.4
0.6
0.8
1.0
Comparison of Empirical cdfs of x and y
-2
-1
0
1
2
dotted line is cdf of y
Figure 6.3: Two clearly different empirical distribution functions.
164
3
The Chi-Square Goodness-of-Fit Test
THE CHI-SQUARE GOODNESS-OF-FIT TEST
The chi-square test is the oldest and best known goodness-of-fit test. It
is a one-sample test that examines the frequency distribution of n
observations grouped into k classes. The observed counts c i in each
class are compared to the expected counts C i from the hypothesized
distribution. The test statistic, due to Pearson, is
k
2
χ̂ =

i=1
( ci – Ci ) 2
----------------------- .
Ci
Under the null hypothesis that the sample comes from the
hypothesized distribution, the test statistic has a χ 2 distribution with
k – 1 degrees of freedom. For any significance level α , reject the null
hypothesis if χ̂ 2 is greater than the critical value ν for which
P( χ2 > ν ) = α .
You perform the chi-square goodness of fit test with the chisq.gof
function. In the simplest case, specify a test vector and a hypothesized
distribution:
> chisq.gof(z, distribution = "normal")
Chi-square Goodness of Fit Test
data: z
Chi-square = 11.8, df = 12, p-value = 0.4619
alternative hypothesis:
True cdf does not equal the normal Distn. for at least
one sample point.
Since we created z as a random sample from a normal distribution, it
is not surprising that we cannot reject the null hypothesis. If we
hypothesize a different distribution, we see that the chi-square test
correctly rejects the hypothesis. In the command below, we test
whether z is a sample from an exponential distribution.
> chisq.gof(z, distribution = "exponential")
165
Chapter 6 Goodness of Fit Tests
Chi-square Goodness of Fit Test
data: z
Chi-square = 271.28, df = 12, p-value = 0
alternative hypothesis:
True cdf does not equal the exponential Distn. for at
least one sample point.
The allowable values for the distribution argument are the
following strings:
"beta"
"exponential"
"hypergeometric"
"normal"
"weibull"
"binomial"
"f"
"lognormal"
"poisson"
"wilcoxon"
"cauchy"
"gamma"
"logistic"
"t"
"chisquare"
"geometric"
"negbinomial"
"uniform"
The default value for distribution is "normal".
When the data sample is from a continuous distribution, one factor
affecting the outcome is the choice of partition for determining the
grouping of the observations. This becomes particularly important
when the expected count in one or more cells drops below 1, or the
average expected cell count drops below five (Snedecor and Cochran
(1980), p. 77). You can control the choice of partition using either the
n.classes or cut.points argument to chisq.gof. By default,
chisq.gof uses a default value for n.classes due to Moore (1986).
Use the n.classes argument to specify the number of equal-width
classes:
> chisq.gof(z, n.classes = 5)
Use the cut.points argument to specify the end points of the cells.
The specified points should span the observed values:
> cuts.z <- c(floor(min(z))-1, -1, 0, 1, ceiling(max(z))+1)
> chisq.gof(z, cut.points = cuts.z)
166
The Chi-Square Goodness-of-Fit Test
Chi-square tests apply to any type of variable: continuous, discrete, or
a combination of these. For large sample sizes ( n ≥ 50 ), the chi-square
is the only valid test when the hypothesized distribution is discrete. In
addition, the chi-square test easily adapts to the situation when
parameters of a distribution are estimated. However, information is
lost by grouping the data, especially for continuous variables.
167
Chapter 6 Goodness of Fit Tests
THE KOLMOGOROV-SMIRNOV GOODNESS-OF-FIT TEST
Suppose F 1 and F 2 are two cdfs. In the one-sample situation, F 1 is
the empirical distribution function and F 2 is a hypothesized cdf; in
the two-sample situation, F 1 and F 2 are both empirical distribution
functions. Possible hypotheses and alternatives concerning these cdfs
are:
•
Two-sided
H0: F 1 ( x ) = F 2 ( x ) for all x
HA: F 1 ( x ) ≠ F 2 ( x ) for at least one value of x
•
One-sided (“less” alternative)
H0: F 1 ( x ) ≥ F 2 ( x ) for all x
HA: F 1 ( x ) < F 2 ( x ) for at least one value of x .
•
One-sided (“greater” alternative)
H0: F 1 ( x ) ≤ F 2 ( x ) for all x
HA: F 1 ( x ) > F 2 ( x ) for at least one value of x
The Kolmogorov-Smirnov (KS) test is a method for testing the above
hypotheses. Corresponding to each alternative hypothesis is a test
statistic, as follows.
•
Two-sided Test: T = sup x F 1 ( x ) – F 2 ( x )
•
Less Alternative: T - = sup x F 2 ( x ) – F 1 ( x )
•
Greater Alternative: T + = sup x F 1 ( x ) – F 2 ( x )
Thus, the KS test is based on the maximum vertical distance between
the distributions F 1 ( x ) and F 2 ( x ) . If the test statistic is greater than
some critical value, the null hypothesis is rejected.
168
The Kolmogorov-Smirnov Goodness-of-Fit Test
To perform a KS test, use the function ks.gof. By default, the onesample ks.gof test compares the sample x to a normal distribution
with a mean of mean(x) and a standard deviation of stdev(x). For
example, the following is returned for our normal sample, z:
> ks.gof(z)
One sample Kolmogorov-Smirnov Test of Composite Normality
data: z
ks = 0.0826, p-value = 0.0891
alternative hypothesis:
True cdf is not the normal distn. with estimated
parameters
sample estimates:
mean of x standard deviation of x
0.006999765
1.180401
In the one-sample case, ks.gof can test any of the three hypotheses
through the alternative argument; possible values of alternative
are "two-sided", "greater", and "less". In the two-sample case,
ks.gof can test only the two-sided hypothesis.
You can specify a different distribution using the distribution
argument, which accepts the following values:
"beta"
"exponential"
"hypergeometric"
"normal"
"weibull"
"binomial"
"f"
"lognormal"
"poisson"
"wilcoxon"
"cauchy"
"gamma"
"logistic"
"t"
"chisquare"
"geometric"
"negbinomial"
"uniform"
For example, suppose we think the underlying distribution of z is chisquare with 2 degrees of freedom. The KS test gives strong evidence
against this assumption. In the command below, the ks.gof function
passes the df argument to the functions for the chi-square distribution.
169
Chapter 6 Goodness of Fit Tests
> ks.gof(z, alternative = "greater",
+ distribution = "chisquare", df = 2)
One-sample Kolmogorov-Smirnov Test
Hypothesized distribution = chisquare
data: z
ks = 0.4906, p-value = 0
alternative hypothesis:
True cdf is greater than the chisquare distn. with the
specified parameters
Figure 6.4, created as follows, also shows that this assumption is not
reasonable:
> cdf.compare(z, dist = "chisquare", df = 2)
The chisq.gof test gives further confirmation:
> chisq.gof(z, dist = "chisquare", n.param.est = 0, df = 2)
Chi-square Goodness of Fit Test
data: z
Chi-square = 314.96, df = 12, p-value = 0
alternative hypothesis:
True cdf does not equal the chisquare Distn. for at least
one sample point.
Note that chisq.gof tests only the two sided alternative.
170
The Kolmogorov-Smirnov Goodness-of-Fit Test
0.0
0.2
0.4
0.6
0.8
1.0
Empirical and Hypothesized chisquare CDFs
-3
-2
-1
0
1
2
3
solid line is the empirical d.f.
Figure 6.4: Similar to Figure 6.3, except the dotted line shows a chi-square cdf with
2 degrees of freedom.
171
Chapter 6 Goodness of Fit Tests
THE SHAPIRO-WILK TEST FOR NORMALITY
The Shapiro-Wilk W-statistic is a well-established and powerful test
for detecting departures from normality. The test statistic W is
defined as:
n


  a i x i
i=1 
W = ---------------------------n
 ( xi – x )
2
2
i=1
where x 1, x 2, …, x n
are the ordered data values. The vector
a = ( a 1, a 2, …, a n ) is
T
–1
m V
T
a = --------------------------------- ,
T –1 –1
m V V m
where m is the vector of expected values of the order statistics for a
random sample of size n from a standard normal distribution. Here,
V is the variance-covariance matrix for the order statistics, T denotes
–1
the transpose operator, and V is the inverse of V . Thus, a contains
the expected values of the standard normal order statistics, weighted
T
by their variance-covariance matrix and normalized so that a a = 1 .
The W-statistic is attractive because it has a simple, graphical
interpretation: you can think of it as an approximate measure of the
correlation in a normal quantile-quantile plot of the data.
To
calculate
Shapiro-Wilk’s W-statistic in S-PLUS, use the
shapiro.test function. This function works for sample sizes less than
5000; S-PLUS returns an error if there is more than 5000 finite values
in your data set. The following is returned for our normal sample, z:
> shapiro.test(z)
Shapiro-Wilk Normality Test
data: z
W = 0.9853, p-value = 0.3348
172
The Shapiro-Wilk Test for Normality
Small p-values indicate that the null hypothesis of normality is
probably not true. Since we generated z from a normal distribution, it
is not surprising that we cannot reject the null hypothesis.
173
Chapter 6 Goodness of Fit Tests
ONE-SAMPLE TESTS
Comparison of
Tests
As we mention in the section The Chi-Square Goodness-of-Fit Test on
page 165, the chi-square test divides the data into categories. While
this may be appropriate for discrete data, it can be an arbitrary
process when the data are from a continuous distribution. The results
of the chi-square test can vary with how the data are divided,
especially when dealing with continuous distributions. Because of this
characteristic, the one-sample Kolmogorov-Smirnov test is more
powerful than the chi-square test when the hypothesized distribution
is continuous. That is, it is more likely to reject the null hypothesis
when it should.
In general, both the chi-square and Kolmogorov-Smirnov GOF tests
are less powerful for detecting departures from normality than the
Shapiro-Wilk test. This is because the Shapiro-Wilk test is designed
specifically for normal distributions, and does not test the goodness of
fit for other distributions. In addition, the chi-square and
Kolmogorov-Smirnov tests must estimate distribution parameters
from the data if none are provided; we discuss this in detail in the
next section.
Composite
Tests for a
Family of
Distributions
When distribution parameters are estimated from a sample rather
than specified in advance, the tests described in this chapter may no
longer be adequate. Instead, different tables of critical values are
needed. The tables for the Kolmogorov-Smirnov test, for example,
vary according the the following criteria:
•
Different distributions
•
Estimated parameters
•
Methods of estimation
•
Sample sizes
The null hypothesis is composite in these cases: rather than
hypothesizing that the data have a distribution with specific
parameters, we hypothesize only that the distribution belongs to a
particular family of distributions, such as the normal. This family of
distributions results from allowing all possible parameter values.
174
One-Sample Tests
The two functions chisq.gof and ks.gof use different strategies to
solve composite tests. When estimating distribution parameters, the
chisq.gof function requires the user to pass both the number of
estimated parameters and the estimates themselves as arguments. It
then reduces the degrees of freedom for the chi-square by the number
of estimated parameters.
The ks.gof function explicitly calculates the required parameters in
two cases:
•
Normal distribution, where both the mean and variance are
estimated.
•
Exponential distribution, where the mean is estimated.
Otherwise, ks.gof forbids composite hypotheses. When distribution
parameters must be estimated, the KS test tends to be conservative.
This means that the actual significance level for the test is smaller than
the stated significance level. A conservative test may incorrectly fail to
reject the null hypothesis, thus decreasing its power.
The Shapiro-Wilk W-statistic is calculated directly from the data
values, and does not require estimates of the distribution parameters.
Thus, it is more powerful than both the chi-square and KolmogorovSmirnov GOF tests when the hypothesized theoretical distribution is
normal.
As an example, we test whether we can reasonably assume that the
Michelson data arises from a normal distribution; see the section One
Sample: Distribution Shape, Location, and Scale on page 129 for a
definition of the mich data set. We start with an exploratory plot using
cdf.compare, as shown in Figure 6.5:
> cdf.compare(mich, dist = "normal", mean = mean(mich),
+ sd = stdev(mich))
175
Chapter 6 Goodness of Fit Tests
0.0
0.2
0.4
0.6
0.8
1.0
Empirical and Hypothesized normal CDFs
700
800
900
solid line is the empirical d.f.
1000
Figure 6.5: Exploratory plot of cdf of the Michelson data.
We now use the ks.gof function, which estimates parameters for the
mean and variance:
> ks.gof(mich, dist = "normal")
One sample Kolmogorov-Smirnov Test of Composite Normality
data: mich
ks = 0.1793, p-value = 0.0914
alternative hypothesis:
True cdf is not the normal distn. with estimated
parameters
sample estimates:
mean of x standard deviation of x
909
104.926
If distribution parameters are estimated, the degrees of freedom for
chisq.gof depend on the method of estimation. In practice, you may
estimate the parameters from the original data and set the argument
n.param.est to the number of parameters estimated. The chisq.gof
176
One-Sample Tests
function then subtracts one degree of freedom for each parameter
estimated. For example, the command below tests the normal
assumption for the Michelson data using chisq.gof:
> chisq.gof(mich, dist = "normal", n.param.est = 2,
+ mean = mean(mich), sd = stdev(mich))
Chi-square Goodness of Fit Test
Warning messages:
Expected counts < 5. Chi-squared approximation may not
be appropriate.
data: mich
Chi-square = 8.7, df = 4, p-value = 0.0691
alternative hypothesis:
True cdf does not equal the normal Distn. for at least one
sample point.
Note that the distribution theory of the chi-square test is a large
sample theory. Therefore, when any expected cell counts are small,
chisq.gof issues a warning message. You should regard p values with
caution in this case.
In truth, if the parameters are estimated by maximum likelihood, the
degrees of freedom for the chi-square test are bounded between
( m – 1 ) and ( m – 1 – k ), where m is the number of cells and k is the
number of parameters estimated. It is therefore important to compare
the test statistic to the chi-square distribution with both ( m – 1 ) and
( m – 1 – k ) degrees of freedom, especially when the sample size is
small. See Kendall and Stuart (1979), for a more complete discussion.
Both the chi-square and Kolmogorov-Smirnov goodness-of-fit tests
return results for the mich data which make us suspect the null
hypothesis, but don’t allow us to firmly reject it at 95% or 99%
confidence levels. The shapiro.test function returns a similar result:
> shapiro.test(mich)
Shapiro-Wilk Normality Test
data: mich
W = 0.9199, p-value = 0.0988
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Chapter 6 Goodness of Fit Tests
TWO-SAMPLE TESTS
In the two-sample case, you can use the ks.gof function, with the
second sample y filling in for the hypothesized distribution. The
assumptions of the two-sample KS test are:
•
The samples are random samples,
•
The samples are mutually independent, and
•
The data are measured on at least an ordinal scale.
In addition, the test gives exact results only if the underlying
distributions are continuous.
For example, the following commands graphically compare the cdfs
of two vectors, x and y, that are generated from a normal and
exponential distribution, respectively:
>
>
>
>
>
x <- rnorm(30)
y <- rexp(100)
par(mfrow = c(1,2))
qqplot(x, y)
cdf.compare(x, y)
Figure 6.6 shows the results; the qqplot is not linear and the cdfs are
quite different. This graphical evidence is verified by a formal KS test:
> ks.gof(x, y)
Two-Sample Kolmogorov-Smirnov Test
data: x and y
ks = 0.4667, p-value = 0.0001
alternative hypothesis:
cdf of x does not equal the
cdf of y for at least one sample point.
178
Two-Sample Tests
0
0.0
1
0.2
0.4
2
y
0.6
3
0.8
4
1.0
Comparison of Empirical cdfs of x and y
-2
-1
0
1
x
2
3
-2
0
2
4
dotted line is cdf of y
Figure 6.6: Normal and exponential samples compared. In the graph on the right,
the dotted line is the cumulative distribution function for the exponential sample.
179
Chapter 6 Goodness of Fit Tests
REFERENCES
Kendall, M.G. & Stuart, A. (1979). The Advanced Theory of Statistics,
Volume 2: Inference and Relationship (4th ed.). New York: Oxford
University Press.
Millard, S.P. and Neerchal, N.K. (2001). Environmental Statistics with
S-PLUS. Boca Raton, Florida: CRC Press LLC.
Moore, D.S. (1986). Tests of chi-squared type. In D'Agostino, R.B. &
Stevens, M.A. (Eds.) Goodness-of-Fit Techniques. New York: Marcel
Dekker.
Snedecor, G.W. & Cochran, W.G. (1980). Statistical Methods (7th ed.).
Ames, Iowa: Iowa State University Press.
180
STATISTICAL INFERENCE FOR
COUNTS AND PROPORTIONS
7
Introduction
182
Proportion Parameter for One Sample
Setting Up the Data
Hypothesis Testing
Confidence Intervals
184
184
184
185
Proportion Parameters for Two Samples
Setting Up the Data
Hypothesis Testing
Confidence Intervals
186
186
186
188
Proportion Parameters for Three or More Samples
Setting Up the Data
Hypothesis Testing
Confidence Intervals
189
189
190
191
Contingency Tables and Tests for Independence
The Chi-Square and Fisher Tests of Independence
The Chi-Square Test of Independence
Fisher’s Exact Test of Independence
The Mantel-Haenszel Test of Independence
McNemar’s Test for Symmetry Using Matched Pairs
192
193
195
196
196
199
References
201
181
Chapter 7 Statistical Inference for Counts and Proportions
INTRODUCTION
This chapter shows you how to use S-PLUS statistical inference
functions for two types of problems that involve counts or proportions.
With these functions, you can obtain confidence intervals for the
unknown population parameters and p values for hypothesis tests of
the parameter values.
The first type of problem is one where you have one or more
samples, or sets of trials, in which the count for each sample
represents the number of times that a certain interesting outcome
occurs. By common convention, we refer to the occurrence of the
outcome of interest as a “success.” For example, if you play roulette
100 times at a casino, and you bet on red each time, you are
interested in counting the number of times that the color red comes
up. This count is a number between 0 and 100. When you divide this
count by 100 you get a proportion (that is, a number between 0 and
1). This proportion is a natural estimate of the probability that red
comes up on the roulette wheel.
Another example is provided by the famous Salk vaccine trials. These
trials involved two groups, one of which received the vaccine and one
of which received a placebo. For each group, the count of interest was
the number of individuals who contracted polio. The ratio of the
number of individuals who contracted polio to the total number of
individuals in the group is a proportion that provides a natural
estimate of the probability of contracting polio within that group.
The underlying probability model for problems of this first type is the
binomial distribution. For each set of trials i, this distribution is
characterized by the number of trials and the probability p i that a
success occurs on each trial. This probability is called a proportion
parameter. Your main interest is in making statistical inference
statements concerning the probabilities p 1, p 2, …, p m of occurrence of
the event of interest for each of the m sets of trials.
The second type of problem is one where you have counts on the
number of occurrences of several possible outcomes for each of two
variables. For example, you may be studying three types of cancer
and three types of diet (such as low-, medium- and high-fat diets). The
two variables of interest may be “type of cancer” and “type of diet.”
182
Introduction
For a fixed set of individuals, you have counts on the number of
individuals who fall jointly in each of the categories defined by the
simultaneous occurrence of a type of cancer and a diet classification.
For problems of this kind, the data is arranged in a two-way table
called a contingency table.
In this second kind of problem, your main interest is to determine
whether there is any association between the two variables of interest.
The probability model for such problems is one of statistical
independence between the two variables.
The first three sections of this chapter cover the first type of problem
described above, for which the proportion parameters are the
probabilities of success, p 1, p 2, …, p m in m sets of binomial trials. The
last section covers the second type of problem, where you are
interested in testing the null hypothesis of independence between two
variables.
183
Chapter 7 Statistical Inference for Counts and Proportions
PROPORTION PARAMETER FOR ONE SAMPLE
When you play roulette and bet on red, you expect your probability
of winning to be close to, but slightly less than, 0.5. You expect this
because (in the United States) a roulette wheel has 18 red slots, 18
black slots, and two additional slots labeled “0” and “00,” for a total of
38 slots into which the ball can fall. Thus, for a “fair” (that is, perfectly
balanced) wheel, you expect the probability of red to be
p 0 = 18 ⁄ 38 = 0.474 . You hope that the house is not cheating you
by altering the roulette wheel so that the probability of red is less than
0.474.
To test whether a given sample has a particular proportion parameter,
use the binom.test function.
Setting Up the
Data
In the roulette case there is little to do, since the only data are the
number n of trials and the number x of successes. These two values
can be directly supplied as arguments to binom.test, as shown in the
examples below.
Hypothesis
Testing
You can test the null hypothesis that p = p 0 using the function
binom.test. For example, if you bet on red 100 times and red comes
up 42 times, you get a p value for this null hypothesis against the
two-sided alternative that p ≠ 0.474 as follows:
> binom.test(42, 100, p = 0.474)$p.value
[1] 0.3167881
The two-sided alternative is the default alternative for binom.test.
You can get a p value for a one-sided alternative by using the optional
argument alt. For example, in the roulette wheel example you are
concerned that the house might cheat you in some way so that p < p 0 .
Use the following to test the null hypothesis against this one-sided
alternative:
> binom.test(42, 100, p = 0.474, alt = "l")$p.value
[1] 0.1632416
184
Proportion Parameter for One Sample
Here alt="l" specifies the “less than” alternative p < p 0 . To specify
the “greater than” alternative p > p 0 , use alt="g".
The default for the optional argument p, which specifies the null
hypothesis value for p , is p=0.5. For example, suppose you toss a
coin 1000 times, with heads coming up 473 times. To test the coin for
“fairness” (that is, to test that the probability of heads equals 0.5), use
the following:
> binom.test(473, 1000)$p.value
[1] 0.09368729
Confidence
Intervals
The function binom.test does not compute a confidence interval for
the probability p of success. You can get a confidence interval for p
by using the function prop.test. For example, the following shows
how to obtain the 95% confidence interval for p :
> prop.test(45, 100)$conf.int
[1] 0.3514281 0.5524574
attr(, "conf.level"):
[1] 0.95
The function prop.test uses a normal approximation to the binomial
distribution for such computations.
You get different confidence intervals by using the optional argument
with different values. For example, to get a 90%
confidence interval:
conf.level
> prop.test(45, 100, conf.level = 0.9)$conf.int
[1] 0.3657761 0.5370170
attr(, "conf.level"):
[1] 0.9
185
Chapter 7 Statistical Inference for Counts and Proportions
PROPORTION PARAMETERS FOR TWO SAMPLES
In the Salk vaccine trials, two large groups were involved in the
placebo-control phase of the study. The first group, which received
the vaccination, consisted of 200,745 individuals. The second group,
which received a placebo, consisted of 201,229 individuals. There
were 57 cases of polio in the first group and 142 cases of polio in the
second group.
You assume a binomial model for each group, with a probability p 1
of contracting polio in the first group and a probability p 2 of
contracting polio in the second group. You are mainly interested in
knowing whether or not the vaccine is effective. Thus you are mainly
interested in knowing the relationship between p 1 and p 2 .
You can use prop.test to obtain hypothesis test p values concerning
the values of p 1 and p 2 , and to obtain confidence intervals for the
difference between the values p 1 and p 2 .
Setting Up the
Data
The first two arguments to prop.test are vectors containing,
respectively, the number of successes and the total number of trials.
For consistency with the one-sample case, we name these vectors x
and n. In the case of the Salk vaccine trials, a “success” corresponds to
contracting polio (although one hardly thinks of this as a literal
success!). Thus, you create the vectors x and n as follows:
> x <- c(57, 142)
> n <- c(200745, 201229)
Hypothesis
Testing
For two-group problems, you can use either of two null hypotheses:
an equal probabilities null hypothesis that p 1 = p 2 , with no
restriction on the common value of these probabilities other than that
they be between 0 and 1, or a completely specified probabilities null
hypothesis, where you provide specific probabilities for both p 1 and
p 2 , and test whether the true probabilities are equal to those
hypothesized.
186
Proportion Parameters for Two Samples
The Equal
When using the equal probabilities null hypothesis, a common
Probabilities Null alternative hypothesis is the two-sided alternative p 1 ≠ p 2 . These null
Hypothesis
and alternative hypotheses are the defaults for prop.test.
In the Salk vaccine trials, your null hypothesis is that the vaccine has
no effect. For the two-sided alternative that the vaccine has some
effect, either positive or negative, you get a p value by extracting the
p.value component of the list returned by prop.test:
> prop.test(x, n)$p.value
[1] 2.86606e-09
The extremely small p value clearly indicates that the vaccine has
some effect.
To test the one-sided alternative that the vaccine has a positive effect;
that is, that p 1 < p 2 , use the argument alt="l" to prop.test:
> prop.test(x, n, alt = "l")$p.value
[1] 1.43303e-09
Here the p value is even smaller, indicating that the vaccine is highly
effective in protecting against the contraction of polio.
Completely
Specified Null
Hypothesis
Probabilities
You can also use prop.test to test “completely” specified null
hypothesis probabilities. For example, suppose you have some prior
belief that the probabilities of contracting polio with and without the
Salk vaccine are p 01 = 0.0002 and p 02 = 0.0006 , respectively. Then
you supply these null hypothesis probabilities as a vector object, using
the optional argument p. The p value returned is for the joint
probability that both probabilities are equal to the hypothesized
probabilities; that is, 0.0002 and 0.0006 .
> prop.test(x, n, p = c(0.0002, 0.0006))$p.value
[1] 0.005997006
The above p value is very small and indicates that the null hypothesis
is very unlikely and should be rejected.
187
Chapter 7 Statistical Inference for Counts and Proportions
Confidence
Intervals
You obtain a confidence interval for the difference p 1 – p 2 in the
probabilities of success for the two samples by extracting the
conf.int component of prop.test. For example, to get a 95%
confidence interval for the difference in probabilities for the Salk
vaccine trials:
> prop.test(x, n)$conf.int
[1] -0.0005641508 -0.0002792920
attr(, "conf.level"):
[1] 0.95
The 95% confidence level is the default confidence level for
prop.test. You get a different confidence level by using the optional
argument conf.level=. For example, to get a 99% confidence
interval, use:
> prop.test(x, n, conf.level = 0.99)$conf.int
[1] -0.0006073419 -0.0002361008
attr(, "conf.level"):
[1] 0.99
You get a confidence interval for the difference p 1 – p 2 by using
prop.test only when you use the default null hypothesis that
p1 = p2 .
You get all the information provided by prop.test as follows:
> prop.test(x, n, conf.level = 0.90)
2-sample test for equality of proportions with
continuity correction
data: x out of n
X-squared = 35.2728, df = 1, p-value = 0
alternative hypothesis: two.sided
90 percent confidence interval:
-0.0005420518 -0.0003013909
sample estimates:
prop’n in Group 1 prop’n in Group 2
0.0002839423
0.0007056637
188
Proportion Parameters for Three or More Samples
PROPORTION PARAMETERS FOR THREE OR MORE
SAMPLES
Sometimes you may have three or more samples of subjects, with
each subject characterized by the presence or absence of some
characteristic. An alternative, but equivalent, terminology is that you
have three or more sets of trials, with each trial resulting in a success
or failure. For example, consider the data shown in Table 7.1 for four
different studies of lung cancer patients, as presented by Fleiss (1981).
Table 7.1: Smoking status among lung cancer patients in four studies.
Study
Number of Patients
Number of Smokers
1
86
83
2
93
90
3
136
129
4
82
70
Each study has a certain number of patients, as shown in the second
column of the table, and for each study a certain number of the
patients were smokers, as shown in the third column of the table. For
this data, you are interested in whether the probability of a patient
being a smoker is the same in each of the four studies, that is, whether
each of the studies involve patients from a homogeneous population.
Setting Up the
Data
The first argument to prop.test is a vector containing the number of
subjects having the characteristic of interest for each of the groups (or
the number of successes for each set of trials). The second argument
to prop.test is a vector containing the number of subjects in each
group (or the number of trials for each set of trials). As in the one and
two sample cases, we call these vectors x and n.
189
Chapter 7 Statistical Inference for Counts and Proportions
For the smokers data in Table 7.1, you create the vectors x and n as
follows:
> x <- c(83, 90, 129, 70)
> n <- c(86, 93, 136, 82)
Hypothesis
Testing
For problems with three or more groups, you can use either an equal
probabilities null hypothesis or a completely specified probabilities
null hypothesis.
The Equal
In the lung cancer study, the null hypothesis is that the probability of
Probabilities Null being a smoker is the same in all groups. Because the default null
hypothesis for prop.test is that all groups (or sets of trials) have the
Hypothesis
same probability of success, you get a p value as follows:
> prop.test(x, n)$p.value
[1] 0.005585477
The p value of 0.006 is highly significant, so you can not accept the
null hypothesis that all groups have the same probability that a
patient is a smoker. To see all the results returned by prop.test, use:
> prop.test(x, n)
4-sample test for equality of proportions without
continuity correction
data: x out of n
X-squared = 12.6004, df = 3, p-value = 0.0056
alternative hypothesis: two.sided
sample estimates:
prop’n in Group 1 prop’n in Group 2 prop’n in Group 3
0.9651163
0.9677419
0.9485294
prop’n in Group 4
0.8536585
Completely
Specified Null
Hypothesis
Probabilities
190
If you want to test a completely specified set of null hypothesis
probabilities, you need to supply the optional argument p, with the
value of this argument being a vector of probabilities having the same
length as the first two arguments, x and n.
Proportion Parameters for Three or More Samples
For example, in the lung cancer study, to test the null hypothesis that
the first three groups have a common probability 0.95 of a patient
being a smoker, while the fourth group has a probability 0.90 of a
patient being a smoker, create the vector p as follows, then use it as an
argument to prop.test:
> p <- c(0.95, 0.95, 0.95, 0.90)
> prop.test(x, n, p)$p.value
[1] 0.5590245
Warning messages:
Expected counts < 5. Chi-square approximation may not be
appropriate in prop.test(x,n,p).
Alternatively, you could use
> prop.test(x, n, p = c(0.95, 0.95, 0.95, 0.90))$p.value
Confidence
Intervals
Confidence intervals are not computed by prop.test when you have
three or more groups (or sets of trials).
191
Chapter 7 Statistical Inference for Counts and Proportions
CONTINGENCY TABLES AND TESTS FOR INDEPENDENCE
The Salk vaccine trials in the early 1950s resulted in the data
presented in Table 7.2.
Table 7.2: Contingency table of Salk vaccine trials data.
No Polio
Non-paralytic
Polio
Paralytic
Polio
Totals
Vaccinated
200,688
24
33
200,745
Placebo
201,087
27
115
201,229
Totals
401,775
51
148
401,974
There are two categorical variables for the Salk trials: vaccination
status, which has the two levels “vaccinated” and “placebo,” and polio
status, which has the three levels “no polio,” “non-paralytic polio,”
and “paralytic polio.” Of 200,745 individuals who were vaccinated,
24 contracted non-paralytic polio, 33 contracted paralytic polio, and
the remaining 200,688 did not contract any kind of polio. Of 201,229
individuals who received the placebo, 27 contracted non-paralytic
polio, 115 contracted paralytic polio, and the remaining 201,087 did
not contract any kind of polio.
Tables such as Table 7.2 are called contingency tables. A contingency
table lists the number of counts for the joint occurrence of two levels
(or possible outcomes), one level for each of two categorical variables.
The levels for one of the categorical variables correspond to the
columns of the table, and the levels for the other categorical variable
correspond to the rows of the table.
When working with contingency table data, your primary interest is
most often determining whether there is any association in the form
of statistical dependence between the two categorical variables whose
counts are displayed in the table. The null hypothesis is that the two
variables are statistically independent. You can test this null
hypothesis with the functions chisq.test and fisher.test. The
function chisq.test is based on the classic chi-square test statistic,
and the associated p value computation entails some approximations.
192
Contingency Tables and Tests for Independence
The function fisher.test computes an exact p value for tables
having at most 10 levels for each variable. The function fisher.test
also entails a statistical conditioning assumption.
For contingency tables involving confounding variables, which are
variables related to both variables of interest, you can test for
independence using the function mantelhaen.test, which performs
the Mantel-Haenszel test. For contingency tables involving matched
pairs, use the function mcnemar.test to perform McNemar’s
chi-square test.
The functions for testing independence in contingency tables do not
compute confidence intervals, only p-values and the associated test
statistic.
The Chi-Square
and Fisher
Tests of
Independence
The chi-square and Fisher’s exact tests are familiar methods for
testing independence. The Fisher test is often recommended when
expected counts in any cell are below 5, as the chi-square probability
computation becomes increasingly inaccurate when the expected
counts in any cell are low; S-PLUS produces a warning message in
that case. Other factors may also influence your choice of which test
to use, however. Refer to a statistics text for further discussion if you
are unsure which test to use.
Setting Up the
Data
You can set up your contingency table data in several ways. Which
way you choose depends to some extent on the original form of the
data and whether the data involve a large number of counts or a small
to moderate number of counts.
Two-Column
Matrix Objects
If you already have the data in the form of a contingency table in
printed form, as in Table 7.2, the easiest thing to do is to put the data
in matrix form (excluding the marginal totals, if provided in the
original data). For example,
> salk.mat <- rbind(c(200688, 24, 33),c(201087, 27, 115))
> salk.mat
[,1] [,2] [,3]
[1,] 200688
24
33
[2,] 201087
27 115
193
Chapter 7 Statistical Inference for Counts and Proportions
You could obtain the same result in a slightly different way as follows:
> salk.mat <- matrix(c(200688, 24, 33, 201087, 27, 115),
+ 2, 3, byrow = T)
Two Vector
Objects
You may be given the raw data in the form of two equal-length coded
vectors, one for each variable. In such cases, the length of the vectors
corresponds to the number of individuals, with each entry indicating
the level by a numeric coding. For example, suppose you have two
variables from a clinical trial of the drug propranolol (Snow, 1965).
The vector status is coded for control or propranolol status, and the
vector drug is coded yes or no indicating whether the patient survived
at least 28 days with the prescribed drug. The raw data are stored in
two columns of a built-in data frame named propranolol:
> propranolol$status
[1]
[8]
[15]
[22]
[29]
[36]
[43]
[50]
[57]
[64]
[71]
[78]
[85]
control
control
prop
control
control
control
prop
prop
control
control
prop
control
prop
control
prop
control
prop
control
prop
control
prop
control
prop
control
prop
control
control
control
control
control
control
prop
prop
prop
control
control
prop
control
prop
control
prop
prop
control
control
prop
control
control
prop
prop
control
prop
control
prop
prop
prop
prop
prop
control
prop
prop
prop
control
prop
control
control
control
control
prop
control
control
prop
control
prop
control
prop
control
prop
prop
prop
prop
prop
control
prop
control
control
prop
prop
control
prop
control
prop
yes
yes
no
yes
no
yes
yes
no
yes
yes
yes
yes
no
no
yes
yes
yes
yes
> propranolol$drug
[1]
[15]
[29]
[43]
[57]
[71]
[85]
194
yes
yes
no
yes
yes
no
yes
yes
no
yes
yes
yes
no
yes
yes
no
no
yes
yes
yes
yes
no
yes
yes
no
no
yes
no
yes
yes
no
yes
yes
yes
no
yes yes
yes yes
yes yes
no yes
yes yes
yes yes
yes no
yes
no
no
yes
no
yes
yes
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Contingency Tables and Tests for Independence
To obtain the contingency table (without marginal count totals) use
the table function with the status and drug columns as arguments:
> table(propranolol$drug, propranolol$status)
control prop
no
17
7
yes
29
38
Your data may already be in the form of two factor objects, or you
may want to put your data in that form for further analysis in S-PLUS.
To do this, use the factor command as follows:
> status.fac <- factor(propranolol$status)
> drug.fac <- factor(propranolol$drug)
We use status.fac and drug.fac as arguments to the functions
described below.
The Chi-Square
Test of
Independence
You use the function chisq.test to perform a classical chi-square test
of the null hypothesis that the categorical variables of interest are
independent. For example, using the matrix form of data object
salk.mat for the Salk vaccine trials
> chisq.test(salk.mat)$p.value
[1] 1.369748e-10
which yields an exceedingly small p value. This leads to rejection of
the null hypothesis of no association between polio status and
vaccination status.
To get all the information computed by chisq.test, use chisq.test
without specifying a return component, as usual:
> chisq.test(salk.mat)
Pearson’s chi-square test without Yates’ continuity
correction
data: salk.mat
X-squared = 45.4224, df = 2, p-value = 0
195
Chapter 7 Statistical Inference for Counts and Proportions
You could also use the two factor objects status.fac and drug.fac as
follows:
> chisq.test(status.fac, drug.fac)
Pearson's chi-square test with Yates' continuity correction
data: status.fac and drug.fac
X-square = 4.3198, df = 1, p-value = 0.0377
The results are the same no matter which way you have set up the
data.
Fisher’s Exact
Test of
Independence
You can perform an exact test of indepence by using the S-PLUS
function fisher.test. You can use any data object type that can be
used with chisq.test. For example, using the factor objects for the
propranolol clinical trial:
> fisher.test(status.fac, drug.fac)
Fisher's exact test
data: status.fac and drug.fac
p-value = 0.0314
alternative hypothesis: two.sided
When using fisher.test you should be aware that the p value is
computed conditionally on the fixed marginal counts of the
contingency table you are analyzing. That is, the inference does not
extend to all possible tables that might be obtained by repeating the
experiment and getting different marginal counts.
The MantelHaenszel
Test of
Independence
196
A cancer study produced the data shown in Table 7.3 and Table 7.4, as
reported by Rosner (1986). In these tables, “case” refers to an
individual who had cancer and “control” refers to an individual who
did not have cancer. A “passive” smoker is an individual who lives
with a smoker. A smoker can also be a passive smoker if that smoker
lives with a spouse who also smokes.
Contingency Tables and Tests for Independence
Table 7.3: Nonsmokers in cancer study.
Case-Control Status
Passive Smoker
Not a Passive
Smoker
case
120
111
control
80
155
Case-Control Status
Passive Smoker
Not a Passive
Smoker
case
161
117
control
130
124
Table 7.4: Smokers in cancer study.
For each of these tables, you can use chisq.test or fisher.test to
test for independence between cancer status and passive smoking
status. The data are presented in separate tables because “smoking
status,” that is, being a smoker or not being a smoker, could be a
confounding variable, because both smoking status and passive smoking
status are related to the outcome, cancer status, and because smoking
status may be related to the smoking status of the spouse. You would
like to be able to combine the information in both tables so as to
produce an overall test of independence between cancer status and
passive smoking status. You can do so for two or more two-by-two
tables, by using the function mantelhaen.test, which performs the
Mantel-Haenszel test.
Since the data are now associated with three categorical variables, the
two main variables of interest plus a confounding variable, you can
prepare your data in any one of the three forms listed below.
197
Chapter 7 Statistical Inference for Counts and Proportions
•
a three-dimensional array which represents the three
dimensional contingency table (two-by-two tables stacked on
top of one another)
•
three numerical vectors representing each of the three
categorical variables, two of primary interest and one a
confounding variable
•
three factor objects for the three categorical variables
Which form you use depends largely on the form in which the data
are presented to you. For example, the data in Table 7.3 and Table 7.4
are ideal for use with a three-dimensional array:
> x.array <- array(c(120, 80, 111, 155, 161, 130, 117, 124),
+ c(2, 2, 2))
> x.array
, , 1
[,1] [,2]
120 111
80 155
[1,]
[2,]
, , 2
[1,]
[2,]
[,1] [,2]
161 117
130 124
> mantelhaen.test(x.array)$p.value
[1] 0.0001885083
> mantelhaen.test(x.array)
Mantel-Haenszel chi-square test with
continuity correction
data: x.array
Mantel-Haenszel chi-square = 13.9423, df = 1,
p-value = 2e-04
198
Contingency Tables and Tests for Independence
McNemar’s
Test for
Symmetry
Using Matched
Pairs
In some experiments with two categorical variables, one of the
variables specifies two or more groups of individuals who receive
different treatments. In such situations, matching of individuals is
often carried out in order to increase the precision of statistical
inference. However, when matching is carried out the observations
usually are not independent. In such cases, the inference obtained
from chisq.test, fisher.test and mantelhaen.test is not valid
because these tests all assume independent observations. The
function mcnemar.test allows you to obtain a valid inference for
experiments where matching is carried out.
Consider, for example, the data in Table 7.5, as reported by Rosner
(1986). In this table, each entry represents one pair. For instance, the
“5” in the lower left cell means that in 5 pairs, the individual with
treatment A died, while the individual that that person was paired
with, who received treatment B, survived.
Table 7.5: Matched pair data for cancer study.
Survive With
Treatment B
Die With
Treatment B
survive with treatment A
90
16
die with treatment A
5
510
Your interest is in the relative effectiveness of treatments A and B in
treating a rare form of cancer. Each count in the table is associated
with a matched pair of individuals.
A pair in the table for which one member of a matched pair survives
while the other member dies is called a discordant pair. There are 16
discordant pairs in which the individual who received treatment A
survives and the individual who received treatment B dies. There are
5 discordant pairs with the reverse situation in which the individual
who received treatment A dies and the individual who received
treatment B survives.
If both treatments are equally effective, then you expect these two
types of discordant pairs to occur with “nearly” equal frequency. Put
in terms of probabilities, your null hypothesis is that p 1 = p 2 , where
199
Chapter 7 Statistical Inference for Counts and Proportions
p 1 is the probability that the first type of discordancy occurs in a
matched pair of individuals, and p 2 is the probability that the second
type of discordancy occurs.
We illustrate the use of mcnemar.test on the above data, putting the
data into the form of a matrix object:
> x.matched <- cbind(c(90, 5),c(16, 510))
> x.matched
[,1] [,2]
[1,]
90
16
[2,]
5 510
> mcnemar.test(x.matched)$p.value
[1] 0.02909633
> mcnemar.test(x.matched)
McNemar’s chi-square test with continuity
correction
data: x.matched
McNemar’s chi-square = 4.7619, df = 1, p-value = 0.0291
You can use mcnemar.test with two numeric vector objects, or two
factor objects, as the data arguments (just as with the other functions
in this section). You can also use mcnemar.test with matched pair
tables having more than two rows and more than two columns. In
such cases, the null hypothesis is symmetry of the probabilities p ij
associated with each row and column of the table; that is, the null
hypothesis is that p ij = p ji for each combination of i and j .
200
References
REFERENCES
Bishop, Y.M.M. and Fienberg, S.J., & Holland, P.W. (1980). Discrete
Multivariate Analysis: Theory and Practice. Cambridge, MA: The MIT
Press.
Conover, W.J. (1980). Practical Nonparametric Statistics (2nd ed.). New
York: John Wiley & Sons, Inc.
Fienberg, S.E. (1983). The Analysis of Cross-Classified Categorical Data,
(2nd ed.). Cambridge, MA: The MIT Press.
Fleiss, J.L. (1981). Statistical Methods for Rates and Proportions (2nd ed.).
New York: John Wiley & Sons, Inc.
Lehmann, E.L. (1975). Nonparametrics: Statistical Methods Based on
Ranks. San Francisco: Holden-Day.
Rosner, B. (1986). Fundamentals of Biostatistics. Boston: Duxbury
Press.
Snedecor, G.W. & Cochran, W.G. (1980). Statistical Methods (7th ed.).
Ames, Iowa: Iowa State University Press.
Snow, P.J.D. (1965). Effect of propranolol in myocardial infarction.
Lancet 2: 551-553.
201
Chapter 7 Statistical Inference for Counts and Proportions
202
CROSS-CLASSIFIED DATA
AND CONTINGENCY TABLES
8
Introduction
204
Choosing Suitable Data Sets
209
Cross-Tabulating Continuous Data
213
Cross-Classifying Subsets of Data Frames
216
Manipulating and Analyzing Cross-Classified Data
219
203
Chapter 8 Cross-Classified Data and Contingency Tables
INTRODUCTION
Much data of interest is categorical in nature. Did patients receive
treatment A, B, or C and did they survive? Do the people in a sample
population smoke? Do they have high cholesterol counts? Have they
had heart trouble? These data are stored in S-PLUS as factors, that is,
as vectors where the elements indicate one of a number of levels. A
useful way of looking at these data is to cross-classify it and get a count
of the number of cases sharing a given combination of levels, and
then create a multi-way contingency table (a cross-tabulation) showing
the levels and the counts.
Consider the data set claims. It contains the number of claims for
auto insurance received broken down by the following variables: age
of claimant, age of car, type of car, and the average cost of the claims.
We can disregard the costs for the moment, and consider the question
of which groups of claimants generate the most claims. To make the
work easier we create a new data frame claims.src which does not
contain the cost variable:
> claims.src <- claims[, -4]
> summary(claims.src)
age
17-20
:16
21-24
:16
25-29
:16
30-34,35-39 :32
40-49
:16
50-59
:16
60+
:16
car.age
0-3:32
4-7:32
8-9:32
10+:32
type
A:32
B:32
C:32
D:32
number
Min.
: 0.00
1st Qu.: 9.00
Median : 35.50
Mean
: 69.86
3rd Qu.: 96.25
Max. :434.00
Use the function crosstabs to generate tables of cross-classified data.
The following call to crosstabs generates output showing car age vs.
car type.
204
Introduction
> crosstabs(number ~ car.age + type, data = claims.src)
Call:
crosstabs(number ~ car.age + type, claims.src)
8942 cases in table
+----------+
|N
|
|N/RowTotal|
|N/ColTotal|
|N/Total
|
+----------+
car.age|type
|A
|B
|C
|D
|RowTotl|
-------+-------+-------+-------+-------+-------+
0-3
| 391
|1538
|1517
| 688
|4134
|
|0.0946 |0.3720 |0.3670 |0.1664 |0.462 |
|0.3081 |0.3956 |0.5598 |0.6400 |
|
|0.0437 |0.1720 |0.1696 |0.0769 |
|
-------+-------+-------+-------+-------+-------+
4-7
| 538
|1746
| 941
| 324
|3549
|
|0.1516 |0.4920 |0.2651 |0.0913 |0.397 |
|0.4240 |0.4491 |0.3472 |0.3014 |
|
|0.0602 |0.1953 |0.1052 |0.0362 |
|
-------+-------+-------+-------+-------+-------+
8-9
| 187
| 400
| 191
| 44
|822
|
|0.2275 |0.4866 |0.2324 |0.0535 |0.092 |
|0.1474 |0.1029 |0.0705 |0.0409 |
|
|0.0209 |0.0447 |0.0214 |0.0049 |
|
-------+-------+-------+-------+-------+-------+
10+
| 153
| 204
| 61
| 19
|437
|
|0.3501 |0.4668 |0.1396 |0.0435 |0.049 |
|0.1206 |0.0525 |0.0225 |0.0177 |
|
|0.0171 |0.0228 |0.0068 |0.0021 |
|
-------+-------+-------+-------+-------+-------+
ColTotl|1269
|3888
|2710
|1075
|8942
|
|0.14
|0.43
|0.30
|0.12
|
|
-------+-------+-------+-------+-------+-------+
Test for independence of all factors
Chi^2 = 588.2952 d.f.=9 (p=0)
Yates’ correction not used
205
Chapter 8 Cross-Classified Data and Contingency Tables
The first argument to crosstabs is a formula that tells which variables
to include in the table. The second argument is the data set where the
variables are found. The complete call to crosstabs is stored in the
resulting object as the attribute "call" and is printed at the top of the
table.
Following the formula at the top of the table, the next item of
information is the number of cases; that is, the total count of all the
variables considered. In this example, this is the total of the number
variable, sum(claims.src$number). After the total number of cases,
the output from crosstabs provides a key that tells you how to
interpret the cells of the table. In the key, N is the count. Below N are
the proportions of the whole that the count represents: the proportion
of the row total, the proportion of the column total, and the
proportion of the table total. If there are only two terms in the
formula, the table total is the same as the number of cases.
A quick look at the counts in the table, and in particular at the row
totals (4134, 3549, 822, 437), shows that there are fewer older cars than
newer cars. Relatively few cars survive to be eight or nine years old,
and the number of cars over ten years old is one-tenth that of cars
three years or newer. It is slightly more surprising to note the four
types of cars don’t seem to age equally. You can get an inkling of this
by comparing the cells near the top of the table with those near the
bottom; however, if you compare the third figure in each cell, the one
the key tells us is N/ColTotal, the progression becomes clear. Of cars
of type D, 64% are no more than three years old, while only 4% are
eight or nine, and less than 2% are over 10. Compare this to type A
cars, where there are slightly more in the 4-7 year age group than in
the 0-3 year, the proportion between eight and nine is 0.1474 and the
proportion over ten years is 0.1206.
It seems as if the type of car is related to its age. If we look below the
2
table where the results of the χ test for independence are written, we
see that the p value is so small it appears as 0. Of course, we must
remember these data are from insurance claims forms. This is not a
sample of all the cars on the road, but just those that were accidents
and had insurance policies with the company that collected the data.
There may also be an interaction between car type/car age and the
age of the owner (which seems likely), and between the age of the
owner and the likelihood of an automobile accident.
206
Introduction
With crosstabs, it is possible to tabulate all of these data at once and
print the resulting table in a series of layers, each showing two
variables. Thus, when we type crosstabs(number ~ car.age + type
+ age, data=claims.src), we get a series of 8 layers, one for each
factor (age group) in the variable age. The variable represented by the
first term in the formula to the right of the ~, car.age, is represented
by the rows of each layer. The second term, type is represented by
the columns, and each level of the third, age, produces a separate
layer. If there were more than three variables, there would be one
layer for each possible combination of levels in the variables after the
first two. Part of the first of these layers is shown below. Note that the
number written in the bottom right margin is the sum of the row
totals, and is not the same as the number of cases in the entire table,
which is still found at the top of the display and which is used to
compute N/Total, the fourth figure in each cell.
> crosstabs(number ~ car.age + type + age,
+ data = claims.src)
Call:
crosstabs(number ~ car.age + type + age, claims.src)
8942 cases in table
+----------+
|N
|
|N/RowTotal|
|N/ColTotal|
|N/Total
|
+----------+
age=17-20
car.age|type
|A
|B
|C
|D
|RowTotl|
-------+-------+-------+-------+-------+-------+
0-3
| 8
| 10
| 9
| 3
|30
|
|0.27
|0.33
|0.3
|0.1
|0.34
|
|0.38
|0.25
|0.39
|0.6
|
|
|8.9e-4 |0.0011 |0.001 |3.4e-4 |
|
-------+-------+-------+-------+-------+-------+
4-7
| 8
| 28
| 13
| 2
|51
|
|0.16
|0.55
|0.25
|0.039 |0.57
|
|0.38
|0.7
|0.57
|0.4
|
|
|8.9e-4 |0.0031 |0.0015 |2.2e-4 |
|
-------+-------+-------+-------+-------+-------+
207
Chapter 8 Cross-Classified Data and Contingency Tables
-------+-------+-------+-------+-------+-------+
8-9
| 4
| 1
| 1
| 0
|6
|
|0.67
|0.17
|0.17
|0
|0.067 |
|0.19
|0.025 |0.043 |0
|
|
|4.5e-4 |1.1e-4 |1.1e-4 |0
|
|
-------+-------+-------+-------+-------+-------+
10+
| 1
| 1
| 0
| 0
|2
|
|0.5
|0.5
|0
|0
|0.022 |
|0.048 |0.025 |0
|0
|
|
|1.1e-4 |1.1e-4 |0
|0
|
|
-------+-------+-------+-------+-------+-------+
ColTotl|21
|40
|23
|5
|89
|
|0.24
|0.45
|0.26
|0.056 |
|
-------+-------+-------+-------+-------+-------+
208
Choosing Suitable Data Sets
CHOOSING SUITABLE DATA SETS
Cross-tabulation is a technique for categorical data. You tabulate the
number of cases for each combination of factors between your
variables. In the claims data set these numbers were already
tabulated. However, when looking at data that have been gathered as
a count, you must always keep in mind exactly what is being
counted—thus we can tell that of the 40-49 year old car owners who
submitted insurance claims, 43% owned cars of type B, and of the cars
of type B whose owners submitted insurance claims, 25% were owned
by 40-49 year olds.
The data set guayule also has a response variable which is a count,
while all the predictor variables are factors. Here, the thing being
counted is the number of rubber plants that sprouted from seeds of a
number of varieties subjected to a number of treatments. However,
this experiment was designed so that the same number of seeds were
planted for each possible combination of the factors of the controlling
variables. Since we know the exact make-up of the larger population
from which our counts are taken, we can observe the relative size of
counts with complaisance and draw conclusions with great
confidence. The difference between guayule and claims is that with
the former we can view the outcome variable as a binomial response
variable (“sprouted”/“didn’t sprout”) for which we have tabulated one
of the outcomes (“sprouted”), and in the claims data set we can’t.
Another data set in which all the controlling variables are factors is
solder.
> summary(solder)
Opening
Solder
Mask
S:300
Thin :450 A1.5:180
M:300
Thick:450 A3 :270
L:300
A6 : 90
B3 :180
B6 :180
PadType Panel
skips
L9
: 90 1:300 Min.
: 0.00
W9
: 90 2:300 1st Qu.: 0.00
L8
: 90 3:300 Median : 2.00
L7
: 90
Mean
: 5.53
D7
: 90
3rd Qu.: 7.00
L6
: 90
Max.
:48.00
(Other):360
209
Chapter 8 Cross-Classified Data and Contingency Tables
The response variable is the number of skips appearing on a finished
circuit board. Since any skip on a board renders it unusable, we can
easily turn this into a binary response variable:
> attach(solder)
> good <- factor(skips == 0)
Then, when we want to look at the interaction between the variables,
crosstabs counts up all the cases with like levels among the factors:
> crosstabs( ~ Opening + Mask + good)
Call:
crosstabs( ~ Opening + Mask + good)
900 cases in table
+----------+
|N
|
|N/RowTotal|
|N/ColTotal|
|N/Total
|
+----------+
good=FALSE
Opening|Mask
|A1.5
|A3
|A6
|B3
|B6
|RowTotl|
-------+-------+-------+-------+-------+-------+-------+
S
|49
|76
|30
|60
|60
|275
|
|0.1782 |0.2764 |0.1091 |0.2182 |0.2182 |0.447 |
|0.5326 |0.5033 |0.3371 |0.4444 |0.4054 |
|
|0.0544 |0.0844 |0.0333 |0.0667 |0.0667 |
|
-------+-------+-------+-------+-------+-------+-------+
M
|22
|35
|59
|39
|51
|206
|
|0.1068 |0.1699 |0.2864 |0.1893 |0.2476 |0.335 |
|0.2391 |0.2318 |0.6629 |0.2889 |0.3446 |
|
|0.0244 |0.0389 |0.0656 |0.0433 |0.0567 |
|
-------+-------+-------+-------+-------+-------+-------+
L
|21
|40
| 0
|36
|37
|134
|
|0.1567 |0.2985 |0.0000 |0.2687 |0.2761 |0.218 |
|0.2283 |0.2649 |0.0000 |0.2667 |0.2500 |
|
|0.0233 |0.0444 |0.0000 |0.0400 |0.0411 |
|
-------+-------+-------+-------+-------+-------+-------+
ColTotl|92
|151
|89
|135
|148
|615
|
|0.1496 |0.2455 |0.1447 |0.2195 |0.2407 |
|
-------+-------+-------+-------+-------+-------+-------+
210
Choosing Suitable Data Sets
good=TRUE
Opening|Mask
|A1.5
|A3
|A6
|B3
|B6
|RowTotl|
-------+-------+-------+-------+-------+-------+-------+
S
|11
|14
| 0
| 0
| 0
|25
|
|0.4400 |0.5600 |0.0000 |0.0000 |0.0000 |0.088 |
|0.1250 |0.1176 |0.0000 |0.0000 |0.0000 |
|
|0.0122 |0.0156 |0.0000 |0.0000 |0.0000 |
|
-------+-------+-------+-------+-------+-------+-------+
M
|38
|25
| 1
|21
| 9
|94
|
|0.4043 |0.2660 |0.0106 |0.2234 |0.0957 |0.330 |
|0.4318 |0.2101 |1.0000 |0.4667 |0.2812 |
|
|0.0422 |0.0278 |0.0011 |0.0233 |0.0100 |
|
-------+-------+-------+-------+-------+-------+-------+
L
|39
|80
| 0
|24
|23
|166
|
|0.2349 |0.4819 |0.0000 |0.1446 |0.1386 |0.582 |
|0.4432 |0.6723 |0.0000 |0.5333 |0.7188 |
|
|0.0433 |0.0889 |0.0000 |0.0267 |0.0256 |
|
-------+-------+-------+-------+-------+-------+-------+
ColTotl|88
|119
|1
|45
|32
|285
|
|0.3088 |0.4175 |0.0035 |0.1579 |0.1123 |
|
-------+-------+-------+-------+-------+-------+-------+
Test for independence of all factors
Chi^2 = 377.3556 d.f.= 8 (p=0)
Yates' correction not used
In the first example above we specified where to look for the
variables age, car.age and type by giving the data frame claims.src
as the second argument of crosstabs. In the second example, we
attached the data frame solder and let crosstabs find the variables in
the search list. Both methods work because, when crosstabs goes to
interpret a term in the formula, it looks first in the data frame
specified by the argument data and then in the search list.
You can specifiy a data set to crosstabs with the name of a data
frame, or a frame number in which to find an attached data frame.
Using a frame number gives the advantage of speed that comes from
attaching the data frame, while protecting against the possibility of
having masked the name of one of the variables with something in
your .Data directory.
211
Chapter 8 Cross-Classified Data and Contingency Tables
For example,
> attach(guayule)
> search()
[1] ".Data"
[2] "guayule" . . .
> rubber <- crosstabs(plants ~ variety + treatment,
+ data = 2)
If you specify a data frame and do not give a formula, crosstabs uses
the formula ~ ., that is, it cross-classifies all the variables in the data
frame. Any variable names not found in the specified data frame
(which is all of them if you don’t specify any) are sought in the search
list.
212
Cross-Tabulating Continuous Data
CROSS-TABULATING CONTINUOUS DATA
As seen in the example of the solder data frame above, it is fairly
easy to turn a continuous response variable into a binomial response
variable. Clearly, we could have used any logical expression that
made sense to do so; we could have chosen any cutoff point for
acceptable numbers of skips.
A somewhat harder problem is presented by the case where you want
a multinomial factor from continuous data. You can make judicious
use of the cut function to turn the continuous variables into factors,
but you need to put care and thought into the points at which to
separate the data into ranges. The quartiles given by the function
summary offer a good starting point. The data frame kyphosis
represents data on 81 children who have had corrective spinal
surgery. The variables here are whether a postoperative deformity
(kyphosis) is present, the age of the child in months, the number of
vertebrae involved in the operation, and beginning of the range of
vertebrae involved.
> summary(kyphosis)
Kyphosis
absent :64
present:17
Age
Min.
: 1.00
1st Qu.: 26.00
Median : 87.00
Mean
: 83.65
3rd Qu.:130.00
Max.
:206.00
Number
Min.
: 2.000
1st Qu.: 3.000
Median : 4.000
Mean
: 4.049
3rd Qu.: 5.000
Max.
:10.000
Start
Min.
: 1.00
1st Qu.: 9.00
Median :13.00
Mean
:11.49
3rd Qu.:16.00
Max.
:18.00
The summary of these variables suggests that two year intervals might
be a reasonable division for the age. We use the cut function to break
the variable Age into factors at a sequence of points at 24 month
intervals and to label the resulting levels with the appropriate range of
years. Since there are at most nine values for Number we leave it alone
for the moment. Since the mean of the Start variable is close to the
first quartile, a fairly coarse division of Start is probably sufficient.
We could require that cut simply divide the data into four segments
of equal length with the command cut(Start, 4), but the results of
this, while mathematically correct, look a bit bizarre; the first level
213
Chapter 8 Cross-Classified Data and Contingency Tables
created is "0.830+ thru 5.165". The pretty function divides the
range of Start into equal intervals with whole number end points,
and the cut function makes them into levels with reasonable names:
>
>
+
+
+
+
>
>
attach(kyphosis)
kyphosis.fac <- data.frame(Kyphosis = Kyphosis,
Age = cut(Age, c(seq(from=0, to=144, by=24), 206),
labels = c("0-2", "2-4", "4-6", "6-8", "8-10",
"10-12", "12+")),
Number = Number, Start = cut(Start, pretty(Start, 4)))
detach(2)
summary(kyphosis.fac)
Kyphosis
absent :64
present:17
Age
0-2 :20
2-4 : 7
4-6 : 8
6-8 : 9
8-10 :11
10-12:14
12+ :12
Number
Min.
: 2.000
1st Qu.: 3.000
Median : 4.000
Mean
: 4.049
3rd Qu.: 5.000
Max.
:10.000
0+
5+
10+
15+
Start
thru 5:13
thru 10:14
thru 15:32
thru 20:22
The cross-tabulation of this data can then be easily examined:
> crosstabs(~ Age + Kyphosis, data = kyphosis.fac)
Call:
crosstabs( ~ Age + Kyphosis, kyphosis.fac)
81 cases in table
+----------+
|N
|
|N/RowTotal|
|N/ColTotal|
|N/Total
|
+----------+
Age
|Kyphosis
|absent |present|RowTotl|
-------+-------+-------+-------+
0-2
|19
| 1
|20
|
|0.950 |0.050 |0.247 |
|0.297 |0.059 |
|
|0.235 |0.012 |
|
-------+-------+-------+-------+
214
Cross-Tabulating Continuous Data
2-4
| 6
|
| 1
|7
|
|
. . .
|
|
-------+-------+-------+-------+
10-12 | 9
| 5
|14
|
|0.643 |0.357 |0.173 |
|0.141 |0.294 |
|
|0.111 |0.062 |
|
-------+-------+-------+-------+
12+
|11
| 1
|12
|
|0.917 |0.083 |0.148 |
|0.172 |0.059 |
|
|0.136 |0.012 |
|
-------+-------+-------+-------+
ColTotl|64
|17
|81
|
|0.79
|0.21
|
|
-------+-------+-------+-------+
Test for independence of all factors
Chi^2 = 9.588004 d.f.= 6 (p=0.1431089)
Yates' correction not used
Some expected values are less than 5,
don't trust stated p-value
215
Chapter 8 Cross-Classified Data and Contingency Tables
CROSS-CLASSIFYING SUBSETS OF DATA FRAMES
There are two ways to subset a data frame for cross-classification.
First, the crosstabs function cross-tabulates only those variables
specified in the formula. If there is one variable in the data frame in
which you are not interested, don’t mention it. Second, you can
choose which rows you want to consider with the subset argument.
You can use anything you would normally use to subscript the rows
of a data frame. Thus, the subset argument can be an expression that
evaluates to a logical vector, or a vector of row numbers or row
names. See the chapter Writing Functions in Spotfire S+ in the
Programmer’s Guide for details on subscripting.
As an example, recall the solder data set. You can look at the relation
between the variables without turning skips explicitly into a binomial
variable by using it to subscript the rows of the data frame:
> crosstabs(~ Solder + Opening, data = solder,
+ subset = skips < 10)
Call:
crosstabs( ~ Solder+Opening, solder, subset = skips<10)
729 cases in table
+----------+
|N
|
|N/RowTotal|
|N/ColTotal|
|N/Total
|
+----------+
Solder |Opening
|S
|M
|L
|RowTotl|
-------+-------+-------+-------+-------+
Thin
| 50
|133
|140
|323
|
|0.155 |0.412 |0.433 |0.44
|
|0.294 |0.494 |0.483 |
|
|0.069 |0.182 |0.192 |
|
-------+-------+-------+-------+-------+
Thick |120
|136
|150
|406
|
|0.296 |0.335 |0.369 |0.56
|
|0.706 |0.506 |0.517 |
|
|0.165 |0.187 |0.206 |
|
216
Cross-Classifying Subsets of Data Frames
-------+-------+-------+-------+-------+
-------+-------+-------+-------+-------+
ColTotl|170
|269
|290
|729
|
|0.23
|0.37
|0.40
|
|
-------+-------+-------+-------+-------+
Test for independence of all factors
Chi^2 = 20.01129 d.f.= 2 (p=4.514445e-05)
Yates' correction not used
A more common use of the subscript is to look at some of the
variables while considering only a subset of the levels of another:
> crosstabs( ~ Solder + Opening + good,
+ subset = Panel == "1")
Call:
crosstabs( ~ Solder+Opening+good, subset = Panel == "1")
300 cases in table
+----------+
|N
|
|N/RowTotal|
|N/ColTotal|
|N/Total
|
+----------+
good=FALSE
Solder |Opening
|S
|M
|L
|RowTotl|
-------+-------+-------+-------+-------+
Thin
|49
|33
|31
|113
|
|0.4336 |0.2920 |0.2743 |0.59
|
|0.5444 |0.5410 |0.7949 |
|
|0.1633 |0.1100 |0.1033 |
|
-------+-------+-------+-------+-------+
Thick |41
|28
| 8
|77
|
|0.5325 |0.3636 |0.1039 |0.41
|
|0.4556 |0.4590 |0.2051 |
|
|0.1367 |0.0933 |0.0267 |
|
-------+-------+-------+-------+-------+
ColTotl|90
|61
|39
|190
|
|0.474 |0.321 |0.205 |
|
-------+-------+-------+-------+-------+
217
Chapter 8 Cross-Classified Data and Contingency Tables
good=TRUE
Solder |Opening
|S
|M
|L
|RowTotl|
-------+-------+-------+-------+-------+
Thin
| 1
|17
|19
|37
|
|0.0270 |0.4595 |0.5135 |0.34
|
|0.1000 |0.4359 |0.3115 |
|
|0.0033 |0.0567 |0.0633 |
|
-------+-------+-------+-------+-------+
Thick | 9
|22
|42
|73
|
|0.1233 |0.3014 |0.5753 |0.66
|
|0.9000 |0.5641 |0.6885 |
|
|0.0300 |0.0733 |0.1400 |
|
-------+-------+-------+-------+-------+
ColTotl|10
|39
|61
|110
|
|0.091 |0.355 |0.555 |
|
-------+-------+-------+-------+-------+
Test for independence of all factors
Chi^2 = 82.96651 d.f.= 2 (p=3.441691e-15)
Yates' correction not used
218
Manipulating and Analyzing Cross-Classified Data
MANIPULATING AND ANALYZING CROSS-CLASSIFIED
DATA
When you apply crosstabs to a data frame, you get a
multidimensional array whose elements are the counts and whose
dimensions are the variables involved in the cross-tabulations. The
first factor variable is the first (or row) dimension, the second is the
second (or column) dimension, the third is the third dimension, etc. If
you wish to do more than tabulate data, say compute means or sums
of cross-classified data, you can apply functions to the elements of the
array with the function tapply; see the online help for tapply for
more information.
219
Chapter 8 Cross-Classified Data and Contingency Tables
220
POWER AND SAMPLE SIZE
9
Introduction
222
Power and Sample Size Theory
223
Normally Distributed Data
One-Sample Test of Gaussian Mean
Comparing Means from Two Samples
224
224
226
Binomial Data
One-Sample Test of Binomial Proportion
Comparing Proportions from Two Samples
229
229
230
References
234
221
Chapter 9 Power and Sample Size
INTRODUCTION
When contemplating a study, one of the first statistical questions that
arises is “How big does my sample need to be?” The required sample
size is a function of the alternative hypothesis, the probabilities of
Type I and Type II errors, and the variability of the population(s)
under study. Two functions are available in S-PLUS for computing
power and sample size requirements: normal.sample.size and
binomial.sample.size. Depending on the input, these functions
provide the following:
•
For given power and alternative hypothesis, the required
sample size;
•
For given sample size and power, the detectable difference;
•
For given sample size and alternative hypothesis, the power to
distinguish between the hypotheses.
These functions can be applied in one- and two-sample studies. They
produce tables from vectorized input that are suitable for passing to
Trellis graphics functions.
222
Power and Sample Size Theory
POWER AND SAMPLE SIZE THEORY
Intuitively, we have a sense that the sample size required for a study
depends on how small of a difference we’re trying to detect, how
much variability is inherent in our data, and how certain we want to
be of our results. In a classical hypothesis test of H0 (null hypothesis)
versus Ha (alternative hypothesis), there are four possible outcomes,
two of which are erroneous:
•
Don’t reject H0 when is H0 true.
•
Reject H0 when H0 is false.
•
Reject H0 when H0 is true (type I error).
•
Don’t reject H0 when H0 is false (type II error).
To construct a test, the distribution of the test statistic under H0 is
used to find a critical region which will ensure the probability of
committing a type I error does not exceed some predetermined level.
This probability is typically denoted α. The power of the test is its
ability to correctly reject the null hypothesis, or 1 - Pr(type II error),
which is based on the distribution of the test statistic under Ha. The
required sample size is then a function of
1. The null and alternative hypotheses;
2. The target α;
3. The desired power to detect Ha;
4. The variability within the population(s) under study.
Our objective is, for a given test, to find a relationship between the
above factors and the sample size that enables us to select a sample
size consistent with the desired α and power.
223
Chapter 9 Power and Sample Size
NORMALLY DISTRIBUTED DATA
One-Sample
Test of
Gaussian Mean
When conducting a one-sample test of a normal mean, we start by
writing our assumptions and hypotheses:
X i ∼ N ( μ, σ 2 )
2
where i = 1,...,n, and σ is known. To perform a two-sided test of
equality the hypotheses is as follows:
H 0 :μ = μ 0
H a :μ = μ a
Our best estimate of μ is the sample mean, which is normally
distributed:
σ2
X ∼ N  μ, ------
 n
The test statistic is
Z =
n ( X – μ0 ) ⁄ σ
Z ∼ N ( μ – μ 0, 1 )
Z ∼ N ( 0, 1 ) for H 0
We reject H0 if Z > Z 1 – α ⁄ 2 , which guarantees a level α test. The
power of the test to detect μ = μ0 is
n ( μ0 – μa )
n ( μa – μ0 )
- – Z 1 – α ⁄ 2 + Φ  ----------------------------- – Z 1 – α ⁄ 2
Power = Φ  ----------------------------



σ
σ
We can think of the left side of the sum as the lower power, or the
power to detect μ a < μ 0 , and the right side as the upper power, or the
power to detect μ a > μ 0 . Solving for n using both the upper and
lower power is difficult, but we note that when μ a – μ 0 < 0 , the
upper power is negligible (< α/2). Similarly, the lower power is small
when μ a – μ 0 > 0 . Therefore, the equation can be simplified by using
224
Normally Distributed Data
the absolute value of the difference between μa and μ0 and
considering only one side of the sum. This results in the following
sample size formula:
n = [ ( σ ( Z 1 – α ⁄ 2 + Z Power ) ) ⁄ μ a – μ o ]
Comments
Examples
2
•
While only one of the upper and lower power is used in
deriving the sample size formula, the S-PLUS function
normal.sample.size uses both the upper and lower power
when computing the power of a two-tailed test for a given
sample size.
•
In practice, the variance of the population is seldom known
and the test statistic is based on the t distribution. Using the
t distribution to derive a sample size requires an iterative
approach, since the sample size is needed to specify the
degrees of freedom. The difference between the quantile
value for the t distribution versus the standard normal
distribution is significant only when small sample sizes are
required. Thus, the standard formula based on the normal
distribution is chosen. Keep in mind that for samples sizes less
than 10, the power of a t test could be significantly less than
the target power.
•
The formula for a one-tailed test is derived along similar lines.
It is exactly the same as the two-tailed formula with the
exception that Z 1 – α ⁄ 2 is replaced by Z 1 – α .
The function for computing sample size for normally distributed data
is normal.sample.size. This function can be used to compute sample
size, power, or minimum detectable difference, and automatically
chooses what to compute based on the input information. Here are
some simple examples:
# One-sample case, using all the defaults
> normal.sample.size(mean.alt = 0.3)
1
mean.null sd1 mean.alt delta alpha power n1
0
1
0.3
0.3 0.05
0.8 88
# Reduce output with summary
> summary(normal.sample.size(mean.alt = 0.3))
225
Chapter 9 Power and Sample Size
1
delta power n1
0.3
0.8 88
#
>
+
+
Upper-tail test, recomputing power
normal.sample.size(mean = 100, mean.alt = 105, sd1 = 10,
power = c(0.8, 0.9, 0.95, 0.99), alt = "greater",
recompute.power = T)
1
2
3
4
mean.null sd1 mean.alt delta alpha
power n1
100 10
105
5 0.05 0.8037649 25
100 10
105
5 0.05 0.9054399 35
100 10
105
5 0.05 0.9527153 44
100 10
105
5 0.05 0.9907423 64
# Calculate power
> normal.sample.size(mean = 100, mean.alt = 105, sd1 = 10,
+ n1 = (1:5)*20)
1
2
3
4
5
mean.null sd1 mean.alt delta alpha
power n1
100 10
105
5 0.05 0.6087795 20
100 10
105
5 0.05 0.8853791 40
100 10
105
5 0.05 0.9721272 60
100 10
105
5 0.05 0.9940005 80
100 10
105
5 0.05 0.9988173 100
# Lower-tail test, minimum detectable difference
> summary(normal.sample.size(mean = 100, sd1 = 10,
+ n1 = (1:5)*20, power = 0.9, alt = "less"))
1
2
3
4
5
mean.alt
93.45636
95.37295
96.22203
96.72818
97.07359
See
the
delta power n1
-6.543641
0.9 20
-4.627053
0.9 40
-3.777973
0.9 60
-3.271821
0.9 80
-2.926405
0.9 100
online
summary.power.table
Comparing
Means from
Two Samples
226
help files for
for more details.
normal.sample.size
and
Extending the formula to two-sampled tests is relatively easy. Given
two independent samples from normal distributions
Normally Distributed Data
X 1, i ∼ N ( μ 1, σ 12 )
i = 1 , …, n 1
X 2, j ∼ N ( μ 2, σ 22 )
j = 1 , …, n 2
we construct a two-sided test of equality of means
H 0 :μ 1 = μ 2
H a :μ 1 ≠ μ 2
This is more conveniently written as
H 0 :μ 2 – μ 1 = 0
H a :μ 2 – μ 1 ≠ 0
The difference of the sample means is normally distributed:
σ2
σ2 σ2
1
( X 2 – X 1 ) ∼ N  μ 2 – μ 1, -----1- + -----2- ∼ N  μ 2 – μ 1, -----  σ 12 + -----2-  .


n1 
n1 n2 
k 
Here, the constant k is the ratio of the sample sizes, k = n 2 ⁄ n 1 . This
leads to the test statistic
X2 – X1
---------------------Z = σ 12 σ 22
------ + -----n1 n2
Derivation of the two-sample formulas proceeds along the same lines
as the one-sample case, producing the following formulas:
σ 2 ( Z ( 1 – α ⁄ 2 ) + Z Power )
n 1 =  σ 1 + -----2- ----------------------------------------------
μ2 – μ1
k
2
n 2 = kn 1
227
Chapter 9 Power and Sample Size
Examples
For two-sample cases, use normal.sample.size with mean2 instead of
mean.alt. A few simple examples are provided below.
# Don't round sample size
> summary(normal.sample.size(mean2 = 0.3, exact.n = T))
1
delta power
n1
n2
0.3
0.8 174.4195 174.4195
# Round sample size, then recompute power
> summary(normal.sample.size(mean2 = 0.3, recompute = T))
1
delta
power n1 n2
0.3 0.8013024 175 175
#
#
#
>
+
Unequal sample sizes, lower tail test
The prop.n2 argument is equal to k from the
above derivation.
normal.sample.size(mean = 100, mean2 = 94, sd1 = 15,
prop.n2 = 2, power = 0.9, alt = "less")
mean1 sd1 mean2 sd2 delta alpha power n1 n2 prop.n2
1
100 15
94 15
-6 0.05
0.9 81 162
2
228
Binomial Data
BINOMIAL DATA
One-Sample
Test of
Binomial
Proportion
Another very common test is for a binomial proportion. Say we have
data sampled from a binomial distribution,
X ∼ B ( n, π )
Here X represents the number of “successes” observed in n Bernoulli
trials, where the probability of a success is equal to π . The mean and
variance of the random variable X is
E(X) = nπ
Var ( X ) = nπ ( 1 – π )
We wish to test the value of the parameter π using a two-sided test:
H 0 :π = π 0
H a :π = π a
We could use an exact binomial test, but if n is sufficiently large and
the distribution is not too skewed (π is not too close to 0 or 1), a
normal approximation can be used instead. A good rule of thumb is
that the normal distribution is a good approximation to the binomial
distribution if
nπ ( 1 – π ) ≥ 5
When using a continuous distribution to approximate a discrete one,
a continuity correction is usually recommended; typically, a value of 1/2
is used to extend the range in either direction. This means that a
probability of Pr ( X l ≤ X ≤ X u ) for a binomial distribution becomes
1
1
Pr  X l – --- ≤ X ≤ X u + ---

2
2
when using a normal approximation.
229
Chapter 9 Power and Sample Size
If the continuity correction is temporarily suppressed, the sample size
formula is derived very much as in the normal case:
n*
π 0 ( 1 – π 0 )Z 1 – α ⁄ 2 + π 0 ( 1 – π 0 )Z Power
= -----------------------------------------------------------------------------------------------------πa – π0
2
There have been several suggestions concerning how to best
incorporate a continuity correction into the sample-size formula. The
one adopted by the S-PLUS function binomial.sample.size for a
one-sample test is
2
*
n = n + -------------------πa – π0
Examples
# One-sample case, using all the defaults
> binomial.sample.size(p.alt = 0.3)
1
p.null p.alt delta alpha power n1
0.5
0.3 -0.2 0.05
0.8 57
# Minimal output
> summary(binomial.sample.size(p.alt = 0.3))
1
delta power n1
-0.2
0.8 57
# Compute power
> binomial.sample.size(p = 0.2, p.alt = 0.12, n1 = 250)
1
Comparing
Proportions
from Two
Samples
p.null p.alt delta alpha
power n1
0.2 0.12 -0.08 0.05 0.8997619 250
The two-sample test for proportions is a bit more involved than the
others we’ve looked at. Say we have data sampled from two binomial
distributions
X 1 ∼ B ( n 1, π 1 )
X 2 ∼ B ( n 2, π 2 )
230
Binomial Data
We construct a two-sided test of equality of means
H 0 :π 1 = π 2
H a :π 1 ≠ π 2
which is more conveniently written as
H 0 :π 1 – π 2 = 0
H a :π 1 – π 2 ≠ 0
Using our best estimators for the parameters π 1 and π 2 , we can begin
constructing a test statistic:
n
1
πˆ1 = ----n1
 X 1, i
i=1
n
1
πˆ2 = ----n2
1
2
 X 2, j
j=1
For large enough sample sizes, we can use a normal approximation:
π 1 ( 1 – π 1 ) π 2 ( 1 – π 2 )
πˆ2 – πˆ1 ∼ N  π 2 – π 1, ------------------------ + ------------------------

n1
n2
Let the constant k be the ratio of the sample sizes, k = n 2 ⁄ n 1 . Then:
π 2 ( 1 – π 2 ) 
1
πˆ2 – πˆ1 ∼ N  π 2 – π 1, -----  π 1 ( 1 – π 1 ) + -----------------------

n1 
k
When the null hypothesis is true, π 2 = π 1 = π and this can be
written as
1 1
π(1 – π)
1
π̂ 2 – π̂ 1 ∼ N  0, π ( 1 – π )  ----- + -----  ∼ N  0, --------------------  1 + --- 

 n 1 n 2 

n1 
k 
Immediately a problem arises: namely, the variance needed to
construct the test statistic depends on the parameters being tested. It
seems reasonable to use all of the data available to estimate the
variances, and this is exactly what S-PLUS does. A weighted average
of the two estimates for the proportions is used to estimate the
variance under H0.
231
Chapter 9 Power and Sample Size
When weighted averages are used to estimate the variance, the test
statistic is:
n 1 π̂ 1 + n 2 π̂ 2
π̂ 1 + kπ̂ 2
π = ---------------------------- = -------------------n1 + n2
1+k
π̂ 2 – π̂ 1
----------------------------------------------Z =
1
1
π ( 1 – π )  ----- + -----
 n 1 n 2
When the null hypothesis is true, this gives Z ∼ N ( 0, 1 ) . We use this
to derive the formula without continuity correction:
π2 ( 1 – π2 )
1
- Z Power + π ( 1 – π )  1 + --- Z 1 – α ⁄ 2
π 1 ( 1 – π 1 ) + -----------------------
k
k
n 1* = -------------------------------------------------------------------------------------------------------------------------------------------------π2 – π1
2
Applying the two-sample adjustment for a continuity correction
produces the final results
k+1
*
n 1 = n 1 + ----------------------k π2 – π1
n 2 = kn 1
Examples
# For two-sample, use p2 instead of p.alt
> summary(binomial.sample.size(p2 = 0.3))
1
delta power n1 n2
-0.2
0.8 103 103
# Don't round sample size or use the continuity correction
> summary(binomial.sample.size(p2 = 0.3, exact.n = T,
+ correct = F))
1
232
delta power
n1
n2
-0.2
0.8 92.99884 92.99884
Binomial Data
# Round sample size, then recompute power
> summary(binomial.sample.size(p2 = 0.3, recompute = T))
delta
power n1 n2
1 -0.2 0.8000056 103 103
#
#
#
>
+
Unequal sample sizes, lower tail test
The prop.n2 argument is equal to k from the
above derivation.
binomial.sample.size(p = 0.1, p2 = 0.25, prop.n2 = 2,
power = 0.9, alt = "less")
p1
p2 delta alpha power n1 n2 prop.n2
1 0.1 0.25 0.15 0.05
0.9 92 184
2
#
#
>
+
Compute minimum detectable difference (delta),
given sample size and power.
binomial.sample.size(p = 0.6, n1 = 500, prop.n2 = 0.5,
power = c(0.8, 0.9, 0.95))
p1
p2
delta alpha power n1 n2 prop.n2
1 0.6 0.7063127 0.1063127 0.05 0.80 500 250
0.5
2 0.6 0.7230069 0.1230069 0.05 0.90 500 250
0.5
3 0.6 0.7367932 0.1367932 0.05 0.95 500 250
0.5
# Compute power
> binomial.sample.size(p = 0.3, p2 = seq(0.31, 0.35,
+ by = 0.01), n1 = 1000, prop.n2 = 0.5)
p1
0.3
0.3
0.3
0.3
0.3
1
2
3
4
5
p2 delta alpha
0.31 0.01 0.05
0.32 0.02 0.05
0.33 0.03 0.05
0.34 0.04 0.05
0.35 0.05 0.05
power
0.06346465
0.11442940
0.20446778
0.32982868
0.47748335
n1 n2 prop.n2
1000 500
0.5
1000 500
0.5
1000 500
0.5
1000 500
0.5
1000 500
0.5
233
Chapter 9 Power and Sample Size
REFERENCES
Rosner, B. (1990). Fundamentals of Biostatistics (3rd ed.). Boston: PWSKent.
Fisher, L.D. & Van Belle, G. (1993). Biostatistics. New York: John
Wiley & Sons, Inc.
Fleiss, J.L. (1981). Statistical Methods for Rates and Proportions. New
York: John Wiley & Sons, Inc.
234
REGRESSION AND
SMOOTHING FOR
CONTINUOUS RESPONSE
DATA
10
Introduction
237
Simple Least-Squares Regression
Diagnostic Plots for Linear Models
Other Diagnostics
239
242
245
Multiple Regression
247
Adding and Dropping Terms from a Linear Model
251
Choosing the Best Model—Stepwise Selection
257
Updating Models
260
Weighted Regression
Example: Weighted Linear Regression
Observation Weights vs. Frequencies
261
261
265
Prediction with the Model
270
Confidence Intervals
272
Polynomial Regression
275
Generalized Least Squares Regression
Example: The Ovary Data Set
Manipulating gls Objects
280
282
283
Smoothing
Locally Weighted Regression Smoothing
Using the Super Smoother
Using the Kernel Smoother
Smoothing Splines
Comparing Smoothers
290
290
292
295
298
299
235
Chapter 10 Regression and Smoothing for Continuous Response Data
236
Additive Models
301
More on Nonparametric Regression
Alternating Conditional Expectations
Additivity and Variance Stabilization
Projection Pursuit Regression
307
307
312
318
References
328
Introduction
INTRODUCTION
Regression is a tool for exploring relationships between variables.
Linear regression explores relationships that are readily described by
straight lines, or their generalization to many dimensions. A
surprisingly large number of problems can be analyzed using the
techniques of linear regression, and even more can be attacked by
means of transformations of the original variables that result in linear
relationships among the transformed variables. In recent years, the
techniques themselves have been extended through the addition of
robust methods and generalizations of the classical linear regression
techniques. These generalizations allow familiar problems in
categorical data analysis such as logistic and Poisson regression to be
subsumed under the heading of the generalized linear model (GLM),
while still further generalizations allow a predictor to be replaced by
an arbitrary smooth function of the predictor in building a generalized
additive model (GAM).
This chapter describes regression and smoothing in the case of a
univariate, continuous response. We start with simple regression,
which is regression with a single predictor variable: fitting the model,
examining the fitted models, and analyzing the residuals. We then
examine multiple regression, varying models by adding and dropping
terms as appropriate. Again, we examine the fitted models and
analyze the residuals. We then consider the special case of weighted
regression, which underlies many of the robust techniques and
generalized regression methods.
One important reason for performing regression analysis is to get a
model useful for prediction. The section Prediction with the Model
describes how to use TIBCO Spotfire S+ to obtain predictions from
your fitted model, and the section Confidence Intervals describes
how to obtain pointwise and simultaneous confidence intervals.
The classical linear regression techniques make several strong
assumptions about the underlying data, and the data can fail to satisfy
these assumptions in different ways. For example, the regression line
may be thrown off by one or more outliers or the data may not be
fitted well by any straight line. In the first case, we can bring robust
regression methods into play; these minimize the effects of outliers
237
Chapter 10 Regression and Smoothing for Continuous Response Data
while retaining the basic form of the linear model. Conversely, the
robust methods are often useful in identifying outliers. We discuss
robust regression in detail in a later chapter.
In the second case, we can expand our notion of the linear model,
either by adding polynomial terms to our straight line model, or by
replacing one or more predictors by an arbitrary smooth function of
the predictor, converting the classical linear model into a generalized
additive model (GAM).
Scatterplot smoothers are useful tools for fitting arbitrary smooth
functions to a scatter plot of data points. The smoother summarizes
the trend of the measured response as a function of the predictor
variables. We describe several scatterplot smoothers available in SPLUS, and describe how the smoothed values they return can be
incorporated into additive models.
238
Simple Least-Squares Regression
SIMPLE LEAST-SQUARES REGRESSION
Simple regression uses the method of least squares to fit a continuous,
univariate response as a linear function of a single predictor variable.
In the method of least squares, we fit a line to the data so as to
minimize the sum of the squared residuals. Given a set of n
observations y i of the response variable corresponding to a set of
values x i of the predictor and an arbitrary model ŷ = f̂ ( x ) , the ith
residual is defined as the difference between the ith observation y i
and the fitted value ŷ i = f̂(x i) , that is, r i = y i – ŷ i .
To do simple regression with S-PLUS, use the function lm (for linear
model) with a simple formula linking your chosen response variable to
the predictor variable. In many cases, both the response and the
predictor are components of a single data frame, which can be
specified as the data argument to lm. For example, consider the air
pollution data in the built-in data set air:
> air[, c(1,3)]
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ozone temperature
3.448217
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. .
A scatter plot of the data is shown in Figure 10.1.
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Chapter 10 Regression and Smoothing for Continuous Response Data
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Figure 10.1: Scatter plot of ozone against temperature.
From the scatter plot, we hypothesize a linear relationship between
temperature and ozone concentration. We choose ozone as the
response and temperature as the single predictor. The choice of
response and predictor variables is driven by the subject matter in
which the data arise, rather than by statistical considerations.
To fit the model, use lm as follows:
> ozone.lm <- lm(ozone ~ temperature, data = air)
The first argument, ozone ~ temperature, is the formula specifying
that the variable ozone is modeled as a function of temperature. The
second argument specifies that the data for the linear model is
contained in the data frame air.
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Simple Least-Squares Regression
Use the summary function to obtain a summary of the fitted model:
> summary(ozone.lm)
Call: lm(formula = ozone ~ temperature)
Residuals:
Min
1Q Median
3Q
Max
-1.49 -0.4258 0.02521 0.3636 2.044
Coefficients:
(Intercept)
temperature
Value Std. Error
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0.4614
0.0704
0.0059
t value Pr(>|t|)
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0.0000
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Residual standard error: 0.5885 on 109 degrees of freedom
Multiple R-Squared: 0.5672
F-statistic: 142.8 on 1 and 109 degrees of freedom, the
p-value is 0
Correlation of Coefficients:
(Intercept)
temperature -0.9926
The Value column under Coefficients gives the coefficients of the
linear model, allowing us to read off the estimated regression line as
follows:
ozone = -2.2260 + 0.0704 x temperature
The column headed Std. Error gives the estimated standard error
for each coefficient. The Multiple R-Squared term from the lm
summary tells us that the model explains about 57% of the variation
in ozone. The F-statistic is the ratio of the mean square of the
regression to the estimated variance; if there is no relationship
between temperature and ozone, this ratio has an F distribution with
1 and 109 degrees of freedom. The ratio here is clearly significant, so
the true slope of the regression line is probably not 0.
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Chapter 10 Regression and Smoothing for Continuous Response Data
Diagnostic
Plots for
Linear Models
Suppose we have the linear model defined as follows:
> ozone.lm <- lm(ozone ~ temperature, data = air)
How good is the fitted linear regression model? Is temperature an
adequate predictor of ozone concentration? Can we do better?
Questions such as these are essential any time you try to explain data
with a statistical model. It is not enough to fit a model; you must also
assess how well that model fits the data, being ready to modify the
model or abandon it altogether if it does not satisfactorily explain the
data.
The simplest and most informative method for assessing the fit is to
look at the model graphically, using an assortment of plots that, taken
together, reveal the strengths and weaknesses of the model. For
example, a plot of the response against the fitted values gives a good
idea of how well the model has captured the broad outlines of the
data. Examining a plot of the residuals against the fitted values often
reveals unexplained structure left in the residuals, which in a strong
model should appear as nothing but noise. The default plotting
method for lm objects provides these two plots, along with the
following useful plots:
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useful in identifying outliers and visualizing structure in the
residuals.
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Normal quantile plot of residuals. This plot provides a visual test
of the assumption that the model’s errors are normally
distributed. If the ordered residuals cluster along the
superimposed quantile-quantile line, you have strong
evidence that the errors are indeed normal.
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Residual-Fit spread plot, or r-f plot. This plot compares the
spread of the fitted values with the spread of the residuals.
Since the model is an attempt to explain the variation in the
data, you hope that the spread in the fitted values is much
greater than that in the residuals.
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Cook’s distance plot. Cook’s distance is a measure of the
influence of individual observations on the regression
coefficients.
Simple Least-Squares Regression
Calling plot as follows yields the six plots shown in Figure 10.2:
> par(mfrow = c(2,3))
> plot(ozone.lm)
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Figure 10.2: Default plots for lm objects.
The line y = ŷ is shown as a dashed line in the third plot (far right of
top row). In the case of simple regression, this line is visually
equivalent to the regression line. The regression line appears to
model the trend of the data reasonably well. The residuals plots (left
and center, top row) show no obvious pattern, although five
observations appear to be outliers. By default, as in Figure 10.2, the
three most extreme values are identified in each of the residuals plots
and the Cook’s distance plot. You can request a different number of
points by using the id.n argument in the call to plot; for this model,
id.n=5 is a good choice.
Another useful diagnostic plot is the normal plot of residuals (left plot,
bottom row). The normal plot gives no reason to doubt that the
residuals are normally distributed.
243
Chapter 10 Regression and Smoothing for Continuous Response Data
The r-f plot, on the other hand (middle plot, bottom row), shows a
weakness in this model; the spread of the residuals is actually greater
than the spread in the original data. However, if we ignore the five
outlying residuals, the residuals are more tightly bunched than the
original data.
The Cook’s distance plot shows four or five heavily influential
observations. As the regression line fits the data reasonably well, the
regression is significant, and the residuals appear normally
distributed, we feel justified in using the regression line as a way to
estimate the ozone concentration for a given temperature. One
important issue remains—the regression line explains only 57% of the
variation in the data. We may be able to do somewhat better by
considering the effect of other variables on the ozone concentration.
See the section Multiple Regression for this further analysis.
At times, you are not interested in all of the plots created by the
default plotting method. To view only those plots of interest to you,
call plot with the argument ask=T. This call brings up a menu listing
the available plots:
> par(mfrow = c(1,1))
> plot(ozone.lm, id.n = 5, ask = T)
Make a plot selection (or 0 to exit):
1: plot: All
2: plot: Residuals vs Fitted Values
3: plot: Sqrt of abs(Residuals) vs Fitted Values
4: plot: Response vs Fitted Values
5: plot: Normal QQplot of Residuals
6: plot: r-f spread plot
7: plot: Cook’s Distances
Selection:
Enter the number of the desired plot.
If you want to view all the plots, but want them all to appear in a full
graphics window, do not set par(mfrow=c(2,3)) before calling plot,
and do not use the ask=T argument. Instead, before calling plot, call
par(ask=T). This tells S-PLUS to prompt you before displaying each
additional plot.
244
Simple Least-Squares Regression
Other
Diagnostics
The Durbin-Watson statistic DW can be used to test for first-order
correlation in the residuals of a linear model. The statistic is defined
as:
n–1
 ( et – et + 1 )
2
=1
DW = t------------------------------------,
n
 ( et – e )
2
t=1
where e 1, e 2, …, e n are the residuals and e is their arithmetic mean.
The statistic is bounded between 0 and 4; small values indicate
possible positive autocorrelation and large values indicate possible
negative autocorrelation. For completely independent residuals, DW
is symmetric around 2. If the test is significant, the observations in
your data set may not be independent and you should check the
validity of your model assumptions.
The null distribution for the Durbin-Watson test statistic depends on
the data matrix used to compute the linear model. Thus, significance
tables are not built into Spotfire S+. Instead, you can obtain
approximate bounds for significance levels using the tables found in
Durbin and Watson (1950); these tables are also available in many
general statistics texts.
In S-PLUS, the Durbin-Watson test statistic is implemented in the
function durbinWatson, which has a method for the class "lm" as well
as a default method for numeric vectors. The code used to compute
the statistic is sum((diff(x))^2)/var(x, SumSquares=T), where x is a
vector. Thus, DW is simply the ratio of the sum of squared, successive
differences to the sum of squared deviations from the mean.
For example, we obtain the following from durbinWatson for our
linear model ozone.lm:
> durbinWatson(ozone.lm)
Durbin-Watson Statistic: 1.819424
Number of observations: 111
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Chapter 10 Regression and Smoothing for Continuous Response Data
The Durbin-Watson test statistic works well if the observations are
equispaced in space or time. In general, however, correlated residuals
are difficult to diagnose and it is best to analyze the data collection
process for any potential correlation.
246
Multiple Regression
MULTIPLE REGRESSION
You can construct linear models involving more than one predictor as
easily in Spotfire S+ as models with a single predictor. In general,
each predictor contributes a single term in the model formula; a single
term may contribute more than one coefficient to the fit.
For example, consider the built-in data sets stack.loss and stack.x.
Together, these data sets contain information on ammonia loss in a
manufacturing process. The stack.x data set is a matrix with three
columns representing three predictors: air flow, water temperature,
and acid concentration. The stack.loss data set is a vector
containing the response. To make our computations easier, combine
these two data sets into a single data frame, then attach the data
frame:
> stack.df <- data.frame(stack.loss, stack.x)
> stack.df
stack.loss Air.Flow Water.Temp Acid.Conc.
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. . .
> attach(stack.df)
For multivariate data, it is usually a good idea to view the data as a
whole using the pairwise scatter plots generated by the pairs
function:
> pairs(stack.df)
The resulting plot is shown in Figure 10.3.
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Chapter 10 Regression and Smoothing for Continuous Response Data
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Call lm as follows to model stack.loss as a linear function of the
three predictors:
> stack.lm <- lm(stack.loss ~ Air.Flow + Water.Temp +
+ Acid.Conc.)
> summary(stack.lm)
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Multiple Regression
Call: lm(formula = stack.loss ~ Air.Flow + Water.Temp +
Acid.Conc.)
Residuals:
Min
1Q Median
3Q
Max
-7.238 -1.712 -0.4551 2.361 5.698
Coefficients:
Value Std. Error
(Intercept) -39.9197 11.8960
Air.Flow
0.7156
0.1349
Water.Temp
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0.3680
Acid.Conc. -0.1521
0.1563
t value Pr(>|t|)
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0.0038
5.3066
0.0001
3.5196
0.0026
-0.9733
0.3440
Residual standard error: 3.243 on 17 degrees of freedom
Multiple R-Squared: 0.9136
F-statistic: 59.9 on 3 and 17 degrees of freedom, the
p-value is 3.016e-09
Correlation of Coefficients:
(Intercept) Air.Flow Water.Temp
Air.Flow 0.1793
Water.Temp -0.1489
-0.7356
Acid.Conc. -0.9016
-0.3389
0.0002
When the response is the first variable in the data frame, as in
stack.df, and the desired model includes all the variables in the data
frame, the name of the data frame itself can be supplied in place of
the formula and data arguments:
> lm(stack.df)
Call:
lm(formula = stack.df)
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-39.91967 0.7156402
1.295286 -0.1521225
Degrees of freedom: 21 total; 17 residual
Residual standard error: 3.243364
We examine the default plots to assess the quality of the model (see
Figure 10.4):
249
Chapter 10 Regression and Smoothing for Continuous Response Data
> par(mfrow = c(2,3))
> plot(stack.lm, ask = F)
Both the line y = ŷ and the residuals plots give support to the
2
model. The multiple R and F statistic also support the model. But
would a simpler model suffice?
6
To find out, let’s return to the summary of the stack.lm model. From
the t values, and the associated p -values, it appears that both
Air.Flow and Water.Temp contribute significantly to the fit. But can
we improve the model by dropping the Acid.Conc. term? We explore
this question further in the section Adding and Dropping Terms from
a Linear Model.
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Adding and Dropping Terms from a Linear Model
ADDING AND DROPPING TERMS FROM A LINEAR MODEL
In the section Multiple Regression, we fitted a linear model with three
predictors of which only two appeared to be significant. Can we
improve the model stack.lm by dropping one or more terms?
The drop1 function takes a fitted model and returns an ANOVA table
showing the effects of dropping in turn each term in the model:
> drop1(stack.lm)
Single term deletions
Model:
stack.loss ~ Air.Flow +
Df Sum of Sq
<none>
Air.Flow 1 296.2281
Water.Temp 1 130.3076
Acid.Conc. 1
9.9654
Water.Temp + Acid.Conc.
RSS
Cp
178.8300 262.9852
475.0580 538.1745
309.1376 372.2541
188.7953 251.9118
The columns of the returned value show the degrees of freedom for
each deleted term, the sum of squares corresponding to the deleted
term, the residual sum of squares from the resulting model, and the
C p statistic for the terms in the reduced model.
The C p statistic (actually, what is shown is the AIC statistic, the
likelihood version of the C p statistic—the two are related by the
2
equation AIC = σ̂ (C p + n) ) provides a convenient criterion for
determining whether a model is improved by dropping a term. If any
term has a C p statistic lower than that of the current model (shown on
the line labeled <none>), the term with the lowest C p statistic is
dropped. If the current model has the lowest C p statistic, the model is
not improved by dropping any term. The regression literature
discusses many other criteria for adding and dropping terms. See, for
example, Chapter 8 of Weisberg (1985).
251
Chapter 10 Regression and Smoothing for Continuous Response Data
In our example, the C p statistic shown for Acid.Conc. is lower than
that for the current model. So it is probably worthwhile dropping that
term from the model:
> stack2.lm <- lm(stack.loss ~ Air.Flow + Water.Temp)
> stack2.lm
Call:
lm(formula = stack.loss ~ Air.Flow + Water.Temp)
Coefficients:
(Intercept) Air.Flow Water.Temp
-50.35884 0.6711544
1.295351
Degrees of freedom: 21 total; 18 residual
Residual standard error: 3.238615
A look at the summary shows that we have retained virtually all the
explanatory power of the more complicated model:
> summary(stack2.lm)
Call: lm(formula = stack.loss ~ Air.Flow + Water.Temp)
Residuals:
Min
1Q Median
3Q
Max
-7.529 -1.75 0.1894 2.116 5.659
Coefficients:
Value Std. Error
(Intercept) -50.3588
5.1383
Air.Flow
0.6712
0.1267
Water.Temp
1.2954
0.3675
t value Pr(>|t|)
-9.8006
0.0000
5.2976
0.0000
3.5249
0.0024
Residual standard error: 3.239 on 18 degrees of freedom
Multiple R-Squared: 0.9088
F-statistic: 89.64 on 2 and 18 degrees of freedom, the
p-value is 4.382e-10
Correlation of Coefficients:
(Intercept) Air.Flow
Air.Flow -0.3104
Water.Temp -0.3438
-0.7819
252
Adding and Dropping Terms from a Linear Model
The residual standard error has fallen, from 3.243 to 3.239, while the
2
multiple R has decreased only slightly from 0.9136 to 0.9088.
We create the default set of diagnostic plots as follows:
> par(mfrow = c(2,3))
> plot(stack2.lm, ask = F)
6
These plots, shown in Figure 10.5, support the simplified model.
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Figure 10.5: Diagnostic plots for simplified model.
We turn next to the opposite problem: adding terms to an existing
model. Our first linear model hypothesized a relationship between
temperature and atmospheric ozone, based on a scatter plot showing
an apparent linear relationship between the two variables. The air
data set containing the two variables ozone and temperature also
includes two other variables, radiation and wind. Pairwise scatter
plots for all the variables can be constructed using the pairs function,
as illustrated in the command below.
> pairs(air)
253
Chapter 10 Regression and Smoothing for Continuous Response Data
The resulting plot is shown in Figure 10.6. The plot in the top row,
third column of Figure 10.6 corresponds to the scatter plot shown in
Figure 10.1.
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90
Figure 10.6: Pairwise scatter plots for ozone data.
From the pairwise plots, it appears that the ozone varies somewhat
linearly with each of the variables radiation, temperature, and wind,
and the dependence on wind has a negative slope.
254
Adding and Dropping Terms from a Linear Model
We can use the add1 function to add the terms wind and radiation in
turn to our previously fitted model:
> ozone.add1 <- add1(ozone.lm, ~ temperature + wind +
+ radiation)
> ozone.add1
Single term additions
Model:
ozone ~ temperature
Df Sum of Sq
RSS
Cp
<none>
37.74698 39.13219
wind 1 5.839621 31.90736 33.98517
radiation 1 3.839049 33.90793 35.98575
The first argument to add1 is a fitted model object, the second a
formula specifying the scope; that is, the possible choices of terms to
be added to the model. A response is not necessary in the formula
supplied; the response must be the same as that in the fitted model.
The returned object is an ANOVA table like that returned by drop1,
showing the sum of squares due to the added term, the residual sum
of squares of the new model, and the modified C p statistic for the
terms in the augmented model. Each row of the ANOVA table
represents the effects of a single term added to the base model. In
general, it is worth adding a term if the C p statistic for that term is
lowest among the rows in the table, including the base model term. In
our example, we conclude that it is worthwhile adding the wind term.
Our choice of temperature as the original predictor in the model,
however, was completely arbitrary. We can gain a truer picture of the
effects of adding terms by starting from a simple intercept model:
> ozone0.lm <- lm(ozone ~ 1, data = air)
> ozone0.add1 <- add1(ozone0.lm, ~ temperature + wind +
+ radiation)
255
Chapter 10 Regression and Smoothing for Continuous Response Data
The obvious conclusion from the output is that we should start with
the temperature term, as we did originally:
> ozone0.add1
Single term additions
Model:
ozone ~ 1
Df Sum of Sq
<none>
temperature
wind
radiation
256
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RSS
87.20876
49.46178 37.74698
31.28305 55.92571
15.53144 71.67732
Cp
88.79437
40.91821
59.09694
74.84855
Choosing the Best Model—Stepwise Selection
CHOOSING THE BEST MODEL—STEPWISE SELECTION
Adding and dropping terms using add1 and drop1 is a useful method
for selecting a model when only a few terms are involved, but it can
quickly become tedious. The step function provides an automatic
procedure for conducting stepwise model selection. Essentially what
step does is automate the selection process implied in the section
Adding and Dropping Terms from a Linear Model. That is, it
calculates the C p statistics for the current model, as well as those for
all reduced and augmented models, then adds or drops the term that
reduces C p the most. The step function requires an initial model,
often constructed explicitly as an intercept-only model, such as the
ozone0.lm model constructed in the last section. Because step
calculates augmented models, it requires a scope argument, just like
add1.
For example, suppose we want to find the “best” model involving the
stack loss data, we could create an intercept-only model and then call
step as follows:
> stack0.lm <- lm(stack.loss ~ 1, data = stack.df)
> step(stack0.lm, ~ Air.Flow + Water.Temp + Acid.Conc.)
Start: AIC= 2276.162
stack.loss ~ 1
Single term additions
Model:
stack.loss ~ 1
scale:
103.4619
Df Sum of Sq
<none>
Air.Flow
Water.Temp
Acid.Conc.
1
1
1
RSS
Cp
2069.238 2276.162
1750.122 319.116 732.964
1586.087 483.151 896.998
330.796 1738.442 2152.290
257
Chapter 10 Regression and Smoothing for Continuous Response Data
Step: AIC= 732.9637
stack.loss ~ Air.Flow
Single term deletions
Model:
stack.loss ~ Air.Flow
scale:
103.4619
Df Sum of Sq
RSS
Cp
<none>
319.116 732.964
Air.Flow 1 1750.122 2069.238 2276.162
Single term additions
Model:
stack.loss ~ Air.Flow
scale:
103.4619
Df Sum of Sq
<none>
Water.Temp 1 130.3208
Acid.Conc. 1
9.9785
Call:
lm(formula = stack.loss
RSS
Cp
319.1161 732.9637
188.7953 809.5668
309.1376 929.9090
~ Air.Flow, data = stack.df)
Coefficients:
(Intercept) Air.Flow
-44.13202 1.020309
Degrees of freedom: 21 total; 19 residual
Residual standard error (on weighted scale): 4.098242
258
Choosing the Best Model—Stepwise Selection
The value returned by step is an object of class "lm", and the final
result appears in exactly the same form as the output of lm. However,
by default, step displays the output of each step of the selection
process. You can turn off this display by calling step with the trace=F
argument:
> step(stack0.lm, ~ Air.Flow + Water.Temp + Acid.Conc.,
+ trace = F)
Call:
lm(formula = stack.loss ~ Air.Flow, data = stack.df)
Coefficients:
(Intercept) Air.Flow
-44.13202 1.020309
Degrees of freedom: 21 total; 19 residual
Residual standard error (on weighted scale): 4.098242
259
Chapter 10 Regression and Smoothing for Continuous Response Data
UPDATING MODELS
We built our alternate model for the stack loss data by explicitly
constructing a second call to lm. For models involving only one or
two predictors, this is not usually too burdensome. However, if you
are looking at many different combinations of many different
predictors, constructing the full call repeatedly can be tedious.
The update function provides a convenient way for you to fit new
models from old models, by specifying an updated formula or other
arguments. For example, we could create the alternate model
stack2.lm using update as follows:
> stack2a.lm <- update(stack.lm, .~. - Acid.Conc.,
+ data = stack.df)
> stack2a.lm
Call:
lm(formula = stack.loss ~ Air.Flow + Water.Temp, data =
stack.df)
Coefficients:
(Intercept) Air.Flow Water.Temp
-50.35884 0.6711544
1.295351
Degrees of freedom: 21 total; 18 residual
Residual standard error: 3.238615
The first argument to update is always a model object, and additional
arguments for lm are passed as necessary. The formula argument
typically makes use of the “.” notation on either side of the “~”. The
“.” indicates “as in previous model.” The “-” and “+” operators are
used to delete or add terms. See Chapter 2, Specifying Models in
Spotfire S+, for more information on formulas with update.
260
Weighted Regression
WEIGHTED REGRESSION
You can supply weights in fitting any linear model; this can
sometimes improve the fit of models with repeated values in the
predictor. Weighted regression is the appropriate method in those
cases where it is known a priori that not all observations contribute
equally to the fit.
Example:
Weighted
Linear
Regression
The claims data set contains information on the average cost of
insurance claims for automobile accidents. The 128 rows of the data
frame represent all possible combinations of three predictor
variables: age, car.age, and type. An additional variable, number,
gives the number of claims that correspond to each combination. The
outcome variable, cost, is the average cost of the claims in each
category. An insurance company may be interested in using data like
this to set premiums.
We want to fit a regression model predicting cost from age, car.age,
and type. We begin with a simple scatter plot of the number of claims
versus the average cost:
> plot(claims$number, claims$cost)
The result is displayed in Figure 10.7. The plot shows that the
variability of cost is much greater for the observations with smaller
numbers of claims. This is what we expect: if each combination of
age, car.age,
2
and type has the same variance σ before averaging,
2
then the mean cost for a group of n claims is σ ⁄ n . Thus, as the size
of a group grows, the variability decreases.
261
Chapter 10 Regression and Smoothing for Continuous Response Data
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claims$number
Figure 10.7: Scatter plot of the number of insurance claims versus the average cost.
First, we fit an unweighted linear model to the claims data and view a
plot of the residuals:
> unweighted.claims <- lm(cost ~ age + type + car.age,
+ data = claims, na.action = na.exclude)
> unweighted.claims
Call:
lm(formula = cost ~ age + car.age + type, data = claims,
na.action = na.exclude)
Coefficients:
(Intercept)
age.L
age.Q
age.C
age ^ 4
239.2681 -58.27753 53.31217 -23.83734 -37.09553
age ^ 5 age ^ 6
age ^ 7 car.age.L car.age.Q
-51.57616 9.523087 -12.60742 -112.1761 -20.12425
car.age.C
type1
type2
type3
-1.035686 10.46875 3.519079 25.53023
262
Weighted Regression
Degrees of freedom: 123 total; 109 residual
5 observations deleted due to missing values
Residual standard error: 103.6497
> plot(claims$number, resid(unweighted.claims))
[1] T
> abline(h = 0)
The plot is displayed in the left panel of Figure 10.8. We know the
unweighted.claims model is wrong because the observations are
based on different sample sizes, and therefore have different
variances. In the plot, we again see that the variability in the residuals
is greater for smaller group sizes.
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Figure 10.8: Scatter plots of residuals for two claims models. The plot on the left is for an unweighted
model, and the plot on the right is for a model that includes weights.
263
Chapter 10 Regression and Smoothing for Continuous Response Data
To adjust for the difference in variances, we compute a weighted
linear model using number as our vector of weights. This means, for
example, that the observation based on 434 claims is weighted much
more than the 6 observations that are based on only one claim. This
makes sense, because we expect an average based on many data
points to be more stable and closer to the true group mean than one
based on only a few points.
> weighted.claims <- lm(cost ~ age + type + car.age,
+ data = claims, na.action = na.exclude, weights = number)
> weighted.claims
Call:
lm(formula = cost ~ age + car.age + type, data = claims,
weights = number, na.action = na.exclude)
Coefficients:
(Intercept)
age.L
age.Q
age.C
age ^ 4
250.6384 -58.26074 30.19545 5.962486 -34.10711
age ^ 5
age ^ 6 age ^ 7 car.age.L car.age.Q
-33.5003 -7.180729 18.667 -78.91788 -54.76935
car.age.C
type1
type2
type3
-49.47014 2.661179 9.47081 24.2689
Degrees of freedom: 123 total; 109 residual
5 observations deleted due to missing values
Residual standard error (on weighted scale): 606.2138
> plot(claims$number, resid(weighted.claims))
[1] T
> abline(h = 0)
The plot is displayed in the right panel of Figure 10.8. The plot shows
that the weighted model fits points with large weights more accurately
than the unweighted model. The analysis with weights is more
trustworthy and matches better with standard regression assumptions.
264
Weighted Regression
Observation
Weights vs.
Frequencies
Spotfire S+ implements observation weights through the weights
argument to most regression functions. Observation weights are
appropriate when the variances of individual observations are
inversely proportional to the weights. For a set of weights w i , one
interpretation is that the ith observation is the average of w i other
observations, each having the same predictors and (unknown)
variance. This is the interpretation of the weights we include in the
claims example above.
It is important to note that an observation weight is not the same as a
frequency, or case weight, which represents the number of times a
particular observation is repeated. It is possible to include frequencies
as a weights argument to a S-PLUS regression function; although this
produces the correct coefficients for the model, inference tools such
as standard errors, p values, and confidence intervals are incorrect. In
the examples below, we clarify the difference between the two types
of weights using both mathematical and S-PLUS notation.
Let X j be a set of predictor variables, for j = 1, 2, …, p , and suppose
Y is a vector of n response values. The classical linear model
(weighted or unweighted) is represented by an equation of the form
p
Y = β0 +
 βj Xj + ε ,
j=1
where β 0 is the intercept, β j is the coefficient corresponding to X j , ε
is a vector of residuals of length n , and β 0 +  β j X j represents the
j
fitted values. In this model, there are n observations and p + 1
coefficients to estimate.
For i = 1, 2, …, n , the residuals ε i in an unweighted model are
normally distributed with zero means and identical, unknown
2
variances σ . When observation weights are included in the model,
however, the variances differ between residuals. Suppose we include
a set of weights w i in our linear model. The ith residual ε i in the
weighted model is normally distributed with a zero mean, but its
2
2
variance is equal to σ ⁄ w i for an unknown σ . This type of model is
265
Chapter 10 Regression and Smoothing for Continuous Response Data
appropriate if the ith observation is the average of w i other
2
observations, each having the same variance σ . Another situation in
which this weighted model can be used is when the relative precision
of the observations is known in advance.
Note
Spotfire S+ does not currently support weighted regression when the absolute precision of the
observations is known. This situation arises often in physics and engineering, when the
uncertainty associated with a particular measurement is known in advance due to properties of
2
the measuring procedure or device. In this type of regression, the individual σ i are known,
2
2
weights w i = 1 ⁄ σ i are supplied, and σ need not be estimated. Because of the treatment of
2
weights in S-PLUS , however, σ is always estimated. If you know the absolute precision of your
2
observations, it is possible to supply them as 1 ⁄ σ i to the weights argument in an S-PLUS
regression function. This computes the correct coefficients for your model, but the standard
2
errors and other inference tools will be incorrect, since they are based on estimates of σ .
The main difference between observation weights and frequencies
lies in the degrees of freedom for a particular model. In S-PLUS, the
degrees of freedom for both weighted and unweighted models is
equal to the number of observations minus the number of parameters
estimated. For example, a linear model with n observations and one
predictor has n – 2 degrees of freedom, since both a slope and an
intercept are estimated. In contrast, the degrees of freedom for a
model with frequencies is equal to the sum of the frequencies minus
the number of parameters estimated. The degrees of freedom does
not affect the coefficients in a S-PLUS regression, but it is used to
compute standard errors, p values, and confidence intervals. If you
use a weights argument to represent frequencies in a regression
function, you will need to exercise extreme caution in interpreting the
statistical results.
For example, consider the following three contrived linear models.
First, we create arbitrary vectors x and y, where the first five elements
in x are identical to each other. We then compute a linear model for
the vectors. For reproducibility, we use the set.seed function.
266
Weighted Regression
> set.seed(0)
> x <- c(rep(1, 5), 2:10)
> x
[1]
1
1
1
1
1
2
3
4
5
6
7
8
9 10
> y <- runif(14)
> y
[1] 0.96065916 0.93746001 0.04410193 0.76461851 0.70585769
[6] 0.50355052 0.92864822 0.84027312 0.54710167 0.48780511
[11] 0.39898473 0.26351962 0.92592463 0.42851457
> unweighted.lm1 <- lm(y ~ x)
> unweighted.lm1
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept)
x
0.7162991 -0.02188421
Degrees of freedom: 14 total; 12 residual
Residual standard error: 0.288045
Next, we create vectors x2 and y2 that are identical to x and y, only
the five repeated x values have identical y values. This simulates a
data set with repeated observations. In our example, we choose the
mean of the first five y values to be the repeated y2 value, and then
compute a linear model for the vectors:
> x2 <- x
> y2 <- c(rep(mean(y[1:5]), times=5), y[6:14])
> y2
[1] 0.6825395 0.6825395 0.6825395 0.6825395 0.6825395
[6] 0.5035505 0.9286482 0.8402731 0.5471017 0.4878051
[11] 0.3989847 0.2635196 0.9259246 0.4285146
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Chapter 10 Regression and Smoothing for Continuous Response Data
> unweighted.lm2 <- lm(y2 ~ x2)
> unweighted.lm2
Call:
lm(formula = y2 ~ x2)
Coefficients:
(Intercept)
x2
0.7162991 -0.02188421
Degrees of freedom: 14 total; 12 residual
Residual standard error: 0.1911415
Note that both of these models have fourteen observations and 12
degrees of freedom. Finally, we create vectors x3 and y3 that are
identical to x2 and y2, only the five repeated values are condensed
into one. To account for this, we assign a weight of 5 to the first
observation and compute a weighted regression for x3 and y3:
> x3 <- 1:10
> y3 <- c(y2[1], y2[6:14])
> y3
[1] 0.6825395 0.5035505 0.9286482 0.8402731 0.5471017
[6] 0.4878051 0.3989847 0.2635196 0.9259246 0.4285146
> w3 <- c(5, rep(1, 9))
> w3
[1] 5 1 1 1 1 1 1 1 1 1
> weighted.lm <- lm(y3 ~ x3, weights = w3)
> weighted.lm
Call:
lm(formula = y3 ~ x3, weights = w3)
Coefficients:
(Intercept)
x3
0.7162991 -0.02188421
Degrees of freedom: 10 total; 8 residual
Residual standard error (on weighted scale): 0.2340995
268
Weighted Regression
Unlike the first two models, weighted.lm has only 10 observations
and 8 degrees of freedom. Because S-PLUS implements observation
weights, we expect weighted.lm to accurately represent the first
unweighted regression. In contrast, we would expect weighted.lm to
represent the second unweighted regression if S-PLUS supported
frequencies.
Although the coefficients for the three linear models are the same, the
standard errors for the regression parameters are different, due to the
varying degrees of freedom. This can be seen from the following calls
to summary:
> summary(unweighted.lm1)$coefficients
Value Std. Error
t value
Pr(>|t|)
(Intercept) 0.71629912 0.12816040 5.5890831 0.000118174
x -0.02188421 0.02431325 -0.9000937 0.385777544
> summary(unweighted.lm2)$coefficients
Value Std. Error
t value
Pr(>|t|)
(Intercept) 0.71629912 0.08504493 8.422596 2.211207e-006
x2 -0.02188421 0.01613384 -1.356417 1.999384e-001
> summary(weighted.lm)$coefficients
Value Std. Error
t value
Pr(>|t|)
(Intercept) 0.71629912 0.10415835 6.877021 0.0001274529
x3 -0.02188421 0.01975983 -1.107510 0.3002587236
For weighted.lm to accurately represent unweighted.lm2, its standard
errors should be based on 12 degrees of freedom (the sum of the the
frequencies minus 2).
Depending on the field of study, different categories of weights may
be needed in regression analysis. Observation weights and
frequencies are not the only types used; we present these here simply
to illustrate how S-PLUS implements weights in regression functions.
Although the above discussion is specific to the lm function, it is
applicable to most S-PLUS regression functions that include a
weights option.
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Chapter 10 Regression and Smoothing for Continuous Response Data
PREDICTION WITH THE MODEL
Much of the value of a linear regression model is that, if it accurately
models the underlying phenomenon, it can provide reliable predictions
about the response for a given value of the predictor. The predict
function takes a fitted model object and a data frame of new data, and
returns a vector corresponding to the predicted response. The
variable names in the new data must correspond to those of the
original predictors; the response may or may not be present, but if
present is ignored.
For example, suppose we want to predict the atmospheric ozone
concentration from the following vector of temperatures:
> newtemp <- c(60, 62, 64, 66, 68, 70, 72)
We can obtain the desired predictions using predict as follows:
> predict(ozone.lm, data.frame(temperature = newtemp))
1
2
3
4
5
6
1.995822 2.136549 2.277276 2.418002 2.558729 2.699456
7
2.840183
The predicted values do not stand apart from the original
observations.
You can use the se.fit argument to predict to obtain the standard
error of the fitted value at each of the new data points. When
se.fit=T, the output of predict is a list, with a fit component
containing the predicted values and an se.fit component containing
the standard errors
270
Prediction with the Model
For example,
> predict(ozone.lm, data.frame(temperature = newtemp),
+ se.fit = T)
$fit:
1
2
3
4
5
6
1.995822 2.136549 2.277276 2.418002 2.558729 2.699456
7
2.840183
$se.fit:
1
2
3
4
5
0.1187178 0.1084689 0.09856156 0.08910993 0.08027508
6
7
0.07228355 0.06544499
$residual.scale:
[1] 0.5884748
$df:
[1] 109
You can use this output list to compute pointwise and simultaneous
confidence intervals for the fitted regression line. See the section
Confidence Intervals for details. See the predict help file for a
description of the remaining components of the return list,
residual.scale and df, as well as a description of predict’s other
arguments.
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Chapter 10 Regression and Smoothing for Continuous Response Data
CONFIDENCE INTERVALS
How reliable is the estimate produced by a simple regression?
Provided the standard assumptions hold (that is, normal, identically
distributed errors with constant variance σ ), we can construct
confidence intervals for each point on the fitted regression line based
on the t distribution, and simultaneous confidence bands for the
fitted regression line using the F distribution.
In both cases, we need the standard error of the fitted value, se.fit,
which is computed as follows (Weisberg, 1985, p. 21):

 1--2
1
(x – x)  2
se.fit = σ̂  --- + ---------------------------
2
n
i ( xi – x ) 

where x = a given point in the predictor space. For a fitted object of
class "lm", you can use the predict function as follows to calculate
se.fit:
> predict(ozone.lm, se.fit = T)
For a given point x in the predictor space, a ( 1 – α )% confidence
interval for the fitted value corresponding to x is the set of values y
such that
ŷ – t(α ⁄ 2, n – 2) × se.fit < y < ŷ + t(α ⁄ 2, n – 2) × se.fit ,
where t ( q, d ) computes the qth quantile of the t distribution with d
degrees of freedom. The pointwise function takes the output of
predict (produced with the se.fit=T flag) and returns a list
containing three vectors: the vector of lower bounds, the fitted values,
and the vector of upper bounds giving the confidence intervals for the
fitted values for the predictor. The output from pointwise is suitable,
for example, as input for the error.bar function. The following
command computes pointwise prediction intervals for the ozone.lm
model.
272
Confidence Intervals
> pointwise(predict(ozone.lm, se.fit = T))
$upper:
1
2
3
4
5
6
2.710169 3.011759 3.138615 2.42092 2.593475 2.250401
7
8
9
10
11
12
2.363895 2.828752 2.651621 2.769185 2.193888 2.535673
. . .
$fit:
1
2
3
4
5
6
2.488366 2.840183 2.98091 2.136549 2.347639 1.925458
7
8
9
10
11
12
2.066185 2.629093 2.418002 2.558729 1.855095 2.277276
. . .
$lower:
1
2
3
4
5
6
2.266563 2.668607 2.823205 1.852177 2.101803 1.600516
7
8
9
10
11
12
1.768476 2.429434 2.184384 2.348273 1.516301 2.018878
. . .
It is tempting to believe that the curves resulting from connecting all
the upper points and all the lower points would give a confidence
interval for the entire curve. This, however, is not the case; the
resulting curve does not have the desired confidence level across its
whole range. What is required instead is a simultaneous confidence
interval, obtained by replacing the t distribution with the F
distribution. An S-PLUS function for creating such simultaneous
confidence intervals (and by default, plotting the result) can be
defined with the code below.
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Chapter 10 Regression and Smoothing for Continuous Response Data
"confint.lm"<function(object, alpha = 0.05, plot.it = T, ...) {
f <- predict(object, se.fit = T)
p <- length(coef(object))
fit <- f$fit
adjust <- (p * qf(1 - alpha, p, length(fit) p))^0.5 * f$se.fit
lower <- fit - adjust
upper <- fit + adjust
if(plot.it) {
y <- fit + resid(object)
plot(fit, y)
abline(0, 1, lty = 2)
ord <- order(fit)
lines(fit[ord], lower[ord])
lines(fit[ord], upper[ord])
invisible(list(lower=lower, upper=upper))
}
else list(lower = lower, upper = upper)
}
A plot of our first model of the air data, as generated by the following
command, is shown in Figure 10.9:
> confint.lm(ozone.lm)
•
5
•
4
•
1
2
3
y
•
•
•
• •
• ••
•
•
•
•
•
•••
•
• ••
•
•
•
•
•
•
•
•
•
•
•
• •• •
• •
••
••• •
•
•
•
•
•
• •
•• • ••
••
•
•
•
•
•
•
•
•••
•
• •
• • •• •
••
•
•
•
•
•••
• •• •
•
•
•
• •
•
•
•
•
•
•
2.0
2.5
3.0
3.5
4.0
fit
Figure 10.9: Simultaneous confidence intervals for the ozone data.
274
4.5
Polynomial Regression
POLYNOMIAL REGRESSION
4
Thus far in this chapter, we’ve dealt with data sets for which the
graphical evidence clearly indicated a linear relationship between the
predictors and the response. For such data, the linear model is a
natural and elegant choice, providing a simple and easily analyzed
description of the data. But what about data that does not exhibit a
linear dependence? For example, consider the scatter plot shown in
Figure 10.10. Clearly, there is some functional relationship between the
predictor E (for Ethanol) and the response NOx (for Nitric Oxide), but
just as clearly the relationship is not a straight line.
• •
•
•
• •
• •• •
•
•
3
••
•
••
•
•
•
•
•
•
•••
• •
2
NOx
•
•
1
•
•
•
•
•
•
•
••
•
••
•
•
•
••
••
• ••
•
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•
•
•
• ••
••
0.6
••
•
•
0.8
1.0
••• • •
• ••
• •• ••
• • ••
•
1.2
E
Figure 10.10: Scatter plot showing nonlinear dependence.
275
Chapter 10 Regression and Smoothing for Continuous Response Data
How should we model such data? One approach is to add polynomial
terms to the basic linear model, then use least-squares techniques as
before. The classical linear model (with the intercept term
represented as the coefficient of a dummy variable X 0 of all 1’s) is
represented by an equation of the following form:
n
Y=
 βk Xk + ε
(10.1)
k=0
where the predictors X k enter the equation as linear terms. More
generally, classical linear regression techniques apply to any equation
of the form
n
 βk Zk + ε
Y=
(10.2)
k=0
where the Z k are new variables formed as combinations of the
original predictors. For example, consider a cubic polynomial
relationship given by the following equation:
3
Y=
 βk x
k
+ε
(10.3)
k=0
We can convert this to the desired form by the following assignments:
0
x = Z0
1
x = Z1
2
x = Z2
3
x = Z3
Once these assignments are made, the coefficients β k can be
determined as usual using the classical least-squares techniques.
276
Polynomial Regression
To perform a polynomial regression in S-PLUS, use lm together with
the poly function. Use poly on the right hand side of the formula
argument to lm to specify the independent variable and degree of the
polynomial. For example, consider the following made-up data:
x <- runif(100, 0, 100)
y <- 50 - 43*x + 31*x^2 - 2*x^3 + rnorm(100)
We can fit this as a polynomial regression of degree 3 as follows:
> xylm <- lm(y ~ poly(x, 3))
> xylm
Call:
lm(formula = y ~ poly(x, 3))
Coefficients:
(Intercept) poly(x, 3)1 poly(x, 3)2 poly(x, 3)3
-329798.8
-3681644
-1738826
-333975.4
Degrees of freedom: 100 total; 96 residual
Residual standard error: 0.9463133
The coefficients that appear in the object xylm are the coefficients for
the orthogonal form of the polynomial. To recover the simple
polynomial form, use the function poly.transform:
> poly.transform(poly(x,3), coef(xylm))
x^0
x^1
x^2
x^3
49.9119 -43.01118 31.00052 -2.000005
These coefficients are very close to the exact values used to create y.
If the coefficients returned from a regression involving poly are so
difficult to interpret, why not simply model the polynomial explicitly?
That is, why not use the formula y ~ x + x^2 + x^3 instead of the
formula involving poly? In our example, there is little difference.
However, in problems involving polynomials of higher degree,
severe numerical problems can arise in the model matrix. Using poly
avoids these numerical problems, because poly uses an orthogonal
set of basis functions to fit the various “powers” of the polynomial.
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Chapter 10 Regression and Smoothing for Continuous Response Data
As a further example of the use of poly, let us consider the ethanol
data we saw at the beginning of this section. From Figure 10.10, we
are tempted by a simple quadratic polynomial. However, there is a
definite upturn at each end of the data, so we are safer fitting a quartic
polynomial, as follows:
> ethanol.poly <- lm(NOx ~ poly(E, degree = 4))
> summary(ethanol.poly)
Call: lm(formula = NOx ~ poly(E, degree = 4))
Residuals:
Min
1Q
Median
3Q
Max
-0.8125 -0.1445 -0.02927 0.1607 1.017
Coefficients:
Value Std. Error t value
1.9574
0.0393
49.8407
poly(E,
-1.0747
0.3684
-2.9170
poly(E,
-9.2606
0.3684
-25.1367
poly(E,
-0.4879
0.3684
-1.3243
poly(E,
3.6341
0.3684
9.8644
Pr(>|t|)
(Intercept)
0.0000
poly(E, degree = 4)1
0.0045
poly(E, degree = 4)2
0.0000
poly(E, degree = 4)3
0.1890
poly(E, degree = 4)4
0.0000
Residual standard error: 0.3684 on 83 degrees of freedom
Multiple R-Squared: 0.8991
F-statistic: 184.9 on 4 and 83 degrees of freedom, the
p-value is 0
(Intercept)
degree = 4)1
degree = 4)2
degree = 4)3
degree = 4)4
Correlation of Coefficients:
(Intercept) poly(E, degree = 4)1
poly(E, degree = 4)1 0
poly(E, degree = 4)2 0
0
poly(E, degree = 4)3 0
0
poly(E, degree = 4)4 0
0
poly(E, degree = 4)2 poly(E, degree = 4)3
poly(E, degree = 4)1
poly(E, degree = 4)2
poly(E, degree = 4)3 0
poly(E, degree = 4)4 0
0
278
Polynomial Regression
> poly.transform(poly(E, 4), coef(ethanol.poly))
x^0
x^1
x^2
x^3
x^4
174.3601 -872.2071 1576.735 -1211.219 335.356
In the summary output, the P(>|t|) value for the fourth order
coefficient is equal to zero. Thus, the probability that the model does
not include a fourth order term is zero, and the term is highly
significant. Although the ethanol data looks fairly quadratic in Figure
10.10, a simple quadratic model would result in more error than in the
quartic model ethanol.poly.
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Chapter 10 Regression and Smoothing for Continuous Response Data
GENERALIZED LEAST SQUARES REGRESSION
Generalized least squares models are regression (or ANOVA) models
in which the errors have a nonstandard covariance structure. Like
simple least squares regression, the method of generalized least squares
(GLS) uses maximum likelihood or restricted maximum likelihood to
fit a continuous, univariate response as a linear function of a single
predictor variable. In GLS, however, the errors are allowed to be
correlated and/or to have unequal variances.
To fit a linear model in S-PLUS with generalized least squares
regression, use the function gls. Several arguments are available in
gls, but a typical call is in one of three forms:
gls(model, data, correlation)
# correlated errors
gls(model, data, weights)
# heteroscedastic errors
gls(model, data, correlation, weights)
# both
The model argument is a two-sided linear formula specifying the
model for the expected value of the response variable; this is identical
to the model argument required by lm. In many cases, both the
response and the predictor are components of a single data frame,
which can be specified as the optional data argument to gls.
The arguments that exemplify the flexibility of gls are correlation
and weights. The optional argument correlation specifies the
within-group correlation structure for a grouped data set. In grouped
data, the values of the response variable are grouped according to one
or more factors; these data are discussed in detail in Chapter 14,
Linear and Nonlinear Mixed-Effects Models. The correlation
structures available in gls are organized into corStruct classes, as
shown in Table 10.1. The optional argument weights to gls specifies
the form of the errors variance-covariance function, which is used to
model heteroscedasticity in the within-group errors. The available
variance functions are organized into varFunc classes, as shown in
Table 10.2.
280
Generalized Least Squares Regression
Table 10.1: Classes of correlation structures.
Class
Description
corAR1
AR(1)
corARMA
ARMA(p,q)
corBand
banded
corCAR1
continuous AR(1)
corCompSymm
compound symmetry
corExp
exponential spatial correlation
corGaus
Gaussian spatial correlation
corIdent
multiple of an identity
corLin
linear spatial correlation
corRatio
rational quadratic spatial correlation
corSpatial
general spatial correlation
corSpher
spherical spatial correlation
corStrat
a different corStruct class for each level of a
stratification variable
corSymm
general correlation matrix
Table 10.2: Classes of variance function structures.
Class
Description
varComb
combination of variance functions
varConstPower
constant plus power of a variance covariate
varExp
exponential of a variance covariate
281
Chapter 10 Regression and Smoothing for Continuous Response Data
Table 10.2: Classes of variance function structures.
Class
Description
varFixed
fixed weights, determined by a variance covariate
varIdent
different variances per level of a factor
varPower
power of a variance covariate
You can define your own correlation and variance function classes by
specifying appropriate constructor functions and a few method
functions. For a new correlation structure, method functions must be
defined for at least corMatrix and coef. For examples of these
functions, see the methods for the corSymm and corAR1 classes. A new
variance function requires methods for at least coef, coef<-, and
initialize. For examples of these functions, see the methods for the
varPower class.
Example: The
Ovary Data Set
The Ovary data set has 308 rows and 3 columns. It contains the
number of ovarian follicles detected in different mares at different
times in their estrus cycles.
> Ovary
Grouped
Mare
1
1
2
1
3
1
4
1
5
1
6
1
7
1
. . .
Data: follicles ~ Time | Mare
Time follicles
-0.13636360
20
-0.09090910
15
-0.04545455
19
0.00000000
16
0.04545455
13
0.09090910
10
0.13636360
12
Biological models suggest that the number of follicles may be
modeled as a linear combination of the sin and cosine of 2*pi*Time.
The corresponding S-PLUS model formula is written as:
follicles ~ sin(2*pi*Time) + cos(2*pi*Time)
Let’s fit a simple linear model for the Ovary data first, to demonstrate
the need for considering dependencies among the residuals.
282
Generalized Least Squares Regression
> Ovary.lm <- lm(follicles ~
+ sin(2*pi*Time) + cos(2*pi*Time), data = Ovary)
We can view a plot of the residuals with the following command:
> plot(Ovary.lm, which = 1)
15
The result is shown in Figure 10.11, and suggests that we try a more
general variance-covariance structure for the error term in our model.
82
0
-10
-5
Residuals
5
10
11
47
10
12
14
Fitted : sin(2 * pi * Time) + cos(2 * pi * Time)
Figure 10.11: Residuals plot from a simple linear fit to the Ovary data set.
We use the gls function with a power variance structure instead of
standard linear regression. In our generalized least squares model, the
variance increases with a power of the absolute fitted values.
> Ovary.fit1 <- gls(follicles ~
+ sin(2*pi*Time) + cos(2*pi*Time), data = Ovary,
+ weights = varPower())
Manipulating
gls Objects
The fitted objects returned by the gls function are of class "gls". A
variety of methods are available for displaying, updating, and
evaluating the estimation results.
The print method displays a brief description of the estimation
results returned by gls. For the Ovary.fit1 object, the results are
> Ovary.fit1
283
Chapter 10 Regression and Smoothing for Continuous Response Data
Generalized least squares fit by REML
Model: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time)
Data: Ovary
Log-restricted-likelihood: -895.8303
Coefficients:
(Intercept) sin(2 * pi * Time) cos(2 * pi * Time)
12.22151
-3.292895
-0.8973728
Variance function:
Structure: Power of variance covariate
Formula: ~ fitted(.)
Parameter estimates:
power
0.4535912
Degrees of freedom: 308 total; 305 residual
Residual standard error: 1.451151
A more complete description of the estimation results is returned by
the summary function:
> summary(Ovary.fit1)
Generalized least squares fit by REML
Model: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time)
Data: Ovary
AIC
BIC
logLik
1801.661 1820.262 -895.8303
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Generalized Least Squares Regression
Variance function:
Structure: Power of variance covariate
Formula: ~ fitted(.)
Parameter estimates:
power
0.4535912
Coefficients:
(Intercept)
sin(2 * pi * Time)
cos(2 * pi * Time)
Value
12.22151
-3.29290
-0.89737
Std.Error
0.2693741
0.3792688
0.3591879
t-value p-value
45.37003 <.0001
-8.68222 <.0001
-2.49834
0.013
Correlation:
(Intr) s(2*p*T)
sin(2 * pi * Time) -0.165
cos(2 * pi * Time) -0.321
0.021
Standardized residuals:
Min
Q1
Med
Q3
Max
-2.303092 -0.7832415 -0.02163715 0.6412627 3.827058
Residual standard error: 1.451151
Degrees of freedom: 308 total; 305 residual
Diagnostic plots for assessing the quality of a fitted gls model are
obtained using the plot method. Figure 10.12 shows the plot
displayed by the command:
> plot(Ovary.fit1)
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Chapter 10 Regression and Smoothing for Continuous Response Data
4
Standardized residuals
3
2
1
0
-1
-2
10
12
14
Fitted values
Figure 10.12: Residuals plot from a generalized least squares fit to the Ovary data,
using a power variance function.
Although we included a power variance structure in Ovary.fit1, the
plot in Figure 10.12 still shows evidence of extra variation in the
model. One possibility, given that Time is a covariate in the data, is
that serial correlation exists within the groups. To test this hypothesis,
we use the ACF function as follows:
> ACF(Ovary.fit1)
lag
ACF
1
0 1.0000000
2
1 0.6604265
3
2 0.5510483
4
3 0.4410895
. . .
The ACF function computes the values of the empirical
autocorrelation function that correspond to the residuals of the gls fit.
The values are listed for several lags, and there appears to be
significant autocorrelation at the first few lags. These values,
displayed in Figure 10.13, can be plotted with a simple call to the plot
method for ACF.
> plot(.Last.value)
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Generalized Least Squares Regression
1.0
0.8
Autocorrelation
0.6
0.4
0.2
0.0
-0.2
0
5
10
15
20
Lag
Figure 10.13: Empirical autocorrelation function corresponding to the standardized
residuals of the Ovary.fit1 model object.
Figure 10.13 suggests that an autoregressive process of order 1 may be
adequate to model the serial correlation in the residuals. We use the
correlation argument in gls to re-fit the model using an AR(1)
correlation structure for the residuals. The value returned by ACF for
the first-lag correlation is used as an estimate of the autoregressive
coefficient.
>
+
+
>
Ovary.fit2 <- gls(follicles ~
sin(2*pi*Time) + cos(2*pi*Time), data = Ovary,
correlation = corAR1(0.66), weights = varPower())
plot(Ovary.fit2)
The residuals, displayed in Figure 10.14, look much tighter than for
Ovary.fit1. This indicates that the extra variation we observed in
Ovary.fit1 is adequately modeled with the corAR1 correlation
structure.
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Chapter 10 Regression and Smoothing for Continuous Response Data
In addition, the anova table comparing the two fits shows great
improvement when the serial correlation is considered in the model:
> anova(Ovary.fit1, Ovary.fit2)
Ovary.fit1
Ovary.fit2
Model df
AIC
BIC
logLik
Test
1 5 1801.661 1820.262 -895.8303
2 6 1598.496 1620.818 -793.2479 1 vs 2
L.Ratio p-value
Ovary.fit1
Ovary.fit2 205.1648 <.0001
Standardized residuals
2
1
0
-1
-2
11
12
13
14
Fitted values
Figure 10.14: Residuals plot from a generalized least squares fit to the Ovary data,
using a power variance function and within-group AR(1) serial correlation.
The final generalized least squares model for the Ovary data is:
> Ovary.fit2
Generalized least squares fit by REML
Model: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time)
Data: Ovary
Log-restricted-likelihood: -793.2479
Coefficients:
(Intercept) sin(2 * pi * Time) cos(2 * pi * Time)
12.30864
-1.647776
-0.8714635
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Generalized Least Squares Regression
Correlation Structure: AR(1)
Parameter estimate(s):
Phi
0.7479559
Variance function:
Structure: Power of variance covariate
Formula: ~ fitted(.)
Parameter estimates:
power
-0.7613254
Degrees of freedom: 308 total; 305 residual
Residual standard error: 32.15024
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Chapter 10 Regression and Smoothing for Continuous Response Data
SMOOTHING
Polynomial regression can be useful in many situations. However, the
choice of terms is not always obvious, and small effects can be greatly
magnified or lost completely by the wrong choice. Another approach
to analyzing nonlinear data, attractive because it relies on the data to
specify the form of the model, is to fit a curve to the data points
locally. With this technique, the curve at any point depends only on
the observations at that point and some specified neighboring points.
Because such a fit produces an estimate of the response that is less
variable than the original observed response, the result is called a
smooth, and procedures for producing such fits are called scatterplot
smoothers. S-PLUS offers a variety of scatterplot smoothers:
•
loess.smooth,
a locally weighted regression smoother.
•
smooth.spline, a cubic smoothing spline, with local behavior
similar to that of kernel-type smoothers.
•
ksmooth,
•
supsmu,
a kernel-type scatterplot smoother.
a very fast variable span bivariate smoother.
Halfway between the global parametrization of a polynomial fit and
the local, nonparametric fit provided by smoothers are the parametric
fits provided by regression splines. Regression splines fit a continuous
curve to the data by piecing together polynomials fit to different
portions of the data. Thus, like smoothers, they are local fits. Like
polynomials, they provide a parametric fit. In S-PLUS, regression
splines can be used to specify the form of a predictor in a linear or
more general model, but are not intended for top-level use.
Locally
Weighted
Regression
Smoothing
In locally weighted regression smoothing, we build the smooth
function s ( x ) pointwise as follows:
1. Take a point, say x 0 . Find the k nearest neighbors of x 0 ,
which constitute a neighborhood N ( x 0 ) . The number of
neighbors k is specified as a percentage of the total number of
points. This percentage is called the span.
290
Smoothing
2. Calculate the largest distance between x 0 and another point
in the neighborhood:
Δ ( x 0 ) = max N ( x0 ) x 0 – x 1
3. Assign weights to each point in N ( x 0 ) using the tri-cube
weight function:
x0 – x1
W  -------------------
 Δ ( x0 ) 
where
 ( 1 – u3 )3
W(u) = 
0
for 0 ≤ u < 1
otherwise
4. Calculate the weighted least squares fit of y on the
neighborhood N ( x 0 ) . Take the fitted value ŷ 0 = s ( x 0 ) .
5. Repeat for each predictor value.
Use the loess.smooth function to calculate a locally weighted
regression smooth. For example, suppose we want to smooth the
ethanol data. The following expressions produce the plot shown in
Figure 10.15:
> plot(E, NOx)
> lines(loess.smooth(E, NOx))
The figures shows the default smoothing, which uses a span of 2/3.
For most uses, you will want to specify a smaller span, typically in the
range of 0.3 to 0.5.
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4
Chapter 10 Regression and Smoothing for Continuous Response Data
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E
Figure 10.15: Loess-smoothed ethanol data.
Using the
Super
Smoother
With loess, the span is constant over the entire range of predictor
values. However, a constant value will not be optimal if either the
error variance or the curvature of the underlying function f varies
over the range of x . An increase in the error variance requires an
increase in the span whereas an increase in the curvature of f requires
a decrease. Local cross-validation avoids this problem by choosing a
span for the predictor values x j based on only the leave-one-out
residuals whose predictor values x i are in the neighborhood of x j .
The super smoother, supsmu, uses local cross-validation to choose the
span. Thus, for one-predictor data, it can be a useful adjunct to loess.
For example, Figure 10.16 shows the result of super smoothing the
response NOx as a function of E in the ethanol data (dotted line)
superimposed on a loess smooth. To create the plot, use the
following commands:
> scatter.smooth(E, NOx, span = 1/4)
> lines(supsmu(E, NOx), lty = 2)
292
4
Smoothing
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1.0
1.2
E
Figure 10.16: Super smoothed ethanol data (the dotted line).
Local CrossValidation
Let s ( x k ) denote the linear smoother value at x when span k is
used. We wish to choose k = k ( X ) so as to minimize the mean
squared error
2
e ( k ) = EX Y [ Y – s ( X k ) ]
2
where we are considering the joint random variable model for ( X, Y ) .
Since
2
EX Y [ Y – s ( X k ) ] = EX EY X [ Y – s ( X k ) ]
2
we would like to choose k = k ( x ) to minimize
2
ex ( k ) = EY X = x [ Y – s ( X k ) ]
2
2
= EY X = x [ Y – s ( x k ) ] .
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Chapter 10 Regression and Smoothing for Continuous Response Data
However, we have only the data ( x i, y i ) , i = 1, …, n , and not the
true conditional distribution needed to compute E y X = x , and so we
2
cannot calculate e x ( k ) . Thus we resort to cross-validation and try to
2
minimize the cross-validation estimate of e x ( k ) :
n
2
ê CV ( k )
=
 [ yi – s( i ) ( xi k ) ]
2
.
i=1
Here s i ( x i k ) is the “leave-one-out” smooth at x i , that is, s ( i ) ( x i k ) is
constructed using all the data ( x j, y j ) , j = 1, …, n , except for ( x i, y i ) ,
and then the resultant local least squares line is evaluated at x i
thereby giving s ( i ) ( x k ) . The leave-one-out residuals
r( i ) ( k ) = yi – s( i ) ( xi k )
are easily obtained from the ordinary residuals
ri ( k ) = yi – s ( xi k )
using the standard regression model relation
ri ( k )
r ( i ) ( k ) = ----------- .
h ii
Here h ii , i = 1, …, n , are the diagonals of the so-called “hat” matrix,
T
–1
T
H = X ( X X ) X , where, for the case at hand of local straight-line
regression, X is a 2-column matrix.
294
Smoothing
Using the
Kernel
Smoother
A kernel-type smoother is a type of local average smoother that, for
each target point xi in predictor space, calculates a weighted average ŷ i
of the observations in a neighborhood of the target point:
n
 wij yj
ŷ i =
(10.4)
j=1
where
x i – x j
 ------------K
x i – x j
 b 

w ij = K̃ -------------- = ---------------------------------n
 b 
–
x
x
i
k
-
 K  -------------b 
k=1
are weights which sum to one:
n
 wij = 1 .
j=1
The function K used to calculate the weights is called a kernel
function, which typically has the following properties:
•
K ( t ) ≥ 0 for all t
•
–∞ K ( t ) dt
•
K ( – t ) = K ( t )m for all t (symmetry)
∞
= 1
Note that the first two properties are those of a probability density
function. The parameter b in the equation for the weights is the
bandwidth parameter, which determines how large a neighborhood of
the target point is used to calculate the local average. A large
bandwidth generates a smoother curve, while a small bandwidth
generates a wigglier curve. Hastie and Tibshirani (1990) point out that
the choice of bandwidth is much more important than the choice of
kernel.
To perform kernel smoothing in S-PLUS, use the ksmooth function.
The kernels available in ksmooth are shown in Table 10.3.
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Chapter 10 Regression and Smoothing for Continuous Response Data
Table 10.3: Kernels available for ksmooth.
Kernel
Explicit Form
"box"
1 ,
K box ( t ) = 
0 ,
"triangle"
"parzen"
2
1
t ≤ 0.5
t > 0.5
1– t ⁄ C ,

K tri ( t ) = 
 0,

( k1 – t 2 ) ⁄ k2 ,


K par ( t ) =  ( t 2 ⁄ k 3 ) – k 4 t + k 5 ,

0,

1
t ≤ ---C
1
t > ---C
t ≤ C1
C1 < t ≤ C2
C2 < t
"normal"
K nor ( t ) = ( 1 ⁄ 2πk 6 ) exp ( – t 2 ⁄ 2k 62 )
1
In convolution form,
K tri ( t ) = K box * K box ( t )
In convolution form,
K par ( t ) = K tri * K box ( t )
2
The constants shown in the explicit forms above are used to scale the
resulting kernel so that the upper and lower quartiles occur at ±0.25. Also,
the bandwidth is taken to be 1 and the dependence of the kernel on the
bandwidth is suppressed.
Of the available kernels, the default "box" kernel gives the crudest
smooth. For most data, the other three kernels yield virtually identical
smooths. We recommend "triangle" because it is the simplest and
fastest to calculate.
296
Smoothing
The intuitive sense of the kernel estimate ŷ i is clear: Values of y j such
that x j is close to x i get relatively heavy weights, while values of y j
such that x j is far from x i get small or zero weight. The bandwidth
parameter b determines the width of K ( t ⁄ b ) , and hence controls the
size of the region around x i for which y j receives relatively large
weights. Since bias increases and variance decreases with increasing
bandwidth b , selection of b is a compromise between bias and
variance in order to achieve small mean squared error. In practice
this is usually done by trial and error. For example, we can compute a
kernel smooth for the ethanol data as follows:
>
>
>
+
>
+
plot(E, NOx)
lines(ksmooth(E, NOx, kernel="triangle", bandwidth=0.2))
lines(ksmooth(E, NOx, kernel="triangle", bandwidth=0.1),
lty=2)
legend(0.54, 4.1, c("bandwidth=0.2", "bandwidth=0.1"),
lty = c(1,2))
4
The resulting plot is shown in Figure 10.17.
1
2
NOx
3
bandwidth=0.2
bandwidth=0.1
0.6
0.8
1.0
1.2
E
Figure 10.17: Kernel smooth of ethanol data for two bandwidths.
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Chapter 10 Regression and Smoothing for Continuous Response Data
Smoothing
Splines
A cubic smoothing spline behaves approximately like a kernel smoother,
but it arises as the function f̂ that minimizes the penalized residual sum
of squares given by
n
PRSS =
 ( yi – f ( xi ) )
2
+ λ  ( f″ ( t ) ) dt
2
i=1
over all functions with continuous first and integrable second
derivatives. The parameter λ is the smoothing parameter,
corresponding to the span in loess or supsmu or the bandwidth in
ksmooth.
To generate a cubic smoothing spline in S-PLUS, use the function
smooth.spline to smooth to the input data:
> plot(E, NOx)
> lines(smooth.spline(E, NOx))
You can specify a different λ using the spar argument, although it is
not intuitively obvious what a “good” choice of λ might be. When
the data is normalized to have a minimum of 0 and a maximum of 1,
and when all weights are equal to 1, λ = spar. More generally, the
relationship is given by λ = (max(x)-min(x))^3·mean(w)·spar. You
should either let S-PLUS choose the smoothing parameter, using
either ordinary or generalized cross-validation, or supply an
alternative argument, df, which specifies the degrees of freedom for the
smooth. For example, to add a smooth with approximately 5 degrees
of freedom to our previous plot, use the following:
> lines(smooth.spline(E, NOx, df = 5), lty = 2)
The resulting plot is shown in Figure 10.18.
298
4
Smoothing
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E
Figure 10.18: Smoothing spline of ethanol data with cross-validation (solid line)
and pre-specified degrees of freedom.
Comparing
Smoothers
The choice of a smoother is somewhat subjective. All the smoothers
discussed in this section can generate reasonably good smooths; you
might select one or another based on theoretical considerations or the
ease with which one or another of the smoothing criteria can be
applied. For a direct comparision of these smoothers, consider the
artificial data constructed as follows:
>
>
>
>
set.seed(14)
#set the seed to reproduce the example
e <- rnorm(200)
x <- runif(200)
y <- sin(2 * pi * (1-x)^2) + x * e
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Chapter 10 Regression and Smoothing for Continuous Response Data
A
“perfect”
smooth
would
recapture
the
original
signal,
2
f ( x ) = sin ( 2π ( 1 – x ) ) , exactly. The following commands sort the
input and calculate the exact smooth:
> sx <- sort(x)
> fx <- sin(2 * pi * (1-sx)^2)
The following commands create a scatter plot of the original data,
then superimpose the exact smooth and smooths calculated using
each of the smoothers described in this chapter:
>
>
>
>
>
>
>
+
plot(x, y)
lines(sx, fx)
lines(supsmu(x, y), lty = 2)
lines(ksmooth(x, y), lty = 3)
lines(smooth.spline(x, y), lty = 4)
lines(loess.smooth(x, y),lty = 5)
legend(0, 2, c("perfect", "supsmu", "ksmooth",
"smooth.spline", "loess"), lty = 1:5)
-2
-1
y
0
1
2
The resulting plot is shown in Figure 10.19. This comparison is crude
at best, because by default each of the smoothers does a different
amount of smoothing. A fairer comparison would adjust the
smoothing parameters to be roughly equivalent.
•
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perfect
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x
Figure 10.19: Comparison of S-PLUS smoothers.
300
0.8
1.0
Additive Models
ADDITIVE MODELS
An additive model extends the notion of a linear model by allowing
some or all linear functions of the predictors to be replaced by
arbitrary smooth functions of the predictors. Thus, the standard linear
model
n
Y =  βi Xi + ε
i=0
is replaced by the additive model
n
Y = α + f i ( X i ) + ε .
i=1
The standard linear regression model is a simple case of an additive
model. Because the forms of the f i are generally unknown, they are
estimated using some form of scatterplot smoother.
To fit an additive model in S-PLUS, use the gam function, where gam
stands for generalized additive model. You provide a formula which may
contain ordinary linear terms as well as terms fit using any of the
following:
•
loess smoothers, using the lo function;
•
smoothing spline smoothers, using the s function;
•
natural cubic splines, using the ns function;
•
B-splines, using the bs function;
•
polynomials, using poly.
The three functions ns, bs, and poly result in parametric fits; additive
models involving only such terms can be analyzed in the classical
linear model framework. The lo and s functions introduce
nonparametric fitting into the model. For example, the following call
takes the ethanol data and models the response NOx as a function of
the loess-smoothed predictor E:
> attach(ethanol)
> ethanol.gam <- gam(NOx ~ lo(E, degree = 2))
301
Chapter 10 Regression and Smoothing for Continuous Response Data
> ethanol.gam
Call:
gam(formula = NOx ~ lo(E, degree = 2))
Degrees of Freedom: 88 total; 81.1184 Residual
Residual Deviance: 9.1378
In the call to lo, we specify that the smooth is to be locally quadratic
by using the argument degree=2. For data that is less obviously
nonlinear, we would probably be satisfied with the default, which is
locally linear fitting. The printed gam object closely resembles a
printed lm object from linear regression—the call producing the model
is shown, followed by the degrees of freedom and the residual deviance
which serves the same role as the residual sum of squares in the linear
model. The deviance is a function of the log-likelihood function,
which is related to the probability mass function f ( y i ;μ i ) for the
observation y i given μ i . The log-likelihood for a sample of n
observations is defined as follows:
n
l ( m; y ) =
 log f ( yi ; μi )
i=1
The deviance D ( y; m ) is then defined as
D ( y; m )
-------------------- = 2l ( m∗ ; y ) – 2l ( m; y )
φ
where μ∗ maximizes the log-likelihood over μ unconstrained, and φ
is the dispersion parameter. For a continuous response with normal
errors, as in the models we’ve been considering in this chapter, the
2
dispersion parameter is just the variance σ , and the deviance
reduces to the residual sum of squares. As with the residual sum of
squares, the deviance can be made arbitrarily small by choosing an
interpolating solution. As in the linear model case, however, we
generally have a desire to keep the model as simple as possible. In the
linear case, we try to keep the number of parameters, that is, the
quantities estimated by the model coefficients, to a minimum.
Additive models are generally nonparametric, but we can define for
nonparametric models an equivalent number of parameters, which we
would also like to keep as small as possible.
302
Additive Models
The equivalent number of parameters for gam models is defined in
terms of degrees of freedom, or df. In fitting a parametric model, one
degree of freedom is required to estimate each parameter. For an
additive model with parametric terms, one degree of freedom is
required for each coefficient the term contributes to the model. Thus,
for example, consider a model with an intercept, one term fit as a
cubic polynomial, and one term fit as a quadratic polynomial. The
intercept term contributes one coefficient and requires one degree of
freedom, the cubic polynomial contributes three coefficients and thus
requires three degrees of freedom, and the quadratic polynomial
contributes two coefficients and requires two more degrees of
freedom. Thus, the entire model has six parameters, and uses six
degrees of freedom. A minimum of six observations is required to fit
such a model.
Models involving smoothed terms use both parametric and
nonparametric degrees of freedom; parametric degrees of freedom
result from fitting a linear (parametric) component for each smooth
term, while the nonparametric degrees of freedom result from fitting
the smooth after the linear part has been removed. The difference
between the number of observations and the degrees of freedom
required to fit the model is the residual degrees of freedom. Conversely,
the difference between the number of observations and the residual
degrees of freedom is the degrees of freedom required to fit the
model, which is the equivalent number of parameters for the model.
The summary method for gam objects shows the residual degrees of
freedom, the parametric and nonparametric degrees of freedom for
each term in the model, together with additional information:
> summary(ethanol.gam)
Call: gam(formula = NOx ~ lo(E, degree = 2))
Deviance Residuals:
Min
1Q
Median
3Q
Max
-0.6814987 -0.1882066 -0.01673293 0.1741648 0.8479226
(Dispersion Parameter for Gaussian family taken to be
0.1126477 )
Null Deviance: 111.6238 on 87 degrees of freedom
Residual Deviance: 9.137801 on 81.1184 degrees of freedom
303
Chapter 10 Regression and Smoothing for Continuous Response Data
Number of Local Scoring Iterations: 1
DF for Terms and F-values for Nonparametric Effects
(Intercept)
lo(E, degree = 2)
Df Npar Df
Npar F
Pr(F)
1
2
3.9 35.61398 1.110223e-16
The Deviance Residuals are, for Gaussian models, just the ordinary
residuals y i – μ̂ i . The Null Deviance is the deviance of the model
consisting solely of the intercept term.
The ethanol data set contains a third variable, C, which measures the
compression ratio of the engine. Figure 10.20 shows pairwise scatter
plots for the three variables.
• • ••
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Figure 10.20: Pairs plot of the ethanol data.
304
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Additive Models
Let’s incorporate C as a linear term in our additive model:
> ethanol2.gam <- gam(NOx ~ C + lo(E, degree = 2))
> ethanol2.gam
Call:
gam(formula = NOx ~ C + lo(E, degree = 2))
Degrees of Freedom: 88 total; 80.1184 Residual
Residual Deviance: 5.16751
> summary(ethanol2.gam)
Call: gam(formula = NOx ~ C + lo(E, degree = 2))
Deviance Residuals:
Min
1Q
Median
3Q
Max
-0.6113908 -0.166044 0.0268504 0.1585614 0.4871313
(Dispersion Parameter for Gaussian family taken to be
0.0644985 )
Null Deviance: 111.6238 on 87 degrees of freedom
Residual Deviance: 5.167513 on 80.1184 degrees of freedom
Number of Local Scoring Iterations: 1
DF for Terms and F-values for Nonparametric Effects
(Intercept)
C
lo(E, degree = 2)
Df Npar Df
Npar F Pr(F)
1
1
2
3.9 57.95895
0
305
Chapter 10 Regression and Smoothing for Continuous Response Data
We can use the anova function to compare this model with the
simpler model involving E only:
> anova(ethanol.gam, ethanol2.gam, test = "F")
Analysis of Deviance Table
Response: NOx
Terms Resid. Df Resid. Dev Test Df
1
lo(E, degree = 2)
81.1184
9.137801
2 C + lo(E, degree = 2)
80.1184
5.167513
+C 1
Deviance F Value
Pr(F)
1
2 3.970288 61.55632 1.607059e-11
The model involving C is clearly better, since the residual deviance is
cut almost in half by expending only one more degree of freedom.
Is the additive model sufficient? Additive models stumble when there
are interactions among the various terms. In the case of the ethanol
data, there is a significant interaction between C and E. In such cases, a
full local regression model, fit using the loess function, is often more
satisfactory. We discuss the ethanol data more thoroughly in Chapter
13, Local Regression Models.
306
More on Nonparametric Regression
MORE ON NONPARAMETRIC REGRESSION
The additive models fitted by gam in the section Additive Models are
simple examples of nonparametric regression. The machinery of
generalized additive models, proposed by Hastie and Tibshirani
(1990), is just one approach to such nonparametric models. S-PLUS
includes several other functions for performing nonparametric
regression, including the ace function, which implements the first
proposed technique for nonparametric regression—alternating
conditional expectations. S-PLUS also includes AVAS (Additive and
VAriance Stabilizing transformations) and projection pursuit
regression. This section describes these varieties of nonparametric
regression.
Alternating
Conditional
Expectations
Alternating conditional expectations or ace, is an intuitively appealing
technique introduced by Breiman and Friedman (1985). The idea is to
find nonlinear transformations θ ( y ), φ 1 ( x 1 ), φ 2 ( x 2 ), …, φ p ( x p ) of the
response y and predictors x 1, x 2, …, x p , respectively, such that the
additive model
θ ( y ) = φ1 ( x1 ) + φ2 ( x2 ) + … + φp ( xp ) + ε
(10.5)
is a good approximation for the data y i, x i1, …, x ip , i = 1, …, n . Let
y i, x 1, x 2, …, x p be random variables with joint distribution F , and let
expectations be taken with respect to F . Consider the goodness-of-fit
measure
p
E θ(y) –
2
2
 φk ( xk )
k=1
e = e (θ, φ 1 , ...,φ p) = -----------------------------------------------------2
Eθ ( y )
(10.6)
307
Chapter 10 Regression and Smoothing for Continuous Response Data
The measure e
2
is the fraction of variance not explained by
2
regressing θ ( y ) on φ ( x 1 ), …, φ ( x p ) . The data-based version of e is
p
n

 φ̂k ( xik )
θ̂ ( y i ) –
i=1
k=1
ê 2 = --------------------------------------------------------------n
 θ̂
2
(10.7)
( yi )
i=1
where θ̂ and the φ̂ j , estimates of θ and the φ j , are standardized so
n
that θ̂ ( y i ) and the φ̂ j ( x ij ) have mean zero:
 θ̂ ( yi )
= 0 and
i=1
n
 φ̂k ( xik )
= 0 , k = 1, …, p . For the usual linear regression case,
i=1
where
θ̂ ( y i ) = y i – y
and
φ̂ 1(x i1 – x 1) = ( x i1 – x 1 )β̂ 1 ,…, φ̂ p(x ip – x p) = ( x ip – x p )β̂ p
with β̂ 1 , …, β̂ p the least squares regression coefficients, we have
p
n

( yi – y ) –
 ( xik – xk )β̂k
i=1
k=1
2
ê LS = RSS
---------- ≡ ------------------------------------------------------------------------------n
SSY
 ( yi – y )2
i=1
308
More on Nonparametric Regression
The
squared
R2 = 1 –
2
e LS .
multiple
correlation
coefficient
is
given
by
The transformations θ̂ , φ̂ 1 , …, φ̂ p are chosen to
maximize the correlation between θ̂(y i) and φ̂(x i1) + … + φ̂(x ip) .
Although ace is a useful exploratory tool for determining which of the
response y and the predictors x 1, …, x p are in need of nonlinear
transformations and what type of transformation is needed, it can
produce anomalous results if errors ε and the φ̂ 1(x i) fail to satisfy the
independence and normality assumptions.
To illustrate the use of ace, construct an artificial data set with additive
errors
y i = e 1 + 2xi + ε i i , i = 1, 2, …, 200
with the ε i ’s being N(0,10) random variables (that is, normal random
variables with mean 0 and variance 10 ), independent of the x i ’s, with
the x i ’s being U(0, 2) random variables (that is, random variables
uniformly distributed on the interval from 0 to 2).
>
>
>
>
set.seed(14)
#set the seed to reproduce the example
x <- 2 * runif(200)
e <- rnorm(200, 0, sqrt(10))
y <- exp(1+2*x) + e
Now use ace:
> a <- ace(x, y)
Set graphics for 3 x 2 layout of plots:
> par(mfrow = c(3,2))
Make plots to do the following:
1. Examine original data
2. Examine transformation of y
3. Examine transformation of x
4. Check linearity of the fitted model
5. Check residuals versus the fit
309
Chapter 10 Regression and Smoothing for Continuous Response Data
The following S-PLUS commands provide the desired plots:
>
>
>
>
+
>
+
plot(x, y, sub = "Original Data")
plot(x, a$tx, sub = "Transformed x vs. x")
plot(y, a$ty, sub = "Transformed y vs. y")
plot(a$tx, a$ty, sub = "Transformed y vs.
Continue string: Transformed x")
plot(a$tx, a$ty - a$tx,
ylab = "residuals", sub = "Residuals vs. Fit")
These plots are displayed in Figure 10.21, where the transformed
values θ̂ ( y ) and φ̂ ( y ) are denoted by ty and tx , respectively. The
estimated transformation tx = φ̂ ( x ) seems close to exponential, and
except for the small bend at the lower left, the estimated
transformation ty = θ̂ ( y ) seems quite linear. The linearity of the
plot of ty versus tx reveals that a good additive model of the type
shown in Equation (10.5) has been achieved. Furthermore, the error
variance appears to be relatively constant, except at the very lefthand
end. The plot of residuals, r i = θ̂ ( y i ) – φ̂ ( x i ) versus the fit
tx = φ̂ ( x i ) gives a clearer confirmation of the behavior of the
residuals’ variance.
310
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More on Nonparametric Regression
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Residuals vs. Fit
Figure 10.21: ace example with additive errors .
311
Chapter 10 Regression and Smoothing for Continuous Response Data
Additivity and
Variance
Stabilization
The term AVAS stands for additivity and variance stabilizing
transformation. Like ace, the S-PLUS function avas tries to find
transformations θ ( y ) , φ 1 ( x 1 ), …, φ p ( x p ) such that
θ ( y ) = φ1 ( x1 ) + φ2 ( x2 ) + … + φp ( xp ) + ε
(10.8)
provides a good additive model approximation for the data
y i, x i1, …, x ip , i = 1, 2, …, n . However, avas differs from ace in that
it chooses θ ( y ) to achieve a special variance stabilizing feature. In
particular the goal of avas is to estimate transformations θ, φ 1, …, φ p
which have the properties
p
 φi ( xi )
E [ θ ( y ) x 1, …, x p ] =
(10.9)
i=1
and
p
var θ ( y )
 φi ( xi )
= constant
(10.10)
i=1
Here E [ z w ] is the conditional expectation of z given w . The
additivity structure of Equation (10.9) is the same as for ace, and
correspondingly the φ i ’s are calculated by the backfitting algorithm
φk ( xk ) = E θ ( y ) –
 φi ( xi ) xk
(10.11)
i≠k
cycling through k = 1, 2, …, p until convergence. The variance
stabilizing aspect comes from Equation (10.9). As in the case of ace,
estimates θ̂ ( y i ) and φ j ( x ik ) , k = 1, 2, …, p are computed to
approximately satisfy Equation (10.8) through Equation (10.11), with
the conditional expectations in Equation (10.8) and Equation (10.11)
estimated using the super smoother scatterplot smoother (see supsmu
312
More on Nonparametric Regression
function documentation). The equality in Equation (10.9) is
approximately achieved by estimating the classic stabilizing
transformation.
To illustrate the use of avas, construct an artificial data set with
additive errors
y i = e 1 + 2xi + ε i i , i = 1, ..., 200
with the ε i ’s being N(0, 10) random variables (that is, normal random
variables with mean 0 and variance 10), independent of the x i ’s, with
the x i ’s being U(0, 2) random variables (that is, random variables
uniformly distributed on the interval from 0 to 2).
>
>
>
>
set.seed(14)
#set the seed to reproduce the example
x <- runif(200, 0, 2)
e <- rnorm(200, 0, sqrt(10))
y <- exp(1+2*x) + e
Now use avas:
> a <- avas(x, y)
Set graphics for a 3 x 2 layout of plots:
> par(mfrow = c(3,2))
Make plots to: (1) examine original data; (2) examine transformation
of x ; (3) examine transformation of y ; (4) check linearity of the fitted
model; (5) check residuals versus the fit:
>
>
>
>
>
+
plot(x, y, sub = "Original data")
plot(x, a$tx, sub = "Transformed x vs. x")
plot(y, a$ty, sub = "Transformed y vs. y")
plot(a$tx, a$ty, sub = "Transformed y vs. Transformed x")
plot(a$tx, a$ty - a$tx, ylab = "Residuals",
sub = "Residuals vs. Fit")
These plots are displayed in Figure 10.22 where the transformed
values θ̂ ( y ) and φ̂ ( x ) are denoted by ty and tx , respectively. The
estimated transformation tx = φ̂ ( x ) seems close to exponential, and
the estimated transformation ty = θ̂ ( y ) seems linear. The plot of ty
313
Chapter 10 Regression and Smoothing for Continuous Response Data
versus tx reveals that a linear additive model holds; that is, we have
achieved a good additive approximation of the type in Equation
(10.8). In this plot the error variance appears to be relatively constant.
0.0
0.5
1.0
2
1
a$tx
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60
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80
100
120
140
The plot of residuals, r i = θ̂ ( y i ) – φ̂ ( x i ) , versus the fit tx = φ̂ ( x i )
gives further confirmation of this.
1.5
2.0
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Figure 10.22: avas example with additive errors.
314
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Key Properties
•
Suppose that the true additive model is
p
 φi ( xi ) + ε
0
0
θ (y) =
(10.12)
i=1
with ε independent of x 1, x 2, …, x p , and var(ε) = constant.
Then the iterative avas algorithm for Equation
(10.9) through Equation (10.11), described below for the data
versions of Equation (10.9) through Equation (10.11), yields a
(j)
(j)
(j)
θ , φ 1 , …, φ p
sequence of transformations
0
, which
0
0
converge to the true transformation θ , φ 1 , …, φ p as the
number of iterations j tends to infinity. Correspondingly, the
data-based version of this iteration yields a sequence of
(j)
(j)
(j)
transformations θ̂ , φ̂ 1 , …, φ̂ p , which, at convergence,
provide estimates θ̂, φ̂ 1, …, φ̂ p of the true model
0
0
0
transformations θ , φ 1 , …, φ p .
•
avas appears not to suffer from some of the anomalies of ace,
for example, not finding good estimates of a true additive
model (Equation (10.12)) when normality of ε and joint
normality of φ 1 ( x 1 ), …, φ p ( x p ) fail to hold. See the example
below.
•
avas
is a generalization of the Box and Cox (1964) maximumλ
likelihood procedure for choosing power transformation y of
the response. The function avas also generalizes the Box and
Tidwell (1962) procedure for choosing transformations of the
carriers x 1, x 2, …, x p , and is much more convenient than the
Box-Tidwell procedure. See also Weisberg (1985).
•
θ̂ ( y ) is a monotone transformation, since it is the integral of a
nonnegative function (see the section Further Details on page
316). This is important if one wants to predict y by inverting
θ̂ : monotone transformations are invertible, and hence we
315
Chapter 10 Regression and Smoothing for Continuous Response Data
p
 φ̂i ( xi )
can predict y with ŷ = θ̂ – 1
. This predictor
i=1
has no particular optimality property, but is simply one
straightforward way to get a prediction of y once an avas
model has been fit.
Further Details
Let
p
v ( u ) = VAR θ̂ ( y )
 φi ( xi ) = u
(10.13)
i=1
where θ̂ ( y ) is an arbitrary transformation of y , θ̂ ( y ) will be the
“previous” estimate of θ ( y ) in the overall iterative procedure
described below. Given the variance function v ( u ) , it is known that
p
VAR g ( θ̂ ( y ) )
 φi ( xi ) = u
i=1
will be constant if g is computed according to the rule
g(t) =
du
t ---------------1⁄ 2
c v (u)

(10.14)
for an appropriate constant c . See Box and Cox (1964).
The detailed steps in the population version of the avas algorithm are
as follows:
1. Initialize:
Set θ̂ ( y ) = ( y – Ey ) ⁄ ( VAR
1⁄2
y ) and backfit on x 1, …, x p
to get φ̂ 1, …, φ̂ p . See the description of ace for details of
backfitting.
316
More on Nonparametric Regression
2. Get new transformation of y:
•
Compute variance function:
p
v ( u ) = VAR θ̂ ( y )
 φ̂i ( xi ) = u
i=1
•
Compute variance stabilizing transformation:
g(t) =
•
du
t ---------------1⁄ 2
c v (u)

Set θ̂ ( y ) – g ( θ̂ ( y ) ) and standardize:
θ̂ ( y ) – Eθ̂ ( y )
θ̂ ( y ) – -------------------------------1⁄2
VAR θ̂ ( y )
3. Get new φ̂ i ’s:
Backfit θ̂ ( y ) on x 1, x 2, …, x p to obtain new estimates
φ̂ 1, …, φ̂ p .
4. Iterate steps 2 and 3 until
2
p
2
2
R = 1 – ê = 1 – E θ̂ ( y ) –
 φ̂i ( xi )
(10.15)
i=1
doesn’t change.
Of course the above algorithm is actually carried out using the sample
of data y i, x i1, …, x ip , i = 1, …, n , with expectations replaced by
sample averages, conditional expectations replaced by scatterplot
smoothing techniques and VAR’s replaced by sample variances.
In particular, super smoother is used in the backfitting step to obtain
φ̂ 1 ( x i1 ), …, φ̂ p ( x ip ) , ( i = 1 ), …, n . An estimate v̂ ( u ) of v ( u ) is
obtained
as
follows:
First
the
scatter
plot
of
317
Chapter 10 Regression and Smoothing for Continuous Response Data
2
p
2
logr i
p
 φ̂j ( xij )
= log θ̂ ( y i ) –
versus
j=1
ui =
 φ̂j ( xij )
is
j=1
smoothed using a running straight lines smoother. Then the result is
exponentiated. This gives an estimate v̂ ( u ) ≥ 0 , and v̂ ( u ) is
-10
truncated below at 10 to insure positivity and avoid dividing by
zero in the integral in Equation (10.14); the integration is carried out
using a trapezoidal rule.
Projection
Pursuit
Regression
The basic idea behind projection pursuit regression, ppreg, is as
follows. Let y and x = ( x 1, x 2, …, x p ) T denote the response and
explanatory vector, respectively. Suppose you have observations y i
and corresponding predictors x i = ( x i1, x i2, …x ip ) T , i = 1, 2, …, n .
Let a 1, a 2, … , denote p-dimensional unit vectors, as “direction”
1
vectors, and let y = --n
n
 yi . The ppreg function allows you to find
i=1
M = M 0 , direction vectors a 1, a 2, …, a M
transformations φ 1, φ 2, …, φ M
0
0
and good nonlinear
such that
M0
y≈y+

T
βm φm ( am x )
(10.16)
m=1
provides a “good” model for the data y i , x i , i = 1, 2, …, n . The
“projection” part of the term projection pursuit regression indicates
that the carrier vector x is projected onto the direction vectors
a 1, a 2, …, a M
T
0
to get the lengths a x of the projections, and the
“pursuit” part indicates that an optimization technique is used to find
“good” direction vectors a 1, a 2, …, a M .
0
318
More on Nonparametric Regression
More formally, y and x are presumed to satisfy the conditional
expectation model
M0
E [ y x 1 , x 2 , ..., x p ] = μ y +

T
βm φm ( am x )
(10.17)
m=1
where μ y = E ( y ) , and the φ m have been standardized to have mean
zero and unity variance:
T
Eφ m ( a m x ) = 0,
2
T
Eφ m ( a m x ) = 0, m = 1, ..., M 0
The observations y i , x i = ( x i1, …, x ip )
T
(10.18)
i = 1, …, n , are assumed to
be independent and identically distributed random variables like y
and x , that is, they satisfy the model in Equation (10.17).
The true model parameters β m , φ m , a m , m = 1, …, M 0 in Equation
(10.17) minimize the mean squared error
2
M0
E y – μy –

T
βm φm ( am x )
(10.19)
m=1
over all possible β m , φ m , and a m .
Equation (10.17) includes the additive ace models under the
θ ( y ) = y . This occurs when
M0 = p
restriction
and
T
T
T
a 1 = ( 1, 0, …, 0 ) , a 2 = ( 0, 1, 0, …, 0 ) , a p = ( 0, 0, …, 0, 1 ) , and
the β m ’s are absorbed into the φ m ’s. Furthermore, the ordinary linear
model is obtained when M 0 = 1 , assuming the predictors x are
independent
with
mean
0
and
variance
1.
Then
T
2
2
a = ( b 1 , ..., b p ) ⁄ b 1 + … + b p ,
β1 =
φ1 ( t ) = t ,
and
2
2
b 1 + … + b p , where the b j are the regression coefficients.
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Chapter 10 Regression and Smoothing for Continuous Response Data
The projection pursuit model in Equation (10.17) includes the
possibility of having interactions between the explanatory variables.
For example, suppose that
E [ y x 1, x 2 ] = x 1 x 2
(10.20)
This is described by Equation (10.17) with μ y = 0 , M 0 = 2 ,
1
β 1 = β 2 = --- ,
4
T
a 1 = ( 1, 1 ) ,
T
a 2 = ( 1, – 1 ) ,
2
φ1 ( t ) = t ,
and
2
φ 2 ( t ) = – t . For then
2
T
2
2
φ 1 ( a 1 x ) = ( x 1 + x 2 ) = x 1 + 2x 1 x 2 + x 2
2
T
2
2
φ 2 ( a 2 x ) = – ( x 1 – x 2 ) = – x 1 + 2x 1 x 2 – x 2
so that
2

T
βm φm ( a x ) = x1 x2 .
m=1
Neither ace nor avas is able to model interactions. It is this ability to
pick up interactions that led to the invention of projection pursuit
regression by Friedman and Stuetzle (1981), and it is what makes
ppreg a useful complement to ace and avas.
The two variable interactions shown above can be used to illustrate
the ppreg function. The two predictors, x 1 and x 2 are generated as
uniform random variates on the interval -1 to 1. The response, y , is
the product of x 1 and x 2 plus a normal error with mean zero and
variance 0.04.
>
>
>
>
>
>
320
set.seed(14)
#set the seed to reproduce the example
x1 <- runif(400, -1, 1)
x2 <- runif(400, -1, 1)
eps <- rnorm(400, 0, 0.2)
y <- x1 * x2 + eps
x <- cbind(x1, x2)
More on Nonparametric Regression
Now run the projection pursuit regression with max.term set at 3,
min.term set at 2 and with the residuals returned in the ypred
component (the default if xpred is omitted).
> p <- ppreg(x, y, 2, 3)
Make plots (shown in Figure 10.23) to examine the results of the
regression.
>
>
>
>
+
>
+
>
>
par(mfrow = c(3, 2))
plot(x1, y, sub = "Y vs X1")
plot(x2, y, sub = "Y vs X2")
plot(p$z[,1], p$zhat[,1], sub = "1st Term:
Continue string: Smooth vs Projection Values z1")
plot(p$z[,2], p$zhat[,2], sub = "2nd Term:
Continue string: Smooth vs Projection Values z2")
plot(y-p$ypred, y, sub = "Response vs Fit")
plot(y-p$ypred, p$ypred, sub = "Residuals vs Fit")
The first two plots show the response plotted against each of the
predictors. It is difficult to hypothesize a function form for the
relationship when looking at these plots. The next two plots show the
resulting smooth functions from the regression plotted against their
respective projection of the carrier variables. Both the plots have a
quadratic shape with one being positive and the other negative, the
expected result for this type of interaction function. The fifth plot
shows clearly a linear relationship between the response and the fitted
values. The residuals shown in the last plot do not display any
unusual structure.
321
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Residuals vs Fit
Figure 10.23: Projection pursuit example.
Further Details
The forward stepwise procedure
An initial M-term model of the form given by the right-hand side of
Equation (10.17), with the constraints of Equation (10.18) and M > M 0 ,
is estimated by a forward stepwise procedure, as described by
Friedman and Stuetzle (1981).
322
More on Nonparametric Regression
T
First, a trial direction a 1 is used to compute the values z i1 = a 1 x i ,
1
T
i = 1, …, n , where x i = ( x i1, …, x ip ) . Then, with ỹ i = y i – y , you
have available a scatter plot of data ( ỹ i, z i1 ) , i = 1, …, n , which may
be smoothed to obtain an estimate φ̂ 1 ( z i1 ) of the conditional
expectation
E [ y z 1 ] = E [ y i z i1 ]
for the identically distributed
T
random variables y i , z i1 = a 1 x i . Super Smoother is used for this
purpose; see the documentation for supsmu. This φ̂ 1 depends upon
the trial direction vector a 1 , so we write φ 1 = φ 1, a . Now a 1 is
1
varied to minimize the weighted sum of squares,
n
 wi [ yi – φ̂1, a ( zi1 ) ]
2
(10.21)
1
i=1
where for each a1 in the optimization procedure, a new φ̂ 1, a 1 is
computed using super smoother. The weights w i are user-specified,
with the default being all weights unitary: w i ≡ 1 . The final results of
this optimization will be denoted simply â 1 and φ̂ 1 , where φ̂ 1 has
been standardized according to Equation (10.18) and the
corresponding
value
β̂ 1
is
computed.
We
now
have
the
T
approximation y i ≈ y + β̂ 1 φ̂ 1 ( â 1 x i ) , where i = 1, …, n .
(2)
Next we treat y i
= y i – y – β̂ 1 φ̂ 1 ( z i1 ) as the response, where now
T
T
z i1 = â 1 x i , and fit a second term β̂ 2 φ̂ 2 ( z i2 ) , where z i2 = â 2 x i , to
this modified response, in exactly the same manner that we fitted
T
(1)
(2)
β̂ 1 φ̂ 1 ( â 1 x i ) to y i . This gives the approximation y i
≈ β̂ 2 φ̂ 2 ( z i2 )
or y i ≈ y + β̂ 1 φ̂ 1 ( z i1 ) + β̂ 2 φ̂ 2 ( z i2 ) .
323
Chapter 10 Regression and Smoothing for Continuous Response Data
Continuing in this fashion we arrive at the forward stepwise estimated
model
M
yi ≈ y +

β̂ m φ̂ m ( z im ) , i = 1, …, n
(10.22)
m=1
T
where z im = â m x i , m = 1, …, M .
The backward stepwise procedure
Having fit the M term model in Equation (10.22) in a forward stepwise
manner,
ppreg
fits
all
models
of
decreasing
order
m = M – 1, M – 2, …, M min , where M and M min are user-specified.
For each term in the model, the weighted sum of squared residuals
n
SSR ( m ) =
 wi
i=1
2
m
yi – y –
 βl φl ( al xi )
T
(10.23)
l=1
is minimized through the choice of β l , a 1 , φ l , l = 1, …, m . The
initial values for these parameters, used by the optimization algorithm
which minimizes Equation (10.23), are the solution values for the m
most important out of m + 1 terms in the previous order m + 1 model.
Here importance is measured by
I l = β̂ l
l = 1, …, m + 1
(10.24)
where β̂ l are the optimal coefficients for the m + 1 term model,
m = M – 1, M – 2, …, M min .
Model selection strategy
In order to determine a “good” number of terms M 0 for the ppreg
model, proceed as follows. First, run ppreg with M min = 1 and M set
at a value large enough for the data analysis problem at hand. For a
324
More on Nonparametric Regression
relatively small number of variables p , say p ≤ 4 , you might well
choose M ≥ p . For large p , you would probably choose M < p ,
hoping for a parsimonious representation.
For each order m, 1 ≤ m ≤ M , ppreg will evaluate the fraction of
unexplained variance
SSR ( m )
2
e ( m ) = ----------------------------------n
2
 wi [ yi – y ]
i=1
 wi
2
m
n
yi – y –
i=1
 β̂l φ̂l ( âl xi )
T
l=1
= ---------------------------------------------------------------------------n
 wi [ yi – y ]
2
i=1
2
A plot of e ( m ) versus m which is decreasing in m may suggest a
2
good choice of m = M 0 . Often e ( m ) decreases relatively rapidly
2
when m is smaller than a good model order M 0 (as the (bias)
component of prediction mean-squared error is decreasing rapidly),
and then tend to flatten out and decrease more slowly for m larger
than M 0 . You can choose M 0 with this in mind.
The current version of ppreg has the feature that when fitting models
having m = M min, M min + 1, …, M terms, all of the values β̂ l , â l ,
T
φ̂ l ( z il ) , z il = â l x i , i = 1, …, n , l = 1, …, m , and e 2 ( m ) are
returned for m = M min , whereas all of these except the smoothed
values φ̂ l ( z il ) and their corresponding arguments z il are returned for
all m = M min, …, M . This feature conserves storage requirements. As
a consequence, you must run ppreg twice for m = M min, …, M , using
two different values of M min . The first time M min = 1 is used in order
325
Chapter 10 Regression and Smoothing for Continuous Response Data
2
to examine e ( m ) , m = 1, …, M (among other things) and choose a
good order M 0 . The second time M min = M 0 is used in order obtain
all output, including φ̂ l ( z il ) and z il values.
Multivariate response
All of the preceding discussion has been concentrated on the case of a
single response y , with observed values y 1, …, y n . In fact, ppreg is
designed to handle multivariate responses y 1, …, y q with observed
values y ij , i = 1, …, n , j = 1, …, q . For this case, ppreg allows you
to fit a good model
M0
yj ≈ yj +

T
β̂ mj φ̂ m ( â m x )
(10.25)
m=1
by minimizing the multivariate response weighted sum of squared
residuals
q
SSR q ( m ) =
n
 Wj  wi
j=1
2
m
y ij – y j –
i=1
 β̂lj φ̂l ( al xi )
T
(10.26)
l=1
and choosing a good value m = M 0 . Here the W j are user-specified
response weights (with default W j ≡ 1 ), the w i are user-specified
1
observation weights (with default w i ≡ 1 ), and y j = --n
n
 yij . Note
i=1
that a single set of φ̂ m ’s is used for all responses y ij , j = 1, …, q ,
whereas the different behavior of the different responses is modeled
by different linear combinations of the φ̂ m ’s by virtue of the different
T
sets of coefficients β̂ j = ( β̂ ij , ..., β̂ mj ) , j = 1, …, q .
326
More on Nonparametric Regression
The ppreg procedure for the multivariate response case is similar to
the single response case. For given values of M min and M , ppreg first
does a forward stepwise fitting starting with a single term ( m = 1 ) ,
and ending up with M terms, followed by a backward stepwise
procedure stopping with an M min -term model. When passing from an
m + 1 term model to an m -term model in the multivariate response
case, the relative importance of a term is given by
q
Il =
 Wj β̂jl
l = 1, …, m + 1
j=1
The most important terms are the ones with the largest I l , and the
corresponding values of β̂ jl , φ̂ l , and a l are used as initial conditions
in the minimization of SSR q ( m ) . Good model selection; that is, a
good choice m = M 0 , can be made just as in the case of a single
response, namely, through examination of the multivariate response
fraction of unexplained variation
SSR q ( m )
2
e q ( m ) = -----------------------------------------------------q
n
 Wj  wi [ yij – yj ]
j=1
2
(10.27)
i=1
by first using ppreg with M min = 1 and a suitably large M . Then
ppreg
is run again with M min = M 0 and the same large M .
327
Chapter 10 Regression and Smoothing for Continuous Response Data
REFERENCES
Box, G.E.P. & Tidwell, P.W. (1962). Transformations of independent
variables. Technometrics 4:531-550.
Box, G.E.P. & Cox, D.R. (1964). An analysis of transformations (with
discussion). Journal of the Royal Statistical Society, Series B 26:211-246.
Breiman, L. & Friedman, J.H. (1985). Estimating optimal
transformations for multiple regression and correlation (with
discussion). Journal of the American Statistical Association 80:580-619.
Durbin, J. and Watson, G.S. (1950). Testing for serial correlation in
least squares regression I. Biometrika 37: 409-428.
Friedman, J.H. & Stuetzle, W. (1981). Projection pursuit regression.
Journal of the American Statistical Association 76:817-823.
Friedman, J.H. (1984). SMART Users Guide, no. 1. Stanford, CA:
Laboratory for Computational Statistics, Dept. of Statistics, Stanford
University .
Friedman, J.H. (1984). A Variable Span Smoother, Tech. Rept. 5. Stanford,
CA: Laboratory for Computational Statistics, Dept. of Statistics,
Stanford University.
Friedman, J.H. (1985). Classification and Multiple Regression Through
Projection Pursuit, Tech. Rept. 12. Stanford, CADept. of Statistics,
Stanford University.
Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., & Stahel, W.A.
(1986). Robust Statistics: The Approach Based on Influence Functions. New
York: John Wiley & Sons, Inc.
Hastie, T. & Tibshirani, R. (1990). Generalized Additive Models. London:
Chapman and Hall.
Heiberger, R.M. & Becker, R.A. (1992). Design of an S function for
robust regression using iteratively reweighted least squares. Journal of
Computational and Graphical Statistics 1:181-196.
Huber, P.J. (1973). Robust regression: Asymptotics, conjectures, and
Monte Carlo. Annals of Statistics 1:799-821.
Huber, P.J. (1981). Robust Statistics. New York: John Wiley & Sons, Inc.
328
References
Millard, S.P. and Neerchal, N.K. (2001). Environmental Statistics with
S-PLUS. Boca Raton, Florida: CRC Press LLC.
Rousseeuw, P.J. (1984). Least median of squares regression. Journal of
the American Statistical Association 79:871-888.
Rousseeuw, P.J. & Leroy, A.M. (1987). Robust Regression and Outlier
Detection. New York: John Wiley & Sons, Inc.
Silverman, B.W. (1986). Density Estimation for Statistics and Data
Analysis. London: Chapman and Hall.
Tibshirani, R. (1988). Estimating transformations for regression via
additivity and variance stabilization. Journal of the American Statistical
Association 83:394-405.
Watson, G.S. (1966). Smooth regression analysis. Sankhya, Series A
26:359-378.
Weisberg, S. (1985). Applied Linear Regression (2nd ed.). New York:
John Wiley & Sons, Inc.
329
Chapter 10 Regression and Smoothing for Continuous Response Data
330
ROBUST REGRESSION
11
Introduction
333
Overview of the Robust MM Regression Method
Key Robustness Features of the Method
The Essence of the Method: A Special M-Estimate
The lmRobMM Function
Comparison of Least Squares and Robust Fits
Robust Model Selection
334
334
334
335
336
336
Computing Robust Fits
Example: The oilcity Data
Least Squares and Robust Fits
Least Squares and Robust Model Objects
337
337
338
340
Visualizing and Summarizing Robust Fits
The plot Function
The summary Function
341
341
343
Comparing Least Squares and Robust Fits
Comparison Objects for Least Squares and Robust
Fits
Visualizing Comparison Objects
Statistical Inference from Comparison Objects
345
345
346
347
Robust Model Selection
Robust F and Wald Tests
Robust FPE Criterion
349
349
351
Controlling Options for Robust Regression
Efficiency at Gaussian Model
Alternative Loss Function
Confidence Level of Bias Test
Resampling Algorithms
353
353
353
355
357
331
Chapter 11 Robust Regression
332
Theoretical Details
Initial Estimates
Loss Functions
Robust R-Squared
Robust Deviance
Robust F Test
Robust Wald Test
Robust FPE (RFPE)
Breakdown Points
359
359
360
362
363
364
364
364
365
Other Robust Regression Techniques
Least Trimmed Squares Regression
Least Median Squares Regression
Least Absolute Deviation Regression
M-Estimates of Regression
Comparison of Least Squares, Least Trimmed
Squares, and M-Estimates
367
367
370
370
372
References
378
375
Introduction
INTRODUCTION
Robust regression techniques are an important complement to classical
least squares regression. Robust techniques provide answers similar to
least squares regression when the data are linear and have normally
distributed errors. The results differ significantly, however, when the
errors do not satisfy the normality conditions or when the data
contain significant outliers. TIBCO Spotfire S+ includes several
robust regression techniques; this chapter focuses on robust MM
regression. This is the technique we officially recommend, as it
provides both high-quality estimates and a wealth of diagnostic and
inference tools.
Other robust regression techniques available in Spotfire S+ are least
trimmed squares (LTS) regression, least median squares (LMS) regression,
least absolute deviations (L1) regression, and M-estimates of regression.
These are discussed briefly in the section Other Robust Regression
Techniques.
Spotfire S+ also includes the S+MissingData library, which extends
the statistical modeling capabilities of Spotfire S+ to support modelbased missing data methods. You can load this library into your
Spotfire S+ session by either typing library(missing) in the
Commands window, or if you are using the Windows version,
choose File Load Library from the main menu. For more
information, see the file library/missing/missing.pdf in your
Spotfire S+ program group or if you are on Windows, select Help Online Manuals Missing Data Analysis Library.
333
Chapter 11 Robust Regression
OVERVIEW OF THE ROBUST MM REGRESSION METHOD
This section provides an overview of the Spotfire S+ tools you can
use to compute a modern linear regression model with robust MM
regression. The tools we discuss include both inference for
coefficients and model selection.
Key
Robustness
Features of the
Method
The robust MM method has the following general features:
•
In data-oriented terms, a robust MM fit is minimally
influenced by outliers in the independent variables space, in
the response (dependent variable) space, or in both.
•
In probability-oriented terms, the robust fit minimizes the
maximum possible bias of the coefficients estimates. The bias
minimized is due to a non-Gaussian contamination model
that generates outliers, subject to achieving a desired (large
sample size) efficiency for the coefficient estimates when the
data has a Gaussian distribution.
•
Statistical inference tools produced by the robust fit are based
on large sample size approximations for such quantities as
standard errors and “t-statistics” of coefficients, R-squared
values, etc.
For further information, see the section Theoretical Details.
The Essence of
the Method: A
Special
M-Estimate
A robust MM model has the form
T
y i = x i β + ε i , i = 1, ..., n
where y i is the scalar response associated with ith observation, x i is a
p-dimensional
vector
of
independent
predictor
values,
β = ( β 1, β 2, …, β p ) represents the coefficients, and the ε i are errors.
S-PLUS computes a robust M-estimate β̂ that minimizes the
objective function
n

i=1
334
T
 y i – x i β
ρ  ------------------- .
ŝ 

Overview of the Robust MM Regression Method
Here, ŝ is a robust scale estimate for the residuals and ρ is a
symmetric, bounded loss function. Loss functions are described in the
section Theoretical Details, and two possibilities are shown
graphically in Figure 11.5. Alternatively, β̂ is a solution of the
estimating equation
n

i=1
T
 y i – x i β
x i ψ  ------------------- = 0 ,
ŝ 

where ψ = ρ′ is a redescending (nonmonotonic) function.
Since ρ is bounded, it is nonconvex, and the minimization algorithm
can therefore produce many local minima; correspondingly, the
estimating equation above can have multiple solutions. S-PLUS deals
0
with this issue by computing highly robust initial estimates β and s
0
that have breakdown points of 0.5. The final estimate β̂ is then the
0
local minimum of the objective function that is nearest to β . We
refer to an M-estimate computed in this way as an MM-estimate, a
term first introduced by Yohai (1987). The initial values are computed
using the S-estimate approach described in the section Theoretical
Details, and are thus referred to as initial S-estimates.
Note
The theory for the robust MM method is based on Rousseeuw and Yohai (1984), Yohai, Stahel,
and Zamar (1991), and Yohai and Zamar (1998). The code is based on Alfio Marazzi’s ROBETH
library, with additional work by R. Douglas Martin, Douglas B. Clarkson, and Jeffrey Wang of
Insightful Corporation. The code development was partially supported by an SBIR Phase I grant
entitled “Usable Robust Methods,” funded by the National Institutes of Health.
The lmRobMM
Function
The S-PLUS function that computes robust MM regression estimates
is called lmRobMM. The model object returned by lmRobMM is almost
identical in structure to a least-squares model object returned by lm;
that is, you obtain most of the same fitted model components from
the two functions, such as standard errors and t statistics for
coefficients. Examples using the lmRobMM function are given in the
section Computing Robust Fits.
335
Chapter 11 Robust Regression
Comparison of
Least Squares
and Robust
Fits
S-PLUS includes a special function compare.fits that is specifically
designed to facilitate the comparison of least squares fits and robust
fits for a linear regression model. Objects returned by compare.fits
can be printed, summarized, and plotted, resulting in tabular and
graphical displays that make it easy for you to compare the two types
of fits. Examples using the compare.fits function are given in the
section Comparing Least Squares and Robust Fits.
Robust Model
Selection
It is not enough to use a robust regression method when you try to
decide which of several alternative models to use. You also need a
robust model selection criterion. To this end, you might use one of the
following three tests: the robust F-test, the robust Wald test, and the
robust FPE (RFPE) criterion. See the section Robust Model Selection
for further details.
336
Computing Robust Fits
COMPUTING ROBUST FITS
Example: The
oilcity Data
The S-PLUS data frame oilcity contains monthly excess returns on
the stocks of Oil City Petroleum, Inc., from April 1979 to December
1989. The data set also contains the monthly excess returns of the
market for the same time period. Returns are defined as the relative
change in the stock price over a one-month interval, and excess returns
are computed relative to the monthly return of a 90-day U.S. Treasury
bill at the risk-free rate.
A scatter plot of the oilcity data, displayed in Figure 11.1, shows that
there is one large outlier in the data. The command below produces
the graph.
> plot(oilcity$Market, oilcity$Oil,
+ xlab = "Market Returns", ylab = "Oil City Returns")
3
2
1
Oil City Returns
4
5
•
•
0
•
•
••
•
•
•
••
• • •• • •
•
•
•
•
•
•
•
•
• ••
•
•
•
••• • • • •
•
•
-0.2
•
•
••
• ••• ••••• • ••• ••• • •••••• ••
• • ••• • • •• ••••••• • • •
• ••
• ••• ••
•
•
-0.1
•
•
•
•• •
•
• ••• •• ••
• ••
••
•
•
•
0.0
Market Returns
Figure 11.1: Scatter plot of the oilcity data.
337
Chapter 11 Robust Regression
Normally, financial economists use least squares to fit a straight line
predicting a particular stock return from the market return. The
estimated coefficient of the market return is called the beta, and it
measures the riskiness of the stock in terms of standard deviation and
expected returns. Large beta values indicate that the stock is risky
compared to the market, but also indicate that the expected returns
from the stock are large.
Least Squares
and Robust
Fits
We first fit a least squares model to the oilcity data as follows:
> oil.ls <- lm(Oil ~ Market, data = oilcity)
> oil.ls
Call:
lm(formula = Oil ~ Market, data = oilcity)
Coefficients:
(Intercept) Market
0.1474486 2.85674
Degrees of freedom: 129 total; 127 residual
Residual standard error: 0.4866656
To obtain a robust fit, you can use the lmRobMM function with the same
linear model:
> oil.robust <- lmRobMM(Oil ~ Market, data = oilcity)
> oil.robust
Final M-estimates.
Call:
lmRobMM(formula = Oil ~ Market, data = oilcity)
Coefficients:
(Intercept)
Market
-0.08395796 0.8288795
Degrees of freedom: 129 total; 127 residual
Residual scale estimate: 0.1446265
338
Computing Robust Fits
From the output of the two models, we see that the robust beta
estimate is dramatically different than the least squares estimate. The
least squares method gives a beta of 2.857, which implies that the
stock is 2.857 times as volatile as the market and has about 2.857
times the expected return. The robust MM method gives a beta of
0.829, which implies that the stock is somewhat less volatile and has a
lower expected return. Also, note that the robust scale estimate is
0.14, whereas the least-squares scale estimate is 0.49. The leastsquares scale estimate is based on the sum of squared residuals, and is
thus considerably inflated by the presence of outliers in data.
You can see both models in the same graph with the following set of
commands:
>
+
>
>
>
plot(oilcity$Market, oilcity$Oil,
xlab = "Market Returns", ylab = "Oil City Returns")
abline(coef(oil.robust), lty = 1)
abline(coef(oil.ls), lty = 2)
legend(locator(1), c("oil.robust","oil.ls"), lty=1:2)
The result is displayed in Figure 11.2. In the legend command, the
locator function allows you to interactively choose a location for the
key.
4
5
•
2
1
Oil City Returns
3
oil.robust
oil.ls
•
0
•
•
•
•
•
•
•
•
•
-0.2
•
•
•
• ••
••
•
•
•
•• • •
•• •• • • • • • • ••• •• •••• •• •
• •• • •• • • ••
•
• • • • ••
• •
•
•
•
•
•
•
•
• •
•
•
•
••
•• ••• • ••••• • • • • • •• ••
•••• • • •
•
•
• ••
•••• •••
•
-0.1
•
••
•
•
0.0
Market Returns
Figure 11.2: Least squares and robust fits of the oilcity data.
339
Chapter 11 Robust Regression
Least Squares
and Robust
Model Objects
Objects returned by the lm function are of class "lm":
> data.class(oil.ls)
[1] "lm"
On the other hand, objects returned by lmRobMM are of class
"lmRobMM":
> data.class(oil.robust)
[1] "lmRobMM"
As with objects of class "lm", you can easily visualize, print and
summarize objects of class "lmRobMM" using the generic functions
plot, print and summary. With the names function, you can see that
lmRobMM objects contain many of the same components as lm objects,
in addition to components that are needed for the robust fitting
algorithm:
> names(oil.ls)
[1]
[4]
[7]
[10]
"coefficients"
"effects"
"assign"
"terms"
"residuals"
"R"
"df.residual"
"call"
"fitted.values"
"rank"
"contrasts"
> names(oil.robust)
[1]
[3]
[5]
[7]
[9]
[11]
[13]
[15]
[17]
[19]
[21]
[23]
[25]
[27]
[29]
340
"coefficients"
"scale"
"cov"
"dev"
"residuals"
"r.squared"
"M.weights"
"fitted.values"
"mm.bias"
"iter.refinement"
"iter.final.scale"
"rank"
"robust.control"
"assign"
"terms"
"T.coefficients"
"T.scale"
"T.cov"
"T.dev"
"T.residuals"
"T.r.squared"
"T.M.weights"
"T.fitted.values"
"ls.bias"
"iter.final.coef"
"df.residual"
"est"
"qr"
"contrasts"
"call"
Visualizing and Summarizing Robust Fits
VISUALIZING AND SUMMARIZING ROBUST FITS
The plot
Function
For simple linear regression models, like the ones computed for the
oilcity data in the previous section, it is easy to see outliers in a
scatter plot. In multiple regression models, however, determining
whether there are outliers in the data is not as straightforward.
Nevertheless, Spotfire S+ makes it easy for you to visualize outliers in
a multiple regression. To illustrate this point, we use the well-known
“stack loss” data, which has been analyzed by a large number of
statisticians.
The stack loss data contains the percent loss of ammonia during 21
consecutive days at an oxidation plant. Ammonia is lost when it is
dissolved in water to produce nitric acid. Three variables may
influence the loss of ammonia during this process: air flow, water
temperature, and acid concentration. The stack loss response data is
contained in the vector stack.loss, and the three independent
variables are contained in the matrix stack.x. The following
command combines the response and predictor variables into a data
frame named stack.df:
> stack.df <- data.frame(Loss = stack.loss, stack.x)
To compute a least squares fit for stack.df, use the lm function as
follows:
> stack.ls <- lm(Loss ~
+ Air.Flow + Water.Temp + Acid.Conc., data = stack.df)
To compute a robust fit for the same linear model, use:
> stack.robust <- lmRobMM(Loss ~
+ Air.Flow + Water.Temp + Acid.Conc., data = stack.df)
We now use the plot function to visualize the robust fit, as illustrated
in the command below. Note that plots of Cook's distance values are
not currently available for robust linear model objects.
341
Chapter 11 Robust Regression
> plot(stack.robust, ask = T)
Make a plot selection (or 0 to exit):
1: plot: All
2: plot: Residuals vs Fitted Values
3: plot: Sqrt of abs(Residuals) vs Fitted Values
4: plot: Response vs Fitted Values
5: plot: Normal QQplot of Residuals
6: plot: r-f spread plot
Selection:
You can compare plots of the residuals versus fitted values for
stack.ls and stack.robust using the following commands:
>
>
>
>
>
par(mfrow = c(1,2))
plot(stack.ls, which.plots = 1)
title(main = "LS Fit")
plot(stack.robust, which.plots = 1)
title(main = "Robust Fit")
Figure 11.3 shows the two plots. The robust fit pushes the outliers
away from the majority of the data, so that you can identify them
more easily.
Robust Fit
6
LS Fit
4
4
3
0
Residuals
0
-6
-5
-4
-2
Residuals
2
5
4
3
21
10
20
21
30
Fitted : Air.Flow + Water.Temp + Acid.Conc.
40
10
15
20
Figure 11.3: Plots of the residuals vs. fitted values for the stack.loss data.
342
25
30
Fitted : Air.Flow + Water.Temp + Acid.Conc.
35
Visualizing and Summarizing Robust Fits
The summary
Function
The summary function for lmRobMM objects provides the usual types of
inference tools, including t-values and p-values. In addition, it also
provides some information specific to robust models, such as tests for
bias. For example, the command below displays a detailed summary
of the oil.robust object computed in the section Least Squares and
Robust Fits.
> summary(oil.robust)
Final M-estimates.
Call: lmRobMM(formula = Oil ~ Market, data = oilcity)
Residuals:
Min
1Q Median
3Q
Max
-0.4566 -0.08875 0.03082 0.1031 5.218
Coefficients:
Value Std. Error t value Pr(>|t|)
(Intercept) -0.0840 0.0281
-2.9931 0.0033
Market 0.8289 0.2834
2.9247 0.0041
Residual scale estimate: 0.1446 on 127 degrees of freedom
Proportion of variation in response explained by model:
0.0526
Test for Bias
Statistics
P-value
M-estimate
2.16 0.3396400
LS-estimate
22.39 0.0000138
Correlation of Coefficients:
(Intercept)
Market 0.8169
The seed parameter is : 1313
343
Chapter 11 Robust Regression
Note that the standard errors, t-values, and p-values are displayed in
the same format as they are in lm summaries. The standard errors for
lmRobMM objects are computed from the robust covariance matrix of
the estimates. For technical details regarding the computation of
robust covariance matrices, refer to Yohai, Stahel, and Zamar (1991).
The summary method for lmRobMM provides another piece of useful
information: the Proportion of variation in response explained
by model,
2
usually known as the R value. S-PLUS calculates a robust
version of this statistic, as described in the section Theoretical Details.
Finally, there is a Test for Bias section in the summary output for
objects. This section provides the test statistics of bias for
both the final M-estimates and the least squares (LS) estimates against
the initial S-estimates. In the oil.robust example, the test for bias of
the final M-estimates yields a p-value of 0.33, which suggests that the
bias of the final M-estimates relative to the initial S-estimates is not
significant at the default 0.90 level. This is why the final M-estimates
are reported in the summary output instead of the initial estimates. The
test for bias of the least squares estimates relative to the S-estimates
yields a p-value of 0, which indicates that the LS estimate is highly
biased. This suggests that the robust MM model is preferred over the
least squares model. For technical details regarding the calculations of
the tests for bias, see Yohai, Zamar, and Stahel (1991).
lmRobMM
344
Comparing Least Squares and Robust Fits
COMPARING LEAST SQUARES AND ROBUST FITS
Comparison
Objects for
Least Squares
and Robust
Fits
In the section The plot Function, we compared plots of the residuals
versus fitted values for least squares and robust MM fits of the same
linear model. You might have noticed that the plots in Figure 11.3 do
not have the same vertical scale. S-PLUS provides the function
compare.fits for comparing different fits of a given model. Objects
returned by this function are of class "compare.fits", which has
appropriate plot, print, and summary methods. The plot method
allows you to view different fits on the same scale for easy visual
comparison. In addition, the print and summary methods return
tabular displays that are conveniently aligned for comparison of
inference results.
For example, to compare the oil.ls and oil.robust fits, create a
comparison object with the following command:
> oil.cmpr <- compare.fits(oil.ls, oil.robust)
> oil.cmpr
Calls:
oil.ls lm(formula = Oil ~ Market, data = oilcity)
oil.robust lmRobMM(formula = Oil ~ Market, data = oilcity)
Coefficients:
oil.ls oil.robust
(Intercept) 0.1474
-0.08396
Market 2.8567
0.82888
Residual Scale Estimates:
oil.ls : 0.4867 on 127 degrees of freedom
oil.robust : 0.1446 on 127 degrees of freedom
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Chapter 11 Robust Regression
Visualizing
Comparison
Objects
You can easily plot a compare.fits object to obtain a visual
comparison of least squares and robust fits. To plot the oil.cmpr
object that we created in the previous section, use the command:
> plot(oil.cmpr)
Make a plot selection (or 0 to exit):
1: All
2: Normal QQ-Plots of Residuals
3: Estimated Densities of Residuals
4: Residuals vs Fitted Values
5: Response vs Fitted Values
Selection:
The normal qqplot and estimated densities for oil.cmpr are shown in
Figure 11.4, as generated by the following commands:
> par(mfrow = c(2,1))
> plot(oil.cmpr, which.plot = 1)
> plot(oil.cmpr, which.plot = 2)
The densities of residuals are computed using a kernel density
estimator. In a “good” model fit, the probability density estimates for
the residuals are centered at zero and are as narrow as possible.
Figure 11.4 shows that the density for the oil.ls object is shifted to
the left of the origin, whereas the density for oil.robust is wellcentered. Furthermore, the outlier in the oilcity data is pushed far
from the mode of the density for the MM-estimator, and thus appears
as a pronounced bump in the plot of the residual density estimates. In
the density plot for the least squares fit, the outlier is not as visible.
346
Comparing Least Squares and Robust Fits
oil.robust
0
1
2
3
4
5
oil.ls
-2
-1
1
2
oil.robust
0.0
0.5
1.0
1.5
2.0
oil.ls
0
-1
0
1
2
3
4
5
Figure 11.4: Normal qqplots and residual density estimates for the linear fits in oil.cmpr.
Statistical
Inference from
Comparison
Objects
A detailed comparison of two model fits, including t-values and
p-values, can be obtained with the summary method for compare.fits
objects. For example:
> summary(oil.cmpr)
Calls:
oil.ls lm(formula = Oil ~ Market, data = oilcity)
oil.robust lmRobMM(formula = Oil ~ Market, data = oilcity)
Residual Statistics:
Min
1Q
Median
3Q
Max
347
Chapter 11 Robust Regression
oil.ls -0.6952 -0.17323 -0.05444 0.08407 4.842
oil.robust -0.4566 -0.08875 0.03082 0.10314 5.218
Coefficients:
Value Std. Error t value
oil.ls_(Intercept) 0.14745
0.07072
2.085
oil.robust_(Intercept) -0.08396
0.02805 -2.993
oil.ls_Market 2.85674
0.73175
3.904
oil.robust_Market 0.82888
0.28341
2.925
oil.ls_(Intercept)
oil.robust_(Intercept)
oil.ls_Market
oil.robust_Market
Pr(>|t|)
0.0390860
0.0033197
0.0001528
0.0040852
Residual Scale Estimates:
oil.ls : 0.4867 on 127 degrees of freedom
oil.robust : 0.1446 on 127 degrees of freedom
Proportion of variation in response(s) explained by
model(s):
oil.ls : 0.1071
oil.robust : 0.0526
Correlations:
oil.ls
Market
(Intercept) 0.7955736
oil.robust
Market
(Intercept) 0.8168674
Warning
When the p-values for the tests of bias indicate that the final M-estimates are highly biased
relative to the initial S-estimates, the final M-estimates are not used in a lmRobMM fit. In this case,
the asymptotic approximations for the inference results may not be very good, and you should
thus not trust them.
348
Robust Model Selection
ROBUST MODEL SELECTION
Robust F and
Wald Tests
An important part of statistical inference is hypothesis testing. SPLUS provides two robust tests for determining whether a regression
coefficient is zero: the robust Wald test and the robust F test. To illustrate
how these tests are used, we generate an example data frame
simu.dat with a function called gen.data:
>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
gen.data <- function(coeff, n = 100, eps = 0.1,
sig = 3, snr = 1/20, seed = 837)
{
# coeff : 3 x 1 vector of coefficients
# eps
: the contamination ratio, between 0 and 0.5
# sig
: standard deviation of most observations
# snr
: signal-to-noise ratio,
# Note : the regressors are generated as: rnorm(n,1),
#
rnorm(n,1)^3, exp(rnorm(n,1)). It also
#
generates an unused vector x4.
set.seed(seed)
x <- cbind(rnorm(n,1), rnorm(n,1)^3, exp(rnorm(n,1)))
ru <- runif(n)
n1 <- sum(ru < eps)
u <- numeric(n)
u[ru < eps] <- rnorm(n1, sd = sig/snr)
u[ru > eps] <- rnorm(n - n1, sd = sig)
data.frame(y = x %*% matrix(coeff, ncol = 1) + u,
x1 = x[,1], x2 = x[,2], x3 = x[,3], x4 = rnorm(n,1))
}
> simu.dat <- gen.data(1:3)
The gen.data function creates a data frame with five columns: y, x1,
x2, x3, and x4. The variable y is generated according to the following
equation:
y = b1 x1 + b2 x2 + b3 x3 + u .
349
Chapter 11 Robust Regression
Here b 1 , b 2 , and b 3 are given by the coef argument to gen.data. In
simu.dat,
b 1 = 1 , b 2 = 2 , and b 3 = 3 . The u term in the equation
is sampled from a N(0,3) distribution by default, with 10%
contamination. The x4 column of the resulting data frame is normally
distributed and independent of y, x1, x2, and x3.
First, we model simu.dat using x1, x2, and x3, and x4 as predictor
variables. We use a -1 in the model formula so that an intercept is not
included:
> simu.mm4 <- lmRobMM(y ~ x1+x2+x3+x4-1, data = simu.dat)
> simu.mm4
Final M-estimates.
Call:
lmRobMM(formula = y ~ x1 + x2 + x3 + x4 - 1, data=simu.dat)
Coefficients:
x1
x2
x3
x4
0.6335503 2.048027 3.045304 -0.05288568
Degrees of freedom: 100 total; 96 residual
Residual scale estimate: 3.281144
To test the hypothesis that the coefficient of x4 is actually zero, we fit
another model using only x1, x2, and x3 as predictor variables. We
can then use anova to test the significance of the x4 coefficient:
> simu.mm3 <- update(simu.mm4, .~.-x4)
> anova(simu.mm4, simu.mm3)
Response: y
Terms
1 x1 + x2 + x3 + x4 - 1
2
x1 + x2 + x3 - 1
Df
Wald
P(>Wald)
1 0.04438085 0.8331466
The p-value is greater than 0.8, which implies that you can accept the
null hypothesis that the fourth coefficient is zero.
350
Robust Model Selection
The default test used by the anova method for lmRobMM objects is the
robust Wald test, which is based on robust estimates of the
coefficients and covariance matrix. To use the robust F test instead,
specify the optional test argument to anova:
> anova(simu.mm4, simu.mm3, test = "RF")
Response: y
Terms
1 x1 + x2 + x3 + x4 - 1
2
x1 + x2 + x3 - 1
Df
RobustF P(>RobustF/fH)
1 0.03375381
0.8507215
This gives a result similar to the one returned by the robust Wald test.
Robust FPE
Criterion
In addition to the robust Wald and F tests, S-PLUS provides Robust
Final Prediction Errors (RFPE) as a criterion for model selection. This
criterion is a robust analogue to the classical Final Prediction Errors
(FPE) criterion, and is defined as:
n

i=1
T 1
 yi – xi β 
Eρ  ---------------------- ,
σ


1
where E denotes expectation with respect to both β and y i , the β
1
term is the final M-estimate of β , and σ is the scale parameter for the
observations. The y i , x i , and ρ terms are as defined in the section
Overview of the Robust MM Regression Method. When considering
a variety of models that have different choices of predictor variables,
choose the model with the smallest value of RFPE.
2
Note that when ρ(u) = u , the RFPE criterion reduces to the classical
FPE. It can also be shown that RFPE is asymptotically equivalent to
the robust version of the Akaike Information Criterion (AIC)
proposed by Ronchetti (1985). The section Theoretical Details
provides a technical discussion that supports the use of RFPE.
The RFPE criterion is used as the robust test in the drop1 and add1
methods for lmRobMM objects. For example, use of drop1 on the fitted
model object simu.mm4 gives the output below.
> drop1(simu.mm4)
351
Chapter 11 Robust Regression
Single term deletions
Model:
y ~ x1 + x2 + x3 + x4 - 1
Df
RFPE
<none>
24.24090
x1 1 24.46507
x2 1 52.19715
x3 1 64.32581
x4 1 23.95741
The model obtained by dropping x4 has a lower RFPE than the
model that includes all four predictor variables. This indicates that
dropping x4 results in a better model.
You can also use the add1 function to explore the relevance of
variables. For example, use the following command to investigate
whether x4 helps to predict y in the simu.mm3 model:
> add1(simu.mm3, "x4")
Single term additions
Model:
y ~ x1 + x2 + x3 - 1
Df
RFPE
<none>
24.10179
x4 1 24.38765
As expected, the model without x4 is preferred, since the RFPE
increases when x4 is added.
Warning
When the p-values for the tests of bias indicate that the final M-estimates are highly biased
relative to the initial S-estimates, the final M-estimates are not used in a lmRobMM fit. If this applies
to any of the models considered by drop1 and add1, you should not trust the corresponding
RFPE values.
352
Controlling Options for Robust Regression
CONTROLLING OPTIONS FOR ROBUST REGRESSION
This section discusses a few of the most common control parameters
for robust MM regression. Most of the default settings for the
parameters
can
be
changed
through
the
functions
lmRobMM.robust.control and lmRobMM.genetic.control. For details
about parameters that are not discussed in this section, see the online
help files for the two control functions.
Efficiency at
Gaussian
Model
If the final M-estimates are returned by lmRobMM, they have a default
asymptotic efficiency of 85% compared with the least squares
estimates, when the errors are normally distributed. In some cases,
you may require an efficiency other than 85%. To change the value of
this control parameter, define the efficiency argument to
lmRobMM.robust.control. For example, the following command
computes a robust MM regression model for the oilcity data with an
efficiency of 95%:
> oil.eff <- lmRobMM(Oil ~ Market, data = oilcity,
+ robust.control = lmRobMM.robust.control(efficiency=0.95))
Note that the coefficients of oil.tmp are slightly different than those
of oil.robust, which uses the default efficiency of 85%:
> coef(oil.eff)
(Intercept)
Market
-0.07398854 0.8491129
Alternative
Loss Function
As mentioned in the section Overview of the Robust MM Regression
Method, the final M-estimates are based on initial S-estimates of both
the regression coefficients and the scale parameter. S-PLUS uses a loss
function to compute initial S-estimates and final M-estimates. Two
different loss functions are available in Spotfire S+: Tukey’s bisquare
function, and the optimal loss function recently discovered by Yohai
and Zamar (1998). Figure 11.5 shows Tukey’s bisquare function in the
left panes and the optimal loss function in the right; the top two
graphs in the figure display the loss functions ρ and the bottom two
graphs show ψ = ρ' . The mathematical forms of these functions can
be found in the section Theoretical Details.
353
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Chapter 11 Robust Regression
-4
-2
0
2
4
-4
-2
0
2
4
2
4
1
0
-1
-2
-2
-1
0
1
2
Optimal (Rho)
2
Bisquare (Rho)
-4
-2
0
2
4
-4
Bisquare (Psi)
-2
0
Optimal (Psi)
Figure 11.5: Available loss functions for robust MM regression models.
The optimal loss function has better combined Gaussian efficiency
and non-Gaussian bias control properties, and is therefore used as the
default in lmRobMM models. You can choose the Tukey bisquare
function instead, or a combination of the two loss functions, by
defining the weight argument to lmRobMM.robust.control
accordingly. For example, the following commands use Tukey’s
bisquare function for the initial S-estimates and the optimal loss
function for the final M-estimates:
> control.lossfun <- lmRobMM.robust.control(
+ weight = c("Bisquare","Optimal"), mxr = 100)
> oil.lossfun <- lmRobMM(Oil ~ Market, data = oilcity,
+ robust.control = control.lossfun)
354
Controlling Options for Robust Regression
> coef(oil.lossfun)
(Intercept)
Market
-0.08371941 0.8291027
In the control.lossfun definition, we define the mxr parameter to
increase the maximum number of iterations in the refinement step of
the fitting algorithm.
Confidence
Level of Bias
Test
The default level of the test for bias in lmRobMM is 10%. This means
that whenever the p-value of the test is greater than 0.10, the final
M-estimates are returned; otherwise, the initial S-estimates are
returned. To change the level of the test for bias, define the level
argument in the lmRobMM.robust.control function. A higher value of
level rejects the final M-estimates more often, and a lower value
rejects them less often. For example, you can force the fitting
algorithm to return the initial S-estimates by setting level=1, as the
following commands illustrate:
>
>
+
>
control.s <- lmRobMM.robust.control(level = 1)
oil.s <- lmRobMM(Oil ~ Market, data = oilcity,
robust.control = control.s)
oil.s
Initial S-estimates.
Call:
lmRobMM(formula = Oil ~ Market, data = oilcity,
robust.control = control.s)
Coefficients:
(Intercept)
Market
-0.06246073 0.8270727
Degrees of freedom: 129 total; 127 residual
Residual scale estimate: 0.1446265
Warning messages:
Significant test at level 0%. The bias is high, and
inference based on final estimates is not recommended. Use
initial estimates as exploratory tools.
355
Chapter 11 Robust Regression
Note
The above warning is only relevant when you use levels in the range of 1% to 10%.
Similarly, specifying level=0 forces lmRobMM to return the final
M-estimates:
> control.mm <- lmRobMM.robust.control(level = 0)
> oil.mm <- lmRobMM(Oil ~ Market, data = oilcity,
+ robust.control = control.mm)
If you want to compute the S-estimates only, and do not require the
M-estimates, you can specify the estim argument to
lmRobMM.robust.control as follows:
>
>
+
>
control.s2 <- lmRobMM.robust.control(estim = "S")
oil.s2 <- lmRobMM(Oil ~ Market, data = oilcity,
robust.control = control.s2)
oil.s2
Initial S-estimates.
Call:
lmRobMM(formula = Oil ~ Market, data = oilcity,
robust.control = control.s2)
Coefficients:
(Intercept)
Market
-0.06246073 0.8270727
Degrees of freedom: 129 total; 127 residual
Residual scale estimate: 0.1446265
Similarly, you can obtain only the final M-estimates if you use
estim="MM".
356
Controlling Options for Robust Regression
Sometimes you may want to change the level of the test for bias after
fitting a robust regression model. For this purpose, you can use the
update function and specify a new value with the robust.control
argument. For example, to change the level for oil.s to 20%, use the
following command:
> oil.level <- update(oil.s, level = 0.2)
> oil.level
Final M-estimates.
Call:
lmRobMM(formula = Oil ~ Market, data = oilcity,
robust.control = control.s)
Coefficients:
(Intercept)
Market
-0.08395796 0.8288795
Degrees of freedom: 129 total; 127 residual
Residual scale estimate: 0.1478398
Note that the final M-estimates are now returned. If the formula
argument is missing in the call to update, the function alternates
between the initial S-estimates and final M-estimates.
Resampling
Algorithms
S-PLUS uses one of three resampling schemes to compute initial
S-estimates: random resampling, exhaustive resampling, and a genetic
algorithm. You can choose which scheme to use by specifying the
sampling argument in the lmRobMM.robust.control function. Valid
choices for this control parameter are "Random", "Exhaustive" and
"Genetic"; by default, sampling="Random". Exhaustive resampling is
recommended only when the sample size is small and there are less
than ten predictor variables.
Random resampling is controlled by two parameters: a random seed
and the number of subsamples to draw. By default, the number of
p
subsamples is [ 4.6 ⋅ 2 ] , where p is the number of explanatory
variables and [ ] denotes the operation of rounding a number to its
closest integer. This number of subsamples works well if there are less
than 13 predictor variables, but it may be too large when there are
more predictors, resulting in unreasonably long computation times.
357
Chapter 11 Robust Regression
To choose a different value for the number of subsamples drawn,
define the optional argument nrep. For example, the following
command computes a robust MM regression model for the oilcity
data using 10 subsamples in the random resampling scheme:
> oil.sample <- lmRobMM(Oil ~ Market, data = oilcity,
+ nrep = 10)
You can control the seed of the random resampling by specifying the
seed argument to the lmRobMM.robust.control function.
The genetic resampling algorithm is controlled by a list of parameters
defined in the lmRobMM.genetic.control function. If you choose the
genetic resampling algorithm for your robust MM model, you can
specify control parameters by defining the genetic.control
argument in lmRobMM. This optional argument should be a list, and is
usually returned by a call to lmRobMM.genetic.control. To see the
names and default values of the lmRobMM.genetic.control
arguments, use the following command:
> args(lmRobMM.genetic.control)
function(popsize = NULL, mutate.prob = NULL,
random.n = NULL, births.n = NULL, stock = list(),
maxslen = NULL, stockprob = NULL, nkeep = 1)
For explanations of these arguments, see the online help files for
lmRobMM.genetic.control and ltsreg.default.
358
Theoretical Details
THEORETICAL DETAILS
Initial
Estimates
As mentioned in the section Overview of the Robust MM Regression
Method, the minimization algorithm that lmRobMM uses to compute
coefficients can produce many optimal solutions to the objective
function
n

i=1
T
 y i – x i β
ρ  ------------------- .
ŝ 

(11.1)
Here y i is the scalar response associated with ith observation, x i is a
p-dimensional vector of independent predictor values, and
β = ( β 1, β 2, …, β p ) represents the coefficients. S-PLUS deals with
0
0
this issue by computing highly robust initial estimates β and s for
the coefficients and scale parameter, respectively. The initial estimates
are calculated using the S-estimate method introduced by Rousseeuw
and Yohai (1984), as part of an overall computational strategy
proposed by Yohai, Stahel, and Zamar (1991).
The S-estimate approach has as its foundation an M-estimate ŝ of an
unknown scale parameter for the observations. The observations are
assumed to be robustly centered, in that a robust location estimate has
been subtracted from each y i for i = 1, 2, …, n . The M-estimate ŝ is
obtained by solving the equation
1
--n
n

yi
ρ  ---- = 0.5
 ŝ 
(11.2)
i=1
where ρ is a symmetric, bounded function. It is known that such a
scale estimate has a breakdown point of 0.5 (Huber, 1981), and that
one can find min-max bias robust M-estimates of scale (Martin and
Zamar, 1989 and 1993).
359
Chapter 11 Robust Regression
Consider the following modification of Equation (11.2):
1
-----------n–p
n

i=1
T
 y i – x i β
ρ  ------------------- = 0.5
 ŝ ( β ) 
(11.3)
For each value of β , we have a corresponding robust scale estimate
0
ŝ ( β ) . The initial S-estimate is the value β that minimizes ŝ ( β ) :
0
β = argmin β ŝ ( β )
(11.4)
This presents a second nonlinear optimization problem, one for
which the solution is traditionally found by a random resampling
algorithm followed by a local search, as described in Yohai, Stahel,
and Zamar (1991). Spotfire S+ allows you to use an exhaustive form
of resampling for small problems, or a genetic algorithm in place of
0
the resampling scheme. Once the initial S-estimate β is computed,
the The final M-estimate is the local minimum of Equation (11.1) that
0
is nearest to β .
For details on the numerical algorithms implemented in lmRobMM, see
Marazzi (1993).
Loss Functions
360
A robust M-estimate for the coefficients β in a linear model is
obtained by minimizing Equation (11.1). The ρ in the equation is a
loss function, which is a convex weight function of the residuals; the
derivative of ρ is usually denoted by ψ . In lmRobMM, two different
weight functions can be used for both the initial S-estimates and the
final M-estimates: Tukey’s bisquare function and the optimal weight
function introduced in Yohai and Zamar (1998).
Theoretical Details
Tukey’s bisquare function and its derivative are as follows:


ρ(r) = 




ψ(r) = 


6
4
2
 -r- – 3  -r- + 3  -r- if r ≤ c
 c
 c
 c
if r > c
1
6  r 12  r 3 6  r 5
--- -- – ------ -- + --- -- if r ≤ c
c  c
c  c c  c
if r > c
1
In these equations, c is a tuning constant. The Yohai and Zamar
optimal function and its derivative are:




ρ(r) = 





3.25c
2
if -r- > 3
c
r 2
r 4
r 6
r 8
2
c 1.792 + h 1  -- + h 2  -- + h 3  -- + h 4  --
 c
 c
 c
 c
2
r
---2
if 2 < -r- ≤ 3
c
if -r- ≤ 2
c

 0

3
5
7

ψ(r) =  c g 1 -r- + g 2  -r- + g 3  -r- + g 4  -r-
 c
 c
 c
c


 r

if -r- > 3
c
if 2 < -r- ≤ 3
c
r
if -- ≤ 2
c
where c is a tuning constant and
g 1 = – 1.944, g 2 = 1.728, g 3 = – 0.312, g 4 = 0.016 ,
g
g
g
g
h 1 = ----1-, h 2 = ----2-, h 3 = ----3-, h 4 = ----4- .
2
4
6
8
See Figure 11.5 for the general shapes of these two loss functions.
361
Chapter 11 Robust Regression
Yohai and Zamar (1998) showed that their loss function above is
optimal in the following sense: the final M-estimate obtained using
this function has a breakdown point of 0.5. In addition, it minimizes
the maximum bias under contamination distributions (locally for
small fractions of contamination), subject to achieving a desired
efficiency when the data are Gaussian.
The Gaussian efficiency of the final M-estimate is controlled by the
choice of tuning constant c in the weight function. As discussed in the
section Controlling Options for Robust Regression, you can specify a
desired Gaussian efficiency with the efficiency argument to
lmRobMM.robust.control. Once a value is chosen, S-PLUS
automatically adjusts the tuning parameter to achieve the desired
efficiency.
Robust
R-Squared
2
The robust R statistic is calculated as follows:
•
Initial S-estimator β
0
If an intercept is included in the model, then
2
0 2
( n – 1 )s μ – ( n – p ) ( s )
R = ---------------------------------------------------------,
2
( n – 1 )s μ
2
where n is the number of observations, p is the number of
0
predictor variables, and s is the initial S-estimate for the
scale parameter. The s μ term is the minimized ŝ(μ) from
Equations (11.3) and (11.4), for a regression model that has
only an intercept μ .
If an intercept is included in the model, then
2
0 2
nŝ ( 0 ) – ( n – p ) ( s )
2
R = --------------------------------------------------.
2
nŝ ( 0 )
362
Theoretical Details
•
Final M-estimator β
1
If an intercept μ is included in the model, then
T 1
 y i – μ̂
 yi – xi β 
–
ρ
-

 -------------------- ρ  -----------
0
0



s
s
2
R = -------------------------------------------------------------------------- ,
 y i – μ̂
-
 ρ  -----------0
s 
where y i is the ith response for i = 1, 2, …, n , x i is a
0
p-dimensional vector of predictor values, and s is the initial
S-estimate for the scale parameter. The μ̂ term is the location
M-estimate corresponding to the local minimum of
Qy ( μ ) =
 y i – μ
-
ρ
  -----------0
s 
such that
Q y ( μ̂ ) ≤ Q y ( μ* )
*
where μ is the sample median estimate. If an intercept is not
included in the model, replace μ̂ with 0 in the above formula.
Robust
Deviance
For an M-estimate, the deviance is defined as the optimal value of the
2
objective function (11.1) on the σ scale. That is:
•
Initial S-estimator β
0
0
For simplicity, we use the notation ŝ ( β ) = ŝ 0 where ŝ ( β ) is
from Equations (11.3) and (11.4), so that
2
D = ( ŝ 0 ) .
363
Chapter 11 Robust Regression
•
Final M-estimator β
1
T 1
 yi – xi β 
D = 2 ( ŝ 0 )  ρ  ----------------------
ŝ 0 

i
2
Robust F Test
See Chapter 7 of Hampel, Ronchetti, Rousseeuw, and Stahel (1986),
where this test is referred to as the tau test.
Robust Wald
Test
See Chapter 7 of Hampel, Ronchetti, Rousseeuw, and Stahel (1986).
Robust FPE
(RFPE)
In 1985, Ronchetti proposed to generalize the Akaike Information
Criterion (AIC) to robust model selection. However, Ronchetti’s
results are subject to certain restrictions: they apply only to
M-estimates with zero breakdown points, and the density of the errors
must have a certain form. Yohai (1997) proposed the following Robust
Final Prediction Errors (RFPE) criterion for model selection, which is
not subject to the same restrictions:
A
ε
RFPE = nEρ  --- + p ------- .
 σ
2B
(11.5)
Here n is the number of observations, p is the number of predictor
variables, ε contains the errors for the model, and σ is the scale
parameter for the observations. The A and B terms are
2 ε
A = Eψ  ---
 σ
ε
B = Eψ′  --- ,
 σ
where ψ = ρ' is the derivative of the loss function. This criterion is a
robust analogue to the classical Final Prediction Errors (FPE)
criterion.
364
Theoretical Details
By replacing the expectation with a summation, the first term in
Equation (11.5) can be approximated by
ε
nEρ  --- ≈
 σ
n
ri
A
ρ  --- + p ------- ,
 σ
2B

i=1
T 1
where r i = y i – x i β are the residuals for the model using the final
M-estimates β
estimated by
1
for the coefficients. Equation (11.5) can thus be
n

RFPE ≈
i=1
T 1
 yi – xi β 
Â
ρ  ---------------------- + p --- ,
ŝ
B̂


0
(11.6)
0
where ŝ 0 = ŝ ( β ) is from Equations (11.3) and (11.4). The  and B̂
terms are:
1
 = --n
n

i=1
ri
ψ 2  ----
 ŝ 0
1
B̂ = --n
n

i=1
ri
ψ ′  ----
 ŝ 0
The approximation on the right-hand side of Equation (11.6) is used
as the RFPE criterion in S-PLUS.
Breakdown
Points
The breakdown point of a regression estimator is the largest fraction of
data that may be replaced by arbitrarily large values without making
the Euclidean norm of the resulting estimate tend to infinity. The
Euclidean norm β̂ of an estimate is defined as follows:
p
β̂
2
2
=  β̂ i .
i=1
Any estimator with a breakdown point of approximately 1/2 is called
a high breakdown point estimator, and is highly robust.
365
Chapter 11 Robust Regression
To illustrate the concept of breakdown point, consider the simple
problem of estimating location, where the most common estimator is
1
the sample mean y = --n
n
 yi
. The breakdown point of the mean is
i=1
0, since if any single y i → ± ∞ , then y → ± ∞ . On the other hand, the
sample median has breakdown point of approximately 1/2. For
convenience, consider an odd sample size n : it is possible to set
n = 1 ⁄ 2 of the observations to ± ∞ without the median tending to
±∞ .
366
Other Robust Regression Techniques
OTHER ROBUST REGRESSION TECHNIQUES
Least Trimmed
Squares
Regression
Least trimmed squares (LTS) regression, introduced by Rousseeuw
(1984), is a highly robust method for fitting a linear regression model.
The LTS estimate β̂ LTS for the coefficients in a linear model
minimizes the following objective function:
q
 ri β ,
2
(11.7)
i=1
where r i β is the ith ordered residual. The value of q is often set to be
slightly larger than half of n , the number of observations in the
model. In contrast, the ordinary least squares estimate β̂ LS for the
regression coefficients minimizes the sum of all squared residuals:
n
 ri β .
2
(11.8)
i=1
Thus, LTS is equivalent to ordering the residuals from a least squares
fit, trimming the observations that correspond to the largest residuals,
and then computing a least squares regression model for the
remaining observations. The ordinary least squares estimator lacks
robustness because a single observation can cause β̂ˆ LS to take on any
value. The same is true of M-estimators, which are discussed in the
section M-Estimates of Regression.
To compute a least trimmed squares regression model, use the ltsreg
function. For the stack.df data introduced in the section Visualizing
and Summarizing Robust Fits, we compute LTS estimates as follows:
> stack.lts <- ltsreg(Loss ~ ., data = stack.df)
367
Chapter 11 Robust Regression
> stack.lts
Method:
Least Trimmed Squares Robust Regression.
Call:
ltsreg.formula(Loss ~ ., data = stack.df)
Coefficients:
Intercept Air.Flow Water.Temp Acid.Conc.
-43.6607
0.9185
0.5242
-0.0623
Scale estimate of residuals:
2.05
Total number of observations:
21
Number of observations that determine the LTS estimate:
18
Comparing the LTS coefficients to those for an ordinary least squares
fit, we see that the robust values are noticeably different:
> stack.lm <- lm(Loss ~ ., data = stack.df)
> coef(stack.lm)
(Intercept) Air.Flow Water.Temp Acid.Conc.
-39.91967 0.7156402
1.295286 -0.1521225
> coef(stack.lts)
Intercept Air Flow Water Temp Acid Conc.
-43.66066 0.9185217 0.5241657 -0.0622979
Plots of the residuals versus fitted values for the two fits, shown in
Figure 11.6, are also revealing:
>
>
+
>
368
par(mfrow = c(1,2))
plot(fitted(stack.lm), resid(stack.lm),
ylim = range(resid(stack.lts)))
plot(fitted(stack.lts), resid(stack.lts))
Other Robust Regression Techniques
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-5
-5
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0
•
resid(stack.lts)
0
resid(stack.lm)
•
-10
-10
•
10
20
fitted(stack.lm)
30
40
•
5
10
15
20
25
30
35
fitted(stack.lts)
Figure 11.6: Residual plots for least squares (left) and least trimmed squares (right)
regression models.
The plot for the least squares fit shows the residuals scattered with no
apparent pattern. In contrast, the plot for the LTS fit shows four clear
outliers: three at the top of the graph and one at the bottom.
If q is the right fraction of n , the least trimmed squares estimator has
the attractive robustness property that its breakdown point is
approximately 1/2. Thus, the LTS estimator is a high-breakdown
point regression estimator. The high breakdown point means that the
T
values x i β̂ LTS , i = 1, …, n , fit the bulk of the data well, even when
the bulk consists of only a little more than 50% of the data.
T
Correspondingly, the residuals r i β̂ LTS = y i – x i β̂ LTS reveal the
outliers quite clearly. Least squares residuals and M-estimate residuals
often fail to reveal problems in the data, as discussed in the section
Comparison of Least Squares, Least Trimmed Squares, and MEstimates.
369
Chapter 11 Robust Regression
Least Median
Squares
Regression
Similar to least trimmed squares regression is a method called least
median of squares (LMS). Rather than minimize a sum of the squared
residuals as LTS does, LMS minimizes the median of the squared
residuals (Rousseeuw 1984). In S-PLUS, the lmsreg function
performs least median of squares regression.
LMS regression has a high breakdown point of almost 50%. That is,
almost half of the data can be corrupted in an arbitrary fashion, and
the estimates obtained by LMS continue to model the majority of the
data well. However, least median of squares is statistically very
inefficient. It is due to this inefficiency that we recommend the
lmRobMM and ltsreg functions over lmsreg.
Least Absolute
Deviation
Regression
The idea of least absolute deviation (L1) regression is actually older than
that of least squares, but until the development of high-speed
computers, it was too cumbersome to have wide applicability. As its
name implies, L1 regression finds the coefficients estimate β̂ L1 that
minimizes the sum of the absolute values of the residuals:
n
 ri β
.
i=1
In S-PLUS, the function l1fit is used to compute a least absolute
deviation regression model (note that the second character in the
function name is the number “1”, not the letter “l”). As an example,
consider again the stack loss data introduced in the section
Visualizing and Summarizing Robust Fits. We construct an L1
regression model using l1fit as follows:
> stack.l1 <- l1fit(stack.x, stack.loss)
> stack.l1
$coefficients:
Intercept Air Flow Water Temp Acid Conc.
-39.68986 0.8318838 0.5739132 -0.06086949
370
Other Robust Regression Techniques
$residuals:
[1] 5.06087255 0.00000000 5.42898655
[5] -1.21739066 -1.79130375 -1.00000000
[9] -1.46376956 -0.02028821 0.52753741
[13] -2.89854980 -1.80289757 1.18260884
[17] -0.42608649 0.00000000 0.48695672
[21] -9.48115635
7.63478327
0.00000000
0.04058089
0.00000000
1.61739194
Plots of the residuals against the fitted values for statck.l1 show the
outliers more clearly than the least squares regression model, but not
as clearly as ltsreg does in Figure 11.6:
>
>
+
>
par(mfrow = c(1,2))
plot(fitted(stack.lm), resid(stack.lm),
ylim = range(resid(stack.l1)))
plot(stack.loss - resid(stack.l1), resid(stack.l1))
The resulting plot is shown in Figure 11.7.
•
•
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5
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-5
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0
•
resid(stack.l1)
0
resid(stack.lm)
•
••
•
•
-10
-10
•
10
20
fitted(stack.lm)
30
40
10
15
20
25
30
35
stack.loss - resid(stack.l1)
Figure 11.7: Residual plots for least squares (left) and least absolute deviation
(right) regression models.
371
Chapter 11 Robust Regression
M-Estimates of
Regression
The M-estimator of regression was first introduced by Huber in 1973. For
a given ρ function, an M-estimate of regression β̂ M minimizes the
objective function:
n

ri β
ρ  ------- .
 σ
(11.9)
i=1
2
Least squares regression corresponds to ρ ( x ) = x and L1 regression
corresponds to ρ ( x ) = x . Generally, the value of β̂ M is dependent
on the value of σ , which is usually unknown.
Although M-estimates are protected against wild values in the
response variable, they are sensitive to high leverage points, which have
very different x values compared to the other data points in a model.
In particular, a typographical error in an explanatory variable can
have a dramatic affect on an M-estimate, while least trimmed squares
handles this situation easily. One advantage of M-estimates is that
they can be computed in much less time than LTS or other highbreakdown point estimators. For more discussion about high leverage
points, see the section Comparison of Least Squares, Least Trimmed
Squares, and M-Estimates.
In S-PLUS, you can calculate M-estimates of regression using the
function, which computes iteratively reweighted least-squares fits. In
the fitting algorithm, an initial model is calculated using traditional
weighted least squares by default. The algorithm computes a new set
of weights based on the results of the initial fit. The new weights are
then used in another weighted least squares fit, and so on. S-PLUS
continues iteratively until some convergence criteria are satisfied or a
specified maximum number of iterations is reached.
rreg
To use the rreg function, the only required arguments are x, the
vector or matrix of explanatory variables, and y, the vector response.
For example, a typical call to rreg using the stack loss data is:
> stack.M <- rreg(stack.x, stack.loss)
372
Other Robust Regression Techniques
> stack.M
$coefficients:
(Intercept) Air Flow Water Temp Acid Conc.
-42.07438 0.8978265
0.731816 -0.1142602
$residuals:
[1]
2.65838630
[5] -1.75017776
[9] -1.89068795
[13] -2.80290885
[17] -0.49471517
[21] -10.01211661
-2.45587390
-2.48199378
-0.03142924
-1.27786270
0.30510621
3.72541082
-1.52824862
0.99691253
2.17952419
0.68755039
6.78619020
-0.52824862
0.61446835
0.83674360
1.52911203
$fitted.values:
[1] 39.341614 39.455874 33.274589 21.213810 19.750178
[6] 20.481994 20.528249 20.528249 16.890688 14.031429
[11] 13.003087 12.385532 13.802909 13.277863 5.820476
[16] 6.163256 8.494715 7.694894 8.312450 13.470888
[21] 25.012117
$w:
[1]
[6]
[11]
[16]
[21]
0.87721539
0.90178786
0.97640677
0.98897286
0.07204303
0.91831885
0.95897484
0.98691782
0.99387986
0.77235329
0.99398847
0.89529949
0.99933718
0.41742415
0.93525890
0.98052477
0.99574820
0.95387576
0.99958817
0.92540436
0.96320721
$int:
[1] T
$conv:
[1] 0.175777921 0.036317063 0.021733577 0.013181419
[5] 0.007426725 0.003341872 0.093998053 0.055029889
$status:
[1] "converged"
You can control the choice of ρ by specifying a weight function as the
method argument to rreg. Currently, there are eleven weight
functions built into S-PLUS, and there is not yet a consensus on
which method is the “best.” See the rreg help file for details on each
373
Chapter 11 Robust Regression
of the weight functions available. The default weight function uses
Huber’s function until convergence, and then a bisquare function for
two more iterations. Huber’s function is defined as:
 1

ρ(x) =  c
--------------- abs
(x)

abs ( x ) < c
abs ( x ) ≥ c
where c is a tuning constant. The bisquare function implemented in
rreg is:

x 2 2
  1 –  -- 
c
ρ(x) = 

0

x<c
.
x≥c
Here again, c is a tuning parameter.
The following call to rreg defines a simple weight function for the
2
stack loss data that corresponds to the least squares choice ρ ( x ) = x :
> stack.MLS <- rreg(stack.x, stack.loss,
+ method = function(u) 2*abs(u), iter = 100)
Warning messages:
failed to converge in 100 steps
> coef(stack.MLS)
(Intercept) Air Flow Water Temp Acid Conc.
-39.68049 0.7166834
1.298541 -0.156553
> coef(stack.lm)
(Intercept) Air.Flow Water.Temp Acid.Conc.
-39.91967 0.7156402
1.295286 -0.1521225
374
Other Robust Regression Techniques
Comparison of
Least Squares,
Least Trimmed
Squares, and
M-Estimates
Plots of residuals are often used to reveal outliers in linear models. As
discussed in the section Least Trimmed Squares Regression, LTS is an
robust method that isolates outliers quite clearly in plots. However,
residuals from least squares and M-estimator regression models often
fail to reveal problems in the data. We illustrate this point with a
contrived example.
First, we construct an artificial data set with sixty percent of the data
scattered about the line y = x , and the remaining forty percent in an
outlying cluster centered at (6,2) .
#
>
>
>
>
>
>
>
>
set the seed to reproduce this example
set.seed(14)
x30 <- runif(30, mean = 0.5, sd = 4.5)
e30 <- rnorm(30, mean = 0, sd = 0.2)
y30 <- 2 + x30 + e30
x20 <- rnorm(20, mean = 6, sd = 0.5)
y20 <- rnorm(20, mean = 2, sd = 0.5)
x <- c(x30, x20)
y <- c(y30, y20)
We plot the data, and then fit three different regression lines: the
ordinary least squares line, an M-estimate line, and the least trimmed
squared line.
>
>
>
>
>
>
>
plot(x, y)
abline(lm(y ~ x))
text(5, 3.4, "LS")
abline(rreg(x, y))
text(4, 3.2, "M")
abline(ltsreg(x, y))
text(4, 6.5, "LTS")
The resulting plot is shown in Figure 11.8. Note that all three
regression lines are influenced by the leverage points in the outlying
cluster.
375
Chapter 11 Robust Regression
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•
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•
1
2
3
4
5
6
7
x
Figure 11.8: Least trimmed squares, least squares, and M-estimates regression. Note that the outlying cluster of
leverage points influences all three fits.
The ltsreg function has a quan argument that allows you to specify
the number of residuals included in the least trimmed squares
calculations. The default value of quan includes approximately 90% of
the data points. However, we can change this value to include only a
little more than 50% of the data, since LTS regression has a
breakdown point of nearly 1 ⁄ 2 . In the commands below, we use
about 60% of the data in the LTS fit:
>
>
>
>
>
>
>
376
plot(x, y)
abline(lm(y ~ x))
text(5, 3.4, "LS")
abline(rreg(x, y))
text(4, 3.2, "M")
abline(ltsreg(x, y, quan = floor(0.6*length(x))))
text(3.7, 6.0, "LTS")
Other Robust Regression Techniques
The result is shown in Figure 11.9. Note that the outlying cluster of
points pulls both the ordinary least squares line and the M-estimate
away from the bulk of the data. Neither of these two fitting methods is
robust to leverage points (i.e., outliers in the x direction). The LTS line
recovers the linear structure in the bulk of the data and essentially
ignores the outlying cluster. In higher dimensions, such outlying
clusters are extremely hard to identify using classical regression
techniques, which makes least trimmed squares an attractive robust
method.
6
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2
3
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5
6
7
x
Figure 11.9: Least trimmed squares regression, as compared to least squares and
M-estimates regression.
377
Chapter 11 Robust Regression
REFERENCES
Hampel, F., Ronchetti, E.M., Rousseeuw, P.J., & Stahel, W.A. (1986).
Robust Statistics: the Approach Based on Influence Functions. New York:
John Wiley & Sons, Inc.
Huber, P.J. (1981). Robust Statistics. New York: John Wiley & Sons, Inc.
Marazzi, A. (1993). Algorithms, Routines, and S Functions for Robust
Statistics. Pacific Grove, CA: Wadsworth & Brooks/Cole.
Martin, R.D. & Zamar, R.H. (1989). Asymptotically min-max robust
M-estimates of scale for positive random variables. Journal of the
American Statistical Association 84: 494-501.
Martin, R.D. & Zamar, R.H. (1993). Bias robust estimates of scale.
Annals of Statistics 21: 991-1017.
Ronchetti, E. (1985). Robust model selection in regression. S-PLUS
Statistics & Probability Letters 3: 21-23.
Rousseeuw, P.J. (1984). Least median of squares regression. Journal of
the American Statistical Association 79: 871-881.
Rousseeuw, P.J. & Yohai, V. (1984). Robust regression by means of
S-estimators. In Robust and Nonlinear Time Series Analysis, J. Franke, W.
Hardle, & R.D. Martin (Eds.). Lecture Notes in Statistics, 26: 256-272.
New York: Springer-Verlag.
Wilcox, R.R. (1997). Introduction to Robust Estimation and Hypothesis
Testing. San Diego: Academic Press.
Yohai, V.J. (1987). High Breakdown-Point and High Efficiency
Estimates for Regression. Annals of Statistics 15: 642-665.
Yohai, V.J. (1997). A New Robust Model Selection Criterion for
Linear Models: RFPE (unpublished note).
Yohai, V., Stahel, W.A., & Zamar, R.H. (1991). A procedure for robust
estimation and inference in linear regression. In Directions in Robust
Statistics and Diagnostics, Part II, W.A. Stahel & S.W. Weisberg (Eds.).
New York: Springer-Verlag.
Yohai, V.J. & Zamar, R.H. (1998). Optimal locally robust M-estimates
of regression. Journal of Statistical Planning and Inference 64: 309-323.
378
GENERALIZING THE LINEAR
MODEL
12
Introduction
380
Generalized Linear Models
381
Generalized Additive Models
385
Logistic Regression
Fitting a Linear Model
Fitting an Additive Model
Returning to the Linear Model
Legal Forms of the Response Variable
387
388
394
398
402
Probit Regression
404
Poisson Regression
407
Quasi-Likelihood Estimation
415
Residuals
418
Prediction from the Model
Predicting the Additive Model of Kyphosis
Safe Prediction
420
420
422
Advanced Topics
Fixed Coefficients
Family Objects
424
424
425
References
432
379
Chapter 12 Generalizing the Linear Model
INTRODUCTION
Least squares estimation of regression coefficients for linear models
dates back to the early nineteenth century. It met with immediate
success as a simple way of mathematically summarizing relationships
between observed variables of real phenomena. It quickly became
and remains one of the most widely used statistical methods of
practicing statisticians and scientific researchers.
Because of the simplicity, elegance, and widespread use of the linear
model, researchers and statisticians have tried to adapt its
methodology to different data configurations. For example, it should
be possible to relate a categorical response (or some transformation of
it) to a set of predictor variables, similar to the role a continuous
response takes in the linear model. Although conceptually plausible,
the development of regression models for categorical responses
lacked solid theoretical foundation until the introduction of the
generalized linear model by Nelder and Wedderburn (1972).
This chapter focuses on generalized linear models and generalized
additive models, as they apply to categorical responses. In particular,
we focus on logistic, probit, and Poisson regressions. We also include
a brief discussion on the quasi-likelihood method, which fits models
when an exact likelihood cannot be specified.
380
Generalized Linear Models
GENERALIZED LINEAR MODELS
The linear model discussed in Chapter 10, Regression and Smoothing
for Continuous Response Data, is a special case of the generalized
linear model. A linear model provides a way of estimating the
response variable Y , conditional on a linear function of the values
x 1, x 2, …, x p of some set of predictors variables, X 1, X 2, …, X p .
Mathematically, we write this as:
p
E ( Y x ) = β0 +
 βi xi .
(12.1)
i=1
For the linear model, the variance of Y is assumed to be constant, and
2
is denoted by var ( Y ) = σ .
A generalized linear model (GLM) provides a way of estimating a
function of the mean response as a linear combination of some set of
predictors. This is written as:
p
g ( E ( Y x ) ) = g ( μ ) = β0 +
 βi xi
= η(x) .
(12.2)
i=1
The function of the mean response, g ( μ ) , is called the link function,
and the linear function of the predictors, η ( x ) , is called the linear
predictor. For the generalized linear model, the variance of Y may be a
function of the mean response μ :
var ( Y ) = φ V ( μ ) .
To compute generalized linear models in S-PLUS, we can use the glm
function.
381
Chapter 12 Generalizing the Linear Model
Three special cases of generalized linear models are the logistic,
probit, and Poisson regressions. Logistic regression models data in
which the response variable is categorical and follows a binomial
distribution. To do a logistic regression in S-PLUS, we declare the
binomial family in glm. This uses the logit link function
p
g ( p ) = logit (p) = log ------------ ,
1–p
and the variance function defined by
p .
var ( Y ) = φ ----------1–p
Here, p is the probability of an event occurring, and corresponds to
the mean response of a binary (0-1) variable. In logistic regression, we
model the probability of some event occurring as a linear function of
a set of predictors. The most common examples of logistic response
variables include the presence/absence of AIDS, the presence/
absence of a plant species in a vegetation sample, and the failure/nonfailure of a electronic component in a radio.
Like logistic regression, probit regression models data in which the
response variable follows a binomial distribution. It describes the
probability of some event occurring as a linear function of predictors,
and therefore uses the same variance function as logistic models:
p .
var ( Y ) = φ ----------1–p
However, probit regression uses the probit link function
–1
g( p) = F (p ) ,
–1
where F is the Gaussian cumulative distribution function, and F is
its inverse. To do a probit regression in S-PLUS, we declare the
binomial(link=probit) family in glm. This kind of regression is
popular in bioassay problems.
382
Generalized Linear Models
Poisson regression models data in which the response variable
represents counts. To do a Poisson regression in S-PLUS, we declare
the poisson family in glm. This uses the log link function
g ( μ ) = log ( μ ) ,
and the variance function defined by
var ( Y ) = φμ .
The Poisson family is useful for modeling count data that typically
follows a Poisson distribution. Common examples include tables of
rates, in which the rate of a particular event is classified according to a
number of categorical predictors. The example we present in the
section Poisson Regression models the number of soldering skips as a
function of various controlled factors in a solder experiment.
Usually, φ is fixed to be 1 in the variance function of a generalized
linear model. When we cannot assume that φ = 1 , we must use the
quasi family in glm for quasi-likelihood estimation. This is the case of
over- or under-dispersion, as discussed in McCullagh and Nelder
(1989). The quasi-likelihood family allows us to estimate the
parameters in a model without specifying the underlying distribution
function. In this case, the link and variance functions are all that are
used to fit the model. Once these are known, the same iterative
procedure used for fitting the other families can be used to estimate
the model parameters. For more details, see Chambers and Hastie
(1992) and McCullagh and Nelder (1989).
Other families are available in glm for modeling various kinds of data
as linear functions of predictors. For example, normal and inverse
normal distributions are specified with the gaussian and
inverse.gaussian families. Table 12.1 lists the distribution families
available for use with the glm function.
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Chapter 12 Generalizing the Linear Model
Table 12.1: Link and variance functions for the generalized linear and generalized
additive models.
Distribution
Family
Link
Variance
Normal/Gaussian
gaussian
μ
1
Binomial
binomial
log(μ/(1-μ))
μ(1-μ)/n
Poisson
poisson
log(μ)
μ
Gamma
gamma
1/μ
μ
Inverse Normal/
Gaussian
inverse.gaussian
1/μ
Quasi
quasi
g(μ)
2
2
3
μ
V(μ)
Each of these distributions belongs to the one-parameter exponential
family of distributions. The link function for each family listed in
Table 12.1 is referred to as the canonical link, because it relates the
canonical parameter of the distribution family to the linear predictor,
η ( x ) . For more details on the parameterization of these distributions,
see McCullagh and Nelder (1989).
The estimates of regression parameters in a generalized linear model
are maximum likelihood estimates, produced by iteratively
reweighted least-squares (IRLS). Essentially, the log-likelihood l ( β, y )
is maximized by solving the score equations:
∂l( β, y ) ⁄ ∂β = 0 .
(12.3)
Since the score equations are nonlinear in β , they are solved
iteratively. For more details, see Chambers and Hastie (1992) or
McCullagh and Nelder (1989).
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Generalized Additive Models
GENERALIZED ADDITIVE MODELS
The section Generalized Linear Models discusses an extension of
linear models to data with error distributions other than normal
(Gaussian). By using the glm function, we can fit data with Gaussian,
binomial, Poisson, gamma, or inverse Gaussian errors. This
dramatically broadens the kind of data for which we can build
regression models.
The primary restriction of a GLM is the fact that the linear predictor
η ( x ) is still a linear function of the parameters in the model. The
generalized additive model (GAM) extends the generalized linear
model by fitting nonparametric functions to estimate relationships
between the response and the predictors. The nonparametric
functions are estimated from the data using smoothing operations. To
compute generalized additive models in S-PLUS, we can use the gam
function. Because GLMs are a special instance of GAMs, we can fit
genearlized linear models using the gam function as well.
The form of a generalized additive model is:
p
g( E( Y x ) ) = g(μ ) = α +
 fi ( xi )
= η(x) ,
(12.4)
i=1
where g ( μ ) is the link function and α is a constant intercept term. In
Equation (12.4), f i corresponds to the nonparametric function
describing the relationship between the transformed mean response
g ( μ ) and the ith predictor. In this context, η ( x ) is referred to as the
additive predictor, and is entirely analogous to the linear predictor of a
GLM as defined in Equation (12.2). As for the generalized linear
model, the variance of Y in a GAM may be function of the mean
response μ :
var ( Y ) = φV ( μ ) .
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Chapter 12 Generalizing the Linear Model
All of the distribution families listed in Table 12.1 are available for
generalized additive models. Thus fully nonparametric, nonlinear
additive regression models can be fit to binomial data (logistic and
probit regression) and count data (Poisson regression), as well as to
data with error distributions given by the other families in Table 12.1.
Two functions that are useful for fitting a gam are s and lo. Both of
these functions are used to fit smooth relationships between the
transformed response and the predictors. The s function fits cubic
B-splines to estimate the smooth, and lo fits a locally weighted leastsquares regression to estimate the smooth. For more details on using
these functions, see their help files.
386
Logistic Regression
LOGISTIC REGRESSION
To fit a logistic regression model, use either the glm function or the
gam function with a formula to specify the model, and set the family
argument to binomial. As an example, consider the built-in data
frame kyphosis. A summary of the data frame produces the
following:
> attach(kyphosis)
> summary(kyphosis)
Kyphosis
absent :64
present:17
Age
Min. : 1.00
1st Qu.: 26.00
Median : 87.00
Mean
: 83.65
3rd Qu.:130.00
Max.
:206.00
Number
Min. : 2.000
1st Qu.: 3.000
Median : 4.000
Mean : 4.049
3rd Qu.: 5.000
Max. :10.000
Start
Min. : 1.00
1st Qu.: 9.00
Median :13.00
Mean :11.49
3rd Qu.:16.00
Max. :18.00
The list below describes the four variables in the kyphosis data set.
•
Kyphosis: a binary variable indicating the presence/absence
of a postoperative spinal deformity called Kyphosis.
•
Age:
•
Number: the number of vertebrae involved in the spinal
operation.
•
Start:
the age of the child in months.
the beginning of the range of the vertebrae involved in
the operation.
A convenient way of examining the bivariate relationship between
each predictor and the binary response, Kyphosis, is with a set of
boxplots produced by plot.factor:
> par(mfrow = c(1,3), cex = 0.7)
> plot.factor(kyphosis)
Setting the mfrow parameter to c(1,3) produces three plots in a row.
The character expansion is set to 0.7 times the normal size using the
cex parameter of the par function. Figure 12.1 displays the result.
387
0
2
5
10
Start
8
4
6
Number
100
50
Age
150
15
200
10
Chapter 12 Generalizing the Linear Model
absent present
absent present
absent present
Kyphosis
Kyphosis
Kyphosis
Figure 12.1: Boxplots of the predictors of kyphosis versus Kyphosis.
Both Start and Number show strong location shifts with respect to the
presence or absence of Kyphosis. The Age variable does not show
such a shift in location.
Fitting a
Linear Model
The logistic model we start with relates the probability of developing
Kyphosis to the three predictor variables, Age, Number, and Start. We
fit the model using glm as follows:
> kyph.glm.all <- glm(Kyphosis ~ Age + Number + Start,
+ family = binomial, data = kyphosis)
The summary function produces a summary of the resulting fit:
> summary(kyph.glm.all)
Call: glm(formula = Kyphosis ~ Age + Number + Start,
family = binomial, data = kyphosis)
Deviance Residuals:
Min
1Q
Median
3Q
Max
-2.312363 -0.5484308 -0.3631876 -0.1658653 2.16133
Coefficients:
Value Std. Error
t value
(Intercept) -2.03693225 1.44918287 -1.405573
Age
0.01093048 0.00644419 1.696175
Number
0.41060098 0.22478659 1.826626
Start
-0.20651000 0.06768504 -3.051043
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Logistic Regression
(Dispersion Parameter for Binomial family taken to be 1 )
Null Deviance: 83.23447 on 80 degrees of freedom
Residual Deviance: 61.37993 on 77 degrees of freedom
Number of Fisher Scoring Iterations: 5
Correlation of Coefficients:
(Intercept)
Age
Age -0.4633715
Number -0.8480574
0.2321004
Start -0.3784028 -0.2849547
Number
0.1107516
The summary includes:
1. a replica of the call that generated the fit,
2. a summary of the deviance residuals (we discuss residuals
later in this chapter),
3. a table of estimated regression coefficients, their standard
errors, and the partial t-test of their significance,
4. estimates of the null and residual deviances, and
5. a correlation matrix of the coefficient estimates.
The partial t-tests indicate that Start is important even after adjusting
for Age and Number, but they provide little information on the other
two variables.
You can produce an analysis of deviance for the sequential addition
of each variable by using the anova function, specifying the chi-square
test to test for differences between models. The command below
shows this test for the kyph.glm.all model object.
> anova(kyph.glm.all, test = "Chi")
Analysis of Deviance Table
Binomial model
Response: Kyphosis
Terms added sequentially (first to last)
389
Chapter 12 Generalizing the Linear Model
NULL
Age
Number
Start
Df Deviance Resid. Df Resid. Dev
Pr(Chi)
80
83.23447
1 1.30198
79
81.93249 0.2538510
1 10.30593
78
71.62656 0.0013260
1 10.24663
77
61.37993 0.0013693
Here we see that Number is important after adjusting for Age. We
already know that Number loses its importance after adjusting for Age
and Start. In addition, Age does not appear to be important as a
linear predictor.
You can examine the bivariate relationships between the probability
of Kyphosis and each of the predictors by fitting a “null” model and
then adding each of the terms, one at a time. The null model in this
example has a single intercept term, and is specified with the formula
Kyphosis ~ 1:
> kyph.glm.null <- glm(Kyphosis ~ 1, family = binomial,
+ data = kyphosis)
> add1(kyph.glm.null, ~ . + Age + Number + Start)
Single term additions
Model: Kyphosis ~ 1
Df Sum of Sq
<none>
Age
1
1.29546
Number
1 10.55222
Start
1 16.10805
RSS
81.00000
79.70454
70.44778
64.89195
Cp
83.02500
83.75454
74.49778
68.94195
The Cp statistic is used to compare models that are not nested. A small
Cp value corresponds to a better model, in the sense of a smaller
residual deviance penalized by the number of parameters that are
estimated in fitting the model.
From the above analysis, Start is clearly the best single variable to
use in a linear model. These statistical conclusions, however, should
be verified by looking at graphical displays of the fitted values and
residuals. The plot method for generalized linear models is called
plot.glm, and produces four diagnostic plots:
1. a plot of deviance residuals versus the fitted values,
2. a plot of the square root of the absolute deviance residuals
versus the linear predictor values,
390
Logistic Regression
3. a plot of the response versus the fitted values, and
4. a normal quantile plot of the Pearson residuals.
This set of plots is similar to those produced by the plot method for lm
objects.
Systematic curvature in the residual plots might be indicative of
problems in the choice of link, the wrong scale for one of the
predictors, or omission of a quadratic term in a predictor. Large
residuals can also be detected in these plots, and may be indicative of
outlying observations that need to be removed from the analysis. The
plot of the absolute residuals against predicted values gives a visual
check on the adequacy of the assumed variance function. The normal
quantile plot is useful in detecting extreme observations deviating
from a general trend. However, one should exercise caution in not
over-interpreting the shape of this plot, which is not necessarily of
interest in the nonlinear context.
Figure 12.2 displays the four plots for the model involving all three
predictor variables: Age, Number, and Start. The plots are produced
with the following commands:
> par(mfrow = c(2,2))
> plot(kyph.glm.all)
391
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• ••
•
0.0
0.2
0.4
0.6
1.2
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0.8
Fitted : Age + Number + Start
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Kyphosis
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••••
Predicted : Age + Number + Start
Pearson Residuals
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••
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0.8
Fitted : Age + Number + Start
•
••
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0.8
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••
0.4
0
-1
•••••••
••••••••
••••••
• •
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•• •
sqrt(abs(resid(kyph.glm.all)))
•••
1
2
•••
-2
Deviance Residuals
Chapter 12 Generalizing the Linear Model
•
-2
-1
0
1
2
Quantiles of Standard Normal
Figure 12.2: Plots of the generalized linear model of Kyphosis predicted by Age,
Start, and Number.
Residual plots are not useful for binary data such as Kyphosis,
because all of the points lie on one of two curves depending on
whether the response is 0 or 1. A more useful diagnostic plot is
produced by the plot.gam function. By default, plot.gam plots the
estimated relationship between the individual fitted terms and each of
the corresponding predictors. You can request that partial residuals
be added to the plot by specifying the argument resid=T. The scale
argument can be used to keep all of the plots on the same scale for
ease of comparison. Figure 12.3 is produced with the following
commands:
> par(mfrow = c(1,3))
> plot.gam(kyph.glm.all, resid = T, scale = 6)
392
0
100
200
3
2
Age
2
3
•••
1
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Start
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• •
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•
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•
••
Number
1
0
-2
Age
2
3
Logistic Regression
4
6
8 10
0
Number
5
•••
••• • •
• • • •• • •
• • •••• •
••
10
Start
Figure 12.3: Additional plots of the generalized linear model of Kyphosis
predicted by Age, Number, and Start.
These plots give a quick assessment of how well the model fits the
data by examining the fit of each term in the formula. The plots are of
the adjusted relationship for each predictor, versus each predictor.
When the relationship is linear, the label on the vertical axis reduces
to the variable name. We will see the utility of this plot method and
the reason for the labels in the next section, where we plot additive
models produced by gam.
Both plot.glm and plot.gam produce multiple plots. You can,
however, choose which plots you look at by using the argument
ask=T. This option produces a menu of available plots from which
you select the number of the plot that you would like to see. For
example, here is the menu of default GLM plots:
> plot(kyph.glm.all, ask = T)
Make a plot selection (or 0 to exit):
1: plot: All
2: plot: Residuals vs Fitted Values
3: plot: Sqrt of abs(Residuals) vs Predictions
4: plot: Response vs Fitted Values
5: plot: Normal QQplot of Std. Residuals
Selection:
393
Chapter 12 Generalizing the Linear Model
Fitting an
Additive Model
So far we have examined only linear relationships between the
predictors and the probability of developing Kyphosis. We can assess
the validity of the linear assumption by fitting an additive model with
relationships estimated by smoothing operations, and then comparing
it to the linear fit. We use the gam function to fit an additive model as
follows:
> kyph.gam.all <+ gam(Kyphosis ~ s(Age) + s(Number) + s(Start),
+ family = binomial, data = kyphosis)
Including each variable as an argument to the s function instructs gam
to estimate the “smoothed” relationships with each predictor by using
cubic B-splines. Alternatively, we can use the lo function for local
regression smoothing. A summary of the fit is:
> summary(kyph.gam.all)
Call: gam(formula = Kyphosis ~ s(Age) +s(Number)+ s(Start),
family = binomial, data = kyphosis)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.351358 -0.4439636 -0.1666238 -0.01061843 2.10851
(Dispersion Parameter for Binomial family taken to be 1 )
Null Deviance: 83.23447 on 80 degrees of freedom
Residual Deviance: 40.75732 on 68.1913 degrees of freedom
Number of Local Scoring Iterations: 7
DF for Terms and Chi-squares for Nonparametric Effects
(Intercept)
s(Age)
s(Number)
s(Start)
394
Df Npar Df Npar Chisq
P(Chi)
1
1
2.9
5.782245 0.1161106
1
3.0
5.649706 0.1289318
1
2.9
5.802950 0.1139286
Logistic Regression
The summary of a gam fit is similar to the summary of a glm fit. One
noticeable difference, however, is in the analysis of deviance table.
For an additive fit, the tests correspond to approximate partial tests for
the importance of the smooth for each term in the model. These tests
are typically used to screen variables for inclusion in the model. For a
single-variable model, this is equivalent to testing for a difference
between a linear fit and a smooth fit that includes both linear and
smooth terms. The approximate nature of the partial tests is discussed
in detail in Hastie and Tibshirani (1990).
Since Start is the best single variable to use in the Kyphosis model,
we fit a base GAM with a smooth of Start. For comparison, we fit
two additional models that build on the base model: one with a
smooth of the Age variable and one with a smooth of the Number
variable.
> kyph.gam.start <- gam(Kyphosis ~ s(Start),
+ family = binomial, data = kyphosis)
> kyph.gam.start.age <+ gam(Kyphosis ~ s(Start) + s(Age),
+ family = binomial, data = kyphosis)
> kyph.gam.start.number <+ gam(Kyphosis ~ s(Start) + s(Number),
+ family = binomial, data = kyphosis)
We produce the following analysis of deviance tables:
> anova(kyph.gam.start, kyph.gam.start.age, test = "Chi")
Analysis of Deviance Table
Response: Kyphosis
Terms Resid. Df Resid. Dev
1
s(Start) 76.24543
59.11262
2 s(Start) + s(Age) 72.09458
48.41713
Test
Df Deviance
Pr(Chi)
1
2 +s(Age) 4.150842 10.69548 0.0336071
> anova(kyph.gam.start, kyph.gam.start.number,
+ test = "Chi")
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Chapter 12 Generalizing the Linear Model
Analysis of Deviance Table
Response: Kyphosis
Terms Res.Df
Res.Dev
1
s(Start) 76.24543 59.11262
2 s(Start)+s(Number) 72.18047 54.17895
Test
Df Deviance
Pr(Chi)
1
2 +s(Number) 4.064954 4.933668 0.3023856
The indication is that Age is important in the model even with Start
included, whereas Number is not important under the same conditions.
With the following commands, we plot the fit that includes the Age
and Start variables, adding partial residuals and maintaining the
same scale for all figures:
> par(mfrow = c(2,2))
> plot(kyph.gam.start.age, resid = T, scale = 8)
The result is displayed in the top two plots of Figure 12.4. With the
following command, we plot the fit and add pointwise confidence
intervals:
> plot(kyph.gam.start.age, se = T, scale = 10)
The result is displayed in the bottom two plots of Figure 12.4. Notice
the labels on the vertical axes, which reflect the smoothing operation
included in the modeling.
396
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Logistic Regression
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-4
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s(Age)
-5
s(Start)
0
0
2
Start
100
5
10
15
Start
0
50
100
Age
Figure 12.4: The partial fits for the generalized additive logistic regression model of
Kyphosis with Age and Start as predictors.
The summary of the additive fit with smooths of Age and Start
appears as follows:
> summary(kyph.gam.start.age)
Call: gam(formula = Kyphosis ~ s(Start) + s(Age),
family = binomial, data = kyphosis)
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Chapter 12 Generalizing the Linear Model
Deviance Residuals:
Min
1Q
Median
3Q
Max
-1.694389 -0.4212112 -0.1930565 -0.02753535 2.087434
(Dispersion Parameter for Binomial family taken to be 1 )
Null Deviance: 83.23447 on 80 degrees of freedom
Residual Deviance: 48.41713 on 72.09458 degrees of freedom
Number of Local Scoring Iterations: 6
DF for Terms and Chi-squares for Nonparametric Effects
Df Npar Df Npar Chisq
P(Chi)
(Intercept) 1
s(Start) 1
2.9
7.729677 0.0497712
s(Age) 1
3.0
6.100143 0.1039656
Returning to
the Linear
Model
The plots displayed in Figure 12.4 suggest a quadratic relationship for
Age and a piecewise linear relationship for Start. We return to a
generalized linear model to fit these relationships instead of relying
on the more complicated additive models. In general, it is best to fit
relationships with a linear model if possible, as it results in a simpler
model without losing too much precision in predicting the response.
For Age, we fit a second degree polynomial. For Start, recall that its
values indicate the beginning of the range of the vertebrae involved
in the operation. Values less than or equal to 12 correspond to the
thoracic region of the spine, and values greater than 12 correspond to
the lumbar region. From Figure 12.4, we see that the relationship for
Start is fairly flat for values approximately less than or equal to 12,
and then drops off linearly for values greater than 12. Because of this,
we try fitting a linear model with the term I((Start 12) * (Start > 12)):
> kyph.glm.istart.age2 <+ glm(Kyphosis ~ poly(Age,2) + I((Start-12) * (Start>12)),
+ family = binomial, data = kyphosis)
398
Logistic Regression
The I function is used here to prevent the "*" from being used for
factor expansion in the formula sense. Figure 12.5 displays the
resulting fit, along with the partial residuals and pointwise confidence
intervals. To generate these plots, we use the plot.gam function in the
same way that we did for Figure 12.4:
> par(mfrow = c(2,2))
> plot.gam(kyph.glm.istart.age2, resid = T, scale = 8)
> plot.gam(kyph.glm.istart.age2, se = T, scale = 10)
•
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10
0
50
100
Age
150
200
5
10
15
Start
Figure 12.5: The partial fits for the generalized linear logistic regression model of
Kyphosis with quadratic fit for Age and piecewise linear fit for Start.
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Chapter 12 Generalizing the Linear Model
The summary of the fit follows:
> summary(kyph.glm.istart.age2)
Call: glm(formula = Kyphosis ~ poly(Age, 2) +
I((Start - 12) * (Start > 12)), family = binomial,
data = kyphosis)
Deviance Residuals:
Min
1Q
Median
3Q
Max
-1.42301 -0.5014355 -0.1328078 -0.01416602 2.116452
Coefficients:
Value Std. Error
t value
(Intercept) -0.6849607 0.4570976 -1.498500
poly(Age, 2)1
5.7719269 4.1315471 1.397038
poly(Age, 2)2 -10.3247767 4.9540479 -2.084109
I((Start-12)*(Start>12)) -1.3510122 0.5072018 -2.663658
(Dispersion Parameter for Binomial family taken to be 1 )
Null Deviance: 83.23447 on 80 degrees of freedom
Residual Deviance: 51.95327 on 77 degrees of freedom
Number of Fisher Scoring Iterations: 6
Correlation of Coefficients:
(Intercept) poly(Age,2)1 poly(Age,2)2
poly(Age, 2)1 -0.1133772
poly(Age, 2)2 0.5625194 0.0130579
I((Start-12)*(Start>12)) -0.3261937 -0.1507199 -0.0325155
Contrasting the summary of the linear fit kyph.glm.istart.age2 with
the additive fit kyph.gam.start.age, we can see the following
important details:
1. The linear fit is more parsimonious. The effective number of
parameters estimated in the linear model is approximately 5
less than for the additive model with smooths.
400
Logistic Regression
2. The residual deviance in the linear fit is not significantly
higher than the residual deviance in the additive fit. The
deviance in the linear fit is only about 3.5 more, even though
the effective number of parameters in the linear model is
lower.
3. With a linear fit, we can produce an analytical expression for
the model, which cannot be done for an additive model with
smooth fits. This is because the coefficients in a linear model
are estimated for a parametric relationship, whereas the
smooths in an additive model are nonparametric estimates. In
general, these nonparametric estimates have no analytical
form and are based on an iterative computer algorithm. This
is an important distinction to consider when choosing
between linear models and additive models with smooth
terms.
Finally, we can use the anova function to verify that there is no
difference between the two models kyph.glm.istart.age2 and
kyph.gam.start.age:
> anova(kyph.glm.istart.age2, kyph.gam.start.age,
+ test = "Chi")
Analysis of Deviance Table
Response: Kyphosis
Terms Res. Df Res. Dev
1 poly(Age,2)+I((Start-12)*(Start>12)) 77.00000 51.95327
2
s(Start) + s(Age) 72.09458 48.41713
Test
Df Deviance
Pr(Chi)
1
2 1 vs. 2 4.905415 3.536134 0.6050618
401
Chapter 12 Generalizing the Linear Model
Legal Forms of
the Response
Variable
The required formula argument to glm is in the same format as most
other formulas in S-PLUS, with the response on the left side of a tilde
(~) and the predictor variables on the right. In logistic regression,
however, the response can assume a few different forms:
1. If the response is a logical vector or a two-level factor, it is
treated as a 0/1 binary vector. The zero values correspond to
failures and the ones correspond to successes. This is the form
of the response variable in all of the example kyphosis
models above.
2. If the response is a multilevel factor, S-PLUS assumes the first
level codes failures (0) and all of the remaining levels code
successes (1).
3. If the response is a two-column matrix, S-PLUS assumes the
first column holds the number of successes for each trial and
the second column holds the number of failures.
4. If the response is a general numeric vector, S-PLUS assumes
that it holds the proportion of successes. That is, the ith value
in the response vector is s i ⁄ n i , where s i denotes the number
of successes out of n i total trials. In this case, the n i must be
given as weights to the weights argument in glm.
As an simple example of a two-column response, we tabulate the data
in the Kyphosis variable of the kyphosis data set:
> kyph.table <- table(kyphosis$Kyphosis)
> kyph.mat <- t(as.matrix(kyph.table))
> kyph.mat
absent present
[1,]
64
17
The following call to glm creates a generalized linear model using the
first column of kyph.mat as the response. Because it is the first column
of the matrix, absent is assumed to be a success in the model:
> kyph1.glm <- glm(kyph.mat ~ 1, family = binomial)
402
Logistic Regression
> kyph1.glm
Call:
glm(formula = kyph.mat ~ 1, family = binomial)
Coefficients:
(Intercept)
1.32567
Degrees of Freedom: 1 Total; 0 Residual
Residual Deviance: 0
If we use the full vector Kyphosis in a similar call, S-PLUS assumes
that present is a success in the model. This is because present is the
second level of the factor variable and is therefore coded to the binary
value 1 (success). Likewise, absent is the first level of Kyphosis, and is
therefore coded to 0 (failure):
> levels(kyphosis$Kyphosis)
[1] "absent"
"present"
> kyph2.glm <- glm(Kyphosis ~ 1, family = binomial,
+ data = kyphosis)
> kyph2.glm
Call:
glm(formula = Kyphosis ~ 1, family = binomial, data =
kyphosis)
Coefficients:
(Intercept)
-1.32567
Degrees of Freedom: 81 Total; 80 Residual
Residual Deviance: 83.23447
We can rename absent to be the success indicator with the following
command:
> kyph3.glm <- glm(Kyphosis=="absent" ~ 1,
+ family = binomial, data = kyphosis)
403
Chapter 12 Generalizing the Linear Model
PROBIT REGRESSION
To fit a probit regression model, use either the glm function or the gam
function with a formula to specify the model, and set the family
argument to binomial(link=probit). As an example, consider the
data frame kyphosis. In the previous section, we computed various
logistic regression models for the variables in kyphosis. From our
analysis,
we
determined
that
the
best
model
was
kyph.glm.istart.age2:
> kyph.glm.istart.age2
Call:
glm(formula = Kyphosis ~ poly(Age, 2) + I((Start - 12) *
(Start > 12)),
family = binomial, data = kyphosis)
Coefficients:
(Intercept) poly(Age, 2)1 poly(Age, 2)2
-0.6849607
5.771927
-10.32478
I((Start - 12) * (Start > 12))
-1.351012
Degrees of Freedom: 81 Total; 77 Residual
Residual Deviance: 51.95327
To compute the same model as a probit regression, use the probit
link function as follows:
> kyph.probit <- glm(Kyphosis ~ poly(Age, 2) +
+ I((Start - 12) * (Start > 12)),
+ family = binomial(link=probit), data = kyphosis)
> summary(kyph.probit)
Call: glm(formula = Kyphosis ~ poly(Age, 2) + I((Start - 12)
* (Start > 12)), family = binomial(link = probit), data
= kyphosis)
Deviance Residuals:
Min
1Q
Median
3Q
Max
-1.413873 -0.5227573 -0.09664452 -0.0005086466 2.090332
404
Probit Regression
Coefficients:
Value Std. Error
(Intercept) -0.3990572 0.2516421
poly(Age, 2)1 3.4305340 2.2995511
poly(Age, 2)2 -6.1003327 2.6288017
I((Start - 12) * (Start > 12)) -0.7516299 0.2564483
t value
(Intercept) -1.585813
poly(Age, 2)1 1.491828
poly(Age, 2)2 -2.320575
I((Start - 12) * (Start > 12)) -2.930922
(Dispersion Parameter for Binomial family taken to be 1 )
Null Deviance: 83.23447 on 80 degrees of freedom
Residual Deviance: 51.63156 on 77 degrees of freedom
Number of Fisher Scoring Iterations: 6
Correlation of Coefficients:
(Intercept) poly(Age, 2)1
poly(Age, 2)1 -0.0536714
poly(Age, 2)2 0.4527154
0.0306960
I((Start - 12) * (Start > 12)) -0.3762806 -0.1765981
poly(Age, 2)2
poly(Age, 2)1
poly(Age, 2)2
I((Start - 12) * (Start > 12))
0.00393
Often, it is difficult to distinguish between logistic and probit models,
since the underlying distributions approximate each other well in
many circumstances. That is, the logistic distribution is similar to the
Gaussian distribution, only with longer tails. Unless the sample size is
extremely large, the subtle differences between the two distributions
can be difficult to see. If a substantial proportion of responses are
concentrated in the tails of the distribution, where the logistic and
Gaussian distributions differ, then the probit and logit links can give
significantly different results. When both models fit well, the
405
Chapter 12 Generalizing the Linear Model
parameter estimates in a logistic model are about 1.6 to 1.8 times the
esimates in the probit model. For more details, see either Venables &
Ripley (1997) or Agresti (1990).
406
Poisson Regression
POISSON REGRESSION
To fit a Poisson regression model use either the glm function or the
gam function with a formula to specify the model, and set the family
argument to poisson. In this case, the response variable is discrete
and takes on non-negative integer values. Count data is frequently
modeled as a Poisson distribution. As an example, consider the builtin data frame solder.balance. A summary of the data frame
produces the following:
> attach(solder.balance)
> summary(solder.balance)
Opening
Solder
Mask
S:240 Thin :360 A1.5:180
M:240 Thick:360 A3 :180
L:240
B3 :180
B6 :180
PadType
L9 : 72
W9 : 72
L8 : 72
L7 : 72
D7 : 72
L6 : 72
(Other):288
Panel
1:240
2:240
3:240
skips
Min.
: 0.000
1st Qu.: 0.000
Median : 2.000
Mean : 4.965
3rd Qu.: 6.000
Max. :48.000
The solder experiment, contained in solder.balance, was designed
and implemented in one of AT&T’s factories to investigate
alternatives in the “wave-soldering” procedure for mounting
electronic components on circuit boards. Five different factors were
considered as having an effect on the number of solder skips. A brief
description of each of the factors follows. For more details, see the
paper by Comizzoli, Landwehr, and Sinclair (1990).
•
Opening:
•
Solder:
•
Mask:
•
PadType:
•
Panel:
•
skips:
The amount of clearance around the mounting pad.
The amount of solder.
The type and thickness of the material used for the
solder mask.
The geometry and size of the mounting pad.
The panel number. In the experiment, each board was
divided into three panels, with three runs on a board.
The number of visible solder skips on a circuit board.
407
Chapter 12 Generalizing the Linear Model
Two useful preliminary plots of the data are a histogram of the
response variable skips, and plots of the mean response for each
level of the predictor. Figure 12.6 and Figure 12.7 display the plots, as
generated by the commands below. Figure 12.6 shows the skewness
and long-tailedness typical of count data. We model this behavior
using a Poisson distribution.
0
100
200
300
400
500
> par(mfrow = c(1,1))
> hist(skips)
> plot(solder.balance)
0
10
20
30
40
50
skips
Figure 12.6: A histogram of skips for the solder.balance data.
408
Poisson Regression
S
10
B6
8
Thin
6
D4
D7
W4
B3
2
4
mean of skips
L4
M
L
Thick
L8
D6
L7
L9
L6
2
3
1
A3
A1.5
W9
Opening Solder Mask PadType Panel
Factors
Figure 12.7: A plot of the mean response for each level of each factor.
The plot of the mean skips for different levels of the factors displayed
in Figure 12.7 shows a very strong effect due to Opening. For levels M
and L, only about two skips were seen on average, whereas for level S,
more then 10 skips were seen. Effects almost as strong were seen for
different levels of Mask.
If we do boxplots of skips for each level of the two factors, Opening
and Mask, we get an idea of the distribution of the data across levels of
the factors. Figure 12.8 displays the results of doing “factor” plots on
these two factors.
> par(mfrow = c(1, 2))
> plot.factor(skips ~ Opening + Mask)
Examining Figure 12.8, it is clear that the variance of skips increases
as its mean increases. This is typical of Poisson distributed data.
409
40
30
0
10
20
skips
20
0
10
skips
30
40
Chapter 12 Generalizing the Linear Model
S
M
L
A1.5
Opening
A3
B3
B6
Mask
Figure 12.8: Boxplots for each level of the two factors Opening and Mask.
We proceed now to model skips as a function of the controlled
factors in the experiment. We start with a simple-effects model for
skips as follows:
> paov <- glm(skips ~ ., family = poisson,
+ data = solder.balance)
> anova(paov, test = "Chi")
Analysis of Deviance Table
Poisson model
Response: skips
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL
719
6855.690
Opening 2 2524.562
717
4331.128
Solder 1 936.955
716
3394.173
Mask 3 1653.093
713
1741.080
PadType 9 542.463
704
1198.617
Panel 2
68.137
702
1130.480
410
Pr(Chi)
0.000000e+00
0.000000e+00
0.000000e+00
0.000000e+00
1.554312e-15
Poisson Regression
The chi-squared test is requested in this case because glm assumes that
the dispersion parameter φ = 1 in the variance function; in other
words, glm assumes that there is no under- or over-dispersion in the
model. We use the quasi-likelihood family in glm when we want to
estimate the dispersion parameter as part of the model fitting
computations. We could also set the argument disp to 0 in the
summary function to obtain chi-squared estimates of φ :
> summary(paov, disp = 0)
According to the analysis of deviance, it appears that all of the factors
considered have a very significant influence on the number of solder
skips. The solder experiment contained in solder.balance is
balanced, so we need not be concerned with the sequential nature of
the analysis of deviance table; the tests of a sequential analysis are
identical to the partial tests of a regression analysis when the
experiment is balanced.
Now we fit a second order model. We fit all the simple effects and all
the second order terms except those including Panel (we have looked
ahead and discovered that the interactions with Panel are nonsignificant, marginal, or of less importance than the other
interactions). The analysis of deviance table follows:
> paov2 <- glm(skips ~ . +
+ (Opening + Solder + Mask + PadType) ^ 2,
+ family = poisson, data = solder.balance)
> anova(paov2, test = "Chi")
Analysis of Deviance Table
Poisson model
Response: skips
Terms added sequentially (first to last)
Df Deviance Res.Df Resid. Dev
NULL
719
6855.690
Opening 2 2524.562
717
4331.128
Solder 1 936.955
716
3394.173
Mask 3 1653.093
713
1741.080
PadType 9 542.463
704
1198.617
Pr(Chi)
0.0000000000
0.0000000000
0.0000000000
0.0000000000
411
Chapter 12 Generalizing the Linear Model
Panel 2
Opening:Solder 2
Opening:Mask 6
Opening:PadType 18
Solder:Mask 3
Solder:PadType 9
Mask:PadType 27
68.137
27.978
70.984
47.419
59.806
43.431
61.457
702
700
694
676
673
664
637
1130.480
1102.502
1031.519
984.100
924.294
880.863
819.407
0.0000000000
0.0000008409
0.0000000000
0.0001836068
0.0000000000
0.0000017967
0.0001694012
All of the interactions estimated in paov2 are quite significant.
To verify the fit, we do several different kinds of plots. The first four
are displayed in Figure 12.9, and result from the standard plotting
method for a glm object.
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40
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-2
Deviance Residuals
> par(mfrow = c(2, 2))
> plot(paov2)
50
-4
-2
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2
4
10
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30
40
50
0
2
4
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8
0
Opening + Solder + Mask + PadType + Panel +
Pearson Residuals
• • •• ••
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-2
30
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skips
50
Opening + Solder + Mask + PadType + Panel +d : Opening + Solder + Mask + PadType + Panel
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-2
-1
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Quantiles of Standard Normal
Figure 12.9: Plots of the second order model of skips.
The plot of the observations versus the fitted values shows no great
departures from the model. The plot of the absolute deviance
residuals shows striations due to the discrete nature of the data.
Otherwise, the deviance residual plot does not reveal anything to
make us uneasy about the fit.
412
Poisson Regression
The other plots that are useful for examining the fit are produced by
plot.gam. Figure 12.10 displays plots of the adjusted fit with partial
residuals overlaid for each predictor variable. Since all the variables
are factors, the resulting fit is a step function; a constant is fitted for
each level of a factor. Figure 12.10 is produced by the following
commands:
PadType
B6
4
2
0
B3
A1
.5
A3
k
Mask
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0
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•
•• •
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•••• •••••
•• •••
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••• ••
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ic
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in
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0
•
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•
•
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-2
Opening
2
•
•
••••
••••••••••
••••••••••••••••
••••••••••••••••
• •••
•••
••••••••••••••
••
••••
••••••••••••
••••••••
•••
-2
4
S
M
> par(mfrow = c(2,3))
> plot.gam(paov2, resid = T)
Panel
Figure 12.10: Partial residual plots of the second order model of skips.
The plot.gam function adds a bit of random noise to the coded factor
levels to spread the plotted points out. This allows you to see their
vertical locations more clearly.
413
Chapter 12 Generalizing the Linear Model
Note
The warning message about interaction terms not being saved can be safely ignored here.
These plots produced by plot.gam indicate that the data is modeled
reasonably well. Please note, however, that the default plots will show
only glaring lack of fit.
414
Quasi-Likelihood Estimation
QUASI-LIKELIHOOD ESTIMATION
Quasi-likelihood estimation allows you to estimate regression
relationships without fully knowing the error distribution of the
response variable. Essentially, you provide link and variance
functions that are used in the estimation of the regression coefficients.
Although the link and variance functions are typically associated with
a theoretical likelihood, the likelihood need not be specified, and fewer
assumptions are made in estimation and inference.
As a simple analogy, there is a connection between normal-theory
regression models and least-squares regression estimates. Leastsquares estimation gives identical parameter estimates to those
produced from normal-theory models. However, least-squares
estimation assumes far less; only second moment assumptions are
made by least-squares, compared to full distribution assumptions of
normal-theory models.
Quasi-likelihood estimation for the distributions of Table 12.1 is
analogous to least-squares estimation for the normal distribution. For
the Gaussian family, IRLS is equivalent to standard least-squares
estimation. Used in this context, quasi-likelihood estimation allows us
to estimate the dispersion parameter in under- or over-dispersed
regression models. For example, an under- or over-dispersed logistic
regression model can be estimated using quasi-likelihood
methodology, by supplying the appropriate link and variance
functions for the binomial family.
However, quasi-likelihood estimation extends beyond the families
represented in Table 12.1. Any modeling situation for which suitable
link and variance functions can be derived can be modeled using the
quasi-likelihood methodology. Several good examples of this kind of
application are presented in McCullagh and Nelder (1989).
415
Chapter 12 Generalizing the Linear Model
As an example of quasi-likelihood estimation, we return to a Poisson
regression model for the solder.balance data frame. Recall that we
modeled skips as a function of all the factors, plus all the two-way
interactions except those including Panel. The modeling call was:
> paov2$call
glm(formula = skips ~ . + (Opening + Solder +
Mask + PadType)^2, family = poisson, data
= solder.balance)
When we declare the family argument to be Poisson, the dispersion
parameter is set to 1. In many problems, this assumption is not valid.
We can use the quasi-likelihood methodology to force the estimation
of the dispersion parameter. For the solder experiment, we
accomplish this as follows:
>
+
+
+
paov3 <-glm(formula = skips ~ . +
(Opening + Solder + Mask + PadType) ^ 2,
family = quasi(link="log", var="mu"),
data = solder.balance)
A summary of the fit reveals that the dispersion parameter is
estimated to be 1.4, suggesting over-dispersion:
> summary(paov3)$dispersion
Quasi-likelihood
1.400785
We now recompute the ANOVA table, computing F-statistics to test
for effects:
> anova(paov3, test = "F")
Analysis of Deviance Table
Quasi-likelihood model
Response: skips
Terms added sequentially (first to last)
Df Deviance R.Df Res. Dev F Value
Pr(F)
NULL
719 6855.690
Opening 2 2524.562 717 4331.128 901.1240 0.00000000
Solder 1 936.955 716 3394.173 668.8786 0.00000000
416
Quasi-Likelihood Estimation
Mask 3 1653.093
PadType 9 542.463
Panel 2
68.137
Opening:Solder 2
27.978
Opening:Mask 6
70.984
Opening:PadType 18
47.419
Solder:Mask 3
59.806
Solder:PadType 9
43.431
Mask:PadType 27
61.457
713
704
702
700
694
676
673
664
637
1741.080 393.3729 0.00000000
1198.617 43.0285 0.00000000
1130.480 24.3210 0.00000000
1102.502
9.9864 0.00005365
1031.519
8.4457 0.00000001
984.100
1.8806 0.01494805
924.294 14.2316 0.00000001
880.863
3.4449 0.00036929
819.407
1.6249 0.02466031
All of the factors and interactions are still significant even when we
model the over-dispersion. This gives us more assurance in our
previous conclusions.
417
Chapter 12 Generalizing the Linear Model
RESIDUALS
Residuals are the principal tool for assessing how well a model fits the
data. For regression models, residuals are used to assess the
importance and relationship of a term in the model, as well as to
search for anomalous values. For generalized models, we have the
additional task of assessing and verifying the form of the variance as a
function of the mean response.
Generalized models require a generalization of the residual, so that it
can be used in the same way as the Gaussian residuals of a linear
model. In fact, four different kinds of residuals are defined to assess
how well a generalized model fits, to determine the form of the
variance function, and to diagnose problem observations.
•
"deviance":
Deviance residuals are defined as
D
r i = sign ( y i – μ̂ i ) d i
where d i is the contribution of the ith observation to the
deviance.
The deviance itself is D =
D 2
 i ( ri )
. Consequently,
deviance residuals are reasonable for detecting observations
with unduly large influence in the fitting process, since they
reflect the same criterion that is used in the fitting.
•
"working": Working residuals are the difference between the
working response and the linear predictor at the final iteration
of the IRLS algorithm. They are defined as:
∂η̂ i
W
r i = ( y i – μ̂ i ) -------- .
∂μ̂ i
These residuals are returned when you extract the residuals
component directly from a glm object.
•
"pearson":
The Pearson residuals are defined as
y i – μ̂ i
P
r i = ------------------- .
V ( μ̂ i )
418
Residuals
Their sum-of-squares
2
n
( y i – μ̂ i )
χ =  ---------------------V ( μ̂ i )
i=1
2
is the chi-squared statistic. Pearson residuals are a rescaled
version of the working residuals. When proper account is
P
taken of the associated weights, r i =
•
"response":
W
wi ri .
The response residuals are simply y i – μ̂ i .
You compute residuals for glm and gam objects with the residuals
function, or resid for short. The type argument allows you to specify
one of "deviance", "working", "pearson", or "response". By default,
deviance residuals are computed. To plot the deviance residuals
versus the fitted values of a model, type the following command:
> plot(fitted(glmobj), resid(glmobj))
Alternatively, to plot the Pearson residuals versus the fitted values,
type:
> plot(fitted(glmobj), resid(glmobj, type = "pearson"))
Selecting which residuals to plot is somewhat a matter of personal
preference. The deviance residual is the default because a large
deviance residual corresponds to an observation that does not fit the
model well, in the same sense that a large residual for the linear
model does not fit well. You can find additional detail on residuals in
McCullagh and Nelder (1989).
419
Chapter 12 Generalizing the Linear Model
PREDICTION FROM THE MODEL
Prediction for generalized linear models and generalized additive
models is similar to prediction for linear models. An important point
to remember, however, is that for either of the generalized models,
predictions can be on one of two scales. You can predict:
•
on the scale of the linear predictor, which is the transformed
scale after applying the link function, or
•
on the scale of the original response variable.
Since prediction is based on the linear predictor η ( x ) , computing
predicted values on the scale of the original response effectively
transforms η ( x ) (evaluated at the predictor data) via the inverse link
function.
The type argument to either predict.glm or predict.gam allows you
to choose one of three options for predictions.
1. "link": Computes predictions on the scale of the linear
predictor (the link scale).
2. "response": Computes predictions on the scale of the
response.
3. "terms": Computes a matrix of predictions on the scale of the
linear predictor, one column for each term in the model.
Specifying type="terms" allows you to compute the component of
the prediction for each term separately. Summing the columns of the
matrix and adding the intercept term is equivalent to specifying
type="link".
Predicting the
Additive Model
of Kyphosis
As an example, consider the additive model with Kyphosis modeled
as smooths of Start and Age:
> kyph.gam.start.age
Call:
gam(formula = Kyphosis ~ s(Start) + s(Age),
family = binomial, data = kyphosis)
Degrees of Freedom: 81 total; 72.09458 Residual
Residual Deviance: 48.41713
420
Prediction from the Model
If we are interested in plotting the prediction surface over the range of
the data, we start by generating appropriate sequences of values for
each predictor. We then store the sequences in a data frame with
variable labels that correspond to the variables in the model:
>
>
+
+
attach(kyphosis)
kyph.margin <- data.frame(
Start = seq(from=min(Start), to=max(Start), length=40),
Age = seq(from=min(Age), to=max(Age), length=40))
Since a GAM is additive, we need to do predictions only at the
margins and then sum them together to form the entire prediction
surface. We produce the marginal fits by specifying type="terms".
> margin.fit <- predict(kyph.gam.start.age, kyph.margin,
+ type = "terms")
Now generate the surface for the marginal fits.
> kyph.surf <- outer(margin.fit[,1], margin.fit[,2], "+")
> kyph.surf <- kyph.surf + attr(margin.fit, "constant")
> kyph.surf <- binomial()$inverse(kyph.surf)
The first line adds the marginal pieces of the predictions together to
create a matrix of surface values, the second line adds in the constant
intercept term, and the third line applies the inverse link function to
transform the predictions back to the scale of the original response.
Now we produce the plot using the persp function (or contour or
image if we wish):
> persp(kyph.margin[,1], kyph.margin[,2], kyph.surf,
+ xlab = "Start", ylab = "Age", zlab = "Kyphosis")
Figure 12.11 displays the resulting plot.
421
Kyphosis
0 0.2 0.4 0.6 0.8 1
Chapter 12 Generalizing the Linear Model
20
0
15
0
15
10
Ag
0
e
50
5
10
rt
Sta
Figure 12.11: Plot of the probability surface for developing Kyphosis based age in
months and start position.
Safe Prediction
Prediction for linear and generalized linear models is a two-step
procedure.
1. Compute a model matrix using the new data where you want
predictions.
2. Multiply the model matrix by the coefficients extracted from
the fitted model.
This procedure works perfectly fine as long as the model has no
composite terms that are dependent on some overall summary of a
variable. For example:
(x - mean(x))/sqrt(var(x))
(x - min(x))/diff(range(x))
poly(x)
bs(x)
ns(x)
The reason that the prediction procedure does not work for such
composite terms is that the resulting coefficients are dependent on the
summaries used in computing the terms. If the new data are different
from the original data used to fit the model (which is more than likely
when you provide new data), the coefficients are inappropriate. One
way around this problem is to eliminate such dependencies on data
422
Prediction from the Model
summaries. For example, change mean(x) and var(x) to their
numeric values, rather than computing them from the data at the time
of fitting the model. For the spline functions bs and ns, provide the
knots explicity in the call to the function, rather than letting the
function compute them from the overall data. If the removal of
dependencies on the overall data is possible, prediction can be made
safe for new data. However, when the dependencies cannot be
removed, as is the case when using s or lo in gam, use the
predict.gam function explicitly. This function computes predictions
in as safe a way as possible, given the need for generality. To illustrate
this method, suppose that the data used to produce a generalized fit is
named old.data, and new.data is supplied for predictions:
1. A new data frame, both.data, is constructed by combining
old.data and new.data.
2. The model frame and model matrix are constructed from the
combined data frame both.data. The model matrix is
O
n
separated into two pieces X and X , corresponding to
old.data and new.data.
O
3. The parametric part of fit is refit using X .
n
4. The coefficients from this new fit are then applied to X to
obtain the new predictions.
5. For gam objects with both parametric and nonparametric
components, an additional step is taken to evaluate the fitted
nonlinear functions at the new data values.
This procedure works perfectly for terms with mean and var in them,
as well as for poly. For other kinds of composite terms, such as bs
knots placed at equally spaced (in terms of percentiles) quantiles of
the distribution of the predictor, predict.gam works approximately.
Because the knots produced by the combined data will, in general, be
different from the knots produced by the original data, there will be
some error in predicting the new data. If the old data and the new
data have roughly the same distribution, the error in predicting the
new data should be small.
423
Chapter 12 Generalizing the Linear Model
ADVANCED TOPICS
Fixed
Coefficients
A commonly used device in generalized linear models is the offset,
which is a component of the linear predictor that has a fixed
coefficient. The effect of these components is to offset the value of the
linear predictor by a certain fixed amount. In S-PLUS, you can
specify offsets in GLMs by including offset terms directly in the
model formula. For example, consider the following simple logistic
regression model for the kyphosis data set:
> fit1 <- glm(Kyphosis ~ Age + Start,
+ family=binomial, data=kyphosis)
The coef function returns the coefficients of the model:
> coef(fit1)
(Intercept)
Age
Start
0.2250435 0.009507095 -0.237923
With the following syntax, we can force the intercept to be 0.25 and
the coefficient for Age to be 0.01:
> fit2 <- glm(Kyphosis ~
+ offset(0.25 + 0.01*Age) + Start - 1,
+ family=binomial, data=kyphosis)
> coef(fit2)
Start
-0.2443723
The -1 in the model formula is needed to prevent the fitting of an
intercept term, since it is already included in the offset component.
Offsets allow for a kind of residual analysis in generalized linear
models. By specifying offsets, you can evaluate the contribution of
particular terms to a fit, while holding other terms constant. In
addition, a variable can be included as both a regression term and an
offset in a model formula. With this kind of model, you can test the
hypothesis that the variable’s regression coefficient is any fixed value.
424
Advanced Topics
Family Objects
The combination of a link and variance function comprise a family in
generalized linear models and generalized additive models. An SPLUS family object includes the link function, its derivative, the
variance and deviance functions, and a method for obtaining starting
values in the fitting algorithm. There are many combinations of link
and variance functions that are common in GLMs, but only some are
included in S-PLUS. If you would like to use a family in your analysis
that is not yet part of S-PLUS, you will need to use the make.family
function. This constructor requires the arguments listed below.
•
name:
A character string giving the name of the family.
•
link: A list containing information about the link function,
including its inverse, derivative, and initialization expression.
•
variance: A list supplying the variance and deviance
functions.
The data sets glm.links and glm.variances provide the necessary
information for the link and variance functions included in S-PLUS.
The information in these data sets can be used as templates when
defining custom links and variances. For example, the following
command lists the necessary information for the probit link:
> glm.links[, "probit"]
$names:
[1] "Probit: qnorm(mu)"
$link:
function(mu)
qnorm(mu)
$inverse:
function(eta)
pnorm(eta)
$deriv:
function(mu)
sqrt(2 * pi) * exp((qnorm(mu)^2)/2)
425
Chapter 12 Generalizing the Linear Model
$initialize:
expression({
if(is.matrix(y)) {
if(dim(y)[2] > 2)
stop("only binomial response matrices (2
columns)")
n <- drop(y %*% c(1, 1))
y <- y[, 1]
}
else {
if(is.category(y))
y <- y != levels(y)[1]
else y <- as.vector(y)
n <- rep(1, length(y))
}
w <- w * n
n[n == 0] <- 1
y <- y/n
mu <- y + (0.5 - y)/n
}
)
We provide two examples below: one defines a new variance
function for quasi-likelihood estimation, and one defines a new family
for the negative binomial distribution.
Example: quasi-likelihood estimation
In S-PLUS, quasi-likelihood estimation is performed with the
family=quasi option in glm and gam. This option allows you to
specify any combination of the link and variance functions from
Table 12.1. No distributional assumptions are made, and the model is
fit directly from the combination of the link and variance. If you
require a link or variance function for your quasi-likelihood model
that is not included in Table 12.1, you will need to create a new one.
We use the leaf blotch example from McCullagh and Nelder (1989) to
illustrate one approach for doing this.
The data in Table 12.2 is from a 1965 experiment concerning the
incidence of Rhynchosporium secalis, or leaf blotch. Ten varieties of
barley were grown at each of nine sites, and the percentage of total
leaf area affected by the disease was recorded.
426
Advanced Topics
Table 12.2: Percentage of total leaf area affected by Rhynchosporium secalis, for ten varieties of barley grown at
nine different sites.
Variety
Site
1
2
3
4
5
6
7
8
9
10
1
0.05
0.00
0.00
0.10
0.25
0.05
0.50
1.30
1.50
1.50
2
0.00
0.05
0.05
0.30
0.75
0.30
3.00
7.50
1.00
12.70
3
1.25
1.25
2.50
16.60
2.50
2.50
0.00
20.00
37.50
26.25
4
2.50
0.50
0.01
3.00
2.50
0.01
25.00
55.00
5.00
40.00
5
5.50
1.00
6.00
1.10
2.50
8.00
16.50
29.50
20.00
43.50
6
1.00
5.00
5.00
5.00
5.00
5.00
10.00
5.00
50.00
75.00
7
5.00
0.10
5.00
5.00
50.00
10.00
50.00
25.00
50.00
75.00
8
5.00
10.00
5.00
5.00
25.00
75.00
50.00
75.00
75.00
75.00
9
17.50
25.00
42.50
50.00
37.50
95.00
62.50
95.00
95.00
95.00
Wedderburn (1974) suggested a linear logistic model for these data,
with a variance function given by the square of the variance for the
binomial distribution:
2
2
var ( Y ) = μ ( 1 – μ ) .
As this variance is not included in S-PLUS, we must first define it
before continuing with the analysis.
To build a new variance function, a set of names, a variance, and a
deviance are all needed. We use the binomial variance, stored in the
"mu(1-mu)" column of glm.variances, as a template for creating our
squared.binomial variance function.
427
Chapter 12 Generalizing the Linear Model
>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
squared.binomial <- list(
name = "Binomial Squared: mu^2*(1-mu)^2",
variance = function(mu) mu^2 * (1 - mu)^2,
deviance = function(mu, y, w, residuals = F)
{
devy <- y
nz <- y != 0
devy[nz] <- (2*y[nz]-1) * log(y[nz] / (1-y[nz])) - 2
devmu <- (2*y-1)*log(mu/(1-mu)) - y/mu - (1-y)/(1-mu)
if(any(small <- mu^2*(1-mu^2) < .Machine$double.eps))
{
warning("fitted values close to 0 or 1")
smu <- mu[small]
sy <- y[small]
smu <- ifelse(smu < .Machine$double.eps,
.Machine$double.eps, smu)
onemsmu <- ifelse((1 - smu) < .Machine$double.eps,
.Machine$double.eps, 1 - smu)
devmu[small] <- (2*sy-1)*(log(smu)-log(onesmu)) sy/smu - (1 - sy)/(onesmu)
}
devi <- 2 * (devy - devmu)
if(residuals) sign(y - mu) * sqrt(abs(devi) * w)
else sum(devi)
}
)
We can now use the squared binomial variance when computing
quasi-likelihood models. For example, the commands below compute
Wedderburn’s model for the leaf blotch data. We create an R.secalis
data set containing the information from Table 12.2, and then call glm
with the family=quasi option. For clarity, we convert the data values
to decimal percentages.
> R.secalis <- data.frame(
+ fac.design(c(9,10), factor.names = list(
+ site = 1:9, variety = 1:10)),
+ incidence = scan())
1: 0.0005 0 0.0125 0.025 0.055 0.01 0.05 0.05 0.175
10: 0 0.0005 0.0125 0.005 0.01 0.05 0.001 0.1 0.25
19: 0 0.0005 0.025 0.0001 0.06 0.05 0.05 0.05 0.425
28: 0.001 0.003 0.166 0.03 0.011 0.05 0.05 0.05 0.5
428
Advanced Topics
37:
46:
55:
64:
73:
82:
91:
0.0025 0.0075 0.025 0.025 0.025 0.05 0.5 0.25 0.375
0.0005 0.003 0.025 0.0001 0.08 0.05 0.1 0.75 0.95
0.005 0.03 0 0.25 0.165 0.1 0.5 0.5 0.625
0.013 0.075 0.2 0.55 0.295 0.05 0.25 0.75 0.95
0.015 0.01 0.375 0.05 0.2 0.5 0.5 0.75 0.95
0.015 0.127 0.2625 0.4 0.435 0.75 0.75 0.75 0.95
> R.secalis
site variety incidence
1
1
1
0.0005
2
2
1
0.0000
3
3
1
0.0125
4
4
1
0.0250
5
5
1
0.0550
6
6
1
0.0100
7
7
1
0.0500
8
8
1
0.0500
9
9
1
0.1750
10
1
2
0.0000
. . .
# Set treatment contrasts before calling glm.
> options(contrasts = c("contr.treatment", "contr.poly"))
>
+
+
+
secalis.quasi <- glm(incidence ~ site + variety,
data = R.secalis,
family = quasi(link=logit, variance=squared.binomial),
control = glm.control(maxit = 50))
The coefficients and standard errors for our model match those
originally computed by Wedderburn:
> coef(secalis.quasi)
(Intercept)
site2
site3
site4
site5
site6
-7.920978 1.382404 3.857455 3.557023 4.10487 4.30132
site7
site8
site9
variety2 variety3 variety4
4.917166 5.691471 7.065438 -0.4641615 0.0816659 0.9547215
variety5 variety6 variety7 variety8 variety9 variety10
1.352033 1.333007 2.339617 3.262141 3.135984 3.887684
429
Chapter 12 Generalizing the Linear Model
Example: negative binomial distribution
The negative binomial distribution arises when modeling
“overdispersed Poisson data,” which is frequency data in which the
variance is greater than mean. This type of data can arise in Poisson
processes that have variable length, or in processes where each event
contributes a variable amount to the total. The negative binomial
distribution assumes many forms in these contexts; we create a new
family for a particular form in which the variance is quadratic. For
additional technical details, see Venables and Ripley (1997) and
McCullagh and Nelder (1989).
Suppose we have a response variable Y that is Poisson with a mean of
Z . We assume that Z itself is random, and follows a gamma
2
distribution with mean μ and variance μ + μ ⁄ θ , for a parameter θ .
Thus, the variance of Z is proportional to the square of its mean. This
mixture of distributions results in the following negative binomial
distribution for Y :
y θ
Γ ( θ + y )μ θ
,
f μ, θ ( y ) = ------------------------------------------θ+y
Γ ( θ )y! ( μ + θ )
where y = 1, 2, … and Γ is the gamma function. For fixed θ , the
negative binomial distribution in this form has a canonical link given
by
μ
η ( μ ) = log  ------------
 μ + θ
2
and the variance function var ( Y ) = μ + μ ⁄ θ .
We use the make.family function to create a family for the negative
binomial distribution. For simplicity, we use the code for the log and
logit link functions as templates for creating the negative binomial
link. The code for the variance function below is taken from Venables
and Ripley (1997).
> neg.binomial <- function(theta =
+
stop("theta must be given")) {
+
nb.link <- list(
+
names = "log(mu/(mu + theta))",
+
link = substitute(function(mu, th = .Theta)
430
Advanced Topics
+
+
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+
log(mu/(mu + th)),
frame = list(.Theta = theta)),
inverse = substitute(function(eta, th = .Theta)
{
tmp <- care.exp(eta)
return((tmp * th) / (1 - tmp))
},
frame = list(.Theta = theta)),
deriv = substitute(function(mu, th = .Theta)
{
d <- mu * (mu + th)
if(any(tiny <- (d < .Machine$double.eps))) {
warning("Model unstable")
d[tiny] <- .Machine$double.eps
}
return(th / d)
},
frame = list(.Theta = theta)),
initialize = expression(mu <- y + (y==0)/6)
)
nb.variance <- list(
names = "mu + mu^2/theta",
variance = substitute(function(mu, th = .Theta)
mu * (1 - mu/th),
frame = list(.Theta = theta)),
deviance = substitute(
function(mu, y, w, residuals = F, th = .Theta)
{
devi <- 2 * w * (y * log(pmax(1,y) / mu) (y + th) * log((y + th) / (mu + th)))
if(residuals)
return(sign(y - mu) * sqrt(abs(devi)))
else
return(sum(devi))
},
frame = list(.Theta = theta))
)
make.family(
name = "Negative binomial",
link = nb.link,
variance = nb.variance) }
431
Chapter 12 Generalizing the Linear Model
REFERENCES
Agresti, Alan. (1990). Categorical Data Analysis. New York: John &
Sons.
Chambers, J.M. and Hastie, T.J. (Eds.) (1993). Statistical Models in S.
London: Chapman and Hall.
Comizzoli R.B., Landwehr J.M., and Sinclair J.D. (1990). Robust
materials and processes: Key to reliability. AT&T Technical Journal,
69(6): 113--128.
Hastie, T. and Tibshirani, R. (1990). Generalized Additive Models.
London: Chapman and Hall.
McCullagh, P. and Nelder, J.A. (1989). Generalized Linear Models (2nd
ed.). London: Chapman and Hall.
Nelder, J.A. and Wedderburn, R.W.M. (1972). Generalized linear
models. Journal of the Royal Statistical Society (Series A) 135: 370-384.
Venables, W.N. and Ripley, B.D. (1997). Modern Applied Statistics with
S-PLUS (2nd ed.). New York: Springer-Verlag, Inc.
Wedderburn, R.W.M. (1974). Quasilikelihood functions, generalized
linear models and the Gauss-Newton method. Biometrika 61: 439-447.
432
LOCAL REGRESSION MODELS
13
Introduction
434
Fitting a Simple Model
435
Diagnostics: Evaluating the Fit
436
Exploring Data with Multiple Predictors
Conditioning Plots
Creating Conditioning Values
Constructing a Conditioning Plot
Analyzing Conditioning Plots
439
439
441
441
443
Fitting a Multivariate Loess Model
446
Looking at the Fitted Model
452
Improving the Model
455
433
Chapter 13 Local Regression Models
INTRODUCTION
In both Chapter 10, Regression and Smoothing for Continuous
Response Data, and Chapter 12, Generalizing the Linear Model, we
discuss fitting curves or surfaces to data. In both of these earlier
chapters, a significant limitation on the surfaces considered was that
the effects of the terms in the model were expected to enter the model
additively, without interactions between terms.
Local regression models provide much greater flexibility in that the
model is fitted as a single smooth function of all the predictors. There
are no restrictions on the relationships among the predictors.
Local regression models in S-PLUS are created using the loess
function, which uses locally weighted regression smoothing, as
described in the section Smoothing on page 290. In that section, the
focus was on the smoothing function as an estimate of one predictor’s
contribution to the model. In this chapter, we use locally weighted
regression to fit the complete regression surface.
434
Fitting a Simple Model
FITTING A SIMPLE MODEL
As a simple example of a local regression model, we return to the
ethanol data discussed in Chapter 10, Regression and Smoothing for
Continuous Response Data. We start by considering only the two
variables NOx and E. We smoothed these data with loess.smooth in
the section Smoothing on page 290. Now we use loess to create a
complete local regression model for the data.
We fit an initial model to the ethanol data as follows, using the
argument span=1/2 to specify that each local neighborhood should
contain about half of the observations:
> ethanol.loess <- loess(NOx ~ E, data = ethanol,
+ span = 1/2)
> ethanol.loess
Call:
loess(formula = NOx ~ E, data = ethanol, span = 1/2)
Number of Observations:
88
Equivalent Number of Parameters: 6.2
Residual Standard Error:
0.3373
Multiple R-squared:
0.92
Residuals:
min
1st Q
median 3rd Q
max
-0.6656 -0.1805 -0.02148 0.1855 0.8656
The equivalent number of parameters gives an estimate of the complexity
of the model. The number here, 6.2, indicates that the local regression
model is somewhere between a fifth and sixth degree polynomial in
complexity. The default print method for "loess" objects also
2
includes the residual standard error, multiple R , and a five number
summary of the residuals.
435
Chapter 13 Local Regression Models
DIAGNOSTICS: EVALUATING THE FIT
How good is our initial fit? The following function calls plot the loess
object against a scatter plot of the original data:
attach(ethanol)
plot(ethanol.loess, xlim = range(E),
ylim = range(NOx, fitted(ethanol.loess)))
points(E, NOx)
4
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E
Figure 13.1: Locally weighted smooth of ethanol data.
The resulting plot, shown in Figure 13.1, captures the trend
reasonably well. The following expressions plot the residuals against
the predictor E to check for lack of fit:
> scatter.smooth(E, resid(ethanol.loess), span = 1,
+ degree = 1)
> abline(h = 0)
The resulting plot, shown in Figure 13.2, indicates no lack of fit.
436
Diagnostics: Evaluating the Fit
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Figure 13.2: Residual plot for loess smooth.
Is there a surplus of fit? That is, can we increase the span of the data
and still get a good fit? To see, let’s refit our model, using update:
> ethanol.loess2 <- update(ethanol.loess, span = 1)
> ethanol.loess2
Call:
loess(formula = NOx ~ E, data = ethanol, span = 1)
Number of Observations:
88
Equivalent Number of Parameters: 3.5
Residual Standard Error:
0.5126
Multiple R-squared:
0.81
Residuals:
min
1st Q median 3rd Q
max
-0.9791 -0.4868 -0.064 0.3471 0.9863
437
Chapter 13 Local Regression Models
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3
4
By increasing the span, we reduce somewhat the equivalent number
of parameters; this model is thus more parsimonious than our first
model. We do seem to have lost some fit and gained some residual
error. The diagnostic plots, shown in Figure 13.3, reveal a less
satisfying fit in the main plot, and much obvious structure left in the
residuals.
1.2
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Figure 13.3: Diagnostic plots for loess fit with span 1.
The residuals are also more broadly spread than those of the first
model. We confirm this with a call to anova as follows:
> anova(ethanol.loess2, ethanol.loess)
Model 1:
loess(formula = NOx ~ E, data = ethanol, span = 1)
Model 2:
loess(formula = NOx ~ E, data = ethanol, span = 1/2)
Analysis of Variance Table
ENP
RSS
Test
F Value
Pr(F)
1
3.5 22.0840
1 vs 2
32.79 8.2157e-15
2
6.2 9.1685
The difference between the models is highly significant, so we stick
with our original model.
438
Exploring Data with Multiple Predictors
EXPLORING DATA WITH MULTIPLE PREDICTORS
Conditioning
Plots
The ethanol data set actually has three variables, with the
compression ratio, C, of the engine as another predictor joining the
equivalence ratio E and the response, nitric oxide emissions, NOx. A
summary of the data is shown below:
> summary(ethanol)
NOx
Min.
:0.370
1st Qu.:0.953
Median :1.754
Mean
:1.957
3rd Qu.:3.003
Max.
:4.028
C
Min.
: 7.500
1st Qu.: 8.625
Median :12.000
Mean
:12.030
3rd Qu.:15.000
Max.
:18.000
E
Min.
:0.5350
1st Qu.:0.7618
Median :0.9320
Mean
:0.9265
3rd Qu.:1.1100
Max.
:1.2320
A good place to start an analysis with two or more predictors is a
pairwise scatter plot, as generated by the pairs function:
> pairs(ethanol)
The resulting plot is shown in Figure 13.4. The top row shows the
nonlinear dependence of NOx on E, and no apparent dependence of
NOx on C. The middle plot in the bottom row shows E plotted against
C. This plot reveals no apparent correlation between the predictors,
and shows that the compression ratio C takes on only 5 distinct values.
Another useful plot for data with two predictors is the perspective
plot. This lets us view the response as a surface over the predictor
plane.
> persp(interp(E, C, NOx), xlab = "E", ylab = "C",
+ zlab = "NOx")
The resulting plot is shown in Figure 13.5.
439
Chapter 13 Local Regression Models
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18
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Figure 13.5: Perspective plot of ethanol data.
440
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E
1
1.1
1.2
Exploring Data with Multiple Predictors
One conclusion we cannot draw from the pairwise scatter plot is that
there is no effect of C on NOx. Such an effect might well exist, but be
masked by the strong effect of E. Another type of plot, the conditioning
plot, or coplot, can reveal such hidden effects.
A coplot shows how a response depends upon a predictor given other
predictors. Basically, the idea is to create a matrix of conditioning
panels; each panel graphs the response against the predictor for those
observations whose value of the given predictor lie in an interval.
To create a coplot:
1. (Optional) Create the conditioning values. The coplot
function creates default values if conditioning values are
omitted, but they are not usually as good as those created
specifically for the data at hand.
2. Use the coplot function to create the plot.
We discuss these steps in detail in the following subsections.
Creating
Conditioning
Values
How you create conditioning values depends on the nature of the
values taken on by the predictor, whether continuous or discrete.
For continuous data, the conditioning values are intervals, created
using the function co.intervals. For example, the following call
creates nine intervals for the predictor E:
> E.intervals <- co.intervals(E, number = 9, overlap = 1/4)
For data taking on discrete values, the conditioning values are the
sorted, unique values. For example, the following call creates the
conditioning values for the predictor C:
> C.points <- sort(unique(C))
Constructing a
Conditioning
Plot
To construct a conditioning plot, use coplot using a formula with the
special form A ~ B | C, where A is the response, B is the predictor of
interest, and C is the given predictor. Thus, to see the effect of C on
NOx given E, use the formula NOx ~ C | E.
441
Chapter 13 Local Regression Models
In most cases, you also want to specify one or both of the following
arguments:
•
given.values:
•
panel:
The conditioning values created above.
A function of x and y used to determine the method of
plotting in the dependence panels. The default is points.
To create the conditioning plot shown in Figure 13.6:
> coplot(NOx ~ C | E, given.values = E.intervals)
Given : E
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Figure 13.6: Conditioning plot of ethanol data.
•
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C
442
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12
14
16
18
Exploring Data with Multiple Predictors
To read the coplot, move from left to right, bottom to top. The scatter
plots on the bottom row show an upward trend, while those on the
upper two rows show a flat trend. We can more easily see the trend
by using a smoothing function inside the conditioning panels, which
we can do by specifying the panel argument to coplot as follows:
> coplot(NOx ~ C | E, given.values = E.intervals,
+ panel = function(x, y) panel.smooth(x, y,
+ degree = 1, span = 1))
The resulting plot is shown in Figure 13.7.
Given : E
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Figure 13.7: Smooth conditioning plot of ethanol data.
443
Chapter 13 Local Regression Models
This plot clearly shows that for low values of E, NOx increases linearly
with C, while for higher values of E, NOx remains constant with C.
Conversely, the coplot for the effects of E on NOx given C is created
with the following call to coplot, and shown in Figure 13.8:
> coplot(NOx ~ E | C, given.values = C.points,
+ panel = function(x, y) panel.smooth(x, y, degree = 2,
+ span = 2/3))
Given : C
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Figure 13.8: Smooth conditioning plot of ethanol data, conditioned on C.
444
•
Exploring Data with Multiple Predictors
Comparing the two coplots, we can see that NOx changes more rapidly
as a function of E with C fixed than as a function of C with E fixed.
Also, the variability of the residuals is small compared to the effect of
E, but noticeable compared to the effect of C.
445
Chapter 13 Local Regression Models
FITTING A MULTIVARIATE LOESS MODEL
We have learned quite a bit about the ethanol data without fitting a
model: there is a strong nonlinear dependence of NOx on E and there
is an interaction between C and E. We can use this knowledge to
shape our initial local regression model. First, we specify a formula
that includes as predictors both E and C, namely NOx ~ C * E. Then,
we accept the default of local quadratic fitting to better model the
nonlinear dependence.
> ethanol.m <- loess(NOx ~ C * E, data = ethanol)
> ethanol.m
Call:
loess(formula = NOx ~ C * E, data = ethanol)
Number of Observations:
88
Equivalent Number of Parameters: 9.4
Residual Standard Error:
0.3611
Multiple R-squared:
0.92
Residuals:
min
1st Q
median 3rd Q
max
-0.7782 -0.3517 -0.05283 0.195 0.6338
We search for lack of fit by plotting the residuals against each of the
predictors:
>
>
>
>
>
par(mfrow = c(1,2))
scatter.smooth(C, residuals(ethanol.m), span = 1, deg=2)
abline(h = 0)
scatter.smooth(E, residuals(ethanol.m), span = 1, deg=2)
abline(h = 0)
The resulting plot is shown in Figure 13.9. The right-hand plot in the
figure shows considerable lack of fit, so we reduce the span from the
default 0.75 to 0.4:
> ethanol.m2 <- update(ethanol.m, span = .4)
446
Fitting a Multivariate Loess Model
> ethanol.m2
Call: loess(formula = NOx ~ C * E, data = ethanol,
span = 0.4)
Number of Observations:
88
Equivalent Number of Parameters: 15.3
Residual Standard Error:
0.2241
Multiple R-squared:
0.97
Residuals:
min
1st Q
median 3rd Q
max
-0.4693 -0.1865 -0.03518 0.1027 0.3739
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Repeating the commands for generating the diagnostic plots with
ethanol.m2 replacing ethanol.m yields the plot shown in Figure
13.10.
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Figure 13.9: Diagnostic plot for loess model of ethanol data.
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Figure 13.10: Diagnostic plot for first revised model.
447
Chapter 13 Local Regression Models
The right-hand plot in Figure 13.10 looks better but still has some
quadratic structure, so we shrink the span still further, and try again:
> ethanol.m3 <- update(ethanol.m, span = .25)
> ethanol.m3
Call:
loess(formula = NOx ~ C * E, data = ethanol, span = 0.25)
Number of Observations:
Equivalent Number of Parameters:
Residual Standard Error:
Multiple R-squared:
Residuals:
min
1st Q median
3rd Q
-0.3975 -0.09077 0.00862 0.06205
88
21.6
0.1761
0.98
max
0.3382
Again, we create the appropriate residuals plots to check for lack of
fit. The result is shown in Figure 13.11. This time the fit is much better.
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Figure 13.11: Diagnostic plot for second revised model.
Another check on the fit is provided by coplots using the residuals as
the response variable:
>
+
+
+
+
448
coplot(residuals(ethanol.m3) ~ C | E,
given = E.intervals,
panel= function(x, y)
panel.smooth(x, y, degree = 1, span = 1,
zero.line = TRUE))
Fitting a Multivariate Loess Model
>
+
+
+
coplot(residuals(ethanol.m3) ~ E | C, given = C.points,
panel= function(x, y)
panel.smooth(x, y, degree = 1, span = 1,
zero.line = TRUE))
The resulting plots are shown in Figure 13.12 and Figure 13.13. The
middle row of Figure 13.12 shows some anomalies—the residuals are
virtually all positive. However, the effect is small, and limited in
scope, so it can probably be ignored.
Given : E
0.6
0.8
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Figure 13.12: Conditioning plot on E for second revised model.
449
Chapter 13 Local Regression Models
Given : C
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Figure 13.13: Conditioning plot on C for second revised model.
As a final test, we create several additional diagnostic plots to check
the distribution of the error terms. The plots generated by the
following commands are shown in Figure 13.14.
>
>
>
>
450
par(mfrow=c(2, 2))
plot(fitted(ethanol.m3), sqrt(abs(resid(ethanol.m3))))
plot(C, sqrt(abs(resid(ethanol.m3))))
plot(E, sqrt(abs(resid(ethanol.m3))))
Fitting a Multivariate Loess Model
> qqnorm(resid(ethanol.m3))
> qqline(resid(ethanol.m3))
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Quantiles of Standard Normal
Figure 13.14: Diagnostic plots for second revised model.
The model passes these checks; the errors appear to be Gaussian, or
nearly so.
451
Chapter 13 Local Regression Models
LOOKING AT THE FITTED MODEL
Examining the fitted model graphically is no less important than
graphically examining the data. One way to test the model is to
compare the predicted surface with the data surface shown in Figure
13.5 . We can create the corresponding perspective plot for the model
as follows. First, define an evenly-spaced grid of points spanning the
range of E and C:
> newC <- seq(from = min(C), to = max(C), length = 40)
> newE <- seq(from = min(E), to = max(E), length = 40)
> new.ethanol <- expand.grid(E = newE, C = newC)
The expand.grid function returns a data frame with 1600 rows and 2
columns, corresponding to all possible combinations of newC and
newE. We can then use predict with the fitted model and these new
data points to calculate predicted values for each of these grid points:
> eth.surf <- predict(ethanol.m3, new.ethanol)
The perspective plot of the surface is then created readily as follows:
> persp(newE, newC, eth.surf, xlab = "E",
+ ylab = "C")
0 1
2
Z
3
4
5
The resulting plot is shown in Figure 13.15.
18
16
14
C
12
10
8
Figure 13.15: Perspective plot of the model.
452
0.6
0.7
0.8
0.9
E
1
1.1
1.2
Looking at the Fitted Model
Not surprisingly, the surfaces look quite similar, with the model
surface somewhat smoother than the data surface. The data surface
has a noticeable wrinkle for E ⬇ 0.7, C ⬇ 14. This wrinkle is smoothed
out in the model surface. Another graphical view is probably
worthwhile.
The default graphical view for "loess" objects with multiple
predictors is a set of coplots, one per predictor, created using the plot
function.
> par(ask=T)
> plot(ethanol.m3, confidence = 7)
The resulting plots are shown in Figure 13.16 and Figure 13.17. One
feature that is immediately apparent, and somewhat puzzling, is the
curvy form of the bottom row of Figure 13.16. Our preliminary
coplots revealed that the dependence of NOx on C was approximately
linear for small values of E. Thus, the model as fitted has a noticeable
departure from our understanding of the data.
Given : E
0.6
0.8
10
12
14
16
1.2
18
-1
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2
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4
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0
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4
8
1.0
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18
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C
Figure 13.16: Default conditioning plot of the model, first predictor.
453
Chapter 13 Local Regression Models
Given : C
8
10
12
0.8
1.0
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1.2
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2
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4
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0
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4
0.6
14
0.6
0.8
1.0
1.2
0.6
0.8
1.0
E
Figure 13.17: Default conditioning plot of the model, second predictor.
454
1.2
Improving the Model
IMPROVING THE MODEL
The model in ethanol.m3 is fit using local quadratic fitting for all
terms corresponding to C*E. This means that the model contains the
2
2
following fitting variables: a constant, E, C, EC, C , and E . However,
our original look at the data led us to believe that the effect of C was
piecewise linear; it thus makes sense to fit C parametrically, and drop
the quadratic term. We can make these changes using the update
function as follows:
> ethanol.m4 <- update(ethanol.m3, drop.square = "C",
+ parametric = "C")
> ethanol.m4
Call:
loess(formula = NOx ~ C * E, span = 0.25, parametric = "C",
drop.square = "C")
Number of Observations:
88
Equivalent Number of Parameters: 18.2
Residual Standard Error:
0.1808
Multiple R-squared:
0.98
Residuals:
min
1st Q
median
3rd Q
max
-0.4388 -0.07358 -0.009093 0.06616 0.5485
The coplot, Figure 13.18 and Figure 13.19, now shows the appropriate
linear fit, and we have introduced no lack of fit, as shown by the
residuals plots in Figure 13.20.
455
Chapter 13 Local Regression Models
Given : E
0.6
0.8
10
12
14
16
1.2
18
-2
-1
0
1
2
3
4
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0
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4
8
1.0
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12
14
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18
8
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12
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18
C
Figure 13.18: Default conditioning plot of improved model, first predictor.
456
Improving the Model
Given : C
8
10
12
0.8
1.0
16
18
1.2
-2
0
2
4
-2
NOx
0
2
4
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14
0.6
0.8
1.0
1.2
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1.0
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E
Figure 13.19: Default conditioning plot of improved model, second predictor.
457
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Chapter 13 Local Regression Models
18
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Figure 13.20: Residual plot of improved model.
In fact, comparing the plot of residuals against E for the latest model
with that for ethanol.m3 (Figure 13.21) indicates we may be able to
increase the span for the latest model and not introduce any lack of
fit:
> ethanol.m5 <- update(ethanol.m4, span = 1/2)
> ethanol.m5
Call:
loess(formula = NOx ~ C * E, span = 1/2, parametric = "C",
drop.square = "C")
Number of Observations:
88
Equivalent Number of Parameters: 9.2
Residual Standard Error:
0.1842
Multiple R-squared:
0.98
Residuals:
min
1st Q median
3rd Q
max
-0.5236 -0.0972 0.01386 0.07326 0.5584
We gain a much more parsimonious model—the Equivalent Number
of Parameters drop from approximately 18 to about 9. An F-test using
anova shows no significant difference between our first acceptable
model and the latest, more parsimonious model.
458
Improving the Model
> anova(ethanol.m3, ethanol.m5)
Model 1:
loess(formula = NOx ~ C * E, span = 0.25)
Model 2:
loess(formula = NOx ~ C * E, span = 1/2, parametric = "C",
drop.square = "C")
Analysis of Variance Table
ENP
RSS
Test
F Value
Pr(F)
1
21.6 1.7999
1 vs 2
1.42
0.16486
2
9.2 2.5433
0.8
1.0
1.2
E
Fitted model ethanol.m3
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Fitted model ethanol.m4
Figure 13.21: Comparison of residual plots for original and improved models.
459
Chapter 13 Local Regression Models
460
LINEAR AND NONLINEAR
MIXED-EFFECTS MODELS
14
Introduction
463
Representing Grouped Data Sets
The groupedData Class
Example: The Orthodont Data Set
Example: The Pixel Data Set
Example: The CO2 Data Set
Example: The Soybean Data Set
465
465
466
470
472
476
Fitting Models Using the lme Function
Model Definitions
Arguments
479
479
481
Manipulating lme Objects
The print Method
The summary Method
The anova Method
The plot method
Other Methods
483
483
484
486
487
489
Fitting Models Using the nlme Function
Model Definition
Arguments
493
493
494
Manipulating nlme Objects
The print Method
The summary Method
The anova Method
The plot Method
Other Methods
497
497
499
501
501
502
Advanced Model Fitting
Positive-Definite Matrix Structures
Correlation Structures and Variance Functions
505
505
507
461
Chapter 14 Linear and Nonlinear Mixed-Effects Models
Self-Starting Functions
Modeling Spatial Dependence
References
462
513
520
523
Introduction
INTRODUCTION
Mixed-effects models provide a powerful and flexible tool for
analyzing grouped data, which is data that can be classified according
to one or more grouping variables. Mixed-effects models incorporate
both fixed and random effects:
•
Fixed effects are parameters associated with an entire
population, or with repeatable levels of experimental factors.
•
Random effects are parameters associated with experimental
units drawn at random from a population.
Such models typically describe relationships between a response
variable and covariates that are grouped according to one or more
classification factors. Common applications are longitudinal data,
repeated measures data, multilevel data, and block designs. By
associating common random effects to observations sharing the same
level of a classification factor, mixed-effects models flexibly represent
the covariance structure induced by grouping.
This chapter describes a set of functions, classes, and methods for the
analysis of linear and nonlinear mixed-effects models in S-PLUS. The
methods provide a comprehensive set of tools for analyzing linear
and nonlinear mixed-effects models with an arbitrary number of
nested grouping levels. They supersede the modeling facilities
available in release 3 of S (Chambers and Hastie, 1992) and releases
5.1 (Unix) and 2000 (Windows) of S-PLUS.
This chapter illustrates how to:
•
Represent grouped data sets using the groupedData class.
•
Fit basic linear mixed-effects models using the lme function
and manipulate the returned objects.
•
Fit basic nonlinear mixed-effects models using the nlme
function and manipulate the returned objects.
•
Fit advanced linear and nonlinear mixed-effects models by
defining positive-definite matrices, correlation structures, and
variance functions.
The analysis of several sample data sets illustrates many of the
available features. A detailed description of all functions, classes, and
methods can be found in the on-line help files.
463
Chapter 14 Linear and Nonlinear Mixed-Effects Models
The code for the methods discussed in this chapter was contributed
by Douglas M. Bates of the University of Wisconsin and José C.
Pinheiro of Bell Laboratories. Their book, Mixed Effects Models in S and
S-PLUS (2000), contains a careful description of the statistical theory
behind mixed-effects models, as well as detailed examples of the
software for fitting and displaying them. For discussions of advanced
topics not presented in this chapter, we refer the reader to the
Pinheiro and Bates text.
464
Representing Grouped Data Sets
REPRESENTING GROUPED DATA SETS
The data sets used for fitting mixed-effects models have several
characteristics in common. They consist of measurements of a
continuous response at several levels of a covariate (for example,
time, dose, or treatment). The measurements are grouped according
to one or more factors. Additional covariates may also be present,
some of which may vary within a group (inner covariates) and some of
which may not (outer covariates).
A natural way to represent such data in S-PLUS is as a data frame
containing the response, the primary covariate, the grouping factor(s),
and any additional factors or continuous covariates. The different
roles of the variables in the data frame can be described by a formula
of the form
response ~ primary | grouping1/grouping2/...
This is similar to the display formula in a Trellis plot, as discussed in
Becker, Cleveland, and Shyu (1996).
The
groupedData
Class
The formula and the data for a grouped data set are packaged
together in a groupedData object. The constructor (the function used
to create objects of a given class) for groupedData takes a formula and
a data frame as arguments. The call to the constructor establishes the
roles of the variables, stores descriptive labels for plots, and converts
the grouping factors to ordered factors so the panels in plots are
ordered in a natural way. By default, the order of the grouping factors
is determined by a summary function applied to the response and
split according to the groups, taking into account the nesting order.
The default summary function is the maximum. Additionally, labels
can be given for the response and the primary covariate, and their
units can be specified as arbitrary strings. The reason for separating
the labels and the units is to allow the units to propagate to derived
quantities, such as the residuals from a fitted model.
When outer factors are present, they are given by a formula such as
or outer = ~ Treatment*Type. When multiple
grouping factors are present, a list of such formulas must be supplied.
Inner factors are described in a similar way. When establishing the
outer = ~Sex
465
Chapter 14 Linear and Nonlinear Mixed-Effects Models
order of the levels of the grouping factor, and hence the order of
panels in a plot, re-ordering is only permitted within combinations of
levels for the outer factors.
Trellis parameters can be used to control the graphical presentation of
grouped data. See the online help files for plot.nffGroupedData,
plot.nfnGroupedData and plot.nmGroupedData for details. The first
two functions plot groupedData objects with single levels of grouping,
and plot.nmGroupedData displays objects with multiple grouping
levels.
Extractor functions can be used on groupedData objects to obtain the
different components of the display formula. Functions such as
getGroups, getCovariate, and getResponse can be applied to extract
the corresponding element in the data set. In addition, groupedData
objects can be summarized by group using the function gsummary.
Example: The
Orthodont
Data Set
As a first example of grouped data, consider the orthodontic study
presented in Potthoff and Roy (1964). These data consist of four
distance measurements (in millimeters) made at ages 8, 10, 12, and 14
years, on 16 boys and 11 girls. The measurements represent the
distance from the center of the pituitary to the pterygomaxillary
fissure.
The data from the orthodontic study are stored in the example data
set Orthodont, which has the following variables:
•
The 108 observations in the data set are grouped into 27
categories by Subject.
•
The 27 subjects are classified into two groups by Sex, an
indicator variable assuming the value "Male" for boys and
"Female" for girls.
•
Each of the subjects has four measures of distance,
corresponding to the four age values.
This is an example of balanced repeated measures data, with a single
level of grouping (Subject). We wish to predict distance from age,
using Subject as a grouping variable and Sex as an outer covariate.
To create a new groupedData object for Orthodont, use the class
constructor as follows:
# Assign Orthodont to your working directory.
466
Representing Grouped Data Sets
>
>
+
+
+
+
Orthodont <- Orthodont
Orthodont <- groupedData(distance ~ age | Subject,
data = Orthodont, outer = ~ Sex,
labels = list(x = "Age",
y="Distance from pituitary to pterygomaxillary fissure"),
units = list(x = "(yr)", y = "(mm)"))
The print method returns the display formula and the data frame
associated with a groupedData object.
> print(Orthodont)
Grouped Data: distance ~
distance age Subject
1
26.0
8
M01
2
25.0 10
M01
3
29.0 12
M01
4
31.0 14
M01
...
105
24.5
8
F11
106
25.0 10
F11
107
28.0 12
F11
108
28.0 14
F11
age | Subject
Sex
Male
Male
Male
Male
Female
Female
Female
Female
You can also use the names and formula methods to return the
variable names and their roles in a groupedData object.
> names(Orthodont)
[1] "distance" "age" "Subject" "Sex"
> formula(Orthodont)
distance ~ age | Subject
467
Chapter 14 Linear and Nonlinear Mixed-Effects Models
One advantage of using a formula to describe the roles of variables in
a groupedData object is that this information can be used within the
model-fitting functions to make the model specification easier. For
example, obtaining preliminary linear regression fits by Subject is as
simple as the following command:
> Ortho.lis <- lmList(Orthodont)
The lmList function partitions data according to the levels of a
grouping factor, and individual linear models are fit for each data
partition. The linear models use the formula defined in the
groupedData object; in this example, lmList fits models for each
Subject according to the formula distance~age.
You can plot the Orthodont data with:
> plot(Orthodont, layout = c(8,4),
+ between = list(y = c(0, 0.5, 0)))
The result is displayed in Figure 14.1. When establishing the order of
the levels of the grouping factor, and hence the order of panels in a
plot, re-ordering is only permitted within combinations of levels for
the outer factors. In the Orthodont data, Sex is an outer factor, which
is why the panels for males and females are grouped separately in
Figure 14.1. Within each gender group, panels are ordered by
maximum distance measurements.
The plot method for the groupedData class allows an optional
argument outer which can be given a logical value or a formula. A
logical value of TRUE (or T) indicates that the outer formula stored with
the data should be used in the plot. The right side of the explicit or
inferred formula replaces the grouping factor in the trellis formula.
The grouping factor is then used to determine which points are joined
with lines. For example:
> plot(Orthodont, outer = T)
The plot is displayed in Figure 14.2. The two panels in the figure
correspond to males and females. Within the panels, the four
measurements for each Subject are joined with lines.
468
Representing Grouped Data Sets
8
F03
10
12
14
F04
F11
30
Distance from pituitary to pterygomaxillary fissure (mm)
25
20
F10
F09
F06
F01
F05
F08
F07
F02
M04
M12
M06
M13
M15
M01
M09
M10
30
25
20
30
25
20
M11
M16
M08
M05
M14
M02
M07
M03
30
25
20
8
10
12
14
8
10
12
14
8
10
12
14
8
10
12
14
Age (yr)
Figure 14.1: Orthodontic growth patterns in 16 boys (M) and 11 girls (F) between 8
and 14 years of age. Panels within each gender group are ordered by maximum
response.
469
Distance from pituitary to pterygomaxillary fissure (mm)
Chapter 14 Linear and Nonlinear Mixed-Effects Models
8
Male
9
10
11
12
13
14
Female
30
25
20
8
9
10
11
12
13
14
Age (yr)
Figure 14.2: Orthodontic growth patterns in 16 boys and 11 girls between 8 and 14
years of age, with different panels per gender.
Example: The
Pixel Data Set
An example of grouped data with two levels of grouping is from an
experiment conducted by Deborah Darien at the School of Veterinary
Medicine, University of Wisconsin at Madison. The radiology study
consisted of repeated measures of mean pixel values from CT scans of
10 dogs. The pixel values were recorded over a period of 14 days after
the application of a contrast, and measurements were taken from both
the right and left lymph nodes in the axillary region of the dogs.
The data from the radiology study are stored in the example data set
Pixel, which has the following variables:
470
•
The observations in the data set are grouped into 10
categories by Dog.
•
The 10 dogs have two measurements (Side) for each day a
pixel value was recorded: "L" indicates that the CT scan was
on the left lymph node, and "R" indicates that it was on the
right lymph node.
•
The mean pixel values are recorded in the pixel column of
the data set.
Representing Grouped Data Sets
The purpose of the experiment was to model the mean pixel value as
a function of time, in order to estimate the time when the maximum
mean pixel value was attained. We therefore wish to predict pixel
from day, using both Dog and Side as grouping variables.
To create a new groupedData object for the Pixel data, use the class
constructor as follows:
#
>
>
+
+
+
Assign Pixel to your working directory.
Pixel <- Pixel
Pixel <- groupedData(pixel ~ day | Dog/Side,
data = Pixel, labels = list(
x = "Time post injection", y = "Pixel intensity"),
units = list(x = "(days)"))
> Pixel
Grouped Data: pixel ~ day | Dog/Side
Dog Side day pixel
1
1
R
0 1045.8
2
1
R
1 1044.5
3
1
R
2 1042.9
4
1
R
4 1050.4
5
1
R
6 1045.2
6
1
R 10 1038.9
7
1
R 14 1039.8
8
2
R
0 1041.8
9
2
R
1 1045.6
10
2
R
2 1051.0
11
2
R
4 1054.1
12
2
R
6 1052.7
13
2
R 10 1062.0
14
2
R 14 1050.8
15
3
R
0 1039.8
. . .
Plot the grouped data with the following command:
> plot(Pixel, displayLevel = 1, inner = ~Side)
The result is displayed in Figure 14.3. The grouping variable Dog
determines the number of panels in the plot, and the inner factor Side
determines which points in a panel are joined by lines. Thus, there
471
Chapter 14 Linear and Nonlinear Mixed-Effects Models
are 10 panels in Figure 14.3, and each panel contains a set of
connected points for the left and right lymph nodes. The panels are
ordered according to maximum pixel values.
When multiple levels of grouping are present, the plot method allows
two optional arguments: displayLevel and collapseLevel. These
arguments specify, respectively, the grouping level that determines
the panels in the Trellis plot, and the grouping level over which to
collapse the data.
L
0
5
10
7
10
8
6
15
R
20
1160
1140
1120
1100
1080
1060
1040
4
5
Pixel intensity
1160
1140
1120
1100
1080
1060
1040
1
2
3
9
1160
1140
1120
1100
1080
1060
1040
0
5
10
15
20
0
5
10
15
20
Time post injection (days)
Figure 14.3: Mean pixel intensity of the right (R) and left (L) lymph nodes in the
axillary region, versus time from intravenous application of a contrast. The pixel
intensities were obtained from CT scans.
Example: The
CO2 Data Set
As an example of grouped data with a nonlinear response, consider
an experiment on the cold tolerance of a C4 grass species, Echinochloa
crus-galli, described in Potvin, Lechowicz, and Tardif (1990). A total of
twelve four-week-old plants, six from Quebec and six from
Mississippi, were divided into two groups: control plants that were
kept at 26°C, and chilled plants that were subject to 14 hours of
chilling at 7°C. After 10 hours of recovery at 20°C, CO2 uptake rates
2
(in μmol/m s) were measured for each plant at seven concentrations of
472
Representing Grouped Data Sets
ambient CO2: 100, 175, 250, 350, 500, 675, and 1000 μL/L. Each
plant was subjected to the seven concentrations of CO2 in increasing,
consecutive order.
The data from the CO2 study are stored in the example data set CO2,
which has the following variables:
•
The 84 observations in the data set are grouped into 12
categories by Plant.
•
The 12 plants are classified into two groups by Type, an
indicator variable assuming the values "Quebec" and
"Mississippi".
•
The 12 plants are classified into two additional groups
according to Treatment, which indicates whether a plant was
"nonchilled" or "chilled".
•
Each plant has seven uptake measurements, corresponding to
the seven concentration (conc) values.
The objective of the experiment was to evaluate the effect of plant
type and chilling treatment on the CO2 uptake. We therefore wish to
predict uptake from conc, using Plant as a grouping variable and
both Treatment and Type as outer covariates.
To create a new groupedData object for the CO2 data, use the class
constructor as follows:
#
>
>
+
+
+
+
Assign CO2 to your working directory.
CO2 <- CO2
CO2 <- groupedData(uptake ~ conc | Plant, data = CO2,
outer = ~ Treatment * Type,
labels = list(x = "Ambient carbon dioxide concentration",
y = "CO2 uptake rate"),
units = list(x = "(uL/L)", y = "(umol/m^2 s)"))
> CO2
Grouped Data: uptake ~ conc | Plant
Plant
Type Treatment conc uptake
1
Qn1 Quebec nonchilled
95
16.0
2
Qn1 Quebec nonchilled 175
30.4
3
Qn1 Quebec nonchilled 250
34.8
. . .
473
Chapter 14 Linear and Nonlinear Mixed-Effects Models
Plot the grouped data with the following command:
> plot(CO2)
The result is shown in Figure 14.4. As in the Orthodont example, you
can use the optional argument outer=T to indicate that the outer
formula stored with the data should be used in the plot. For example:
> plot(CO2, outer = T)
The plot is displayed in Figure 14.5. The outer covariates, Treatment
and Type, determine the number of plots in the figure. The grouping
variable Plant determines the points that are connected by lines in
each panel.
200
Mn3
400
600
800
200
Mn2
Mn1
400
600
800
200
Mc2
Mc3
400
600
800
Mc1
CO2 uptake rate (umol/m^2 s)
40
30
20
10
Qn1
Qn2
Qn3
Qc1
Qc3
Qc2
40
30
20
10
200
400
600
800
200
400
600
800
200
400
600
800
Ambient carbon dioxide concentration (uL/L)
Figure 14.4: CO2 uptake versus ambient CO2 concentration for Echinochloa crusgalli plants, six from Quebec and six from Mississippi. Half the plants of each type
were chilled overnight before the measurements were taken.
474
Representing Grouped Data Sets
200
Mississippi
nonchilled
400
600
800
Mississippi
chilled
40
CO2 uptake rate (umol/m^2 s)
30
20
10
Quebec
nonchilled
Quebec
chilled
40
30
20
10
200
400
600
800
Ambient carbon dioxide concentration (uL/L)
Figure 14.5: CO2 uptake versus ambient CO2 by Treatment and Type.
475
Chapter 14 Linear and Nonlinear Mixed-Effects Models
We can also obtain a numeric summary of the CO2 data by group,
using the gsummary function as follows:
> gsummary(CO2)
Qn1
Qn2
Qn3
Qc1
Qc3
Qc2
Mn3
Mn2
Mn1
Mc2
Mc3
Mc1
Example: The
Soybean Data
Set
Plant
Qn1
Qn2
Qn3
Qc1
Qc3
Qc2
Mn3
Mn2
Mn1
Mc2
Mc3
Mc1
Type
Quebec
Quebec
Quebec
Quebec
Quebec
Quebec
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Treatment conc
uptake
nonchilled 435 33.22857
nonchilled 435 35.15714
nonchilled 435 37.61429
chilled 435 29.97143
chilled 435 32.58571
chilled 435 32.70000
nonchilled 435 24.11429
nonchilled 435 27.34286
nonchilled 435 26.40000
chilled 435 12.14286
chilled 435 17.30000
chilled 435 18.00000
Another example of grouped data with a nonlinear response comes
from an experiment described in Davidian and Giltinan (1995),
which compares growth patterns of two genotypes of soybean. One
genotype is a commercial variety, Forrest, and the other is an
experimental strain, Plant Introduction #416937. The data were
collected in the three years from 1988 to 1990. At the beginning of the
growing season in each year, 16 plots were planted with seeds (8 plots
with each genotype). Each plot was sampled eight to ten times at
approximately weekly intervals. At sampling time, six plants were
randomly selected from each plot, leaves from these plants were
weighed, and the average leaf weight per plant was calculated for the
plot. Different plots in different sites were used in different years.
The data from the soybean study are stored in the example data set
Soybean, which has the following variables:
476
•
The observations in the data set are grouped into 48
categories by Plot, a variable that provides unique labels for
the 16 plots planted in each of the 3 years.
•
The 48 plots are classified into three groups by Year, which
indicates whether the plot was planted in "1988", "1989", or
"1990".
Representing Grouped Data Sets
•
The 48 plots are classified into two additional groups
according to Variety, which indicates whether a plot
contained the commercial strain of plants (F) or the
experimental strain (P).
•
The average leaf weight at each Time for the plots is recorded
in the weight column of the data set.
The objective of the soybean experiment was to model the growth
pattern in terms of average leaf weight. We therefore wish to predict
weight from Time, using Plot as a grouping variable and both
Variety and Year as outer covariates.
To create a new groupedData object for the Soybean data, use the class
constructor as follows:
#
>
>
+
+
+
+
Assign Soybean to your working directory.
Soybean <- Soybean
Soybean <- groupedData(weight ~ Time | Plot,
data = Soybean, outer = ~ Variety * Year,
labels = list(x = "Time since planting",
y = "Leaf weight/plant"),
units = list(x = "(days)", y = "(g)"))
> Soybean
Grouped Data: weight ~ Time | Plot
Plot Variety Year Time
weight
1 1988F1
F 1988
14 0.10600
2 1988F1
F 1988
21 0.26100
3 1988F1
F 1988
28 0.66600
4 1988F1
F 1988
35 2.11000
5 1988F1
F 1988
42 3.56000
6 1988F1
F 1988
49 6.23000
7 1988F1
F 1988
56 8.71000
8 1988F1
F 1988
63 13.35000
9 1988F1
F 1988
70 16.34170
10 1988F1
F 1988
77 17.75083
11 1988F2
F 1988
14 0.10400
12 1988F2
F 1988
21 0.26900
. . .
477
Chapter 14 Linear and Nonlinear Mixed-Effects Models
Plot the grouped data with the following command:
> plot(Soybean, outer= ~ Year * Variety)
The result is shown in Figure 14.6.
20
P
1988
40
60
80
P
1989
P
1990
30
25
20
Leaf weight/plant (g)
15
10
5
0
F
1988
F
1989
F
1990
30
25
20
15
10
5
0
20
40
60
80
20
40
60
80
Time since planting (days)
Figure 14.6: Average leaf weight in plots of soybeans, versus time since planting. The
plots are from three different years and represent two different genotypes of soybeans.
478
Fitting Models Using the lme Function
FITTING MODELS USING THE LME FUNCTION
The S-PLUS function lme fits a linear mixed-effects model as
described in Laird and Ware (1982), or a multilevel linear mixedeffects model as described in Longford (1993) and Goldstein (1995).
The models are fitted using either maximum likelihood or restricted
maximum likelihood. The lme function produces objects of class
"lme".
Model
Definitions
Example: the Orthodont data
The plot of the individual growth curves in Figure 14.1 suggests that a
linear model might adequately explain the orthodontic distance as a
function of age. However, the intercepts and slopes of the lines seem
to vary with the individual patient. The corresponding linear mixedeffects model is given by the following equation:
d ij = ( β 0 + b i0 ) + ( β 1 + b i1 )age j + ε ij
(14.1)
where d ij represents the distance for the ith individual at age j , and
β0 and β1 are the population average intercept and the population
average slope, respectively. The b i0 and b i1 terms are the effects in
intercept and slope associated with the ith individual, and ε ij is the
T
within-subject error term. It is assumed that the bi = (bi0,bi1) are
2
independent and identically distributed with a N(0,σ D) distribution,
2
where σ D represents the covariance matrix for the random effects.
Furthermore, we assume that the εij are independent and identically
2
distributed with a N(0,σ ) distribution, independent of the bi.
479
Chapter 14 Linear and Nonlinear Mixed-Effects Models
One of the questions of interest for these data is whether the curves
show significant differences between boys and girls. The model given
by Equation (14.1) can be modified as
d ij = ( β 00 + β 01 sex i + b i0 ) +
(14.2)
( β 10 + β 11 sex i + b i1 )age j + ε ij
to test for sex-related differences in intercept and slope. In Equation
(14.2), sexi is an indicator variable assuming the value 0 if the ith
individual is a boy and 1 if she is a girl. The β00 and β10 terms
represent the population average intercept and slope for the boys; β01
and β11 are the changes (with respect to β00 and β10) in population
average intercept and slope for girls. Differences between boys and
girls can be evaluated by testing whether β01 and β11 are significantly
different from zero. The remaining terms in Equation (14.2) are
defined as in Equation (14.1).
Example: the Pixel data
In Figure 14.3, a second order polynomial seems to adequately
explain the evolution of pixel intensity with time. Preliminary
analyses indicated that the intercept varies with Dog, as well as with
Side nested in Dog. In addition, the linear term varies with Dog, but
not with Side. The corresponding multilevel linear mixed-effects
model is given by the following equation:
2
y ijk = ( β 0 + b 0i + b 0i, j ) + ( β 1 + b 1i )t ijk + β 2 t ijk + ε ijk
(14.3)
where i = 1, 2, …, 10 refers to the dog number, j = 1, 2 refers to
the lymph node side ( j = 1 corresponds to the right side and j = 2
corresponds to the left), and k refers to time. The β0, β1, and β2 terms
denote, respectively, the intercept, the linear term, and the quadratic
term fixed effects. The b0i term denotes the intercept random effect at
the Dog level, b0i,j denotes the intercept random effect at the Side
within Dog level, and b1i denotes the linear term random effect at the
480
Fitting Models Using the lme Function
level. The y variable is the pixel intensity, t is the time since
contrast injection, and εijk is the error term. It is assumed that the
Dog
bi = (b0i ,b1i )
T
are independent and identically distributed with a
2
2
N(0,σ D1) distribution, where σ D 1 represents the covariance matrix
for random effects at the Dog level. The bi,j = [b0i,j ] are independent of
2
the bi, and independent and identically distributed with a N(0,σ D2)
2
distribution, where σ D 2 represents the covariance matrix for
random effects at the Side within Dog level. The εijk are independent
2
and identically distributed with a N(0,σ ) distribution, independent of
the bi and the bi,j .
Arguments
The typical call to the lme function is of the form
lme(fixed, data, random)
Only the first argument is required. The arguments fixed and random
are generally given as formulas. Any linear formula is allowed for
both arguments, giving the model formulation considerable
flexibility. The optional argument data specifies the data frame in
which the model’s variables are available.
Other arguments in the lme function allow for flexible definitions of
the within-group correlation and heteroscedasticity structures, the
subset of the data to be modeled, the method to use when fitting the
model, and the list of control values for the estimation algorithm. See
the lme online help file for specific details on each argument.
Example: the Orthodont data
For the model given by Equation (14.1), the fixed and random
formulas are written as follows:
fixed = distance ~ age, random = ~ age
For the model given by Equation (14.2), these formulas are:
fixed = distance ~ age * Sex, random = ~ age
Note that the response variable is given only in the formula for the
fixed argument, and that random is usually a one-sided linear
formula. If the random argument is omitted, it is assumed to be the
same as the right side of the fixed formula.
481
Chapter 14 Linear and Nonlinear Mixed-Effects Models
Because Orthodont is a groupedData object, the grouping structure is
extracted from the groupedData display formula, and does not need
to be explicitly included in random. Alternatively, the grouping
structure can be included in the formula as a conditioning expression:
random = ~ age | Subject
A simple call to lme that fits the model in Equation (14.1) is as follows:
> Ortho.fit1 <- lme(fixed = distance ~ age,
+ data = Orthodont, random = ~ age | Subject)
To fit the model given by Equation (14.2), you can update Ortho.fit1
as follows:
#
>
>
+
set contrasts for desired parameterization
options(contrasts = c("contr.treatment", "contr.poly"))
Ortho.fit2 <- update(Ortho.fit1,
fixed = distance ~ age * Sex)
Example: the Pixel data
When multiple levels of grouping are present, as in the Pixel
example, random must be given as a list of formulas. For the model
given by Equation (14.3), the fixed and random formulas are:
fixed = pixel ~ day + day^2
random = list(Dog = ~ day, Side = ~ 1)
Note that the names of the elements in the random list correspond to
the names of the grouping factors; they are assumed to be in
outermost to innermost order. As with all S-PLUS formulas, a model
with a single intercept is represented by ~ 1.
The multilevel model given by Equation (14.3) is fitted with the
following command:
> Pixel.fit1 <- lme(fixed = pixel ~ day + day^2,
+ data = Pixel, random = list(Dog = ~ day, Side = ~1))
482
Manipulating lme Objects
MANIPULATING LME OBJECTS
A call to the lme function returns an object of class "lme". The online
help file for lmeObject contains a description of the returned object
and each of its components. There are several methods available for
lme objects, including print, summary, anova, and plot. These
methods are described in the following sections.
The print
Method
A brief description of the lme estimation results is returned by the
print method. It displays estimates of the fixed effects, as well as
standard deviations and correlations of random effects. If fitted, the
within-group correlation and variance function parameters are also
printed. For the Ortho.fit1 object defined in the section Arguments
on page 481, the results are as follows:
> print(Ortho.fit1)
Linear mixed-effects model fit by REML
Data: Orthodont
Log-restricted-likelihood: -221.3183
Fixed: distance ~ age
(Intercept)
age
16.76111 0.6601852
Random effects:
Formula: ~ age | Subject
Structure: General positive-definite
StdDev
Corr
(Intercept) 2.3270357 (Inter
age 0.2264279 -0.609
Residual 1.3100396
Number of Observations: 108
Number of Groups: 27
483
Chapter 14 Linear and Nonlinear Mixed-Effects Models
The summary
Method
A complete description of the lme estimation results is returned by the
summary function. For the Ortho.fit2 object defined in the section
Arguments on page 481, the results are given by the following
command:
> summary(Ortho.fit2)
Linear mixed-effects model fit by REML
Data: Orthodont
AIC
BIC
logLik
448.5817 469.7368 -216.2908
Random effects:
Formula: ~ age | Subject
Structure: General positive-definite
StdDev
Corr
(Intercept) 2.4055020 (Inter
age 0.1803458 -0.668
Residual 1.3100393
Fixed effects: distance ~ age +
Value Std.Error
(Intercept) 16.34062 1.018532
age
0.78438 0.086000
Sex
1.03210 1.595733
age:Sex -0.30483 0.134735
Correlation:
(Intr)
age
Sex
age -0.880
Sex -0.638 0.562
age:Sex 0.562 -0.638 -0.880
Sex + age:Sex
DF
t-value p-value
79 16.04331 <.0001
79
9.12069 <.0001
25
0.64679 0.5237
79 -2.26243 0.0264
Standardized Within-Group Residuals:
Min
Q1
Med
Q3
Max
-3.168077 -0.3859386 0.007103473 0.4451539 3.849464
Number of Observations: 108
Number of Groups: 27
The approximate standard errors for the fixed effects are computed
using an algorithm based on the asymptotic theory described in
Pinheiro (1994). In the results for Ortho.fit2, the significant, negative
fixed effect between age and Sex indicate that the orthodontic
484
Manipulating lme Objects
distance increases faster in boys than in girls. However, the nonsignificant fixed effect for Sex indicates that the average intercept is
common to boys and girls.
To summarize the estimation results for the Pixel.fit1 object defined
on page 482, use the following:
> summary(Pixel.fit1)
Linear mixed-effects model fit by REML
Data: Pixel
AIC
BIC
logLik
841.2102 861.9712 -412.6051
Random effects:
Formula: ~ day | Dog
Structure: General positive-definite
StdDev
Corr
(Intercept) 28.36994 (Inter
day 1.84375 -0.555
Formula: ~ 1 | Side %in% Dog
(Intercept) Residual
StdDev:
16.82424 8.989609
Fixed effects: pixel ~ day + day^2
Value Std.Error DF
(Intercept) 1073.339 10.17169 80
day
6.130
0.87932 80
I(day^2)
-0.367
0.03395 80
Correlation:
(Intr)
day
day -0.517
I(day^2) 0.186 -0.668
t-value p-value
105.5222 <.0001
6.9708 <.0001
-10.8218 <.0001
Standardized Within-Group Residuals:
Min
Q1
Med
Q3
Max
-2.829056 -0.4491807 0.02554919 0.557216 2.751964
Number of Observations: 102
Number of Groups:
Dog Side %in% Dog
10
20
485
Chapter 14 Linear and Nonlinear Mixed-Effects Models
The anova
Method
A likelihood ratio test can be used to test the difference between fixed
effects in different lme models. The anova method provides this
capability for lme objects.
Warning
Likelihood comparisons between restricted maximum likelihood (REML) fits with different
fixed effects structures are not meaningful. To compare such models, you should re-fit the objects
using maximum likelihood (ML) before calling anova.
As an example, we compare the Ortho.fit1 and Ortho.fit2 objects
defined for the Orthodont data set. Since the two models have
different fixed effects structures, we must re-fit them using maximum
likelihood estimation before calling the anova function. Use the
update function to re-fit the objects as follows:
> Ortho.fit1.ML <- update(Ortho.fit1, method = "ML")
> Ortho.fit2.ML <- update(Ortho.fit2, method = "ML")
The call to anova produces:
> anova(Ortho.fit1.ML, Ortho.fit2.ML)
Model df
AIC
BIC
logLik
Ortho.fit1.ML
1 6 451.2116 467.3044 -219.6058
Ortho.fit2.ML
2 8 443.8060 465.2630 -213.9030
Test L.Ratio p-value
Ortho.fit1.ML
Ortho.fit2.ML 1 vs 2 11.40565 0.0033
Recall that Ortho.fit2.ML includes terms that test for sex-related
differences in the data. The likelihood ratio test strongly rejects the
null hypothesis of no differences between boys and girls. For small
sample sizes, likelihood ratio tests tend to be too liberal when
comparing models with nested fixed effects structures, and should
therefore be used with caution. We recommend using the Wald-type
tests provided by the anova method (when a single model object is
passed to the function), as these tend to have significance levels close
to nominal, even for small samples.
486
Manipulating lme Objects
The plot
method
Diagnostic plots for assessing the quality of a fitted lme model are
obtained with the plot method. This method takes several optional
arguments, but a typical call is of the form
plot(object, form)
The first argument is an lme object and the second is a display
formula for the Trellis graphic to be produced. The fitted object can
be referenced by the period symbol ‘‘.’’ in the form argument. For
example, the following command produces a plot of the standardized
residuals versus the fitted values for the Ortho.fit2 object, grouped
by gender:
> plot(Ortho.fit2,
+ form = resid(., type = "p") ~ fitted(.) | Sex)
The result is displayed in Figure 14.7.
The form expression above introduces two other common methods
for lme objects: resid and fitted, which are abbreviations for
residuals and fitted.values. The resid and fitted functions are
standard S-PLUS extractors, and return the residuals and fitted values
for a model object, respectively. The argument type for the
residuals.lme method accepts the strings "pearson" (or "p"),
"normalized", and "response"; the standardized residuals are
returned when type="p". By default the raw or "response" (or
standardized) residuals are calculated.
Figure 14.7 provides some evidence that the variability of the
orthodontic distance is greater in boys than in girls. In addition, it
appears that a few outliers are present in the data. To assess the
predictive power of the Ortho.fit2 model, consider the plot of the
observed values versus the fitted values for each Subject. The plots,
shown in Figure 14.8, are obtained with the following command:
> plot(Ortho.fit2, form = distance ~ fitted(.) | Subject,
+ layout = c(4,7), between = list(y = c(0, 0, 0, 0.5)),
+ aspect = 1.0, abline = c(0,1))
487
Chapter 14 Linear and Nonlinear Mixed-Effects Models
18
20
22
24
Male
26
28
30
32
Female
4
Standardized residuals
2
0
-2
18
20
22
24
26
28
30
32
Fitted values (mm)
Figure 14.7: Standardized residuals versus fitted values for the Ortho.fit2 model
object, grouped by gender.
18
22
26
30
F03
F04
F11
F05
F07
F02
30
Distance from pituitary to pterygomaxillary fissure (mm)
25
20
F08
30
25
20
F10
F09
F06
F01
M06
M04
M01
M10
30
25
20
30
25
20
M13
M14
M09
M15
M07
M08
M03
M12
30
25
20
30
25
20
M16
M05
M02
M11
30
25
20
18
22
26
30
18
22
26
30
Fitted values (mm)
Figure 14.8: Observed distances versus fitted values by Subject for the
Ortho.fit2 model object.
488
Manipulating lme Objects
For most of the subjects, there is very good agreement between the
observed and fitted values, indicating that the fit is adequate.
The form argument to the plot method for lme objects provides
virtually unlimited flexibility in generating customized diagnostic
plots. As a final example, consider the plot of the standardized
residuals (at the Side within Dog level) for the Pixel.fit1 object,
grouped by Dog. The plot, similar to the one shown in Figure 14.9, is
obtained with the following command:
> plot(Pixel.fit1, form = Dog ~ resid(., type = "p"))
9
8
7
Dog
6
5
4
3
2
10
1
-3
-2
-1
0
1
2
Standardized residuals
Figure 14.9: Standardized residuals by Dog for the Pixel.fit1 model object.
The residuals seem to be symmetrically scattered around zero with
similar variabilities, except possibly for dog number 4.
Other Methods
Standard S-PLUS methods for extracting components of fitted
objects, such as residuals, fitted.values, and coefficients, can
also be used on lme objects. In addition, lme includes the methods
fixed.effects and random.effects for extracting the fixed effects
and the random effects estimates; abbreviations for these functions
are fixef and ranef, respectively. For example, the two commands
below return coefficients and fixed effects.
489
Chapter 14 Linear and Nonlinear Mixed-Effects Models
> coef(Ortho.fit2)
M16
M05
...
F04
F11
(Intercept)
age
Sex
age:Sex
15.55737 0.6957276 1.032102 -0.3048295
14.69529 0.7759009 1.032102 -0.3048295
18.00174 0.8125880 1.032102 -0.3048295
18.53692 0.8858555 1.032102 -0.3048295
> fixef(Pixel.fit1)
(Intercept)
day
I(day^2)
1073.339 6.129597 -0.3673503
The next command returns the random effects at the Dog level for the
Pixel.fit1 object:
> ranef(Pixel.fit1, level = 1)
1
10
2
3
4
5
6
7
8
9
-24.714229
19.365854
-23.582059
-27.080310
-16.658544
25.299771
10.823243
49.353938
-7.053961
-5.753702
-1.19537074
-0.09936872
-0.43243128
2.19475596
3.09597260
-0.56127136
-1.03699983
-2.27445838
0.99025533
-0.68108358
Random effects estimates can be visualized with the S-PLUS function
plot.ranef.lme, designed specifically for this purpose. This function
offers great flexibility for the display of random effects. The simplest
display produces a dot plot of the random effects for each coefficient,
as in the following example:
> plot(ranef(Pixel.fit1, level = 1))
490
Manipulating lme Objects
Predicted values for lme objects are returned by the predict method.
For example, if you are interested in predicting the average
orthodontic measurement for both boys and girls at ages 14, 15, and
16, as well as for subjects M01 and F10 at age 13, first create a new data
frame as follows:
>
+
+
+
+
Orthodont.new <- data.frame(
Sex = c("Male", "Male", "Male", "Female", "Female",
"Female", "Male", "Female"),
age = c(14, 15, 16, 14, 15, 16, 13, 13),
Subject = c(NA, NA, NA, NA, NA, NA, "M01", "F10"))
You can then use the following command to compute the subjectspecific and population predictions:
> predict(Ortho.fit2, Orthodont.new, level = c(0,1))
Subject predict.fixed predict.Subject
NA
27.32188
NA
NA
28.10625
NA
NA
28.89063
NA
NA
24.08636
NA
NA
24.56591
NA
NA
25.04545
NA
M01
26.53750
29.17264
F10
23.60682
19.80758
1
2
3
4
5
6
7
8
The level argument is used to define the desired prediction levels,
with zero referring to the population predictions.
Finally, the intervals method for lme objects computes confidence
intervals for the parameters in a mixed-effects model:
> intervals(Ortho.fit2)
Approximate 95% confidence intervals
Fixed effects:
(Intercept)
age
Sex
age:Sex
lower
14.3132878
0.6131972
-2.2543713
-0.5730137
est.
16.3406250
0.7843750
1.0321023
-0.3048295
upper
18.36796224
0.95555282
4.31857585
-0.03664544
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
Random Effects:
Level: Subject
lower
est.
upper
sd((Intercept)) 1.00636826 2.4055020 5.7498233
sd(age) 0.05845914 0.1803458 0.5563649
cor((Intercept),age) -0.96063585 -0.6676196 0.3285589
Within-group standard error:
lower
est.
upper
1.084768 1.310039 1.582092
The models considered so far do not assume any special form for the
random effects variance-covariance matrix. See the section Advanced
Model Fitting for a variety of specifications of both the random effects
covariance matrix and the within-group correlation structure. Beyond
the available covariance structures, customized structures can be
designed by the user; this topic is also addressed in the section
Advanced Model Fitting.
492
Fitting Models Using the nlme Function
FITTING MODELS USING THE NLME FUNCTION
Nonlinear mixed-effects models, which generalize nonlinear models
as well as linear mixed-effects models, can be analyzed with the SPLUS function nlme. The nlme function fits nonlinear mixed-effects
models as defined in Lindstrom and Bates (1990), using either
maximum likelihood or restricted maximum likelihood. These
models are of class "nlme" and inherit from the class "lme", so
methods for lme objects apply to nlme objects as well.
There are many advantages to using nonlinear mixed-effects models.
For example, the model or expectation function is usually based on
sound theory about the mechanism generating the data. Hence, the
model parameters usually have a physical meaning of interest to the
investigator.
Model
Definition
Example: the CO2 data
Recall the CO2 data set, which was introduced in the section
Representing Grouped Data Sets as an example of grouped data with
a nonlinear response. The objective of the data collection was to
evaluate the effect of plant type and chilling treatment on their CO2
uptake. The model used in Potvin, et al. (1990) is
U ij = φ 1i { 1 – exp [ – φ 2i ( C j – φ 3i ) ] } + ε ij
(14.4)
where Uij denotes the CO2 uptake rate of the ith plant at the jth CO2
ambient concentration. The φ1i , φ2i , and φ3i terms denote the
asymptotic uptake rate, the uptake growth rate, and the maximum
ambient CO2 concentration at which no uptake is verified for the ith
plant, respectively. The Cj term denotes the jth ambient CO2 level,
and the εij are independent and identically distributed error terms
2
with a common N(0,σ ) distribution.
493
Chapter 14 Linear and Nonlinear Mixed-Effects Models
Arguments
Several optional arguments can be used with the nlme function, but a
typical call is of the form
nlme(model, data, fixed, random, start)
The model argument is required and consists of a formula specifying
the nonlinear model to be fitted. Any S-PLUS nonlinear formula can
be used, giving the function considerable flexibility.
The arguments fixed and random are formulas (or lists of formulas)
that define the structures of the fixed and random effects in the
model. Only the fixed argument is required; by default, random is
equivalent to fixed, so the random argument can be omitted. As in all
S-PLUS formulas, a 1 on the right side of the fixed or random
formulas indicates that a single intercept is associated with the effect.
However, any linear formula can be used instead. Again, this gives
the model considerable flexibility, as time-dependent parameters can
be easily incorporated. This occurs, for example, when a fixed
formula involves a covariate that changes with time.
Usually, every parameter in a mixed-effects model has an associated
fixed effect, but it may or may not have an associated random effect.
Since we assume that all random effects have zero means, the
inclusion of a random effect without a corresponding fixed effect is
unusual. Note that the fixed and random formulas can be
incorporated directly into the model declaration, but the approach
used in nlme allows for more efficient derivative calculations.
The data argument to nlme is optional and names a data frame in
which the variables for the model, fixed, and random formulas are
found. The optional start argument provides a list of starting values
for the iterative algorithm. Only the fixed effects starting estimates are
required; the default starting estimates for the random effects are zero.
Example: the CO2 data
For the CO2 uptake data, we obtain the following model formula
from Equation (14.4):
uptake ~ A * (1 - exp(-B * (conc - C)))
where A = φ1, B = φ2, and C = φ3. To force the rate parameter φ2 to be
positive while preserving an unrestricted parametrization, you can
transform B with lB = log ( B ) as follows:
uptake ~ A * (1 - exp(-exp(lB) * (conc - C)))
494
Fitting Models Using the nlme Function
Alternatively, you can define an S-PLUS function that contains the
model formula:
> CO2.func <+ function(conc, A, lB, C) A*(1 - exp(-exp(lB)*(conc - C)))
The model argument in nlme then looks like
uptake ~ CO2.func(conc, A, lB, C)
The advantage of the latter approach is that the analytic derivatives of
the model function can be passed to nlme as a gradient attribute of
the value returned by CO2.func. The analytic derivatives can then be
used in the optimization algorithm. For example, we use the S-PLUS
function deriv to create expressions for the derivatives:
> CO2.func <+ deriv(~ A * ( 1 - exp(-exp(lB) * (conc - C))),
+ c("A", "lB", "C"), function(conc, A, lB, C){})
If the value returned by a function like CO2.func does not have a
gradient attribute, numerical derivatives are used in the optimization
algorithm.
To fit a model for the CO2 data in which all parameters are random
and no covariates are included, use the following fixed and random
formulas:
fixed = A + lB + C ~ 1, random = A + lB + C ~ 1
Alternatively, the random argument can be omitted since it is
equivalent to the fixed formula by default. Because CO2 is a
groupedData object, the grouping structure does not need to be
explicitly given in random, as it is extracted from the groupedData
display formula. However, it is possible to include the grouping
structure as a conditioning expression in the formula:
random = A + lB + C ~ 1 | Plant
If you want to estimate the (fixed) effects of plant type and chilling
treatment on the parameters in the model, use
fixed = A + lB + C ~ Type * Treatment,
random = A + lB + C ~ 1
495
Chapter 14 Linear and Nonlinear Mixed-Effects Models
The following simple call to nlme fits the model given by Equation
(14.4):
>
+
+
+
CO2.fit1 <nlme(model = uptake ~ CO2.func(conc, A, lB, C),
fixed = A + lB + C ~ 1, data = CO2,
start = c(30, log(0.01), 50))
The initial values for the fixed effects are obtained from Potvin, et al.
(1990).
496
Manipulating nlme Objects
MANIPULATING NLME OBJECTS
Objects returned by the nlme function are of class "nlme". The online
help file for nlmeObject contains a description of the returned object
and each of its components. The nlme class inherits from the lme class,
so that all methods described for lme objects are also available for
nlme objects. In fact, with the exception of the predict method, all
methods are common to both classes. We illustrate their uses here
with the CO2 uptake data.
The print
Method
The print method provides a brief description of the nlme estimation
results. It displays estimates of the standard deviations and
correlations of random effects, the within-group standard deviation,
and the fixed effects. For the CO2.fit1 object defined in the section
Arguments on page 494, the results are as follows:
> print(CO2.fit1)
Nonlinear mixed-effects model fit by maximum likelihood
Model: uptake ~ CO2.func(conc, A, lB, C)
Data: CO2
Log-likelihood: -201.3103
Fixed: A + lB + C ~ 1
A
lB
C
32.47374 -4.636204 43.5424
Random effects:
Formula: list(A ~ 1 , lB ~ 1 , C ~ 1 )
Level: Plant
Structure: General positive-definite
StdDev
Corr
A 9.5100551 A
lB
lB 0.1283327 -0.160
C 10.4010223 0.999 -0.139
Residual 1.7664129
Number of Observations: 84
Number of Groups: 12
497
Chapter 14 Linear and Nonlinear Mixed-Effects Models
Note that there is strong correlation between the A and the C random
effects, and that both of these have small correlations with the lB
random effect. A scatterplot matrix provides a graphical description
of the random effects correlation structure. We generate a scatterplot
matrix with the pairs method:
> pairs(CO2.fit1, ~ranef(.))
The result is shown in Figure 14.10.
10
-5
0
5
10
5
0
-5
C
-5
-10
-15
-20 -15 -10
-5
-20
0.00 0.05 0.10
0.10
0.05
0.00
lB
-0.05
-0.10
-0.15
-0.15 -0.10 -0.05
0
10
5
10
5
0
A
-5
-10
-15
-15
-10
-5
Figure 14.10: Scatterplot matrix of the estimated random effects in CO2.fit1.
The correlation between A and C may be due to the fact that the plant
type and chilling treatment, which are not included in the CO2.fit1
model, affect A and C in similar ways. The plot.ranef.lme function
can be used to explore the dependence of individual parameters on
plant type and chilling factor. The following command produces the
plot displayed in Figure 14.11.
> plot(ranef(CO2.fit1, augFrame = T),
+ form = ~Type*Treatment, layout = c(3,1))
498
Manipulating nlme Objects
A
lB
C
Mississippi chilled
Type * Treatment
Mississippi nonchilled
Quebec chilled
Quebec nonchilled
-15
-10
-5
0
5
10 0.2
-0.1
0.0
0.1
-20
-10
0
10
Random effects
Figure 14.11: Estimated random effects versus plant type and chilling treatment.
These plots indicate that chilled plants tend to have smaller values of
A and C. However, the Mississippi plants seem to be much more
affected than the Quebec plants, suggesting an interaction effect
between plant type and chilling treatment. There is no clear pattern of
dependence between lB and the treatment factors, suggesting that lB
is not significantly affected by either plant type or chilling treatment.
We can update CO2.fit1, allowing the A and C fixed effects to depend
on the treatment factors, as follows:
> CO2.fit2 <- update(CO2.fit1,
+ fixed = list(A+C ~ Treatment * Type, lB ~ 1),
+ start = c(32.55, 0, 0, 0, 41.56, 0, 0, 0, -4.6))
The summary
Method
The summary method provides detailed information for fitted nlme
objects. For the CO2.fit2 object defined in the previous section, the
results are as follows:
> summary(CO2.fit2)
499
Chapter 14 Linear and Nonlinear Mixed-Effects Models
Nonlinear mixed-effects model fit by maximum likelihood
Model: uptake ~ CO2.func(conc, A, lB, C)
Data: CO2
AIC
BIC
logLik
392.4073 431.3004 -180.2037
Random effects:
Formula: list(A ~ 1 , lB ~ 1 , C ~ 1 )
Level: Plant
Structure: General positive-definite
StdDev
Corr
A.(Intercept) 2.3709337 A.(In) lB
lB 0.1475418 -0.336
C.(Intercept) 8.1630618 0.355 0.761
Residual 1.7113057
Fixed effects: list(A + C ~ Treatment * Type, lB ~ 1)
Value Std.Error DF
t-value
A.(Intercept) 42.24934
1.49761 64 28.21125
A.Treatment -3.69231
2.05807 64 -1.79407
A.Type -11.07858
2.06458 64 -5.36603
A.Treatment:Type -9.57430
2.94275 64 -3.25352
C.(Intercept) 46.30206
6.43499 64
7.19536
C.Treatment
8.82823
7.22978 64
1.22109
C.Type
3.00775
8.04748 64
0.37375
C.Treatment:Type -49.01624 17.68013 64 -2.77239
lB -4.65063
0.08010 64 -58.06061
A.(Intercept)
A.Treatment
A.Type
A.Treatment:Type
C.(Intercept)
C.Treatment
C.Type
C.Treatment:Type
lB
Correlation:
. . .
The
small
p-value
<.0001
0.0775
<.0001
0.0018
<.0001
0.2265
0.7098
0.0073
<.0001
p-values of the t-statistics associated with the
effects indicate that both factors have a significant
effect on parameters A and C. This implies that their joint effect is not
just the sum of the individual effects.
Treatment:Type
500
Manipulating nlme Objects
The anova
Method
For the fitted object CO2.fit2, you can investigate the joint effect of
Treatment and Type on both A and C using the anova method.
> anova(CO2.fit2,
+ Terms = c("A.Treatment", "A.Type", "A.Treatment:Type"))
F-test for: A.Treatment, A.Type, A.Treatment:Type
numDF denDF F-value p-value
1
3
64 51.77643 <.0001
> anova(CO2.fit2,
+ Terms = c("C.Treatment", "C.Type", "C.Treatment:Type"))
F-test for: C.Treatment, C.Type, C.Treatment:Type
numDF denDF F-value p-value
1
3
64 2.939699 0.0397
The p-values of the Wald F-tests suggest that Treatment and Type have
a stronger influence on A than on C.
The plot
Method
Diagnostic plots for nlme objects can be obtained with the plot
method, in the same way that they are generated for lme objects. For
the CO2.fit2 model, plots grouped by Treatment and Type of the
standardized residuals versus fitted values are shown in Figure 14.12.
The figure is obtained with the following command:
> plot(CO2.fit2, form =
+ resid(., type = "p") ~ fitted(.) | Type * Treatment,
+ abline = 0)
The plots do not indicate any departures from the assumptions in the
model: no outliers seem to be present and the residuals are
symmetrically scattered around the y = 0 line, with constant spread
for different levels of the fitted values.
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
10
20
chilled
Quebec
30
40
chilled
Mississippi
3
2
1
Standardized residuals
0
-1
-2
nonchilled
Quebec
nonchilled
Mississippi
-3
3
2
1
0
-1
-2
-3
10
20
30
40
Fitted values (umol/m^2 s)
Figure 14.12: Standardized residuals versus fitted values for the CO2.fit2 model,
grouped by plant type and chilling treatment.
Other Methods
Predictions for nlme objects are returned by the predict method. For
example, to obtain population predictions of the CO2 uptake rate for
Quebec and Mississippi plants under chilling and no chilling, at
ambient CO2 concentrations of 75, 100, 200, and 500 μL/L, first
define a new data frame as follows:
>
+
+
+
CO2.new <- data.frame(
Type = rep(c("Quebec", "Mississippi"), c(8, 8)),
Treatment=rep(rep(c("chilled","nonchilled"),c(4,4)),2),
conc = rep(c(75, 100, 200, 500), 4))
You can then use the following command to compute the desired
predictions:
> predict(CO2.fit2, CO2.new, level = 0)
502
Manipulating nlme Objects
[1] 6.667665 13.444072 28.898614 38.007573 10.133021
[6] 16.957656 32.522187 41.695974 8.363796 10.391096
[11] 15.014636 17.739766 6.785064 11.966962 23.785004
[16] 30.750597
attr(, "label"):
[1] "Predicted values (umol/m^2 s)"
The augPred method can be used to plot smooth fitted curves for
predicted values. The method works by calculating fitted values at
closely spaced points. For example, Figure 14.13 presents fitted curves
for the CO2.fit2 model. Individual curves are plotted for all twelve
plants in the CO2 data, evaluated at 51 concentrations between 50 and
1000 μL/L. The curves are obtained with the following command:
> plot(augPred(CO2.fit2))
The CO2.fit2 model explains the data reasonably well, as evidenced
by the close agreement between its fitted values and the observed
uptake rates.
200
400
600
800
1000
200
400
600
Mn1
Mc2
Mc3
Mc1
Qc3
Qc2
Mn3
Mn2
800
1000
40
CO2 uptake rate (umol/m^2 s)
30
20
10
40
30
20
10
Qn1
Qn2
Qn3
Qc1
40
30
20
10
200
400
600
800
1000
200
400
600
800
1000
Ambient carbon dioxide concentration (uL/L)
Figure 14.13: Individual fitted curves for the twelve plants in the CO2 uptake data,
based on the CO2.fit2 object.
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
Methods for extracting components from a fitted nlme object are also
available, and are identical to those for lme objects. Some of the most
commonly used methods are coef, fitted, fixef, ranef, resid, and
intervals. For more details on these extractors, see the online help
files and the section Other Methods on page 489.
504
Advanced Model Fitting
ADVANCED MODEL FITTING
In many practical applications, we want to restrict the random effects
variance-covariance matrix to special forms that have fewer
parameters. For example, we may want to assume that the random
effects are independent so that their variance-covariance matrix is
diagonal. We may also want to make specific assumptions about the
within-group error structure. Both the lme and nlme functions include
advanced options for defining positive-definite matrices, correlation
structures, and variance functions.
PositiveDefinite Matrix
Structures
Different positive-definite matrices can be used to represent the
random effects variance-covariance structures in mixed-effects
models. The available matrices, listed in Table 14.1, are organized in
S-PLUS as different pdMat classes. To use a pdMat class when fitting
mixed-effects models, specify it with the random argument to either
lme or nlme.
Table 14.1: Classes of positive-definite matrices.
Class
Description
pdBand
band diagonal
pdBlocked
block diagonal
pdCompSymm
compound symmetry
pdDiag
diagonal
pdIdent
multiple of an identity
pdKron
Kronecker product
pdStrat
a different pdMat class for each level of
a stratification variable
pdSymm
general positive-definite
505
Chapter 14 Linear and Nonlinear Mixed-Effects Models
By default, the pdSymm class is used to represent a random effects
covariance matrix. You can define your own pdMat class by specifying
a constructor function and, at a minimum, methods for the functions
pdConstruct, pdMatrix and coef. For examples of these functions, see
the methods for the pdSymm and pdDiag classes.
Example: the Orthodont data
We return to the Ortho.fit2 model that we created in the section
Arguments on page 494. To fit a model with independent slope and
intercept random effects, we include a diagonal variance-covariance
matrix using the pdDiag class:
> Ortho.fit3 <- update(Ortho.fit2, random = pdDiag(~age))
> Ortho.fit3
Linear mixed-effects model fit by REML
Data: Orthodont
Log-restricted-likelihood: -216.5755
Fixed: distance ~ age + Sex + age:Sex
(Intercept)
age
Sex
age:Sex
16.34062 0.784375 1.032102 -0.3048295
Random effects:
Formula: ~ age | Subject
Structure: Diagonal
(Intercept)
age Residual
StdDev:
1.554607 0.08801665 1.365502
Number of Observations: 108
Number of Groups: 27
The grouping structure is inferred from the groupedData display
formula in the Orthodont data. Alternatively, the grouping structure
can be passed to the random argument as follows:
random = list(Subject = pdDiag(~age))
Example: the CO2 data
Recall the CO2.fit2 object defined in the section The print Method
on page 497. We wish to test whether we can assume that the random
effects in CO2.fit2 are independent. To do this, use the commands
below.
506
Advanced Model Fitting
> CO2.fit3 <- update(CO2.fit2, random = pdDiag(A+lB+C~1))
> anova(CO2.fit2, CO2.fit3)
Model df
AIC
BIC
logLik
Test
CO2.fit2
1 16 392.4073 431.3004 -180.2037
CO2.fit3
2 13 391.3921 422.9927 -182.6961 1 vs 2
L.Ratio p-value
CO2.fit2
CO2.fit3 4.984779
0.1729
As evidenced by the large p-value for the likelihood ratio test in the
anova output, the independence of the random effects seems
plausible. Note that because the two models have the same fixed
effects structure, the test based on restricted maximum likelihood is
meaningful.
Correlation
Structures and
Variance
Functions
The within-group error covariance structure can be flexibly modeled
by combining correlation structures and variance functions.
Correlation structures are used to model within-group correlations
that are not captured by the random effects. These are generally
associated with temporal or spatial dependencies. The variance
functions are used to model heteroscedasticity in the within-group
errors.
Similar to the positive-definite matrices described in the previous
section, the available correlation structures and variance functions are
organized into corStruct and varFunc classes. Table 14.2 and Table
14.3 list the standard classes for each structure.
Table 14.2: Classes of correlation structures.
Class
Description
corAR1
AR(1)
corARMA
ARMA(p,q)
corBand
banded
corCAR1
continuous AR(1)
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
Table 14.2: Classes of correlation structures. (Continued)
Class
Description
corCompSymm
compound symmetry
corExp
exponential spatial correlation
corGaus
Gaussian spatial correlation
corIdent
multiple of an identity
corLin
linear spatial correlation
corRatio
rational quadratic spatial correlation
corSpatial
general spatial correlation
corSpher
spherical spatial correlation
corStrat
a different corStruct class for each level of a
stratification variable
corSymm
general correlation matrix
Table 14.3: Classes of variance function structures.
508
Class
Description
varComb
combination of variance functions
varConstPower
constant plus power of a variance covariate
varExp
exponential of a variance covariate
varFixed
fixed weights, determined by a variance covariate
Advanced Model Fitting
Table 14.3: Classes of variance function structures.
Class
Description
varIdent
different variances per level of a factor
varPower
power of a variance covariate
In either lme or nlme, the optional argument correlation specifies a
correlation structure, and the optional argument weights is used for
variance functions. By default, the within-group errors are assumed to
be independent and homoscedastic.
You can define your own correlation and variance function classes by
specifying appropriate constructor functions and a few method
functions. For a new correlation structure, method functions must be
defined for at least corMatrix and coef. For examples of these
functions, see the methods for the corSymm and corAR1 classes. A new
variance function requires methods for at least coef, coef<-, and
initialize. For examples of these functions, see the methods for the
varPower class.
Example: the Orthodont data
Figure 14.7 displays a plot of the residuals versus fitted values for the
Ortho.fit2 model. It suggests that different variance structures
should be allowed for boys and girls. We test this by updating the
Ortho.fit3 model (defined in the previous section) with the varIdent
variance function:
> Ortho.fit4 <- update(Ortho.fit3,
+ weights = varIdent(form = ~ 1|Sex))
> Ortho.fit4
Linear mixed-effects model fit by REML
Data: Orthodont
Log-restricted-likelihood: -206.0841
Fixed: distance ~ age + Sex + age:Sex
(Intercept)
age
Sex
age:Sex
16.34062 0.784375 1.032102 -0.3048295
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
Random effects:
Formula: ~ age | Subject
Structure: Diagonal
(Intercept)
age Residual
StdDev:
1.448708 0.1094042 1.65842
Variance function:
Structure: Different standard deviations per stratum
Formula: ~ 1 | Sex
Parameter estimates:
Male
Female
1 0.425368
Number of Observations: 108
Number of Groups: 27
> anova(Ortho.fit3, Ortho.fit4)
Ortho.fit3
Ortho.fit4
Model df
AIC
BIC
logLik
1 7 449.9235 468.4343 -217.9618
2 8 430.9407 452.0958 -207.4704
Test L.Ratio p-value
Ortho.fit3
Ortho.fit4 1 vs 2 20.98281
<.0001
There is strong indication that the orthodontic distance is less variable
in girls than in boys.
We can test for the presence of an autocorrelation of lag 1 in the by
updating Ortho.fit4 as follows:
> Ortho.fit5 <- update(Ortho.fit4, corr = corAR1())
> Ortho.fit5
Linear mixed-effects model fit by REML
Data: Orthodont
Log-restricted-likelihood: -206.037
Fixed: distance ~ age + Sex + age:Sex
(Intercept)
age
Sex
age:Sex
16.31726 0.7859872 1.060799 -0.3068977
510
Advanced Model Fitting
Random effects:
Formula: ~ age | Subject
Structure: Diagonal
(Intercept)
age Residual
StdDev:
1.451008 0.1121105 1.630654
Correlation Structure: AR(1)
Formula: ~ 1 | Subject
Parameter estimate(s):
Phi
-0.05702521
Variance function:
Structure: Different standard deviations per stratum
Formula: ~ 1 | Sex
Parameter estimates:
Male
Female
1 0.4250633
Number of Observations: 108
Number of Groups: 27
> anova(Ortho.fit4, Ortho.fit5)
Ortho.fit4
Ortho.fit5
Model df
AIC
BIC
logLik
Test
1 8 428.1681 449.3233 -206.0841
2 9 430.0741 453.8736 -206.0370 1 vs 2
L.Ratio p-value
Ortho.fit4
Ortho.fit5 0.094035 0.7591
The large p-value of the likelihood ratio test indicates that the
autocorrelation is not present.
Note that the correlation structure is used together with the variance
function, representing a heterogeneous AR(1) process (Littel, et al.,
1996). Because the two structures are defined and constructed
separately, a given correlation structure can be combined with any of
the available variance functions.
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
Example: the Pixel data
In the form argument of the varFunc constructors, a fitted lme or nlme
object can be referenced with the period ‘‘.’’ symbol. For example,
recall the Pixel.fit1 object defined in the section Arguments on
page 481. To use a variance function that is an arbitrary power of the
fitted values in the model, update Pixel.fit1 as follows:
> Pixel.fit2 <- update(Pixel.fit1,
+ weights = varPower(form = ~ fitted(.)))
> Pixel.fit2
Linear mixed-effects model fit by REML
Data: Pixel
Log-restricted-likelihood: -412.4593
Fixed: pixel ~ day + day^2
(Intercept)
day
I(day^2)
1073.314 6.10128 -0.3663864
Random effects:
Formula: ~ day | Dog
Structure: General positive-definite
StdDev
Corr
(Intercept) 28.503164 (Inter
day 1.872961 -0.566
Formula: ~ 1 | Side %in% Dog
(Intercept)
Residual
StdDev:
16.66015 4.4518e-006
Variance function:
Structure: Power of variance covariate
Formula: ~ fitted(.)
Parameter estimates:
power
2.076777
Number of Observations: 102
Number of Groups:
Dog Side %in% Dog
10
20
> anova(Pixel.fit1, Pixel.fit2)
512
Advanced Model Fitting
Pixel.fit1
Pixel.fit2
Model df
AIC
BIC
logLik
Test
1 8 841.2102 861.9712 -412.6051
2 9 842.9187 866.2747 -412.4593 1 vs 2
L.Ratio p-value
Pixel.fit1
Pixel.fit2 0.2915376 0.5892
There is no evidence of heteroscedasticity in this case, as evidenced
by the large p-value of the likelihood ratio test in the anova output.
Because the default value for form in varPower is ~fitted(.), it
suffices to use weights = varPower() in this example.
Example: the CO2 data
As a final example, we test for the presence of serial correlation in the
within-group errors of the nonlinear CO2.fit3 model (defined in the
previous section). To do this, we use the corAR1 class as follows:
> CO2.fit4 <- update(CO2.fit3, correlation = corAR1())
> anova(CO2.fit3, CO2.fit4)
CO2.fit3
CO2.fit4
Model df
AIC
BIC
logLik
Test
1 13 391.3921 422.9927 -182.6961
2 14 393.2980 427.3295 -182.6490 1 vs 2
L.Ratio p-value
CO2.fit3
CO2.fit4 0.09407508 0.7591
There does not appear to be evidence of within-group serial
correlation.
Self-Starting
Functions
The S-PLUS function nlsList can be used to create a list of nonlinear
fits for each group of a groupedData object. This function is an
extension of nls, which is discussed in detail in the chapter Nonlinear
Models. As with nlme, you must provide initial estimates for the fixed
effects parameters when using nlsList. You can either provide the
starting values explicitly, or compute them using a self-starting function.
A self-starting function is a class of models useful for particular
applications. We describe below several self-starting functions that are
provided with S-PLUS.
513
Chapter 14 Linear and Nonlinear Mixed-Effects Models
One way of providing initial values to nlsList is to include them in
the data set as a parameters attribute. In addition, both nlsList and
nlme have optional start arguments that can be used to provide the
initial estimates as input. Alternatively, a function that derives initial
estimates can be added to the model formula itself as an attribute.
This constitutes a selfStart function in S-PLUS. When a self-starting
function is used in calls to nlsList and nlme, initial estimates for the
parameters are taken directly from the initial attribute of the
function.
The following four self-starting functions are useful in biostatistics
applications.
•
Biexponential model:
α1 e
β1
–e t
+ α2 e
β2
–e t
The corresponding S-PLUS function is SSbiexp(input, A1,
lrc1, A2, lrc2), where input=t is a covariate and A1=α1,
A2=α2, lrc1=β1, and lrc2=β2 are parameters.
•
First-order Compartment model:
β
γ
γ
–e t
β
–e t
d ⋅ e ⋅ e ⋅ (e
–e )
--------------------------------------------------------α
β
γ
e ⋅ (e – e )
The corresponding S-PLUS function is SSfol(Dose, input,
lCl, lKa, lKe), where Dose=d is a covariate representing the
initial dose, input=t is a covariate representing the time at
which to evaluate the model, and lCl=α, lKa=β, and lKe=γ
are parameters.
•
Four-parameter Logistic model:
β–α
α + ------------------------------–( x – γ ) ⁄ θ
1+e
The corresponding S-PLUS function is SSfpl(input, A, B,
xmid, scal), where input=x is a covariate and A=α, B=β,
xmid=γ, and scal=θ are parameters.
514
Advanced Model Fitting
•
Logistic model:
α
-----------------------------–( t – β ) ⁄ γ
1+e
The corresponding S-PLUS function is SSlogis(time, Asym,
xmid, scal), where time=t is a covariate and Asym=α, xmid=β,
and scal=γ are parameters.
Other S-PLUS self-starting functions are listed in Table 14.4. Details
about each function can be found in its corresponding online help
file. You can define your own self-starting function by using the
selfStart constructor.
Table 14.4: Self-starting models available in S-PLUS.
Function
Model
SSasymp
asymptotic regression
SSasympOff
asymptotic regression with an offset
SSasympOrig
asymptotic regression through the origin
SSbiexp
biexponential model
SSfol
first-order compartment model
SSfpl
four-parameter logistic model
SSlogis
logistic model
SSmicmen
Michaelis-Menten relationship
Example: The Soybean data
We apply the self-starting function SSlogis to the Soybean data
introduced in the section Representing Grouped Data Sets. We want
to verify the hypothesis that a logistic model can be used represent
leaf growth.
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
The nlsList call is as follows:
> Soybean.nlsList <- nlsList(weight ~
+ SSlogis(Time, Asym, xmid, scal) | Plot, data = Soybean)
Error in nls(y ~ 1/(1 + exp((xmid - x)/scal)), data ..:
singular gradient matrix
The error message indicates that nls could not compute a fit for one
of the groups in the data set. The object Soybean.nlsList is
nevertheless created.
Warning
On occasion, nlsList returns errors when it cannot adequately fit one or more groups in the
data set. When this occurs, fits for the remaining groups are still computed.
The results in Soybean.nlsList show that the 1989P8 group in the
Soybean data could not be fitted appropriately with the logistic model.
We can see this directly by using the coef function.
> coef(Soybean.nlsList)
1988F4
1988F2
1988F1
1988F7
1988F5
1988F8
. . .
1989P2
1989P8
1990F2
. . .
1990P5
1990P2
1990P4
Asym
15.151338
19.745503
20.338576
19.871706
30.647205
22.776430
xmid
scal
52.83361 5.176641
56.57514 8.406720
57.40265 9.604870
56.16236 8.069718
64.12857 11.262351
59.32964 9.000267
28.294391
NA
19.459767
67.17185 12.522720
NA
NA
66.28652 13.158397
19.543787
25.787317
26.132712
51.14830 7.291976
62.35974 11.657019
61.20345 10.973765
An nlme method exists for nlsList objects, which allows you to fit
population parameters and individual random effects for an nlsList
model. For example, the following simple call computes a mixedeffects model from the Soybean.nlsList object.
516
Advanced Model Fitting
> Soybean.fit1 <- nlme(Soybean.nlsList)
> summary(Soybean.fit1)
Nonlinear mixed-effects model fit by maximum likelihood
Model: weight ~ SSlogis(Time, Asym, xmid, scal)
Data: Soybean
AIC
BIC
logLik
1499.667 1539.877 -739.8334
Random effects:
Formula: list(Asym ~ 1 , xmid ~ 1 , scal ~ 1 )
Level: Plot
Structure: General positive-definite
StdDev Corr
Asym 5.200969 Asym xmid
xmid 4.196918 0.721
scal 1.403934 0.711 0.959
Residual 1.123517
Fixed effects: list(Asym ~ 1 , xmid ~ 1 , scal ~ 1 )
Value Std.Error DF t-value p-value
Asym 19.25326 0.8031745 362 23.97145 <.0001
xmid 55.02012 0.7272288 362 75.65724 <.0001
scal 8.40362 0.3152215 362 26.65941 <.0001
Correlation:
Asym xmid
xmid 0.724
scal 0.620 0.807
Standardized Within-Group Residuals:
Min
Q1
Med
Q3
Max
-6.086247 -0.2217542 -0.03385827 0.2974177 4.845216
Number of Observations: 412
Number of Groups: 48
The Soybean.fit1 object does not incorporate covariates or withingroup errors. Comparing the estimated standard deviations and
means of Asym, xmid, and scal, the asymptotic weight Asym has the
highest coefficient of variation (5.2/19.25 = 0.27). Modeling this
random effects parameter is the focus of the following analyses.
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
We first attempt to model the asymptotic weight as a function of the
genotype variety and the planting year. To model the within-group
errors, we assume the serial correlation follows an AR(1) process.
Given that the observations are not equally spaced in time, we need
to use the continuous form of the AR process and provide the time
variable explicitly. From Figure 14.14, we conclude that the withingroup variance is proportional to some power of the absolute value of
the predictions. The figure is obtained with the following command:
> plot(Soybean.fit1)
4
Standardized residuals
2
0
-2
-4
-6
0
5
10
15
20
25
Fitted values (g)
Figure 14.14: A plot of the standardized residuals for the Soybean.fit1 model.
We fit an improved model to the Soybean data below. In the new fit,
we model the within-group errors using the corCAR1 correlation
structure and the varPower variance function. Initial estimates for the
parameterization of Asym are derived from the results of
Soybean.nlsList.
518
Advanced Model Fitting
>
+
+
+
+
+
+
+
Soybean.fit2 <- nlme(weight ~
SSlogis(Time, Asym, xmid, scal), data = Soybean,
fixed = list(Asym ~ Variety * Year, xmid ~ 1, scal ~ 1),
random = list(Asym ~ 1, xmid ~ 1, scal ~ 1),
start = c(20.08425, 2.03699, -3.785161, 0.3036094,
1.497311, -1.084704, 55.02058, 8.402632),
correlation = corCAR1(form = ~Time),
weights = varPower())
Figure 14.15 displays a plot the residuals for the updated model,
obtained with the following command:
> plot(Soybean.fit2)
The residuals plot confirms our choice of variance structure. The
anova function is used to compare the Soybean.fit1 and
Soybean.fit2 models. The progress in the log-likelihood, AIC, and
BIC is tremendous.
> anova(Soybean.fit1, Soybean.fit2)
Soybean.fit1
Soybean.fit2
Model df
AIC
BIC
logLik
1 10 1499.667 1539.877 -739.8334
2 17 678.592 746.950 -322.2962
Test L.Ratio p-value
Soybean.fit1
Soybean.fit2 1 vs 2 835.0744 <.0001
We conclude that both the genotype variety and planting year have a
large impact on the limiting leaf weight of the plants. The
experimental strain gains 2.5 grams in the limit.
519
Chapter 14 Linear and Nonlinear Mixed-Effects Models
4
Standardized residuals
3
2
1
0
-1
-2
0
5
10
15
20
Fitted values (g)
Figure 14.15: A plot of the standardized residuals for Soybean.fit2.
Modeling
Spatial
Dependence
Two main classes of dependence among within-group errors can be
modeled using the mixed-effects tools in S-PLUS: temporal and spatial.
To model serial correlation, or temporal dependence, several
correlation structures were introduced in Table 14.2. To assess and
model spatial dependence among the within-group errors, we use the
Variogram function.
The Variogram method for the lme and nlme classes estimates the
sample semivariogram from the residuals of a fitted object. The
semivariogram can then be plotted with its corresponding plot
method. If the residuals show evidence of spatial dependence, then
you need to determine either a model for the dependence or its
correlation structure.
We use the corSpatial function to model spatial dependence in the
within-group errors. This function is a constructor for the corSpatial
class, which represents a spatial correlation structure. This class is
virtual, having five real classes corresponding to five specific spatial
correlation structures: corExp, corGaus, corLin, corRatio, and
corSpher. An object returned by corSpatial inherits from one of
520
Advanced Model Fitting
these real classes, as determined by the type argument. Objects
created with this constructor need to be initialized using the
appropriate initialize method.
Example: the Soybean data
A typical call to the Variogram function for a mixed-effects model
looks like:
> plot(Variogram(Soybean.fit1, form = ~ Time))
The resulting plot, shown in Figure 14.16, does not show a strong
pattern in the semivariogram of the residuals from Soybean.fit1, in
terms of time distance. This implies that spatial correlation may not
be present in the model.
Semivariogram
1.5
1.0
0.5
0.0
10
20
30
40
50
60
Distance
Figure 14.16: Estimate of the sample semivariogram for the Soybean.fit1 model
object.
521
Chapter 14 Linear and Nonlinear Mixed-Effects Models
Refitting Soybean.fit2 without the AR(1) correlation structure shows
that the model may indeed be overparameterized:
> Soybean.fit3 <- update(Soybean.fit2, correlation = NULL)
> anova(Soybean.fit1, Soybean.fit3, Soybean.fit2)
Soybean.fit1
Soybean.fit3
Soybean.fit2
Model
1
2
3
df
AIC
BIC
logLik
10 1499.667 1539.877 -739.8334
16 674.669 739.005 -321.3344
17 678.592 746.950 -322.2962
Test L.Ratio p-value
Soybean.fit1
Soybean.fit3 1 vs 2 836.9981 <.0001
Soybean.fit2 2 vs 3
1.9237 0.1654
This indicates that only the change in the fixed effects model and the
use of a variance function explain the improvement we see in
Soybean.fit2. The model without the correlation structure is simpler,
and therefore preferred.
522
References
REFERENCES
Becker, R.A., Cleveland, W.S., & Shyu, M.J. (1996). The visual design
and control of trellis graphics displays. Journal of Computational and
Graphical Statistics, 5(2): 123-156.
Chambers, J.M. & Hastie, T.J. (Eds.) (1992). Statistical Models in S.
London: Chapman and Hall.
Davidian, M. & Giltinan, D.M. (1995). Nonlinear Models for Repeated
Measurement Data. London: Chapman & Hall.
Goldstein, H. (1995). Multilevel Statistical Models. New York: Halsted
Press.
Laird, N.M. & Ware, J.H. (1982). Random-effects models for
longitudinal data. Biometrics 38: 963-974.
Lindstrom, M.J. & Bates, D.M. (1990). Nonlinear mixed effects
models for repeated measures data. Biometrics 46: 673-687.
Littel, R.C., Milliken, G.A., Stroup, W.W., & Wolfinger, R.D. (1996).
SAS Systems for Mixed Models. Cary, NC: SAS Institute Inc..
Longford, N.T. (1993). Random Coefficient Models. New York: Oxford
University Press.
Milliken, G.A. & Johnson, D.E. (1992). Analysis of Messy Data, Volume
1: Designed Experiments. London: Chapman & Hall.
Pinheiro, J.C. (1994). Topics in Mixed Effect Models. Ph.D. thesis,
Department of Statistics, University of Wisconsin-Madison.
Pinheiro, J.C. & Bates, D.M. (2000). Mixed-Effects Models in S and SPLUS. New York: Springer-Verlag.
Potthoff, R.F. & Roy, S.N. (1964). A generalized multivariate analysis
of variance model useful especially for growth curve problems.
Biometrika 51: 313-326.
Potvin, C., Lechowicz, M.J., & Tardif, S. (1990). The statistical analysis
of ecophysiological response curves obtained from experiments
involving repeated measures. Ecology 71: 1389-1400.
Venables, W.N. & Ripley, B.D. (1997) Modern Applied Statistics with SPLUS, 2nd Edition. New York: Springer-Verlag.
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Chapter 14 Linear and Nonlinear Mixed-Effects Models
524
NONLINEAR MODELS
15
Introduction
526
Optimization Functions
Finding Roots
Finding Local Maxima and Minima of Univariate
Functions
Finding Maxima and Minima of Multivariate
Functions
Solving Nonnegative Least Squares Problems
Solving Nonlinear Least Squares Problems
527
528
530
535
537
Examples of Nonlinear Models
Maximum Likelihood Estimation
Nonlinear Regression
539
539
542
Inference for Nonlinear Models
Likelihood Models
Least Squares Models
The Fitting Algorithms
Specifying Models
Parametrized Data Frames
Derivatives
Fitting Models
Profiling the Objective Function
544
544
544
544
545
547
548
554
560
References
565
529
525
Chapter 15 Nonlinear Models
INTRODUCTION
This chapter covers the fitting of nonlinear models such as in
nonlinear regression, likelihood models, and Bayesian estimation.
Nonlinear models are more general than the linear models usually
discussed. Specifying nonlinear models typically requires one or
more of the following: more general formulas, extended data frames,
starting values, and derivatives.
The two most common fitting criteria for nonlinear models
considered are Minimum sum and Minimum sum-of-squares.
Minimum sum minimizes the sum of contributions from observations
(the maximum likelihood problem). Minimum sum-of-squares
minimizes the sum of squared residuals (the nonlinear least-squares
regression problem).
The first sections of this chapter summarize the use of the nonlinear
optimization functions. Starting with the section Examples of
Nonlinear Models, the use of the ms and nls functions are examined,
along with corresponding examples and theory, in much more detail.
526
Optimization Functions
OPTIMIZATION FUNCTIONS
S-PLUS has several functions for finding roots of equations and local
maxima and minima of functions, as shown in Table 15.1.
Table 15.1: The range of S-PLUS functions for finding roots, maxima, and minima.
Function
Description
polyroot
Finds the roots of a complex polynomial equation.
uniroot
Finds the root of a univariate real-valued function in a user-supplied interval.
peaks
Finds local maxima in a set of discrete points.
optimize
Approximates a local optimum of a continuous univariate function within a
given interval.
ms
Finds a local minimum of a multivariate function.
nlmin
Finds a local minimum of a nonlinear function using a general quasi-Newton
optimizer.
nlminb
Finds a local minimum for smooth nonlinear functions subject to boundconstrained parameters.
nls
Finds a local minimum of the sums of squares of one or more multivariate
functions.
nlregb
Finds a local minimum for sums of squares of nonlinear functions subject to
bound-constrained parameters.
nnls
Finds the least-squares solution subject to the constraint that the coefficients
be nonnegative.
527
Chapter 15 Nonlinear Models
Finding Roots
The function polyroot finds the roots (zeros) of the complex-valued
polynomial equation
polyroot
k
a k z + … + a 1 z + a 0 = 0 . The input to
is the vector of coefficients c ( a 0, …, a k ) . For example, to
2
solve the equation z + 5z + 6 = 0 , use polyroot as follows:
> polyroot(c(6, 5, 1))
[1] -2+2.584939e-26i -3-2.584939e-26i
Since 2.584939e-26i is equivalent to zero in machine arithmetic,
polyroot returns -2 and -3 for the roots of the polynomial, as
expected.
The function uniroot finds a zero of a continuous, univariate, realvalued function within a user-specified interval for which the function
has opposite signs at the endpoints. The input to uniroot includes the
function, the lower and upper endpoints of the interval, and any
additional arguments to the function. For example, suppose you have
the function:
> my.fcn
function(x, amp=1, per=2*pi, horshft=0, vershft=0)
{
amp * sin(((2*pi)/per) * (x-horshft)) + vershft
}
This is the sine function with amplitude abs(amp), period abs(per),
horizontal (phase) shift horshft and vertical shift vershft. To find a
root of the function my.fcn in the interval [π ⁄ 2,3π ⁄ 2] using its
default arguments, type:
> uniroot(my.fcn, interval = c(pi/2, 3*pi/2))
$root
[1] 3.141593
. . .
To find a root of my.fcn in the interval [π ⁄ 4,3π ⁄ 4] with the period
set to π , type the following command.
> uniroot(my.fcn, interval = c(pi/4, 3*pi/4), per = pi)
528
Optimization Functions
$root:
[1] 1.570796
. . .
> pi/2
[1] 1.570796
See the help file for uniroot for information on other arguments to
this function.
Finding Local
Maxima and
Minima of
Univariate
Functions
The peaks function takes a data object x and returns an object of the
same type with logical values: T if a point is a local maximum;
otherwise, F:
> peaks(corn.rain)
1890: F T F F F F T F F F T F T F F F F T F F F F T F F T F
1917: T F F F T F F T F T F
Use peaks on the data object -x to find local minima:
> peaks(-corn.rain)
1890: F F F F T F F F F F F T F F F T F F F F F T F T F F T
1917: F T F F F T F F T F F
To find a local optimum (maximum or minimum) of a continuous
univariate function within a particular interval, use the optimize
function. The input to optimize includes the function to optimize, the
lower and upper endpoints of the interval, which optimum to look for
(maximum versus minimum), and any additional arguments to the
function.
> optimize(my.fcn, c(0, pi), maximum = T)
$maximum:
[1] 1.570799
$objective:
[1] -1
$nf:
[1] 10
$interval:
529
Chapter 15 Nonlinear Models
[1] 1.570759 1.570840
. . .
> pi/2
[1] 1.570799
> optimize(my.fcn, c(0, pi), maximum = F, per = pi)
$minimum:
[1] 2.356196
$objective:
[1] -1
$nf:
[1] 9
$interval:
[1] 2.356155 2.356236
. . .
> 3*pi/4
[1] 2.356194
See the help file for optimize for information on other arguments to
this function.
Finding
Maxima and
Minima of
Multivariate
Functions
530
S-PLUS has two functions to find the local minimum of a multivariate
function: nlminb (Nonlinear Minimization with Box Constraints) and
ms (Minimize Sums).
The two required arguments to nlminb are objective (the function f
to minimize) and start (a vector of starting values for the
minimization). The function f must take as its first argument a vector
of parameters over which the minimization is carried out. By default,
there are no boundary constraints on the parameters. The nlminb
function, however, also takes the optional arguments lower and upper
that specify bounds on the parameters. Additional arguments to f can
be passed in the call to nlminb.
Optimization Functions
1. Example: using nlminb to find a local minimum
> my.multvar.fcn
function(xvec, ctr = rep(0, length(xvec)))
{
if(length(xvec) != length(ctr))
stop("lengths of xvec and ctr do not match")
sum((xvec - ctr)^2)
}
> nlminb(start = c(0,0), objective = my.multvar.fcn,
+ ctr = c(1,2))
$parameters:
[1] 1 2
$objective:
[1] 3.019858e-30
$message:
[1] "ABSOLUTE FUNCTION CONVERGENCE"
. . .
2. Example: using nlminb to find a local maximum
To find a local maximum of f , use nlminb on – f . Since unary minus
cannot be performed on a function, you must define a new function
that returns -1 times the value of the function you want to maximize:
> fcn.to.maximize
function(xvec)
{
- xvec[1]^2 + 2 * xvec[1] - xvec[2]^2 + 20 * xvec[2] + 40
}
> fcn.to.minimize
function(xvec)
{
- fcn.to.maximize(xvec)
}
531
Chapter 15 Nonlinear Models
> nlminb(start = c(0, 0), objective = fcn.to.minimize)
$parameters:
[1] 1 10
$objective:
[1] -141
$message:
[1] "RELATIVE FUNCTION CONVERGENCE"
. . .
3. Example: using nlminb to find a constrained minimum
To find the local minimum of a multivariate function subject to
constraints, use nlminb with the lower and/or upper arguments. As an
example of constrained minimization, consider the following function
norm.neg.2.ll, which is (minus a constant) -2 times the log-likelihood
function of a Gaussian distribution:
>
+
+
+
+
+
norm.neg.2.ll <function(theta, y)
{
length(y) * log(theta[2]) +
(1/theta[2]) * sum((y - theta[1])^2)
}
This function assumes that observations from a normal distribution
are stored in the vector y. The vector theta contains the mean
(theta[1]) and variance (theta[2]) of this distribution. To find the
maximum likelihood estimates of the mean and variance, we need to
find the values of theta[1] and theta[2] that minimize
norm.neg.2.ll for a given set of observations stored in y. We must
use the lower argument to nlminb because the estimate of variance
must be greater than zero:
>
>
>
+
set.seed(12)
my.obs <- rnorm(100, mean = 10, sd = 2)
nlminb(start = c(0,1), objective = norm.neg.2.ll,
lower = c(-Inf, 0), y = my.obs)
$parameters:
[1] 9.863812 3.477773
532
Optimization Functions
$objective:
[1] 224.6392
$message:
[1] "RELATIVE FUNCTION CONVERGENCE"
. . .
> mean(my.obs)
[1] 9.863812
> (99/100) * var(my.obs)
[1] 3.477774
4. Example: using ms
The Minimum Sums function ms also minimizes a multivariate
function, but in the context of the modeling paradigm. It therefore
expects a formula rather than a function as its main argument. Here,
the last example is redone with ms, where mu is the estimate of the
population mean μ and ss is the estimate of the population variance
2
σ .
> ms( ~length(y) * log(ss) + (1/ss) * sum((y - mu)^2),
+ data = data.frame(y = my.obs),
+ start = list(mu = 0, ss = 1))
value: 224.6392
parameters:
mu
ss
9.863813 3.477776
formula:
~length(y) * log(ss) + (1/ss) * sum((y-mu)^2)
1 observations
call: ms(formula = ~length(y) * log(ss) + (1/ss) *
sum((y - mu)^2),
data = data.frame(y=my.obs), start=list(mu=0, ss=1))
533
Chapter 15 Nonlinear Models
Hint
The ms function does not do minimization subject to constraints on the parameters.
If there are multiple solutions to your minimization problem, you may not get the answer you
want using ms. In the above example, the ms function tells us we have “1 observations” because
the whole vector y was used at once in the formula. The Minimum Sum function minimizes the
sum of contributions to the formula, so we could have gotten the same estimates mu and ss with
the formula shown in example 5.
5. Example: using ms with several observations
> ms( ~log(ss) + (1/ss) * (y - mu)^2,
+ data = data.frame(y = my.obs),
+ start = list(mu = 0, ss = 1))
value: 224.6392
parameters:
mu
ss
9.863813 3.477776
formula:
~log(ss) + (1/ss) * (y - mu)^2
100 observations
call: ms(formula = ~log(ss) + (1/ss) * (y - mu)^2,
data = data.frame(y=my.obs), start=list(mu=0,ss=1))
6. Example: using ms with a formula function
If the function you want to minimize is fairly complicated, then it is
usually easier to write a function and supply it in the formula.
> ms( ~norm.neg.2.ll(theta,y), data = data.frame(y =
+ my.obs), start = list(theta = c(0,1)))
value: 224.6392
parameters:
theta1
theta2
9.863813 3.477776
534
Optimization Functions
formula:
~norm.neg.2.ll(theta, y)
1 observations
call: ms(formula = ~norm.neg.2.ll(theta, y), data =
data.frame(y = my.obs),
start = list(theta = c(0, 1)))
Solving
Nonnegative
Least Squares
Problems
Given an m × n matrix A and a vector b of length m , the linear
nonnegative least squares problem is to find the vector x of length n
that minimizes Ax – b , subject to the constraint that x i ≥ 0 for i in
1, …, n .
To solve nonnegative least squares problems in S-PLUS, use the
nnls.fit function. For example, consider the following fit using the
stack data:
$coefficients
Air Flow Water Temp Acid Conc.
0.2858057 0.05715152
0
$residuals:
[1] 17.59245246
[4]
8.90840973
[7] -0.09159027
[10] -3.60545832
[13] -6.60545832
[16] -8.31901267
[19] -6.43331572
12.59245246
-0.97728723
0.90840973
-3.60545832
-5.66260984
-7.37616419
-2.14814995
14.13578403
-1.03443875
-2.89121593
-4.54830680
-7.31901267
-7.37616419
-6.14942983
$dual:
Air Flow
Water Temp Acid Conc.
3.637979e-12 5.400125e-13 -1438.359
$rkappa:
final
minimum
0.02488167 0.02488167
$call:
nnls.fit(x = stack.x, y = stack.loss)
535
Chapter 15 Nonlinear Models
You can also use nlregb to solve the nonnegative least squares
problem, since the nonnegativity constraint is just a simple box
constraint. To pose the problem to nlregb, define two functions,
lin.res and lin.jac, of the form f(x,params) that represent the
residual function and the Jacobian of the residual function,
respectively:
>
>
>
+
+
lin.res <- function(x, b, A) A %*% x - b
lin.jac <- function(x, A) A
nlregb(n = length(stack.loss), start = rnorm(3),
res = lin.res, jac = lin.jac, lower = 0,
A = stack.x, b = stack.loss)
$parameters:
[1] 0.28580571 0.05715152 0.00000000
$objective:
[1] 1196.252
. . .
Generally, nnls.fit is preferred to nlregb for reasons of efficiency,
since nlregb is primarily designed for nonlinear problems. However,
nlregb can solve degenerate problems that can not be handled by
nnls.fit. You may also want to compare the results of nnls.fit with
those of lm. Remember that lm requires a formula and fits an intercept
term by default (which nnls.fit does not). Keeping this in mind, you
can construct the comparable call to lm as follows:
> lm(stack.loss ~ stack.x - 1)
Call:
lm(formula = stack.loss ~ stack.x - 1)
Coefficients:
stack.xAir Flow stack.xWater Temp stack.xAcid Conc.
0.7967652
1.111422
-0.6249933
Degrees of freedom: 21 total; 18 residual
Residual standard error: 4.063987
For the stack loss data, the results of the constrained optimization
methods nnls.fit and nlregb agree completely. The linear model
produced by lm includes a negative coefficient.
536
Optimization Functions
You can use nnls.fit to solve the weighted nonnegative least squares
problem by providing a vector of weights as the weights argument.
The weights used by lm are the square roots of the weights used by
nnls.fit; you must keep this in mind if you are trying to solve a
problem using both functions.
Solving
Nonlinear
Least Squares
Problems
Two functions, nls and nlregb, are available for solving the special
minimization problem of nonlinear least squares. The function nls is
used in the context of the modeling paradigm, so it expects a formula
rather than a function as its main argument. The function nlregb
expects a function rather than a formula (the argument name is
residuals), and, unlike nls, it can perform the minimization subject
to constraints on the parameters.
1. Example: using nls
In this example, we create 100 observations where the underlying
signal is a sine function with an amplitude of 4 and a horizontal
(phase) shift of π . Noise is added in the form of Gaussian random
numbers. We then use the nls function to estimate the true values of
amplitude and horizontal shift.
>
>
>
>
>
>
+
+
set.seed(20)
noise <- rnorm(100, sd = 0.5)
x <- seq(0, 2*pi, length = 100)
my.nl.obs <- 4 * sin(x - pi) + noise
plot(x, my.nl.obs)
nls(y ~ amp * sin(x - horshft),
data = data.frame(y = my.nl.obs, x = x),
start = list(amp = 1, horshft = 0))
Residual sum of squares : 20.25668
parameters:
amp
horshft
-4.112227 0.01059317
formula: y ~ amp * sin(x - horshft)
100 observations
2. Example: using nls with better starting values
The above example illustrates the importance of finding appropriate
starting values. The nls function returns an estimate of amp close to -4
and an estimate of horshft close to 0 because of the cyclical nature of
537
Chapter 15 Nonlinear Models
the sine function: sin ( x – π ) = – sin ( x ) . If we start with initial
estimates of amp and horshft closer to their true values, nls gives us
the estimates we want.
> nls(y ~ amp * sin(x - horshft),
+ data = data.frame(y = my.nl.obs, x = x),
+ start = list(amp = 3, horshft = pi/2))
Residual sum of squares : 20.25668
parameters:
amp horshft
4.112227 -3.131
formula: y ~ amp * sin(x - horshft)
100 observations
3. Example: creating my.new.func and using nlregb
We can use the nlregb function to redo the above example and
specify that the value of amp must be greater than 0:
> my.new.fcn
function(param, x, y)
{
amp <- param[1]
horshft <- param[2]
y - amp * sin(x - horshft)
}
> nlregb(n = 100, start = c(3,pi/2),
+ residuals = my.new.fcn,
+ lower = c(0, -Inf), x = x, y = my.nl.obs)
$parameters:
[1] 4.112227 3.152186
$objective:
[1] 20.25668
$message:
[1] "BOTH X AND RELATIVE FUNCTION CONVERGENCE"
$grad.norm:
[1] 5.960581e-09
538
Examples of Nonlinear Models
EXAMPLES OF NONLINEAR MODELS
Maximum
Likelihood
Estimation
Parameters are estimated by maximizing the likelihood function.
Suppose n independent observations are distributed with probability
densities p i ( θ ) = p ( y i, θ ) , where θ is a vector of parameters. The
likelihood function is defined as
n
L ( y ;θ ) =
∏ pi(θ ) .
(15.1)
i=1
The problem is to find the estimate θ̃ that maximizes the likelihood
function for the observed data. Maximizing the likelihood is
equivalent to minimizing the negative of the log-likelihood:
n
l ( θ ) = – log ( L ( y ;θ ) ) =
 –log ( p i ( θ ) ) .
(15.2)
i=1
Example One:
Ping-Pong
Each member of the U.S. Table Tennis Association is assigned a
rating based on the member’s performance in tournaments. Winning
a match boosts the winner’s rating and lowers the loser’s rating some
number of points depending on the current ratings of the two players.
Using these data, two questions we might like to ask are the following:
1. Do players with a higher rating tend to win over players with
a lower rating?
2. Does a larger difference in rating imply that the higher-rated
player is more likely to win?
Assuming a logistic distribution in which log ( p ⁄ ( 1 – p ) ) is
proportional to the difference in rating and the average rating of the
two players, we get:
Dα+Rβ
i
e i
p i = ------------------------------- .
Di α + Ri β
1+e
(15.3)
539
Chapter 15 Nonlinear Models
In Equation (15.3), D i = W i – L i is the difference in rating between
1
the winner and loser and R i = --- ( W i + L i ) is the average rating for the
2
two players. To fit the model, we need to find α and β which
minimize the negative log-likelihood
 –log ( pi )
Example Two:
Wave-Soldering
Skips
=

  –Di α – Ri β + log ( 1 + e
Di α + Ri β

).

(15.4)
In a 1988 AT&T wave-soldering experiment, several factors were
varied.
Factor
Description
opening
Amount of clearance around the mounting pad
solder
Amount of solder
mask
Type and thickness of the material used for the solder mask
padtype
The geometry and size of the mounting pad
panel
Each board was divided into three panels, with three runs on a board
The results of the experiment gave the number of visible soldering
skips (faults) on a board. Physical theory and intuition suggest a
model in which the process is in one of two states:
1. A “perfect” state where no defects occur;
2. An “imperfect” state where there may or may not be defects.
Both the probability of being in the imperfect state and the
distribution of skips in that state depend on the factors in the
experiment. Assume that some “stress” S induces the process to be in
the imperfect state and also increases the tendency to generate skips
when in the imperfect state.
540
Examples of Nonlinear Models
Assume S depends linearly on the levels of the factors x j , for
j = 1, … , p :
p
Si =
 xij βj ,
(15.5)
j=1
where β is the vector of parameters to be estimated.
Assume the probability P i of being in the imperfect state is
monotonically related to the stress by a logistic distribution:
1
P i = ----------------------( – τ )S i
1+e
(15.6)
As the stress increases, the above function approaches 1.
Given that the process is in an imperfect state, assume the probability
of k i skips is modeled by the Poisson distribution with mean λ i :
P(k i) = e
–λi
k
λii
⋅ -------k i!
(15.7)
The probability of zero skips is the probability of being in the perfect
state plus the probability of being in the imperfect state and having
zero skips. The probability of one or more skips is the probability of
being in the imperfect state and having one or more skips.
Mathematically the probabilities may be written as:
( – τ )S i
–λi

 e
e
- + ----------------------- if y i = 0
 --------------------- 1 + e ( –τ )Si 1 + e ( –τ )Si
P ( y = yi ) = 
y

–λi λ i
1

-----------------------e --------i
if y i > 0

( – τ )S i
y i!

1+e
(15.8)
541
Chapter 15 Nonlinear Models
The mean number of skips in the imperfect state is always positive
Si
and modeled in terms of the stress by λ i = e . The parameters, τ
and β , can be estimated by minimizing the negative log-likelihood.
The ith element of the negative log-likelihood can be written (to
within a constant) as:

Si

 ( –τ )Si
–e 
( – τ )S i
 log  e
+ e  if y i = 0
l i ( β, τ ) = log ( 1 + e
)–



Si

if y i > 0
 yi Si –e
(15.9)
The model depicted above does not reduce to any simple linear
model.
Nonlinear
Regression
Parameters are estimated by minimizing the sum of squared residuals.
Suppose n independent observations y can be modeled as a
nonlinear parametric function f of a vector x of predictor variables
and a vector β of parameters:
y = f ( x ; β) + ε ,
where the errors, ε , are assumed to be normally distributed. The
nonlinear least-squares problem finds parameter estimates β̃ that
minimize:
n
 ( yi – f (x ;β) )
2
.
(15.10)
i=1
Example Three:
Puromycin
542
A biochemical experiment measured reaction velocity in cells with
and without treatment by Puromycin. The data from this experiment
is stored in the example data frame Puromycin, which contains the
three variables described in the table below.
Examples of Nonlinear Models
Variable
Description
conc
The substrate concentration
vel
The reaction velocity
state
Indicator of treated or untreated
Assume a Michaelis-Menten relationship between velocity and
concentration:
V max c
V = -------------- + ε ,
K+c
(15.11)
where V is the velocity, c is the enzyme concentration, V max is a
parameter representing the asymptotic velocity as c → ∞ , K is the
Michaelis parameter, and ε is experimental error. Assuming the
treatment with the drug changes V max but not K , the optimization
function is
S(V max, K) =
( V max + ΔV max I { treated } ( state ) )c i 2
 V – --------------------------------------------------------------------------------,
 i

K+c
(15.12)
i
where I { treated } is the function indicating if the cell was treated with
Puromycin.
543
Chapter 15 Nonlinear Models
INFERENCE FOR NONLINEAR MODELS
Likelihood
Models
With likelihood models, distributional results are asymptotic.
Maximum likelihood estimates tend toward a normal distribution
with a mean equal to the true parameter and a variance matrix given
by the inverse of the information matrix (i.e., the negative of the
second derivatives of the log-likelihood).
Least Squares
Models
In least-squares models approximations to quantities such as standard
errors or correlations of parameter estimates are used. The
approximation proceeds as follows:
1. Replace the nonlinear model with its linear Taylor series
approximation at the parameter estimates.
2. Use the methods for linear statistical inference on the
approximation.
Consequently, the nonlinear inference results are called linear
approximation results.
The Fitting
Algorithms
Minimum-sum algorithm
This section deals with the general optimization of an objective
function modeled as a sum. The algorithm is a version of Newton’s
method based on a quadratic approximation of the objective function.
If both first and second derivatives are supplied, the approximation is
a local one using the derivatives. If no derivatives or only the first
derivative are supplied, the algorithm approximates the second
derivative information. It does this in a way specifically designed for
minimization.
The algorithm actually used is taken from the PORT subroutine
library which evolved from the published algorithm by Gay (1983).
Two key features of this algorithm are:
1. A quasi-Newton approximation for second derivatives.
2. A “trust region” approach controlling the size of the region in
which the quadratic approximation is believed to be accurate.
The algorithm is capable of working with user models specifying 0, 1,
or 2 orders of derivatives.
544
Inference for Nonlinear Models
Nonlinear least-squares algorithm
The Gauss-Newton algorithm is used with a step factor to ensure that
the sum of squares decreases at each iteration. A line-search method
is used, as opposed to the trust region employed in the minimum-sum
algorithm. The step direction is determined by a quadratic model.
The algorithm proceeds as follows:
1. The residuals are calculated, and the gradient is calculated or
approximated (depending on the data) at the current
parameter values.
2. A linear least-squares fit of the residual on the gradient gives
the parameter increment.
3. If applying the full parameter increment increases the sum of
squares rather than decreasing it, the length of the increment
is successively halved until the sum of squares is decreased.
4. The step factor is retained between iterations and started at
min{2*(previous step factor), 1}.
If the gradient is not specified analytically, it is calculated using finite
differences with forward differencing. For partially linear models, the
increment is calculated using the Golub-Pereyra method (Golub and
Pereyra, 1973) as implemented by Bates and Lindstrom (1986).
Specifying
Models
Nonlinear models typically require specifying more details than
models of other types. The information typically required to fit a
nonlinear model, using the S-PLUS functions ms or nls, is:
1. A formula
2. Data
3. Starting values
Formulas
For nonlinear models a formula is a S-PLUS expression involving
data, parameters in the model, and any other relevant quantities. The
parameters must be specified in the formula because there is no
assumption about where they are to be placed (as in linear models, for
example). Formulas are typically specified differently depending on
whether you have a minimum-sum problem or nonlinear leastsquares problem.
545
Chapter 15 Nonlinear Models
In the puromycin example, you would specify a formula for the
simple model (described in Equation (15.11)) by:
vel ~ Vm*conc / (K + conc)
The parameters Vm and K are specified along with the data vel and
conc. Since there is no explicit response for minimum-sum models
(for example, likelihood models), it is left off in the formula.
In the ping-pong example (ignoring the average rating effect), the
formula for Equation (15.4) is:
~ -D*alpha + log(1 + exp(D*alpha))
where D is a variable in the data and alpha is the parameter to fit.
Note that the model here is based only on the difference in ratings,
ignoring for the moment the average rating.
Simplifying
Formulas
Some models can be organized as simple expressions involving one
or more S-PLUS functions that do all the work. Note that D*alpha
occurs twice in the formula for the ping-pong model. You can write a
general function for the log-likelihood in terms of D*alpha.
> lprob <- function(lp) log(1 + exp(lp)) - lp
Recall that lp is the linear predictor for the GLM. A simpler
expression for the model is now:
~ lprob( D * alpha )
Having lprob now makes it easy to add additional terms or
parameters.
Implications of
the Formulas
For nonlinear least-squares formulas the response on the left of ~ and
the predictor on the right must evaluate to numeric vectors of the
same length. The fitting algorithm tries to estimate parameters to
minimize the sum of squared differences between response and
prediction. If the response is left out the formula is interpreted as a
residual vector.
For Minimum-Sum formulas, the right of ~ must evaluate to a
numeric vector. The fitting algorithm tries to estimate parameters to
minimize the sum of this “predictor” vector. The concept here is
linked to maximum-likelihood models. The computational form does
not depend on an MLE concept. The elements of the vector may be
anything and there need not be more than one.
546
Inference for Nonlinear Models
The evaluated formulas can include derivatives with respect to the
parameters. The derivatives are supplied as attributes to the vector
that results when the predictor side of the formula is evaluated. When
explicit derivatives are not supplied, the algorithms use numeric
approximations.
Parametrized
Data Frames
Relevant data for nonlinear modeling includes:
•
Variables
•
Initial estimates of parameters
•
Fixed values occurring in a model formula
Parametrized data frames allow you to “attach” relevant data to a data
frame when the data do not occupy an entire column. Information is
attached as a "parameter" attribute of the data frame. The parameter
function returns or modifies the entire list of parameters and is
analogous to the attributes function. Similarly the param function
returns or modifies one parameter at a time and is analogous to the
attr function. You could supply values for Vm and K to the Puromycin
data frame with:
# Assign Puromycin to your working directory.
> Puromycin <- Puromycin
> parameters(Puromycin) <- list(Vm = 200, K = 0.1)
The parameter values can be retrieved with:
> parameters(Puromycin)
$Vm:
[1] 200
$K:
[1] 0.1
The class of Puromycin is now:
> class(Puromycin)
[1] "pframe"
Now, when Puromycin is attached, the parameters Vm and K are
available when referred to in formulas.
547
Chapter 15 Nonlinear Models
Starting Values;
Identifying
Parameters
Before the formulas can be evaluated, the fitting functions must know
which names in the formula are parameters to be estimated and must
have starting values for these parameters. The fitting functions
determine this in the following way:
1. If the start argument is supplied, its names are the names of
the parameters to be estimated, and its values are the
corresponding starting values.
2. If start is missing, the parameters attribute of the data
argument defines the parameter names and values.
Hint
Explicitly use the start argument to name and initialize parameters.
You can easily see what the starting values are in the call component of the fit and you can
arrange to keep particular parameters constant when that makes sense.
Derivatives
Supplying derivatives of the predictor side of the formula with respect
to the parameters along with the formula can reduce the number of
iterations (thus speeding up the computations), increase numerical
accuracy, and improve the chance of convergence. In general
derivatives should be used whenever possible.
The fitting algorithms can use both first derivatives (the gradient) and
second derivatives (the Hessian). The derivatives are supplied to the
fitting functions as attributes to the formula. Recall that evaluating the
formula gives a vector of n values. Evaluating the first derivative
expression should give n values for each of the p parameters, that is
an n × p matrix. Evaluating the second derivative expression should
give n values for each of the p × p partial derivatives, that is, an
n × p × p array.
First Derivatives
The negative log-likelihood for the simple ping-pong model is:
l(α) =
548
 [ log ( 1 + e
Di α
) – Di α ]
(15.13)
Inference for Nonlinear Models
Differentiating with respect to α and simplifying gives the gradient:
∂l
------- =
∂α

–Di
-----------------------Dα
(1 + e i )
(15.14)
The gradient is supplied to the fitting function as the gradient
attribute of the formula:
>
>
+
>
form.pp <- ~log(1 + exp( D*alpha ) ) - D*alpha
attr(form.pp, "gradient") <~ -D / ( 1 + exp( D*alpha ) )
form.pp
~ log(1 + exp(D * alpha)) - D * alpha
Gradient: ~ - D/(1 + exp(D * alpha))
When a function is used to simplify a formula, build the gradient into
the function. The lprob function is used to simplify the formula
expression to ~lprob(D*alpha):
> lprob <- function(lp) log(1 + exp(lp)) - lp
An improved version of lprob adds the gradient:
> lprob2 <- function(lp, X)
+ {
+ elp <- exp(lp)
+ z <- 1 + elp
+ value <- log(z) - lp
+ attr(value, "gradient") <- -X/z
+ value
+ }
Note lp is again the linear predictor and X is the data in the linear
predictor. With the gradient built into the function, you don’t need to
add it as an attribute to the formula; it is already an attribute to the
object hence used in the formula.
549
Chapter 15 Nonlinear Models
Second
Derivatives
The second derivatives may be added as the hessian attribute of the
formula. In the ping-pong example, the second derivative of the
negative log-likelihood with respect to α is:
2
∂ l
--------- =
2
∂α
2 Di α
Di e
-------------------------
Di α 2
(1 + e )
(15.15)
The lprob2 function is now modified to add the Hessian as follows.
The Hessian is added in a general enough form to allow for multiple
predictors.
> lprob3 <- function(lp, X)
+ {
+ elp <- exp(lp)
+ z <- 1 + elp
+ value <- log(z) - lp
+ attr(value, "gradient") <- -X/z
+ if(length(dx <- dim(X)) == 2)
+ {
+ n <- dx[1]; p <- dx[2]
+ } else
+{
+ n <- length(X); p <- 1
+ }
+ xx <- array(X, c(n, p, p))
+ attr(value, "hessian") <- (xx * aperm(xx, c(1, 3, 2)) *
+ elp)/z^2
+ value
+ }
Interesting points of the added code are:
550
•
The second derivative computations are performed at the
time of the assignment of the hessian attribute.
•
The rest of the code (starting with if(length(...))) is to
make the Hessian general enough for multiple predictors.
•
The aperm function does the equivalent of a transpose on the
second and third dimensions to produce the proper cross
products when multiple predictors are in the model.
Inference for Nonlinear Models
Symbolic
Differentiation
A symbolic differentiation function D is available to aid in taking
derivatives.
Table 15.2: Arguments to D.
Argument
Purpose
expr
Expression to be differentiated
name
Which parameters to differentiate with respect to
The function D is used primarily as a support routine to deriv.
Again referring to the ping-pong example, form contains the
expression of the negative log-likelihood:
> form
expression(log((1 + exp(D * alpha))) - D * alpha)
The first derivative is computed as:
> D(form, "alpha")
(exp(D * alpha) * D)/(1 + exp(D * alpha)) - D
And the second derivative is computed as:
> D(D(form, "alpha"), "alpha")
(exp(D * alpha) * D * D)/(1 + exp(D * alpha))
- (exp(D * alpha) * D * (exp(D * alpha) * D))
/(1 + exp(D * alpha))^2
551
Chapter 15 Nonlinear Models
Improved
Derivatives
The deriv function takes an expression, computes a derivative,
simplifies the result, then returns an expression or function for
computing the original expression along with its derivative(s).
Table 15.3: Arguments to deriv.
Argument
Purpose
expr
Expression to be differentiated, typically a formula, in
which case the expression returned computes the
right side of the ~ and its derivatives.
namevec
Character vector of names of parameters.
function.arg
Optional argument vector or prototype for a function.
tag
Base of the names to be given to intermediate results.
Default is ".expr".
Periods are used in front of created object names to avoid conflict
with user-chosen names. The deriv function returns an expression in
the form expected for nonlinear models.
> deriv(form, "alpha")
expression(
{
.expr1 <- D * alpha
.expr2 <- exp(.expr1)
.expr3 <- 1 + .expr2
.value <- (log(.expr3)) - .expr1
.grad <- array(0, c(length(.value), 1), list(NULL,
"alpha"))
.grad[, "alpha"] <- ((.expr2 * D)/.expr3) - D
attr(.value, "gradient") <- .grad
.value
})
552
Inference for Nonlinear Models
If the function.arg argument is supplied, a function is returned:
> deriv(form, "alpha", c("D", "alpha"))
function(D, alpha)
{
.expr1 <- D * alpha
.expr2 <- exp(.expr1)
.expr3 <- 1 + .expr2
.value <- (log(.expr3)) - .expr1
.actualArgs <- match.call()["alpha"]
if(all(unlist(lapply(as.list(.actualArgs), is,name))))
{
.grad <- array(0, c(length(.value), 1), list(NULL,
"alpha"))
.grad[, "alpha"] <- ((.expr2 * D)/.expr3) - D
dimnames(.grad) <- list(NULL, .actualArgs)
attr(.value, "gradient") <- .grad
}
.value
}
The namevec argument can be a vector:
> deriv(vel ~ Vm * (conc/(K + conc)), c("Vm", "K"))
expression(
{ .expr1 <- K + conc
.expr2 <- conc/.expr1
.value <- Vm * .expr2
.grad <- array(0, c(length(.value), 2), list(NULL,
c("Vm","K")))
.grad[, "Vm"] <- .expr2
.grad[, "K"] <- - (Vm * (conc/(.expr1^2)))
attr(.value, "gradient") <- .grad
.value
})
The symbolic differentiation interprets each parameter as a scalar.
Generalization from scalar to vector parameters (for example, lprob2)
must be done by hand. Use parentheses to help deriv find relevant
subexpressions. Without the redundant parentheses around conc/
(K + conc) the expression returned by deriv is not as simple as
possible.
553
Chapter 15 Nonlinear Models
Fitting Models
There are two different fitting functions for nonlinear models. The ms
function minimizes the sum of the vector supplied as the right side of
the formula. The nls function minimizes the sum of squared
differences between the left and right sides of the formula.
Table 15.4: Arguments to ms.
Argument
Purpose
formula
The nonlinear model formula (without a left side).
data
A data frame in which to do the computations.
start
Numeric vector of initial parameter values.
scale
Parameter scaling.
control
List of control values to be used in the iteration.
trace
Indicates whether intermediate estimates are printed.
Table 15.5: Arguments to nls.
554
Argument
Purpose
formula
The nonlinear regression model as a formula.
data
A data frame in which to do the computations.
start
Numeric vector of initial parameter values.
control
List of control values to be used in the iteration.
algorithm
Which algorithm to use. The default is a GaussNewton algorithm. If algorithm = "plinear", the
Golub-Pereyra algorithm for partially linear leastsquares models is used.
trace
Indicates whether intermediate estimates are printed.
Inference for Nonlinear Models
Fitting a Model
Before fitting a model, take a look at the data displayed in Figure 15.1.
to the Puromycin
> attach(Puromycin)
Data
200
> plot(conc,vel, type = "n")
> text(conc, vel, ifelse(state == "treated", "T", "U"))
T
T
T
T
150
T
T
U
vel
T
T
U
U
U
U
U
100
T
T U
U
U
T
50
U
U
T
0.0
0.2
0.4
0.6
0.8
1.0
conc
Figure 15.1: vel versus conc for treated (T) and untreated (U) groups.
1. Estimating starting values
Obtain an estimate of V max for each group as the maximum value
each group attains.
•
The treated group has a maximum of about 200.
•
The untreated group has a maximum of about 160.
The value of K is the concentration at which V reaches V max ⁄ 2 ,
roughly 0.1 for each group.
555
Chapter 15 Nonlinear Models
2. A simple model
Start by fitting a simple model for the treated group only.
>
>
+
>
Treated <- Puromycin[Puromycin$state == "treated",]
Purfit.1 <- nls(vel ~ Vm*conc/(K + conc), data = Treated,
start = list(Vm = 200, K = 0.1))
Purfit.1
residual sum of squares: 1195.449
parameters:
Vm
K
212.6826 0.06411945
formula: vel~(Vm * conc)/(K + conc)
12 observations
Fit a model for the untreated group similarly but with Vm=160.
> Purfit.2
residual sum of squares: 859.6043
parameters:
Vm
K
160.2769 0.04770334
formula: vel ~ (Vm * conc)/(K + conc)
11 observations
3. A more complicated model
Obtain summaries of the fits with the summary function:
> summary(Purfit.1)
Formula: vel ~ (Vm * conc)/(K + conc)
Parameters:
Value Std. Error t value
Vm 212.6830000 6.94709000 30.61460
K
0.0641194 0.00828075 7.74319
Residual standard error: 10.9337 on 10 degrees of freedom
Correlation of Parameter Estimates:
Vm
K 0.765
556
Inference for Nonlinear Models
> summary(Purfit.2)
Formula: vel ~ (Vm * conc)/(K + conc)
Parameters:
Value Std. Error t value
Vm 160.2770000 6.48003000 24.73400
K
0.0477033 0.00778125 6.13055
Residual standard error: 9.773 on 9 degrees of freedom
Correlation of Parameter Estimates:
Vm
K 0.777
An approximate t-test for the difference in K between the two models
suggests there is no difference:
> (0.06412 - 0.0477)/sqrt(0.00828^2 + 0.00778^2)
[1] 1.445214
The correct test of whether the K s should be different:
>
+
+
>
Purboth <- nls(vel ~ (Vm + delV*(state == "treated")) *
conc/(K + conc), data = Puromycin,
start = list(Vm = 160, delV = 40, K = 0.05))
summary(Purboth)
Formula: vel ~ ((Vm + delV * (state == "treated")) * conc)/
(K + conc)
Parameters:
Value
Vm 166.6030000
delV 42.0254000
K
0.0579696
Std. Error t value
5.80737000 28.68820
6.27209000 6.70038
0.00590999 9.80875
Residual standard error: 10.5851 on 20 degrees of freedom
Correlation of Parameter Estimates:
Vm
delV
delV -0.5410
K 0.6110 0.0644
557
Chapter 15 Nonlinear Models
> combinedSS <- sum(Purfit.1$res^2) + sum(Purfit.2$res^2)
> Fval <- (sum(Purboth$res^2) - combinedSS)/(combinedSS/19)
> Fval
[1] 1.718169
> 1 - pf(Fval, 1, 19)
[1] 0.2055523
Using a single K appears to be reasonable.
Fitting a Model
The example here develops a model based only on the difference in
to the Ping-Pong ratings, ignoring, for the moment, the average rating. The model to fit
is:
Data
~ –D α
+ log(1 + exp ( Dα ) ) ,
where D is a variable representing the difference in rating, and α is
the parameter to fit. There are four stages to the development of the
model.
1. Estimating starting values
A very crude initial estimate for α can be found with the following
process:
•
Replace all the differences in ratings by ± d , where d is the
mean difference.
•
For each match, the probability from the model that the
winner had a higher rating satisfies:
dα = log ( p ⁄ ( 1 – p ) ) .
•
Substitute for p the observed frequency with which the
higher-rated player wins, and then solve the above equation
for α .
The computations in S-PLUS proceed as follows:
>
>
>
>
>
558
pingpong <- pingpong
param(pingpong, "p") <- 0 # make pingpong a "pframe"
attach(pingpong,1)
D <- winner - loser
p <- sum(winner > loser) / length(winner)
Inference for Nonlinear Models
> p
[1] 0.8223401
> alpha <- log(p/(1-p))/mean(D)
> alpha
[1] 0.007660995
> detach(1, save = "pingpong")
2. A simple model
Recall the lprob function which calculates the log-likelihood for the
ping-pong problem:
> lprob
function(lp)
log(1 + exp(lp)) - lp
The model is fitted as follows:
>
>
+
>
attach(pingpong)
fit.alpha <- ms( ~ lprob( D * alpha ),
start = list(alpha = 0.0077))
fit.alpha
value: 1127.635
parameters:
alpha
0.01114251
formula: ~ lprob(D * alpha)
3017 observations
call: ms(formula= ~lprob(D * alpha),
start = list(alpha = 0.0077))
3. Adding the gradient
To fit the model with the gradient added to the formula, use lprob2.
> fit.alpha.2 <- ms( ~ lprob2( D*alpha, D),
+ start = list(alpha = 0.0077))
> fit.alpha.2
559
Chapter 15 Nonlinear Models
value: 1127.635
parameters:
alpha
0.01114251
formula: ~ lprob2(D * alpha, D)
3017 observations
call: ms(formula = ~ lprob2(DV * alpha, DV), start =
list(alpha = 0.0077))
Even for this simple problem, providing the derivative has decreased
the computation time by 20%.
4. Adding the Hessian
To fit the model with the gradient and the Hessian added to the
formula, use lprob3.
> fit.alpha.3 <- ms( ~ lprob3(D*alpha, D),
+ start = list(alpha = .0077))
> fit.alpha.3
value: 1127.635
parameters:
alpha
0.01114251
formula: ~ lprob3(DV * alpha, DV)
3017 observations
call: ms(formula = ~ lprob3(DV * alpha, DV), start =
list(alpha = 0.0077))
Profiling the
Objective
Function
560
Profiling provides a more accurate picture of the uncertainty in the
parameter estimates than simple standard errors do. When there are
only two parameters, contours of the objective function can be
plotted by generating a grid of values. When there are more than two
parameters, examination of the objective function is usually done in
one of two ways, as listed below.
•
Slices: fix all but two of the parameters at their estimated values
and create a grid of the objective function by varying the
remaining two parameters of interest.
•
Projections: vary two parameters of interest over fixed values,
optimizing the objective function over the other parameters.
Inference for Nonlinear Models
Two-dimensional projections are often too time consuming to
compute. One-dimensional projections are called profiles. Profiles are
plots of a t statistic equivalent, called the profile t function, for a
parameter of interest against a range of values for the parameter.
The Profile t
Function
For nls, the profile t function for a given parameter θ p is denoted by
τ ( θ p ) and is computed as follows:
S̃ ( θ p ) – S ( θ̃ )
τ ( θ p ) = sign(θ p – θ̃ p) ------------------------------- ,
s
(15.16)
where θ̃ p is the model estimate of θ p , S̃ ( θ p ) is the sum of squares
based on optimizing all parameters except the fixed θ p , and S(θ̃) is
the sum of squares based on optimizing all parameters.
The profile t function is directly related to confidence intervals for
the corresponding parameter. It can be shown that τ ( θ p ) is
equivalent to the studentized parameter
θ p – θ̃ p
δ ( θ p ) = ----------------- ,
se ( θ̃ p )
(15.17)
for which a 1 – α confidence interval can be constructed as follows:
a
a
– t  N – P ;--- ≤ δ ( θ p ) ≤ t  N – P ;---



2
2
The profile
Function in SPLUS
(15.18)
The profile function produces profiles for nls and ms objects.
Profiles show confidence intervals for parameters as well as the
nonlinearity of the objective function. If a model is linear, the profile
is a straight line through the origin with a slope of 1. You can produce
the profile plots for the Puromycin fit Purboth as follows:
> Purboth.prof <- profile(Purboth)
> plot(Purboth.prof)
561
Chapter 15 Nonlinear Models
The object returned by profile has a component for each parameter
that contains the evaluations of the profile t function, plus some
additional attributes. The component for the Vm parameter is:
> Purboth.prof$Vm
1
2
3
4
5
6
7
8
9
10
11
12
tau par.vals.Vm par.vals.delV par.vals.K
-3.9021051
144.6497
54.60190 0.04501306
-3.1186052
148.8994
52.07216 0.04725929
-2.3346358
153.2273
49.54358 0.04967189
-1.5501820
157.6376
47.01846 0.05226722
-0.7654516
162.1334
44.50315 0.05506789
0.0000000
166.6040
42.02591 0.05797157
0.7548910
171.0998
39.57446 0.06103225
1.5094670
175.6845
37.12565 0.06431820
2.2635410
180.3616
34.67194 0.06783693
3.0171065
185.1362
32.20981 0.07160305
3.7701349
190.0136
29.73812 0.07563630
4.5225948
194.9997
27.25599 0.07995897
Figure 15.2 shows profile plots for the three-parameter Puromycin fit.
Each plot shows the profile t function ( τ ), when the parameter on the
x-axis ranges over the values shown and the other parameters are
optimized. The surface is quite linear with respect to these three
parameters.
562
2
-4
-4
-2
0
tau
0
-2
tau
2
4
4
Inference for Nonlinear Models
150
170
190
30
40
50
60
70
delV
0
-4
-2
tau
2
4
Vm
20
0.04
0.06
0.08
K
Figure 15.2: The profile plots for the Puromycin fit.
563
Chapter 15 Nonlinear Models
Computing
Confidence
Intervals
An example of a simple function to compute the confidence intervals
from the output of profile follows:
>
+
+
+
+
+
+
+
+
+
+
conf.int <- function(profile.obj, variable.name,
confidence.level = 0.95) {
if(is.na(match(variable.name, names(profile.obj))))
stop(paste("Variable", variable.name,
"not in the model"))
resid.df <- attr(profile.obj, "summary")[["df"]][2]
tstat <- qt(1 - (1 - confidence.level)/2, resid.df)
prof <- profile.obj[[variable.name]]
approx(prof[, "tau"], prof[, "par.vals"]
[, variable.name],
c(-tstat, tstat))[[2]] }
The tricky line in conf.int is the last one which calls approx. The
Purboth.prof$Vm component is a data frame with two columns. The
first column is the vector of τ values that we can pick off using
prof[, "tau"]. The second column is named par.vals and contains
a matrix with as many columns as there are parameters in the model.
This results in the strange looking subscripting given by
prof[, "par.vals"][, variable.name]. The first subscript removes
the matrix from the par.vals component, and the second subscript
removes the appropriate column. Three examples using conf.int
and the profile object Purboth.prof follow:
> conf.int(Purboth.prof, "delV", conf = .99)
[1] 24.20945 60.03857
> conf.int(Purboth.prof, "Vm", conf = .99)
[1] 150.4079 184.0479
> conf.int(Purboth.prof, "K", conf = .99)
[1] 0.04217613 0.07826822
The conf.int function can be improved by doing a cubic spline
interpolation rather than the linear interpolation that approx does. A
marginal confidence interval computed from the profile t function is
exact, disregarding any approximations due to interpolation, whereas
the marginal confidence interval computed with the coefficient and its
standard error is only a linear approximation.
564
References
REFERENCES
Bates D.M. & Lindstrom M.J. (1986). Nonlinear least squares with
conditionally linear parametrics. Proceedings of the American Statistical
Computing Section, 152-157.
Comizzoli R.B., Landwehr J.M., & Sinclair J.D. (1990). Robust
materials and processes: Key to reliability. AT&T Technical Journal,
69(6):113--128.
Gay D.M. (1983). Algorithm 611: Subroutines for unconstrained
minimization using a model/trust-region approach. ACM Transactions
on Mathematical Software 9:503-524.
Golub G.H. & Pereyra V. (1973). The differentiation of pseudoinverses and nonlinear least squares problems whose variables
separate. SIAM Journal on Numerical Analysis 10:413-432.
565
Chapter 15 Nonlinear Models
566
DESIGNED EXPERIMENTS
AND ANALYSIS OF VARIANCE
16
Introduction
Setting Up the Data Frame
The Model and Analysis of Variance
568
568
569
Experiments with One Factor
Setting Up the Data Frame
A First Look at the Data
The One-Way Layout Model and Analysis of Variance
570
571
572
574
The Unreplicated Two-Way Layout
Setting Up the Data Frame
A First Look at the Data
The Two-Way Model and ANOVA (One Observation
Per Cell)
578
579
580
The Two-Way Layout with Replicates
Setting Up the Data Frame
A First Look at the Data
The Two-Way Model and ANOVA (with Replicates)
Method for Two-Factor Experiments with Replicates
Method for Unreplicated Two-Factor Experiments
Alternative Formal Methods
591
592
593
594
597
599
601
k
Many Factors at Two Levels: 2 Designs
Setting Up the Data Frame
A First Look at the Data
k
Estimating All Effects in the 2 Model
Using Half-Normal Plots to Choose a Model
References
583
602
602
604
605
610
615
567
Chapter 16 Designed Experiments and Analysis of Variance
INTRODUCTION
This chapter discusses how to analyze designed experiments.
Typically, the data have a numeric response and one or more
categorical variables (factors) that are under the control of the
experimenter. For example, an engineer may measure the yield of
some process using each combination of four catalysts and three
specific temperatures. This experiment has two factors, catalyst and
temperature, and the response is the yield.
Traditionally, the analysis of experiments has centered on the
performance of an Analysis of Variance (ANOVA). In more recent
years graphics have played an increasingly important role. There is a
large literature on the design and analysis of experiments; Box,
Hunter, and Hunter is an example.
This chapter consists of sections which show you how to use TIBCO
Spotfire S+ to analyze experimental data for each of the following
situations:
•
Experiments with one factor
•
Experiments with two factors and a single replicate
•
Experiments with two factors and two or more replicates
•
Experiments with many factors at two levels: 2 designs
k
Each of these sections stands alone. You can read whichever section
is appropriate to your problem, and get the analysis done without
having to read the other sections. This chapter uses examples from
Box, Hunter, and Hunter (1978) and thus is a useful supplement in a
course which covers the material of Chapters 6, 7, 9, 10, and 11 of
Box, Hunter, and Hunter.
Setting Up the
Data Frame
568
In analyzing experimental data using Spotfire S+, the first thing you
do is set up an appropriate data frame for your experimental data. You
may think of the data frame as a matrix, with the columns containing
values of the variables. Each row of the data frame contains an
observed value of the response (or responses), and the corresponding
values of the experimental factors.
Introduction
A First Look at
the Data
Use
The Model and
Analysis of
Variance
It is important that you have a clear understanding of exactly what
model is being considered when you carry out the analysis of
variance. Use aov to carry out the analysis of variance, and use
summary to display the results.
the
functions plot.design, plot.factor,
to graphically explore your data.
and
possibly
interaction.plot
In using aov, you use formulas to specify your model. The examples in
this chapter introduce you to simple uses of formulas. You may
supplement your understanding of how to use formulas in Spotfire S+
by reading Chapter 2, Specifying Models in Spotfire S+ (in this book),
or Chapter 2, Statistical Models, and Chapter 5, Analysis of Variance;
Designed Experiments (in Chambers and Hastie (1992)).
Diagnostic Plots
For each analysis, you should make the following minimal set of plots
to convince yourself that the model being entertained is adequate:
•
Histogram of residuals (using hist)
•
Normal qq-plot of residuals (using qqnorm)
•
Plot of residuals versus fit (using plot)
When you know the time order of the observations, you should also
make plots of the original data and the residuals in the order in which
the data were collected.
The diagnostic plots may indicate inadequacies in the model from
one or more of the following sources: existence of interactions,
existence of outliers, and existence of nonhomogeneous error
variance.
569
Chapter 16 Designed Experiments and Analysis of Variance
EXPERIMENTS WITH ONE FACTOR
The simplest kind of experiments are those in which a single
continuous response variable is measured a number of times for each
of several levels of some experimental factor.
For example, consider the data in Table 16.1 (from Box, Hunter, and
Hunter (1978)), which consists of numerical values of blood
coagulation times for each of four diets. Coagulation time is the
continuous response variable, and diet is a qualitative variable, or
factor, having four levels: A, B, C, and D. The diets corresponding to
the levels A, B, C, and D were determined by the experimenter.
Table 16.1: Blood coagulation times for four diets.
Diet
A
B
C
D
62
63
68
56
60
67
66
62
63
71
71
60
59
64
67
61
65
68
63
66
68
64
63
59
Your main interest is to see whether or not the factor “diet” has any
effect on the mean value of blood coagulation time. The experimental
factor, “diet” in this case, is often called the treatment.
570
Experiments with One Factor
Formal statistical testing for whether or not the factor level affects the
mean is carried out using the method of analysis of variance
(ANOVA). This needs to be complemented by exploratory graphics
to provide confirmation that the model assumptions are sufficiently
correct to validate the formal ANOVA conclusion. Spotfire S+
provides tools for you to do both the data exploration and formal
ANOVA.
Setting Up the
Data Frame
In order to analyze the data, you need to get it into a form that
Spotfire S+ can use for the analysis of variance. You do this by setting
up a data frame. First create a numeric vector coag:
> coag <- scan()
1: 62 60 63 59
5: 63 67 71 64 65 66
11: 68 66 71 67 68 68
17: 56 62 60 61 63 64 63 59
25:
Next, create a factor called diet, that corresponds to coag:
> diet <- factor(rep(LETTERS[1:4], c(4,6,6,8)))
> diet
[1] A A A A B B B B B B C C C C C C D D D D D D D D
Now create a data frame with columns diet and coag:
> coag.df <- data.frame(diet,coag)
The data frame object coag.df is a matrix-like object, so it looks like a
matrix when you display it on your screen:
> coag.df
diet coag
A
62
A
60
A
63
.
.
.
23
D
63
24
D
59
1
2
3
571
Chapter 16 Designed Experiments and Analysis of Variance
A First Look at
the Data
For each level of the treatment factor, you make an initial graphical
exploration of the response data y ij by using the functions
plot.design and plot.factor.
You can make plots of the treatment means and treatment medians
for each level of the experimental factor diet by using the function
plot.design twice, as follows:
>
>
>
>
par(mfrow = c(1,2))
plot.design(coag.df)
plot.design(coag.df, fun = median)
par(mfrow = c(1,1))
68
66
B
66
C
B
62
64
64
median of coag
68
C
62
mean of coag
The results are shown in the two plots of Figure 16.1. In the left-hand
plot, the tick marks on the vertical line are located at the treatment
means for the diets A, B, C, and D, respectively. The mean values of
coagulation time for diets A and D happen to have the same value,
61, and so the labels A and D are overlaid. The horizontal line,
located at 64, indicates the overall mean of all the data. In the righthand plot of Figure 16.1, medians rather than means are indicated.
There is not much difference between the treatment means and the
treatment medians, so you should not be too concerned about
adverse effects due to outliers.
D
D
A
diet
diet
Factors
Factors
Figure 16.1: Treatment means and medians.
572
A
Experiments with One Factor
The function plot.factor produces a box plotbox plot of the
response data for each level of the experimental factor:
> plot.factor(coag.df)
60
coag
65
70
The resulting plot is shown in Figure 16.2. This plot indicates that the
responses for diets A and D are quite similar, while the median
responses for diets B and C are considerably larger relative to the
variability reflected by the heights of the boxes. Thus, you suspect
that diet has an effect on blood coagulation time.
A
B
C
D
diet
Figure 16.2: Box plots for each treatment.
If the exploratory graphical display of the response using
plot.factor indicates that the interquartile distance of the box plots
depends upon the median, then a transformation to make the error
variance constant is called for. The transformation may be selected
with a “spread versus level” plot. See, for example, the section The
Two-Way Layout with Replicates, or Hoaglin, Mosteller, and Tukey
(1983).
573
Chapter 16 Designed Experiments and Analysis of Variance
The One-Way
Layout Model
and Analysis of
Variance
The classical model for experiments with a single factor is
y ij = μ i + ε ij
j = 1, ..., J i
i = 1, ..., I
where μ i is the mean value of the response for the i th level of the
experimental factor. There are I levels of the experimental factor,
and J i measurements y i1, y i2, …, y iJ are taken on the response
variable for level i of the experimental factor.
Using the treatment terminology, there are I treatments, and μ i is
called the i th treatment mean. The above model is often called the
one-way layout model. For the blood coagulation experiment, there are
I = 4 diets, and the means μ1, μ2, μ3, and μ4 correspond to diets A,
B, C, and D, respectively. The numbers of observations are J A = 4 ,
J B = J C = 6 , and J D = 8 .
You carry out the analysis of variance with the function aov:
> aov.coag <- aov(coag ~ diet, coag.df)
The first argument to aov above is the formula coag ~ diet. This
formula is a symbolic representation of the one-way layout model
equation; the formula excludes the error term ε ij . The second
argument to aov is the data frame you created, coag.df, which
provides the data needed to carry out the ANOVA. The names diet
and coag, used in the formula coag ~ diet, need to match the names
of the variables in the data frame coag.df.
To display the ANOVA table, use summary. The p-value returned by
for aov.coag is 0.000047, which is highly significant.
summary
> summary(aov.coag)
Df Sum of Sq Mean Sq F Value
Pr(F)
diet 3
228
76.0 13.5714 4.65847e-05
Residuals 20
112
5.6
574
Experiments with One Factor
You obtain the fitted values and residuals using the fitted.values
and residuals functions on the result of aov. Thus, for example, you
get the fitted values with the following:
> fitted.values(aov.coag)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
61 61 61 61 66 66 66 66 66 66 68 68 68 68 68 68 61 61 61 61 61 61 61 61
The resid and fitted functions are shorter names for residuals and
fitted.values, respectively.
You can check the residuals for distributional shape and outliers by
using hist and qqnorm, with the residuals component of aov.coag as
argument:
> hist(resid(aov.coag))
> qqnorm(resid(aov.coag))
2
4
6
8
Figure 16.3 shows the resulting histogram and Figure 16.4 shows the
quantile-quantile plot.
0
Diagnostic Plots
-6
-4
-2
0
2
4
6
resid(aov.coag)
Figure 16.3: Histogram of residuals.
575
Chapter 16 Designed Experiments and Analysis of Variance
4
•
•
2
• • •
0
• • • • •
• • • •
-2
resid(aov.coag)
•
• • •
•
• • •
-4
•
•
-2
-1
0
1
2
Quantiles of Standard Normal
Figure 16.4: Normal qq-plot of residuals.
The shape of the histogram, and the linearity of the normal qq-plot,
both indicate that the error distribution is quite Gaussian. The flat
sections in the qq-plot are a consequence of tied values in the data.
You can check for nonhomogeneity of error variance and possible
outliers by plotting the residuals versus the fit:
> plot(fitted(aov.coag), resid(aov.coag))
This plot reveals no unusual features and is not shown.
Details
An alternate form of the one-way layout model is the overall mean plus
effects form:
y ij = μ + α i + ε ij
where μ is the overall mean and α i is the effect for level (or
treatment) i . The ith treatment mean μ i in the one-way layout
formulation is related to μ and α i by
μi = μ + αi .
576
Experiments with One Factor
The effects α i satisfy the constraint
n1 α1 + n2 α2 + … + nI αI = 0 ,
where n i is the number of replications for the ith treatment. The
function aov fits the one-way model in the overall mean plus effects
form:
y ij = μ̂ + α̂ i + r ij .
See the section Model Coefficients and Contrasts for more on this.
To obtain the effects, use model.tables as follows:
> model.tables(aov.coag)
Tables of effects
diet
A B C D
-3 2 4 -3
rep 4 6 6 8
Warning messages:
Model was refit to allow projection in:
model.tables(aov.coag)
You can get the treatment means as follows:
> model.tables(aov.coag, type = "means")
Tables of means
Grand mean
64
diet
A B C D
61 66 68 61
rep 4 6 6 8
Warning messages:
Model was refit to allow projection in:
model.tables(aov.coag, type = "means")
577
Chapter 16 Designed Experiments and Analysis of Variance
THE UNREPLICATED TWO-WAY LAYOUT
The data in Table 16.2 (used by Box, Hunter, and Hunter (1978)) were
collected to determine the effect of treatments A, B, C, and D on the
yield of penicillin in a penicillin manufacturing process.
Table 16.2: Effect of four treatments on penicillin yield.
Treatment
Block
A
B
C
D
Blend 1
89
88
97
94
Blend 2
84
77
92
79
Blend 3
81
87
87
85
Blend 4
87
92
89
84
Blend 5
79
81
80
88
The values of the response variable “yield” are the numbers in the
table, and the columns of the table correspond to the levels A, B, C,
and D of the treatment factor. There was a second factor, namely the
blend factor, since a separate blend of the corn-steep liquor had to be
made for each application of the treatments.
Your main interest is in determining whether the treatment factor
affects yield. The blend factor is of only secondary interest; it is a
blocking variable introduced to increase the sensitivity of the
inference for treatments. The order of the treatments within blocks
was chosen at random. Hence, this is a randomized blocks experiment.
The methods we use in this section apply equally well to two-factor
experiments in which both factors are experimentally controlled and
of equal interest.
578
The Unreplicated Two-Way Layout
Setting Up the
Data Frame
Table 16.2 is balanced :each entry or cell of the table (that is, each row
and column combination) has the same number of observations (one
observation per cell, in the present example). With balanced data,
you can use fac.design to create the data frame.
First, create a list fnames with two components named blend and
treatment, where blend contains the level names of the blend factor
and treatment contains the level names of the treatment factor:
> fnames <- list(blend = paste("Blend ", 1:5),
+ treatment = LETTERS[1:4])
Then use fac.design to create the design data frame pen.design
> pen.design <- fac.design(c(5,4), fnames)
The first argument, c(5,4), to fac.design specifies the design as
having two factors because its length is two. The 5 specifies five levels
for the first factor, blend, and the 4 specifies four levels for the second
factor, treatment. The second argument, fnames, specifies the factor
names and the labels for their levels.
The design data frame pen.design that you just created contains the
factors blend and treatment as its first and second columns,
respectively.
Now create yield to match pen.design:
> yield <- scan()
1: 89 84 81 87 79
6: 88 77 87 92 81
11: 97 92 87 89 80
16: 94 79 85 84 88
21:
You can now use data.frame to combine the design data frame
pen.design and the response yield into the data frame pen.df:
> pen.df <- data.frame(pen.design, yield)
Now look at pen.df:
> pen.df
579
Chapter 16 Designed Experiments and Analysis of Variance
blend treatment yield
Blend 1
A
89
Blend 2
A
84
Blend 3
A
81
Blend 4
A
87
Blend 5
A
79
Blend 1
B
88
.
.
.
19 Blend 4
D
84
20 Blend 5
D
88
1
2
3
4
5
6
Alternatively, you could build the model data frame directly from
pen.design as follows:
> pen.design[,"yield"] <- yield
When you plot the object pen.design, S-PLUS uses the method
plot.design, because the object pen.design is of class "design".
Thus, you obtain the same results as if you called plot.design
explicitly on the object pen.df.
A First Look at
the Data
You can look at the (comparative) values of the sample means of the
data for each level of each factor using plot.design:
> plot.design(pen.df)
This function produces the plot shown in Figure 16.5. For the blend
factor, each tick mark is located at the mean of the corresponding row
of Table 16.2. For the treatment factor, each tick mark is located at the
mean of the corresponding column of Table 16.2. The horizontal line
is located at the sample mean of all the data. Figure 16.5 suggests that
the blend has a greater effect on yield than does the treatment.
580
Blend 1
90
92
The Unreplicated Two-Way Layout
Blend 4
86
mean of yield
88
C
D
B
84
Blend 3
A
82
Blend 2
Blend 5
blend
treatment
Factors
Figure 16.5: Sample means in penicillin yield experiment.
Since sample medians are insensitive to outliers, and sample means
are not, you may want to make a plot similar to Figure 16.5 using
sample medians instead of sample means. You can do this with
plot.design, using the second argument fun=median:
> plot.design(pen.df, fun = median)
In this case, the plot does not indicate great differences between
sample means and sample medians.
Use plot.factor to get a more complete exploratory look at the data.
But first use par to get a one row by two column layout for two plots:
> par(mfrow = c(1,2))
> plot.factor(pen.df)
> par(mfrow = c(1,1))
This command produces the plot shown in Figure 16.6.
581
95
90
80
85
yield
85
80
yield
90
95
Chapter 16 Designed Experiments and Analysis of Variance
Blend 1 Blend 2 Blend 3 Blend 4 Blend 5
blend
A
B
C
D
treatment
Figure 16.6: Factor plot for penicillin yield experiment.
The box plots for factors, produced by plot.factor, give additional
information about the data besides the location given by
plot.design. The box plots indicate variability, skewness, and
outliers in the response, for each fixed level of each factor. For this
particular data, the box plots for both blends and treatments indicate
rather constant variability, relatively little overall skewness, and no
evidence of outliers.
For two-factor experiments, you should use interaction.plot to
check for possible interactions (that is, nonadditivity). The
interaction.plot function does not accept a data frame as an
argument. Instead, you must supply appropriate factor names and the
response name. To make these factor and response data objects
available to interaction.plot, you must first attach the data frame
pen.df:
> attach(pen.df)
> interaction.plot(treatment, blend, yield)
These commands produce the plot shown in Figure 16.7. The first
argument to interaction.plot specifies which factor appears along
the x-axis (in this case, treatment). The second argument specifies
which factor is associated with each line plot, or “trace” (in this case,
blend). The third argument is the response variable (in this case,
yield).
582
The Unreplicated Two-Way Layout
95
blend
1
5
3
4
2
80
85
mean of yield
90
Blend
Blend
Blend
Blend
Blend
A
B
C
D
treatment
Figure 16.7: Interaction plot of penicillin experiment.
Without replication it is often difficult to interpret an interaction plot
since random error tends to dominate. There is nothing striking in
this plot.
The Two-Way
Model and
ANOVA (One
Observation
Per Cell)
The additive model for experiments with two factors, A and B, and
one observation per cell is:
A
B
y ij = μ + α i + α i + ε ij
i = 1, …, I
j = 1, …, J
A
where μ is the overall mean, α i is the effect of the i th level of factor
B
A and α j is the effect of the j th level of factor B.
For the penicillin data above, factor A is “blend” and factor B is
“treatment.” Blend has I = 5 levels and treatment has J = 5 levels.
To estimate the additive model, use aov:
> aov.pen <- aov(yield ~ blend + treatment, pen.df)
The formula yield ~ blend + treatment specifies that a two factor
additive model is fit, with yield the response, and blend and
treatment the factors.
583
Chapter 16 Designed Experiments and Analysis of Variance
Display the analysis of variance table with summary:
> summary(aov.pen)
Df Sum of Sq Mean Sq F Value
Pr(F)
blend 4
264 66.0000 3.50442 0.040746
treatment 3
70 23.3333 1.23894 0.338658
Residuals 12
226 18.8333
The p-value for blend is moderately significant, while the p-value for
treatment is insignificant.
Diagnostic Plots
Make a histogram of the residuals.
> hist(resid(aov.pen))
0
1
2
3
4
5
The resulting histogram is shown in Figure 16.8.
-6
-4
-2
0
2
4
6
resid(aov.pen)
Figure 16.8: Histogram of residuals for penicillin yield experiment.
Now make a normal qq-plot of residuals:
> qqnorm(resid(aov.pen))
The resulting plot is shown in Figure 16.9.
584
8
The Unreplicated Two-Way Layout
6
•
•
4
•
•
2
•
0
• •
• • •
-2
resid(aov.pen)
•
• •
• •
-4
•
•
•
-2
•
•
-1
0
1
2
Quantiles of Standard Normal
Figure 16.9: Quantile-quantile plot of residuals for penicillin yield experiment.
The central four cells of the histogram in Figure 16.8 are consistent
with a fairly normal distribution in the middle. The linearity of the
normal qq-plot in Figure 16.9, except near the ends, also suggests that
the distribution is normal in the middle. The relatively larger values
of the outer two cells of the histogram, and the flattening of the
normal qq-plot near the ends, both suggest that the error distribution
is slightly more short-tailed than a normal distribution. This is not a
matter of great concern for the ANOVA F tests.
Make a plot of residuals versus the fit:
> plot(fitted(aov.pen), resid(aov.pen))
The resulting plot is shown in Figure 16.10. The plot of residuals
versus fit gives some slight indication that smaller error variance is
associated with larger values of the fit.
585
Chapter 16 Designed Experiments and Analysis of Variance
6
•
•
4
•
•
2
•
•
•
0
•
•
•
-2
•
•
-4
resid(aov.pen)
•
•
•
•
•
•
80
•
•
85
90
95
fitted(aov.pen)
Figure 16.10: Residuals vs. fitted values for penicillin yield experiment.
Guidance
Since there is some indication of inhomogeneity of error variance, we
now consider transforming the response, yield.
You may want to test for the existence of a multiplicative interaction,
specified by the model
A
B
A B
y ij = μ + α i + α j + θα i α j + ε ij .
When the unknown parameter θ is not zero, multiplicative
interaction exists. A test for the null hypothesis of no interaction may
be carried out using the test statistic T 1df for Tukey’s one degree of
freedom for nonadditivity.
A S-PLUS function, tukey.1, is provided in the section Details. You
can use it to compute T 1df and the p-value. For the penicillin data:
> tukey.1(aov.pen, pen.df)
$T.1df:
[1] 0.09826791
$p.value:
[1] 0.7597822
586
The Unreplicated Two-Way Layout
The statistic T 1df = 0.098 has a p-value of p = 0.76 , which is not
significant. Therefore, there is no indication of a multiplicative
interaction.
Assuming that the response values are positive, you can find out
whether or not the data suggest a specific transformation to remove
multiplicative interaction as follows: Plot the residuals r ij for the
additive fit versus the comparison values
A B
α̂ i α̂ j
c ij = ------------- .
μ̂
If this plot reveals a linear relationship with estimated slope θ̂ , then
you should analyze the data again, using as new response values the
λ
power transformation y ij of the original response variables y ij , with
exponent
λ = 1 – θ̂ .
(If λ = 0 , use log( y ij ).) See Hoaglin, Mosteller, and Tukey (1983) for
details.
A S-PLUS function called comp.plot, for computing the comparison
values c ij , plotting r ij versus c ij , and computing θ̂ , is provided in the
section Details. Applying comp.plot to the penicillin data gives the
results shown below and in Figure 16.11:
> comp.plot(aov.pen, pen.df)
$theta.hat:
[1] 4.002165
$std.error:
[1] 9.980428
$R.squared:
R2
0.008854346
587
Chapter 16 Designed Experiments and Analysis of Variance
•
6
•
4
•
•
2
•
•
0
•
•
•
•
•
-2
as.vector(r.mat)
•
•
•
•
-4
•
•
•
•
-0.1
0.0
0.1
0.2
cij
Figure 16.11: Display from comp.plot.
In this case, the estimated slope is θ̂ = 4 , which gives λ = – 3 .
However, this is not a very sensible exponent for a power
2
transformation. The standard deviation of θ̂ is nearly 10 and the R
is only .009, which indicates that θ may be zero. Thus, we do not
recommend using a power transformation.
Details
The test statistic T 1df for Tukey’s one degree of freedom is given by:
SS θ
T 1df = ( IJ – I – J ) ---------------SS res.1
where
 I J

A B 

α̂ α̂ y
   i j ij
i = 1 j = 1

SS θ = ----------------------------------------------I
J
A 2
B 2
 ( α̂i )  ( α̂j )
i=1
588
j=1
The Unreplicated Two-Way Layout
SS res.1 = SS res – SS θ
I
SS res =
J
  rij
2
i=1 j=1
A
B
A
B
with the α̂ i , α̂ j the additive model estimates of the α i and α j , and
r ij the residuals from the additive model fit. The statistic T 1df has an
F1,IJ-I-J distribution.
Here is a function tukey.1 to compute the Tukey one degree of
freedom for nonadditivity test. You can create your own version of
this function by typing tukey.1 <- and then the definition of the
function.
>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
tukey.1 <- function(aov.obj, data) {
vnames <- names(aov.obj$contrasts)
if(length(vnames) != 2)
stop("the model must be two-way")
vara <- data[, vnames[1]]
varb <- data[, vnames[2]]
na <- length(levels(vara))
nb <- length(levels(varb))
resp <- data[, as.character(attr(aov.obj$terms,
"variables")[attr(aov.obj$terms, "response" )])]
cfs <- coef(aov.obj)
alpha.A <- aov.obj$contrasts[[vnames[1]]] %*% cfs[
aov.obj$assign[[vnames[1]]]]
alpha.B <- aov.obj$contrasts[[vnames[2]]] %*% cfs[
aov.obj$assign[[vnames[2]]]]
r.mat <- matrix(0, nb, na)
r.mat[cbind(as.vector(unclass(varb)), as.vector(
unclass(vara)))] <- resp
SS.theta.num <- sum((alpha.B %*% t(alpha.A)) * r.mat)^2
SS.theta.den <- sum(alpha.A^2) * sum(alpha.B^2)
SS.theta <- SS.theta.num/SS.theta.den
SS.res <- sum(resid(aov.obj)^2)
SS.res.1 <- SS.res - SS.theta
T.1df <- ((na * nb - na - nb) * SS.theta)/SS.res.1
p.value <- 1 - pf(T.1df, 1, na * nb - na - nb)
list(T.1df = T.1df, p.value = p.value) }
589
Chapter 16 Designed Experiments and Analysis of Variance
Here is a function comp.plot for computing a least-squares fit to the
plot of residuals versus comparison values:
>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
590
comp.plot <- function(aov.obj, data)
{
vnames <- names(aov.obj$contrasts)
if(length(vnames) != 2)
stop("the model must be two-way")
vara <- data[, vnames[1]]
varb <- data[, vnames[2]]
cfs <- coef(aov.obj)
alpha.A <- aov.obj$contrasts[[vnames[1]]] %*% cfs[
aov.obj$assign[[vnames[1]]]]
alpha.B <- aov.obj$contrasts[[vnames[2]]] %*% cfs[
aov.obj$assign[[vnames[2]]]]
cij <- alpha.B %*% t(alpha.A)
cij <- c(cij)/cfs[aov.obj$assign$"(Intercept)"]
na <- length(levels(vara))
nb <- length(levels(varb))
r.mat <- matrix(NA, nb, na)
r.mat[cbind(as.vector(unclass(varb)), as.vector(
unclass(vara)))] <- resid(aov.obj)
plot(cij, as.vector(r.mat))
ls.fit <- lsfit(as.vector(cij), as.vector(r.mat))
abline(ls.fit)
output <- ls.print(ls.fit, print.it = F)
list(theta.hat = output$coef.table[2, 1],
std.error = output$coef.table[2, 2],
R.squared = output$summary[2])
}
The Two-Way Layout with Replicates
THE TWO-WAY LAYOUT WITH REPLICATES
The data in Table 16.3 (used by Box, Hunter, and Hunter (1978))
displays the survival times, in units of 10 hours, of animals in a 3 x 4
replicated factorial experiment. In this experiment, each animal was
given one of three poisons, labeled I, II, and III, and one of four
treatments, labeled A, B, C, and D. Four animals were used for each
combination of poison and treatment, making four replicates.
Table 16.3: A replicated factorial experiment.
Treatment
Poison
A
B
C
D
I
0.31
0.82
0.43
0.45
0.45
1.10
0.45
0.71
0.46
0.88
0.63
0.66
0.43
0.72
0.76
0.62
0.36
0.92
0.44
0.56
0.29
0.61
0.35
1.02
0.40
0.49
0.31
0.71
0.23
1.24
0.40
0.38
0.22
0.30
0.23
0.30
0.21
0.37
0.25
0.36
0.18
0.38
0.24
0.31
0.23
0.29
0.22
0.33
II
III
591
Chapter 16 Designed Experiments and Analysis of Variance
Setting Up the
Data Frame
To set up the data frame, first make a list, fnames, with components
treatment and poison, containing the level names of these two
factors:
> fnames <- list(treatment = LETTERS[1:4],
+ poison=c("I", "II", "III"))
Use fac.design, with optional argument rep = 4, to create the design
data frame poisons.design:
> poisons.design <- fac.design(c(4,3), fnames, rep = 4)
Note that since treatments is the first factor in the fnames list and
treatments has 4 levels, 4 is the first argument of c(4,3).
You now need to create the vector surv.time to match
Each replicate of the experiment consists of data in
three rows of Table 16.3. Rows 1, 5, and 9 make up the first replicate,
and so on. The command to get what we want is:
poisons.design.
> surv.time <- scan()
1: .31 .82 .43 .45
5: .36 .92 .44 .56
9: .22 .30 .23 .30
13: .45 1.10 .45 .71
17: .29 .61 .35 1.02
21: .21 .37 .25 .36
25: .46 .88 .63 .66
29: .40 .49 .31 .71
33: .18 .38 .24 .31
37: .43 .72 .76 .62
41: .23 1.24 .40 .38
45: .23 .29 .22 .33
49:
Finally, make the data frame poisons.df:
> poisons.df <- data.frame(poisons.design, surv.time)
592
The Two-Way Layout with Replicates
A First Look at
the Data
Use plot.design, plot.factor, and interaction.plot to get a first
look at the data through summary statistics.
Set par(mfrow = c(3,2)) and use the above three functions to get the
three row and two column layout of plots displayed in Figure 16.12:
II
0.5
D
C
A
III
treatment
B
0.5
median of surv.time
I
II
C
A
poison
1.0
0.2
0.6
surv.time
1.0
0.6
surv.time
poison
Factors
0.2
A
B
C
D
I
II
B
C
treatment
D
0.8
poison
0.6
I
II
III
0.4
0.4
0.6
I
II
III
0.2
0.8
poison
A
III
poison
median of surv.time
treatment
mean of surv.time
III
treatment
Factors
0.2
I
D
0.3
B
0.3
mean of surv.time
> par(mfrow = c(3,2))
A
B
C
D
treatment
Figure 16.12: Initial plots of the data.
593
Chapter 16 Designed Experiments and Analysis of Variance
To obtain the design plot of sample means shown in the upper left
plot of Figure 16.12, use plot.design as follows:
> plot.design(poisons.df)
To obtain the design plot of sample medians shown in the upper
right-hand plot of Figure 16.12, use plot.design again:
> plot.design(poisons.df, fun = median)
The two sets of box plots shown in the middle row of Figure 16.12 are
obtained with:
> plot.factor(poisons.df)
To obtain the bottom row of Figure 16.12, use interaction.plot:
>
>
>
+
attach(poisons.df)
interaction.plot(treatment,poison, surv.time)
interaction.plot(treatment,poison, surv.time,
fun = median)
The main differences between the plots obtained with plot.design
using means and medians are as follows:
•
the difference between the horizontal lines which represents
the mean and median, respectively, for all the data;
•
the difference between the tick marks for the poison factor at
level II.
The box plots resulting from the use of plot.factor indicate a clear
tendency for variability to increase with the (median) level of
response.
The plots made with interaction.plot show stronger treatment
effects for the two poisons with large levels than for the lowest level
poison. This is an indication of an interaction.
The Two-Way
Model and
ANOVA (with
Replicates)
594
When you have replicates, you can consider a model which includes
AB
an interaction term α ij :
y ijk = μ +
A
αi
+
B
αj
+
AB
α ij
+ ε ijk
i = 1, …, I
j = 1, …, J
k = 1, …, K
The Two-Way Layout with Replicates
You can now carry out an ANOVA for the above model using aov as
follows:
> aov.poisons <- aov(surv.time ~ poison * treatment,
+ data = poisons.df)
The expression poison*treatment on the right-hand side of the
formula specifies that aov fit the above model with interaction. This
contrasts with the formula surv.time ~ poison + treatment, which
AB
tells aov to fit an additive model for which α ij is assumed to be zero
for all levels i, j .
You now display the ANOVA table with summary:
> summary(aov.poisons)
Df Sum of Sq
poison
2 1.033013
treatment
3 0.921206
poison:treatment 6 0.250138
Residuals
36 0.800725
Mean Sq F Value
Pr(F)
0.5165063 23.22174 0.0000003
0.3070688 13.80558 0.0000038
0.0416896 1.87433 0.1122506
0.0222424
The p-values for both poisons and treatment are highly significant,
while the p-value for interaction is insignificant.
The colon in poison:treatment denotes an interaction, in this case
the poison-treatment interaction.
Diagnostic Plots
Make a histogram and a normal qq-plot of residuals, arranging the
plots side by side in a single figure with par(mfrow = c(1,2)) before
using hist and qqnorm:
>
>
>
>
par(mfrow = c(1,2))
hist(resid(aov.poisons))
qqnorm(resid(aov.poisons))
par(mfrow = c(1,1))
The call par(mfrow = c(1,1)), resets the plot layout to a single plot
per figure.
The histogram in the left-hand plot of Figure 16.13 reveals a marked
asymmetry, which is reflected in the normal qq-plot in the right-hand
side of Figure 16.13. The latter shows a curved departure from
595
Chapter 16 Designed Experiments and Analysis of Variance
•
0.2
•
0.0
••
-0.2
5
10
15
resid(aov.poisons)
0.4
20
linearity toward the lower left part of the plot, and a break in linearity
in the upper right part of the plot. Evidently, all is not well (see the
discussion on transforming the data in the Guidance section below).
•
0
•
-0.4
-0.2
0.0
0.2
0.4
resid(aov.poisons)
••
••
••••
••••••••••
•
•
•
•
•
•
•
•
••••••
••••
••
•
•
•
•
-2
-1
0
1
2
Quantiles of Standard Normal
Figure 16.13: Histogram and normal qq-plot of residuals.
Make a plot of residuals versus fit:
> plot(fitted(aov.poisons), resid(aov.poisons))
The result, displayed in Figure 16.14, clearly reveals a strong
relationship between the residuals and the fitted values. The
variability of the residuals increases with increasing fitted values. This
is another indication that transformation would be useful.
Guidance
When the error variance for an experiment varies with the expected
value of the observations, a variance stabilizing transformation will
often reduce or eliminate such behavior.
We shall show two methods for determining an appropriate variance
stabilizing transformation, one which requires replicates and one
which does not.
596
The Two-Way Layout with Replicates
0.4
•
0.2
0.0
•
•
••• ••
•
• •
•
•• ••
•
••• ••
•
•
•
•
•
••
•
•
•
••
-0.2
resid(aov.poisons)
•
•
•
•
•
•
•
•
•
•
•
•
•
0.2
0.4
0.6
•
0.8
fitted(aov.poisons)
Figure 16.14: Plot of residuals versus fit.
Method for
Two-Factor
Experiments
with
Replicates
For two-factor experiments with replicates, you can gain insight into
an appropriate variance stabilizing transformation by carrying out the
following informal procedure. First, calculate the within-cell standard
deviations σ̂ ij and means y ij :
>
+
+
>
>
+
+
>
std.poison <- tapply(poisons.df$surv.time,
list(poisons.df$treatment,
poisons.df$poison), stdev)
std.poison <- as.vector(std.poison)
means.poison <- tapply(poisons.df$surv.time,
list(poisons.df$treatment,
poisons.df$poison), mean)
means.poison <- as.vector(means.poison)
Then plot log ( σ̂ ij ) versus log ( y ij ) and use the slope of the
regression line to estimate the variance stabilizing transform:
>
>
+
>
plot(log(means.poison), log(std.poison))
var.fit <- lsfit(log(means.poison),
log(std.poison))
abline(var.fit)
597
Chapter 16 Designed Experiments and Analysis of Variance
> theta <- var.fit$coef[2]
> theta
X
1.97704
Now let λ̂ = 1 – θ̂ and choose λ to be that value among the set of

1
2
1
2

values  – 1, – ---, 0, ---, 1  which is closest to λ̂ . If λ = 0 , then make


the transformation ỹ ij = log y ij . Otherwise, make the power
λ
transformation ỹ ijk = y ijk . Now you should repeat the complete
analysis described in the previous subsections, using the response ỹ ijk
in place of y ijk .
Since for the poisons experiment you get θ̂ ≈ 2 , you choose λ = – 1 .
–1
This gives a reciprocal transformation ỹ ijk = y ijk , where y ijk are the
values you used in the response with surv.time. You can think of the
new response ỹ ijk as representing the rate of dying.
The model can be refit using the transformed response:
> summary(aov(1/surv.time ~ poison*treatment,
+ data = poisons.df))
Df Sum of Sq Mean Sq F Value
Pr(F)
poison
2 34.87712 17.43856 72.63475 0.0000000
treatment
3 20.41429 6.80476 28.34307 0.0000000
poison:treatment 6
1.57077 0.26180 1.09042 0.3867329
Residuals
36
8.64308 0.24009
With the transformation the p-values for the main effects have
decreased while the p-value for the interaction has increased—a more
satisfactory fit. The diagnostic plots with the new response are much
improved also.
598
The Two-Way Layout with Replicates
Method for
Unreplicated
Two-Factor
Experiments
An alternative simple method for estimating the variance stabilizing
transformation is based on the relationship between the log of the
absolute residuals and the log of the fitted values. This method has the
advantage that it can be used for unreplicated designs. This method is
also often preferred to that of plotting log σ̂ ij against y ij even for
cases with replication, because y ij and σ̂ ij are not always adequately
good estimates of the mean and standard deviation for small values of
K(K < 8) .
This method consists of plotting log of absolute residuals versus log of
fitted values, and computing the slope θ̂ of the regression line. You
then set λ̂ = 1 – θ̂ . Residuals with very small absolute values should
usually be omitted before applying this method. Here is some sample
code.
>
+
+
+
>
+
+
+
+
>
>
>
plot(log(abs(fitted(aov.poisons)[
abs(resid(aov.poisons)) > exp(-10)])),
log(abs(resid(aov.poisons)[
abs(resid(aov.poisons)) > exp(-10)])))
logrij.fit <- lsfit(
log(abs(fitted(aov.poisons)[
abs(resid(aov.poisons)) > exp(-10)])),
log(abs(resid(aov.poisons)[
abs(resid(aov.poisons)) > exp(-10)])))
abline(logrij.fit)
theta <- logrij.fit$coef[2]
theta
X
1.930791
You get λ̂ = 1 – θ̂ ≈ – 1 .
Note that the two simple methods described above both lead to
nearly identical choices of power transformation to stabilize variance.
599
Chapter 16 Designed Experiments and Analysis of Variance
Details
You will find that a nonconstant standard deviation for observations
y i ( y ijk for the two-factor experiment with replicates) is wellexplained by a power law relationship in many data sets. In
particular, for some constant B and some exponent θ , we have
σ y ≈ Bη
θ
where σ y is the standard deviation of the y i and η is the mean of the
y i . If you then use a power law transformation
λ
ỹ i = y i
for some fixed exponent λ , it can be shown that the standard
deviation σ ỹ for the transformed data ỹ i , is given by
σ ỹ = Kλη
λ – (1 – θ)
.
You can therefore make σ ỹ have a constant value, independent of
the mean η of the original data y i (and independent of the
λ
approximate mean η of the transformed data ỹ i ), by choosing
λ = 1 – θ.
Note that
log σ y ≈ log K + θlog η .
Suppose you plot log σ̂ ij versus log ŷ ij for a two-factor experiment
with replicates and find that this plot results in a fairly good straight
line fit with slope θ̂ , where σ̂ ij is an estimate of σ y and ŷ ij is an
estimate of η . Then the slope θ̂ provides an estimate of θ , and so
you set λ̂ = 1 – θ̂ . Since a fractional exponent λ̂ is not very natural,
one often chooses the closest value λ̂ in the following “natural” set.
600
The Two-Way Layout with Replicates
Alternative
Formal
Methods
–1
Reciprocal
1
– --2
Reciprocal square root
0
Log
1
--2
Square root
1
No transformation
There are two alternative formal approaches to stabilizing the
variance. One approach is to select the power transformation that
minimizes the residual squared error. This is equivalent to
maximizing the log-likelihood function and is sometimes referred to
as a Box-Cox analysis (see, for example, Weisberg (1985); Box
(1988); Haaland (1989)).
The second approach seeks to stabilize the variance without the use of
a transformation, by including the variance function directly in the
model. This approach is called generalized least squares/variance
function estimation (see, for example, Carroll and Ruppert (1988);
Davidian and Haaland (1990)).
Transformations are easy to use and may provide a simpler, more
parsimonious model (Box (1988)). On the other hand, modeling the
variance function directly allows the analysis to proceed on the
original scale and allows more direct insight into the nature of the
variance function. In cases when the stability of the variance is
critical, either of these methods have better statistical properties than
the simple informal graphical methods described above.
601
Chapter 16 Designed Experiments and Analysis of Variance
K
MANY FACTORS AT TWO LEVELS: 2 DESIGNS
The data in Table 16.4 come from an industrial product development
experiment in which a response variable called conversion is measured
(in percent) for each possible combination of two levels of four
factors, listed below.
•
K:
•
Te:
•
P:
pressure (50 or 80 pounds per square inch)
•
C:
concentration (10% or 12%)
catalyst charge (10 or 15 pounds)
temperature ( 220 or 240°C )
The levels are labeled “-” and “+” in the table. All the factors in the
experiment are quantitative, so the “-” indicates the “low” level and
the “+” indicates the “high” level for each factor. This data set was
used by Box, Hunter, and Hunter (1978).
4
The design for this experiment is called a 2 design because there are
4
2 = 16 possible combinations of two levels for four factors.
Setting Up the
Data Frame
To set up the data frame first create a list of the four factor names with
the corresponding pairs of levels labels:
> fnames <- list(K = c("10","15"), Te = c("220","240"),
+ P = c("50","80"), C = c("10","12"))
k
Now use fac.design to create the 2 design data frame devel.design:
> devel.design <- fac.design(rep(2,4), fnames)
The first argument to fac.design is a vector of length four, which
specifies that there are four factors. Each entry of the vector is a 2,
which specifies that there are two levels for each factor.
Since devel.design matches Table 16.4, you can simply scan in the
coversion data:
> conversion <- scan()
1: 71 61 90 82 68 61 87 80
9: 61 50 89 83 59 51 85 78
17:
602
k
Many Factors at Two Levels: 2 Designs
Table 16.4: Data from product development experiment.
Factor
Observation
Number
K
Te
P
C
Conversion(%)
Run Order
1
–
–
–
–
71
(8)
2
+
–
–
–
61
(2)
3
–
+
–
–
90
(10)
4
+
+
–
–
82
(4)
5
–
–
+
–
68
(15)
6
+
–
+
–
61
(9)
7
–
+
+
–
87
(1)
8
+
+
+
–
80
(13)
9
–
–
–
+
61
(16)
10
+
–
–
+
50
(5)
11
–
+
–
+
89
(11)
12
+
+
–
+
83
(14)
13
–
–
+
+
59
(3)
14
+
–
+
+
51
(12)
15
–
+
+
+
85
(6)
16
+
+
+
+
78
(7)
603
Chapter 16 Designed Experiments and Analysis of Variance
Finally, create the data frame devel.df:
> devel.df <- data.frame(devel.design, conversion)
> devel.df
K Te P C conversion
1 10 220 50 10
71
2 15 220 50 10
61
3 10 240 50 10
90
.
.
.
15 10 240 80 12
85
16 15 240 80 12
78
A First Look at
the Data
Use plot.design and plot.factor to make an initial graphical
exploration of the data. To see the design plot with sample means, use
the following command, which yields the plot shown in Figure 16.15:
10
10
50
80
70
75
80
240
12
65
15
60
mean of conversion
85
> plot.design(devel.df)
220
K
Te
P
C
Factors
Figure 16.15: Sample means for product development experiment.
To see the design plot with sample medians, use:
> plot.design(devel.df, fun = median)
604
k
Many Factors at Two Levels: 2 Designs
To see box plots of the factors, use the following commands, which
yield the plots shown in Figure 16.16:
90
80
70
conversion
50
60
80
70
60
50
conversion
90
> par(mfrow = c(2,2))
> plot.factor(devel.df)
> par(mfrow = c(1,1))
10
15
220
80
70
conversion
50
60
80
70
60
50
conversion
90
Te
90
K
240
50
80
10
P
12
C
Figure 16.16: Factor plot for product development experiment.
Estimating All
Effects in the
k
2 Model
You can use aov to estimate all effects (main effects and all
interactions), and carry out the analysis of variance. Let’s do so, and
store the results in aov.devel:
> aov.devel <- aov(conversion ~ K*Te*P*C, data = devel.df)
605
Chapter 16 Designed Experiments and Analysis of Variance
The product form K*Te*P*C on the right-hand side of the formula tells
4
S-PLUS to fit the above 2 design model with all main effects and all
interactions included. You can accomplish the same thing by using
the power function ^ to raise the expression K+Te+P+C to the fourth
power:
> aov.devel <- aov(conversion ~ (K+Te+P+C)^4,
+ data = devel.df)
This second method is useful when you want to specify only main
effects plus certain low-order interactions. For example, replacing 4
by 2 above results in a model with all main effects and all secondorder interactions.
You can obtain the estimated coefficients using the coef function on
the aov output:
> coef(aov.devel)
(Intercept) K Te
P
C K:Te
K:P
Te:P
K:C
72.25 -4 12 -1.125 -2.75 0.5 0.375 -0.625 -5.464379e-17
Te:C
P:C K:Te:P K:Te:C K:P:C Te:P:C K:Te:P:C
2.25 -0.125 -0.375
0.25 -0.125 -0.375
-0.125
Notice that colons are used to connect factor names to represent
interactions, for example, K:P:C is the three factor interaction
between the factors K, P, and C. For more on the relationship between
coefficients, contrasts, and effects, see the section Experiments with
One Factor and the section The Unreplicated Two-Way Layout.
You can get the analysis of variance table with the summary
command:
> summary(aov.devel)
K
Te
P
C
K:Te
K:P
Te:P
K:C
Te:C
606
Df Sum of Sq Mean Sq
1
256.00 256.00
1
2304.00 2304.00
1
20.25
20.25
1
121.00 121.00
1
4.00
4.00
1
2.25
2.25
1
6.25
6.25
1
0.00
0.00
1
81.00
81.00
k
Many Factors at Two Levels: 2 Designs
P:C
K:Te:P
K:Te:C
K:P:C
Te:P:C
K:Te:P:C
1
1
1
1
1
1
0.25
2.25
1.00
0.25
2.25
0.25
0.25
2.25
1.00
0.25
2.25
0.25
The ANOVA table does not provide any F statistics. This is because
you have estimated 16 parameters with 16 observations. There are no
degrees of freedom left for estimating the error variance, and hence
there is no error mean square to use as the denominator of the F
statistics. However, the ANOVA table can give you some idea of
which effects are the main contributors to the response variation.
k
Estimating All
Effects in the 2
Model With
Replicates
On some occasions, you may have replicates of a 2 design. In this
k
2
case, you can estimate the error variance σ as well as all effects. For
3
example, the data in Table 16.5 is from a replicated 2 pilot plant
607
Chapter 16 Designed Experiments and Analysis of Variance
example used by Box, Hunter, and Hunter (1978). The three factors
are temperature (Te), concentration (C) and catalyst (K), and the response is
yield.
Table 16.5: Replicated pilot plant experiment.
Te
C
K
Rep 1
Rep 2
–
–
–
59
61
+
–
–
74
70
–
+
–
50
58
+
+
–
69
67
–
–
+
50
54
+
–
+
81
85
–
+
+
46
44
+
+
+
79
81
To set up the data frame, first make the factor names list:
> fnames <- list(Te = c("Tl", "Th"), C = c("Cl", "Ch"),
+ K = c("Kl", "Kh"))
Because T is a constant in S-PLUS that stands for the logical value
true, you can not use T as a factor name for temperature. Instead, use
Te, or some such alternative abbreviation. Then make the design data
frame, pilot.design, with M=2 replicates, by using fac.design with
the optional argument rep=2:
> pilot.design <- fac.design(c(2,2,2), fnames, rep = 2)
Now, create the response vector pilot.yield as a vector of length 16,
with the second replicate values following the first replicate values:
> pilot.yield <- scan()
1: 59 74 50 69 50 81 46 79
9: 61 70 58 67 54 85 44 81
608
k
Many Factors at Two Levels: 2 Designs
17:
Finally, use data.frame:
> pilot.df <- data.frame(pilot.design, pilot.yield)
You can now carry out the ANOVA, and because the observations
are replicated, the ANOVA table has an error variance estimate, that
is, mean square for error, and F statistics:
> aov.pilot <- aov(pilot.yield ~ (Te + C + K)^3, pilot.df)
> summary(aov.pilot)
Te
C
K
Te:C
Te:K
C:K
Te:C:K
Residuals
Df Sum of Sq Mean Sq F Value
Pr(F)
1
2116
2116 264.500 0.000000
1
100
100 12.500 0.007670
1
9
9
1.125 0.319813
1
9
9
1.125 0.319813
1
400
400 50.000 0.000105
1
0
0
0.000 1.000000
1
1
1
0.125 0.732810
8
64
8
Temperature is clearly highly significant, as is the temperaturecatalyst interaction, and concentration is quite significant.
Estimating All
Small Order
Interactions
In cases where you are confident that high-order interactions are
unlikely, you can fit a model which includes interactions only up to a
fixed order, through the use of the power function ^ with an
appropriate exponent. For example, in the product development
experiment of Table 16.4, you may wish to estimate only the main
effects and all second-order interactions. In this case, use the
command:
> aov.devel.2 <- aov(conversion ~ (K+Te+P+C)^2,devel.df)
Now you are using 16 observations to estimate 11 parameters: the
mean, the four main effects, and the six two-factor interactions. Since
you only use 11 degrees of freedom for the parameters, out of a total
of 16, you still have 5 degrees of freedom to estimate the error
variance. So the command
> summary(aov.devel.2)
609
Chapter 16 Designed Experiments and Analysis of Variance
produces an ANOVA table with an error variance estimate and F
statistics.
Using HalfNormal Plots
to Choose a
Model
You are usually treading on thin ice if you assume that higher-order
interactions are zero, unless you have extensive first-hand knowledge
k
of the process you are studying with a 2 design. When you are not
sure whether or not higher-order interactions are zero, you should use
a half-normal quantile-quantile plot to judge which effects, including
interactions of any order, are significant. Use the function qqnorm as
follows to produce a half-normal plot on which you can identify
points:
> qqnorm(aov.devel, label = 6)
The resulting figure, with six points labeled, is shown in Figure 16.17.
30
20
Effects
40
Te •
0
10
K •
Te:P
• •
• • • • • • • •
0.0
0.5
Te:C •
P •
1.0
C •
1.5
2.0
Half-normal Quantiles
Figure 16.17: Half-normal plot for product development experiment.
k
In general, there are 2 – 1 points in the half-normal plot, since there
k
are 2 effects and the estimate of the overall mean is not included in
this plot. The y-axis positions of the labeled points are the absolute
values of the estimated effects. The messages you get from this plot
are:
610
•
The effects for temperature, catalyst, concentration, and
temperature by concentration are clearly nonzero.
•
The effect for pressure is also very likely nonzero.
k
Many Factors at Two Levels: 2 Designs
You can examine the marginal effects better by creating a plot with a
smaller y-range:
> qqnorm(aov.devel, label = 6, ylim = c(0,20))
A full qq-plot of the effects can give you somewhat more information.
To get this type of plot, use the following:
> qqnorm(aov.devel, full = T, label = 6)
Having determined from the half-normal plot which effects are
nonzero, now fit a model having terms for the main effects plus the
interaction between temperature and concentration:
> aov.devel.small <- aov(conversion ~ K+P+Te*C,
+ data = devel.df)
You can now get an ANOVA summary, including an error variance
estimate:
> summary(aov.devel.small)
Df Sum of Sq Mean Sq F Value
Pr(F)
K
1
256.00 256.000 136.533 0.000000375
P
1
20.25
20.250
10.800 0.008200654
Te
1
2304.00 2304.000 1228.800 0.000000000
C
1
121.00 121.000
64.533 0.000011354
Te:C
1
81.00
81.000
43.200 0.000062906
Residuals 10
18.75
1.875
Diagnostic Plots
k
Once you have tentatively identified a model for a 2 experiment,
you should make the usual graphical checks based on the residuals
and fitted values. In the product development example, you should
examine the following plots:
> hist(resid(aov.devel.small))
> qqnorm(resid(aov.devel.small))
> plot(fitted(aov.devel.small), resid(aov.devel.small))
611
Chapter 16 Designed Experiments and Analysis of Variance
The latter two plots are shown in Figure 16.18 and Figure 16.19.
1
0
•
•
•
•
•
•
-1
•
• • •
• •
•
•
-2
resid(aov.devel.small)
2
•
•
-2
-1
0
1
2
Quantiles of Standard Normal
Figure 16.18: Quantile-quantile plot of residuals, product development example.
You should also make plots using the time order of the runs:
> run.ord <- scan()
1: 8 2 10 4 15 9 1 13 16 5 11 14 3 12 6 7
17:
> plot(run.ord, resid(aov.devel.small))
> plot(run.ord, fitted(aov.devel.small))
This gives a slight hint that the first runs were more variable than the
latter runs.
612
k
Many Factors at Two Levels: 2 Designs
1
•
•
•
•
•
•
• •
0
•
•
•
•
-1
resid(aov.devel.small)
2
•
•
-2
•
•
50
60
70
80
90
fitted(aov.devel.small)
Figure 16.19: Fitted values vs. residuals, product development example.
Details
The function aov returns, by default, coefficients corresponding to the
following usual ANOVA form for the ηi:
1
2
k
η i = η i1 …i k = μ + α i1 + α i2 + … + α ik
12
k – 1, k
13
+ α i1 i2 + α i1 i3 + … + α ik – 1 ik
+…
123…k
+ α i1 i2 …ik
k
In this form of the 2 model, each i m takes on just two values: 1 and
2. There are 2
k
values of the k -tuple index i 1, i 2, …, i k , and the
m
parameter μ is the overall mean. The parameters α i correspond to
m
mn
the main effects, for m = 1, …, k . The parameters α i i correspond to
m n
lmn
the two-factor interactions, the parameters α i i i correspond to the
l m n
three-factor interactions, and the remaining coefficients are the higherorder interactions.
613
Chapter 16 Designed Experiments and Analysis of Variance
The coefficients for the main effects satisfy the constraint
i
i
i
i
n 1 α 1 + n 2 α 2 = 0 for i = 1, 2, …, k , where the n i denote the
number of replications for the ith treatment. All higher-order
interactions satisfy the constraint that the weighted sum over any
individual
subscript
index
is
zero.
For
example,
12
12
12
12
124
124
n i1 1 α i 1 1 + n i1 2 α i1 2 = 0 , n 1i i α 1i
24
124
2 i4
+ n 2i
124
α
2 i 4 2i 2 i 4
= 0 , etc. Because
of the constraints on the parameters in this form of the model, it
suffices to specify one of the two values for each effect. The function
aov
i
12
returns estimates for the “high” levels (for example, α̂ 2, α̂ 2 ).
k
An estimated effect (in the sense usually used in 2 models) is equal
to the difference between the estimate at the high level minus the
estimate at the low level:
1
1
1
α̂ = α̂ 2 – α̂ 1 .
1 1
1 1
Since n 1 α̂ 1 + n 2 α̂ 2 = 0 , we have
1
α̂ =
1
α̂ 2  1
1
n 2
+ ----1- .

n 1
1
1
In the case of a balanced design, n 1 = n 2 and the estimated effect
simplifies to α̂
614
1
1
= 2α̂ 2 .
References
REFERENCES
Box, G.E.P., Hunter, W.G., and Hunter, J.S. (1978). Statistics for
Experimenters: An Introduction to Design, Data Analysis. New York: John
Wiley & Sons, Inc.
Box, G.E.P. (1988). Signal-to-noise ratios, performance criteria, and
transformations. Technometrics 30:1-17.
Carroll, R.J. & Ruppert, D. (1988). Transformation and Weighting in
Regression. New York: Chapman and Hall.
Chambers, J.M. & Hastie, T.J. (Eds.) (1992). Statistical Models in S.
London: Chapman and Hall.
Davidian, M. & Haaland, P.D. (1990). Regression and calibration
with non-constant error variance. Chemometrics and Intelligent
Laboratory Systems 9:231-248.
Haaland, P. (1989). Experimental Design in Biotechnology. New York:
Marcel Dekker.
Hoaglin, D.C., Mosteller, F., & Tukey, J.W. (1983). Understanding
Robust and Exploratory Data Analysis. New York: John Wiley & Sons,
Inc.
Weisberg, S. (1985). Applied Linear Regression (2nd ed.). New York:
John Wiley & Sons, Inc.
615
Chapter 16 Designed Experiments and Analysis of Variance
616
FURTHER TOPICS IN
ANALYSIS OF VARIANCE
17
Introduction
618
Model Coefficients and Contrasts
619
Summarizing ANOVA Results
Splitting Treatment Sums of Squares Into Contrast
Terms
Treatment Means and Standard Errors
Balanced Designs
626
k
2 Factorial Designs
Unbalanced Designs
Analysis of Unweighted Means
626
629
629
633
634
637
Multivariate Analysis of Variance
654
Split-Plot Designs
656
Repeated-Measures Designs
658
Rank Tests for One-Way and Two-Way Layouts
The Kruskal-Wallis Rank Sum Test
The Friedman Rank Sum Test
662
662
663
Variance Components Models
Estimating the Model
Estimation Methods
Random Slope Example
664
664
665
666
Appendix: Type I Estimable Functions
668
References
670
617
Chapter 17 Further Topics in Analysis of Variance
INTRODUCTION
Chapter 16, Designed Experiments and Analysis of Variance,
describes the basic techniques for using TIBCO Spotfire S+ for
analysis of variance. This chapter extends the concepts to several
related topics as follows:
•
Multivariate analysis of variance (MANOVA);
•
Split-plot designs;
•
Repeated measures;
•
Nonparametric tests for one-way and blocked two-way
designs;
•
Variance components models.
These topics are preceded by a discussion of model coefficients and
contrasts. This information is important in interpreting the available
ANOVA summaries.
618
Model Coefficients and Contrasts
MODEL COEFFICIENTS AND CONTRASTS
This section explains what the coefficients mean in ANOVA models,
and how to get more meaningful coefficients for particular cases.
Suppose we have 5 measurements of a response variable scores for
each of three treatments, "A", "B", and "C", as shown below:
> scores <- scan()
1: 4 5 4 5 4 10 7 7 7 7 7 7 8 7 6
> scores.treat <- factor(c(rep("A",5), rep("B",5),
+ rep("C",5)))
> scores.treat
[1] A A A A A B B B B B C C C C C
In solving the basic ANOVA problem, we are trying to solve the
following simple system of equations:
μ̂ A = μ̂ + α̂ A
μ̂ B = μ̂ + α̂ B
μ̂ C = μ̂ + α̂ C
619
Chapter 17 Further Topics in Analysis of Variance
Consider:
4
1
5
1
4
1
5
1
4
1
10
1
7
1
y= 7 = 1
7
1
7
1
7
1
7
1
8
1
7
1
6
1
1
1
1
1
1
1
1
1
1
1
μ̂
α̂ A
α̂ B
μ̂
+ ε = 1 Xa
α̂ C
α̂ A
α̂ B
+ε
α̂ C
1
1
1
1
1
The problem is that the matrix 1 X a is singular. That is, we cannot
solve for the alphas.
Use the Helmert contrast matrix C a =
620
–1 –1
1 –1 .
0 2
Model Coefficients and Contrasts
The matrix X*= 1 X a C a is nonsingular. Thus, we solve the new
system (using betas rather than alphas):
1 –1 –1
4
1 –1 –1
5
1 –1 –1
4
1 –1 –1
5
1 –1 –1
4
1 1 –1
10
μ
1 1 –1 μ
7
7 = 1 1 –1 β 1 + ε = 1 Xa Ca β1
1 1 –1 β2
7
β2
1 1 –1
7
1 0 2
7
1 0 2
7
1 0 2
8
1 0 2
7
1 0 2
6
621
Chapter 17 Further Topics in Analysis of Variance
The matrix 1 X a C a is nonsingular; therefore, we can solve for the
μ
6.333
the solution β 1 = 1.6 .
0.333
β2
μ̂
Because
y
=
1 Xa
α̂ A
α̂ B
= 1 Xa Ca
α̂ C
αA
Xa αB = Xa Ca
αC
β1
β2
μ
β1 ,
it
follows
that
β2
or simply α = C a β .
Thus, we can calculate the original alphas:
Ca β =
–1 –1
– 1.933
1.6 =
1 –1
1.266 = α
0.333
0 2
0.667
If we use aov as usual to create the aov object scores.aov, we can use
the coef function to look at the solved values μ̂ , β̂ 1 , and β̂ 2 :
> scores.aov <- aov(scores ~ scores.treat)
> coef(scores.aov)
(Intercept) scores.treat1 scores.treat2
6.333333
1.6
0.3333333
In our example, the contrast matrix is as follows:
 –1 –1 


 1 –1 


 0 2 
622
Model Coefficients and Contrasts
You can obtain the contrast matrix for any factor object using the
contrasts function. For unordered factors such as scores.treat,
contrasts returns the Helmert contrast matrix of the appropriate
size:
> contrasts(scores.treat)
A
B
C
[,1] [,2]
-1
-1
1
-1
0
2
The contrast matrix, together with the treatment coefficients returned
by coef, provides an alternative to using model.tables to calculate
effects:
> contrasts(scores.treat) %*% coef(scores.aov)[-1]
[,1]
A -1.9333333
B 1.2666667
C 0.6666667
For ordered factors, the Helmert contrasts are replaced, by default,
with polynomial contrasts that model the response as a polynomial
through equally spaced points. For example, suppose we define an
ordered factor water.temp as follows:
> water.temp <- ordered(c(65, 95, 120))
> water.temp
[1] 65 95 120
65 < 95 < 120
The contrast matrix for water.temp uses polynomial contrasts:
> contrasts(water.temp)
.L
.Q
65 -0.7071068 0.4082483
95 0.0000000 -0.8164966
120 0.7071068 0.4082483
623
Chapter 17 Further Topics in Analysis of Variance
For the polynomial contrasts, β̂ 1 represents the linear component of
the response, β̂ 2 represents the quadratic component, and so on.
When examining ANOVA summaries, you can split a factor’s effects
into contrast terms to examine each component’s contribution to the
model. See the section Splitting Treatment Sums of Squares Into
Contrast Terms for complete details.
624
Model Coefficients and Contrasts
At times it is desirable to give particular contrasts to some of the
coefficients. In our example, you might be interested in a contrast that
has A equal to a weighted average of B and C. This might occur, for
instance, if the treatments were really doses. You can add a contrast
attribute to the factor using the assignment form of the contrasts
function:
> contrasts(scores.treat) <- c(4, -1, -3)
> contrasts(scores.treat)
[,1]
[,2]
4 0.2264554
-1 -0.7925939
-3 0.5661385
A
B
C
Note that a second contrast was automatically added.
Refitting the model, we now get different coefficients, but the fit
remains the same.
> scores.aov2 <- aov(scores ~ scores.treat)
> coef(scores.aov2)
(Intercept) scores.treat1 scores.treat2
6.333333
-0.4230769
-1.06434
More details on working with contrasts can be found in the section
Contrasts: The Coding of Factors in Chapter 2.
625
Chapter 17 Further Topics in Analysis of Variance
SUMMARIZING ANOVA RESULTS
Results from an analysis of variance are typically displayed in an
analysis of variance table, which shows a decomposition of the variation
in the response: the total sum of squares of the response is split into
sums of squares for each treatment and interaction and a residual sum
of squares. You can obtain the ANOVA table, as we have throughout
this chapter, by using summary on the result of a call to aov, such as
this overly simple model for the wafer data:
> attach(wafer, pos = 2)
> wafer.aov <- aov(pre.mean ~ visc.tem + devtime +
+ etchtime)
> summary(wafer.aov)
Df Sum of Sq
visc.tem
2 1.343361
devtime
2 0.280239
etchtime
2 0.103323
Residuals 11 2.008568
Splitting
Treatment
Sums of
Squares Into
Contrast
Terms
Mean Sq F Value
Pr(F)
0.6716807 3.678485 0.0598073
0.1401194 0.767369 0.4875574
0.0516617 0.282927 0.7588959
0.1825971
Each treatment sum of squares in the ANOVA table can be further
split into terms corresponding to the treatment contrasts. By default,
the treatment contrasts are used for unordered factors and polynomial
contrasts for ordered factors. In this example, we continue to use the
Helmert contrasts for unordered factors and polynomial contrasts for
ordered factors.
For instance, with ordered factors you can assess whether the
response is fairly linear in the factor by listing the polynomial
contrasts separately. In the data set wafer, you can examine the linear
and quadratic contrasts of devtime and etchtime by using the split
argument to the summary function:
> summary(wafer.aov, split = list(
+ etchtime = list(L = 1, Q = 2),
+ devtime = list(L = 1, Q = 2)))
visc.tem
devtime
626
Df Sum of Sq
Mean Sq F Value
Pr(F)
2 1.343361 0.6716807 3.678485 0.0598073
2 0.280239 0.1401194 0.767369 0.4875574
Summarizing ANOVA Results
devtime: L
1
devtime: Q
1
etchtime
2
etchtime: L 1
etchtime: Q 1
Residuals
11
0.220865
0.059373
0.103323
0.094519
0.008805
2.008568
0.2208653
0.0593734
0.0516617
0.0945188
0.0088047
0.1825971
1.209577
0.325161
0.282927
0.517636
0.048219
0.2949025
0.5799830
0.7588959
0.4868567
0.8302131
Each of the (indented) split terms sum to their overall sum of squares.
The split argument can evaluate only the effects of the contrasts
used to specify the ANOVA model: if you wish to test a specific
contrast, you need to set it explicitly before fitting the model. Thus, if
you want to test a polynomial contrast for an unordered factor, you
must specify polynomial contrasts for the factor before fitting the
model. The same is true for other nondefault contrasts. For instance,
the variable visc.tem in the wafer data set is a three-level factor
constructed by combining two levels of viscosity (204 and 206) with
two levels of temperature (90 and 105).
> levels(visc.tem)
[1] "204,90"
"206,90"
"204,105"
To assess viscosity, supposing temperature has no effect, we define a
contrast that takes the difference of the middle and the sum of the first
and third levels of visc.tem; the contrast matrix is automatically
completed:
#
>
>
>
Assign visc.tem to your working directory.
visc.tem <- visc.tem
contrasts(visc.tem) <- c(-1, 2, -1)
contrasts(visc.tem)
[,1]
[,2]
204,90
-1 -7.071068e-01
206,90
2 -1.110223e-16
204,105
-1 7.071068e-01
> wafer.aov <- aov( pre.mean ~ visc.tem + devtime +
+ etchtime)
# Detach the data set.
> detach(2)
627
Chapter 17 Further Topics in Analysis of Variance
In this fitted model, the first contrast for visc.aov reflects the effect of
viscosity, as the summary shows below.
628
Summarizing ANOVA Results
> summary(wafer.aov, split = list(
+ visc.tem = list(visc = 1)))
Df Sum of Sq
visc.tem
2 1.343361
visc.tem: visc 1 1.326336
devtime
2 0.280239
etchtime
2 0.103323
Residuals
11 2.008568
Treatment
Means and
Standard
Errors
Mean Sq
0.671681
1.326336
0.140119
0.051662
0.182597
F Value
3.678485
7.263730
0.767369
0.282927
Pr(F)
0.0598073
0.0208372
0.4875574
0.7588959
Commonly the ANOVA model is written in the form grand mean plus
treatment effects,
y ijk = μ + α i + β j + ( αβ ) ij + ε ijk
The treatment effects, α i , β j , and ( αβ ) ij , reflect changes in the
response due to the combination of treatments. In this
parametrization, the effects (weighted by the replications) are
constrained to sum to zero.
Unfortunately, the use of the term effect in ANOVA is not
standardized: in factorial experiments an effect is the difference
between treatment levels, in balanced designs it is the difference from
the grand mean, and in unbalanced designs there are (at least) two
different standardizations that make sense.
The coefficients of an aov object returned by coef(aov.object) are
coefficients for the contrast variables derived by the aov function,
rather than the grand-mean-plus-effects decomposition. The functions
dummy.coef and model.tables translate the internal coefficients into
the more natural treatment effects.
Balanced
Designs
In a balanced design, both computing and interpreting effects are
straightforward. The following example uses the gun data frame,
which is a design object with 36 rows representing runs of teams of
three men loading and firing naval guns, attempting to get off as
many rounds per minute as possible. The three predictor variables
specify the team, the physiques of the men on it, and the loading
method used. The outcome variable is the rounds fired per minute.
629
Chapter 17 Further Topics in Analysis of Variance
> gun.aov <- aov(Rounds ~ Method + Physique/Team,
+ data = gun)
> coef(gun.aov)
(Intercept)
Method Physique.L Physique.Q
19.33333 -4.255556 -1.154941 -0.06123724
PhysiqueSTeam1 PhysiqueATeam1 PhysiqueHTeam1
1.9375
0.45
-0.45
PhysiqueSTeam2 PhysiqueATeam2 PhysiqueHTeam2
-0.4875
0.008333333
-0.1083333
The dummy.coef function translates the coefficients into the more
natural effects:
> dummy.coef(gun.aov)
$"(Intercept)":
(Intercept)
19.33333
$Method:
M1
M2
4.255556 -4.255556
$Physique:
[1] 0.7916667
0.0500000 -0.8416667
$"Team %in% Physique":
1T1
2T1
3T1
1T2
2T2
-1.45 -0.4583333 0.5583333 2.425 0.4416667
3T2
1T3
2T3
3T3
-0.3416667 -0.975 0.01666667 -0.2166667
For the default contrasts, these effects always sum to zero.
The same information is returned in a tabulated form by
model.tables. Note that model.tables calls proj; hence, it is helpful
to use qr=T in the call to aov.
630
Summarizing ANOVA Results
> model.tables(gun.aov, se = T)
Tables of effects
Method
M1
M2
4.256 -4.256
Physique
S
A
H
0.7917 0.05 -0.8417
Team %in% Physique
Dim 1 : Physique
Dim 2 : Team
T1
T2
T3
S -1.450 2.425 -0.975
A -0.458 0.442 0.017
H 0.558 -0.342 -0.217
Standard errors of effects
Method Physique Team %in% Physique
0.3381
0.4141
0.7172
rep 18.0000 12.0000
4.0000
Warning messages:
Model was refit to allow projection in:
model.tables(gun.aov, se = T)
Using the first method, the gunners fired on average 4.26 more
rounds than the overall mean. The standard errors for the effects are
simply the residual standard error scaled by the replication factor,
rep, the number of observations at each level of the treatment. For
instance, the standard error for the Method effect is:
se(Residual)
1.434
se(Method) = -------------------------------------------------------- = -------------- = 0.3381
replication(Method)
18
The model.tables function also computes cell means for each of the
treatments. This provides a useful summary of the analysis that is
more easily related to the original data.
631
Chapter 17 Further Topics in Analysis of Variance
> model.tables(gun.aov, type = "means", se = T)
Tables of means
Grand mean
19.33
Method
M1
M2
23.59 15.08
Physique
S
A
H
20.13 19.38 18.49
Team %in% Physique
Dim 1 : Physique
Dim 2 : Team
T1
T2
T3
S 18.68 22.55 19.15
A 18.93 19.83 19.40
H 19.05 18.15 18.28
Standard errors for differences of means
Method Physique Team %in% Physique
0.4782
0.5856
1.014
rep 18.0000 12.0000
4.000
Model was refit to allow projection in:
model.tables(gun.aov, type = "means", se = T)
The first method had an average firing rate of 23.6 rounds. For the
tables of means, standard errors of differences between means are
given, as these are usually of most interest to the experimenter. For
instance the standard error of differences for Team %in% Physique is:
SED =
2.0576
2 × ----------------- = 1.014
4
To gauge the statistical significance of the difference between the first
and second small physique teams, we can compute the least significant
difference (LSD) for the Team %in% Physique interaction. The validity
of the statistical significance is based on the assumption that the
model is correct and the residuals are Gaussian. The plots of the
632
Summarizing ANOVA Results
residuals indicate these are not unreasonable assumptions for this
data set. You can verify this by creating a histogram and normal
qq-plot of the residuals as follows:
> hist(resid(gun.aov))
> qqnorm(resid(gun.aov))
The LSD at the 95% level is:
t ( 0.975, 26 ) × SED ( Team %*% Physique )
We use the t-distribution with 26 degrees of freedom because the
residual sum of squares has 26 degrees of freedom. In Spotfire S+, we
type the following:
> qt(0.975, 26) * 1.014
[1] 2.084307
Since the means of the two teams differ by more than 2.08, the teams
are different at the 95% level of significance. From an interaction plot
it is clear that the results for teams of small physique are unusually
high.
k
2 Factorial
Designs
In factorial experiments, where each experimental treatment has only
two levels, a treatment effect is, by convention, the difference between
the high and low levels. Interaction effects are half the average
difference between paired levels of an interaction. These factorial
effects are computed when type="feffects" is used in the
model.tables function:
> catalyst.aov <- aov(Yield ~ ., data = catalyst, qr = T)
> model.tables(catalyst.aov, type = "feffects", se = T)
Table of factorial effects
Effects
se
Temp
23.0 5.062
Conc
-5.0 5.062
Cat
1.5 5.062
633
Chapter 17 Further Topics in Analysis of Variance
Unbalanced
Designs
When designs are unbalanced (there are unequal numbers of
observations in some cells of the experiment), the effects associated
with different treatment levels can be standardized in different ways.
For instance, suppose we use only the first 35 observations of the gun
data set:
> gunsmall.aov <- aov(Rounds ~ Method + Physique/Team,
+ data = gun, subset = 1:35, qr = T)
The dummy.coef function standardizes treatment effects to sum to
zero:
> dummy.coef(gunsmall.aov)
$"(Intercept)":
(Intercept)
19.29177
$Method:
M1
M2
4.297115 -4.297115
$Physique:
[1] 0.83322650
0.09155983 -0.92478632
$"Team %in% Physique":
1T1
2T1
3T1
1T2
2T2
-1.45 -0.4583333 0.6830128 2.425 0.4416667
3T2
1T3
2T3
-0.2169872 -0.975 0.01666667
3T3
-0.466025
The model.tables function computes effects that are standardized so
the weighted effects sum to zero:
T
 ni τi
= 0,
i=1
where n i is the replication of level i and τ i the effect. The
model.tables effects are identical to the values of the projection
vectors computed by proj(gunsmall.aov), as the command below
shows.
634
Summarizing ANOVA Results
> model.tables(gunsmall.aov)
Tables of effects
Method
M1
4.135
rep 18.000
M2
-4.378
17.000
Physique
S
A
0.7923 0.05065
rep 12.0000 12.00000
H
-0.9196
11.0000
Team %in% Physique
Dim 1 : Physique
Dim 2 : Team
T1
T2
T3
S
-1.450 2.425 -0.975
rep 4.000 4.000 4.000
A
-0.458 0.442 0.017
rep 4.000 4.000 4.000
H
0.639 -0.261 -0.505
rep 4.000 4.000 3.000
With this standardization, treatment effects are orthogonal:
consequently cell means can be computed by simply adding effects to
the grand mean; standard errors are also more readily computed.
> model.tables(gunsmall.aov, type = "means", se = T)
Standard error information not returned as design is
unbalanced.
Standard errors can be obtained through se.contrast.
Tables of means
Grand mean
19.45
Method
M1
M2
23.59 15.08
rep 18.00 17.00
635
Chapter 17 Further Topics in Analysis of Variance
Physique
S
A
H
20.25 19.5 18.53
rep 12.00 12.0 11.00
Team %in% Physique
Dim 1 : Physique
Dim 2 : Team
T1
T2
T3
S
18.80 22.67 19.27
rep 4.00 4.00 4.00
A
19.05 19.95 19.52
rep 4.00 4.00 4.00
H
19.17 18.27 18.04
rep 4.00 4.00 3.00
Note that the (Intercept) value returned by dummy.coef is not the
grand mean of the data, and the coefficients returned are not a
decomposition of the cell means. This is a difference that occurs only
with unbalanced designs. In balanced designs the functions
dummy.coef and model.tables return identical values for the effects.
In the unbalanced case, the standard errors for comparing two means
depend on the replication factors, hence it could be very complex to
tabulate all combinations. Instead, they can be computed directly
with se.contrast. For instance, to compare the first and third teams
of heavy physique:
>
+
+
+
se.contrast(gunsmall.aov, contrast = list(
Physique == "S" & Team == "T1",
Physique == "S" & Team == "T3"),
data = gun[1:35,])
[1] 1.018648
By default, the standard error of the difference of the means specified
by contrast is computed. Other contrasts are specified by the
argument coef. For instance, to compute the standard error of the
contrast tested in the section Splitting Treatment Sums of Squares Into
Contrast Terms for the variable visc.tem, use the commands below.
636
Summarizing ANOVA Results
>
>
+
+
+
+
attach(wafer)
se.contrast(wafer.aov, contrast = list(
visc.tem ==levels(visc.tem)[1],
visc.tem == levels(visc.tem)[2],
visc.tem == levels(visc.tem)[3]),
coef = c(-1,2,-1), data = wafer)
Refitting model to allow projection
[1] 0.4273138
# Detach the data set.
> detach(2)
The
value
of
the
model.tables(wafer.aov).
contrast can be computed
The effects for visc.tem are:
from
visc.tem
204,90 206,90 204,105
0.1543 -0.3839 0.2296
The contrast is -0.3839 - mean(c(0.1543,0.2296)) = -0.5758. The
standard error for testing whether the contrast is zero is 0.0779;
clearly, the contrast is nonzero.
Analysis of
Unweighted
Means
Researchers implementing an experimental design frequently lose
experimental units and find themselves with unbalanced, but
complete data. The data are unbalanced in that the number of
replications is not constant for each treatment combination; the data
are complete in that at least one experimental unit exists for each
treatment combination. In this type of circumstance, an experimenter
may find the analysis of unweighted means is appropriate, and that the
unweighted means are of more interest than the weighted means. In
such an analysis, the Type III sum of squares is computed instead of
the Type I (sequential) sum of squares.
In a Type I analysis, the model sum of squares is partitioned into its
term components, where the sum of squares for each term listed in
the ANOVA table is adjusted for the terms listed in the previous
rows. For unbalanced data, the sequential sums of squares (and the
hypotheses they test) depend on the order in which the terms are
specified in the model formula. In a Type III analysis, however, the
sum of squares for each term listed in the ANOVA table is adjusted
for all other terms in the model. These sums of squares are
637
Chapter 17 Further Topics in Analysis of Variance
independent of the order that the terms are specified in the model
formula. If the data are balanced, the sequential sum of squares equals
the Type III sum of squares. If the data are unbalanced but complete,
then the Type III sums of squares are those obtained from Yates'
weighted squares-of-means technique. In this case, the hypotheses
tested by the Type III sums of squares for the main effects is that the
levels of the unweighted means are equal.
For general observational studies, the sequential sum of squares may
be of more interest to an analyst. For a designed experiment, an
analyst may find the Type III sum of squares of more use.
The argument ssType to the methods anova.lm and summary.aov
compute the Type III sums of squares. To obtain the Type III
analysis for an aov object, use the option ssType=3 in the call to anova
or summary. In addition, the multicomp function can be used to
compute unweighted means. In this section, we provide examples to
demonstrate these capabilities in an analysis of a designed
experiment.
The Baking Data
The fat-surfactant example below is taken from Milliken and Johnson
(1984, p. 166), where they analyze an unbalanced randomized block
factorial design. Here, the specific volume of bread loaves baked from
dough that is mixed from each of nine Fat and Surfactant treatment
combinations is measured. The experimenters blocked on four Flour
types. Ten loaves had to be removed from the experiment, but at
least one loaf existed for each Fat x Surfactant combination and all
marginal means are estimable. Therefore, the Type III hypotheses are
testable. The data are given in Table 17.1.
The commands below create a Baking data set from the information
in Table 17.1.
>
+
+
+
+
+
+
+
+
+
638
Baking <- data.frame(
Fat = factor(
c(rep(1,times=12), rep(2,times=12), rep(3,times=12))),
Surfactant = factor(
rep(c(1,1,1,1,2,2,2,2,3,3,3,3), times=3)),
Flour = factor(rep(1:4, times=9)),
Specific.Vol = c(6.7, 4.3, 5.7, NA, 7.1, NA, 5.9, 5.6,
NA, 5.5, 6.4, 5.8, NA, 5.9, 7.4, 7.1, NA, 5.6, NA, 6.8,
6.4, 5.1, 6.2, 6.3, 7.1, 5.9, NA, NA, 7.3, 6.6,
8.1, 6.8, NA, 7.5, 9.1, NA))
Summarizing ANOVA Results
> Baking
Fat Surfactant Flour Specific.Vol
1
1
1
1
6.7
2
1
1
2
4.3
3
1
1
3
5.7
4
1
1
4
NA
5
1
2
1
7.1
6
1
2
2
NA
7
1
2
3
5.9
8
1
2
4
5.6
9
1
3
1
NA
10
1
3
2
5.5
. . .
Table 17.1: Specific volumes from a baking experiment.
Fat
1
2
3
Surfactant
Flour 1
Flour 2
Flour 3
1
6.7
4.3
5.7
2
7.1
Flour 4
5.9
5.6
3
5.5
6.4
5.8
1
5.9
7.4
7.1
2
5.6
6.2
6.3
6.6
8.1
6.8
7.5
9.1
3
6.4
5.1
1
7.1
5.9
2
7.3
3
6.8
The overparametrized model is:
μ ijk = μ + b i + f j + s k + ( fs ) jk
for i = 1, …, 4 , j = 1, 2, 3 , and k = 1, 2, 3 . In this model, the b i
are coefficients corresponding to the levels in Fat, the f j correspond
to Flour, the s k correspond to Surfactant, and the ( fs ) jk are
639
Chapter 17 Further Topics in Analysis of Variance
coefficients for the Fat x Surfactant interaction. Because the data
are unbalanced, the Type III sums of squares for Flour, Fat, and
Surfactant test more useful hypotheses than the Type I analysis.
Specifically, the Type III hypotheses are that the unweighted means
are equal:
H Flour : μ 1.. = μ 2.. = μ 3.. = μ 4..
H Fat : μ .1. = μ .2. = μ .3.
H Surfactant : μ ..1 = μ ..2 = μ ..3
where
 μikj
j, k
μ i.. = -------------3⋅3
 μijk
i, k
μ .j. = -------------4⋅3
 μijk
i, j
μ ..k = -------------4 3
The hypotheses tested by the Type I sums of squares are not easily
interpreted, since they depend on the order in which the terms are
specified. In addition, the Type I sums of squares involve the cell
replications, which can be viewed as random variables when the data
are unbalanced in a truly random fashion. Moreover, the hypothesis
tested by the blocking term, Flour, involves parameters of the Fat,
Flour, and Fat x Flour terms.
The following command computes an analysis of variance model for
the Baking data.
>
+
+
+
ANOVA Tables
640
Baking.aov <- aov(Specific.Vol ~ Flour + Fat*Surfactant,
data = Baking, contrasts = list(Flour = contr.sum(4),
Fat = contr.sum(3), Surfactant = contr.sum(3)),
na.action = na.exclude)
The ANOVA tables for both the Type I and Type III sums of squares
are given below for comparison. Using the Type III sums of squares
for the Baking.aov object, we see that the block effect, Flour, is
Summarizing ANOVA Results
significant. In addition, Fat appears to be significant, but Surfactant
is not (at a test size of α = 0.05 ). In the presence of a significant
interaction, however, the test of the marginal means probably has
little meaning for Fat and Surfactant.
> anova(Baking.aov)
Analysis of Variance Table
Response: Specific.Vol
Terms added sequentially (first to last)
Df Sum of Sq Mean Sq F Value
Flour 3
6.39310 2.131033 12.88269
Fat 2 10.33042 5.165208 31.22514
Surfactant 2
0.15725 0.078625 0.47531
Fat:Surfactant 4
5.63876 1.409691 8.52198
Residuals 14
2.31586 0.165418
Pr(F)
0.0002587
0.0000069
0.6313678
0.0010569
> anova(Baking.aov, ssType = 3)
Analysis of Variance Table
Response: Specific.Vol
Type III Sum of Squares
Df Sum of Sq
Flour 3
8.69081
Fat 2 10.11785
Surfactant 2
0.99721
Fat:Surfactant 4
5.63876
Residuals 14
2.31586
Unweighted
Means
Mean Sq F Value
Pr(F)
2.896937 17.51280 0.00005181
5.058925 30.58263 0.00000778
0.498605 3.01421 0.08153989
1.409691 8.52198 0.00105692
0.165418
The unweighted means computed below estimate the means given in
the Type III hypotheses for Flour, Fat, and Surfactant. The means
for Flour x Surfactant in the overparametrized model are
 μijk
i
-.
μ .jk = -------------4
641
Chapter 17 Further Topics in Analysis of Variance
We
use the multicomp function with
comparisons="none" to compute the unweighted
the argument
means and their
standard errors.
# Unweighted means for Flour.
> multicomp(Baking.aov, comparisons="none", focus="Flour")
95 % simultaneous confidence intervals for specified
linear combinations, by the Sidak method
critical point: 2.8297
response variable: Specific.Vol
intervals excluding 0 are flagged by '****'
1
2
3
4
Estimate Std.Error Lower Bound Upper Bound
7.30
0.199
6.74
7.87 ****
5.71
0.147
5.29
6.12 ****
6.98
0.162
6.52
7.44 ****
6.54
0.179
6.04
7.05 ****
# Unweighted means for Fat.
> multicomp(Baking.aov, comparisons="none", focus="Fat")
95 % simultaneous confidence intervals for specified
linear combinations, by the Tukey method
critical point: 2.6177
response variable: Specific.Vol
intervals excluding 0 are flagged by '****'
1
2
3
642
Estimate Std.Error Lower Bound Upper Bound
5.85
0.136
5.49
6.21 ****
6.58
0.148
6.19
6.96 ****
7.47
0.156
7.06
7.88 ****
Summarizing ANOVA Results
# Unweighted means for Surfactant.
> multicomp(Baking.aov, comparisons = "none",
+ focus = "Surfactant")
95 % simultaneous confidence intervals for specified
linear combinations, by the Tukey method
critical point: 2.6177
response variable: Specific.Vol
intervals excluding 0 are flagged by '****'
1
2
3
Estimate Std.Error Lower Bound Upper Bound
6.4
0.150
6.00
6.79 ****
6.6
0.143
6.22
6.97 ****
6.9
0.147
6.52
7.29 ****
# Unweighted means for Fat x Surfactant.
> multicomp(Baking.aov, comparisons="none", focus="Fat",
+ adjust = list(Surfactant = seq(3)))
95 % simultaneous confidence intervals for specified
linear combinations, by the Sidak method
critical point: 3.2117
response variable: Specific.Vol
intervals excluding 0 are flagged by '****'
1.adj1
2.adj1
3.adj1
1.adj2
2.adj2
3.adj2
1.adj3
2.adj3
3.adj3
Estimate Std.Error Lower Bound Upper Bound
5.54
0.240
4.76
6.31 ****
7.02
0.241
6.25
7.80 ****
6.63
0.301
5.66
7.59 ****
5.89
0.239
5.12
6.66 ****
6.71
0.301
5.74
7.67 ****
7.20
0.203
6.55
7.85 ****
6.12
0.241
5.35
6.90 ****
6.00
0.203
5.35
6.65 ****
8.59
0.300
7.62
9.55 ****
643
Chapter 17 Further Topics in Analysis of Variance
In the output from multicomp, the unweighted means are given in the
Estimate column. In the table for the Fat x Surfactant interaction,
the adjX labels represent the levels in Surfactant. Thus, the value
7.02 is the estimated mean specific volume at the second level in Fat
and the first level in Surfactant.
Multiple
Comparisons
The F statistic for the Fat x Surfactant interaction in the Type III
ANOVA table is significant, so the tests for the marginal means of Fat
and Surfactant have little meaning. We can, however, use multicomp
to find all pairwise comparisons of the mean Fat levels for each level
of Surfactant, and those of Surfactant for each level of Fat.
> multicomp(Baking.aov, focus = "Fat",
+ adjust = list(Surfactant = seq(3)))
95 % simultaneous confidence intervals for specified
linear combinations, by the Sidak method
critical point: 3.2117
response variable: Specific.Vol
intervals excluding 0 are flagged by '****'
1.adj1-2.adj1
1.adj1-3.adj1
2.adj1-3.adj1
1.adj2-2.adj2
1.adj2-3.adj2
2.adj2-3.adj2
1.adj3-2.adj3
1.adj3-3.adj3
2.adj3-3.adj3
Estimate Std.Error Lower Bound Upper Bound
-1.490
0.344
-2.590
-0.381 ****
-1.090
0.377
-2.300
0.120
0.394
0.394
-0.872
1.660
-0.817
0.390
-2.070
0.434
-1.310
0.314
-2.320
-0.300 ****
-0.492
0.363
-1.660
0.674
0.123
0.316
-0.891
1.140
-2.470
0.378
-3.680
-1.250 ****
-2.590
0.363
-3.750
-1.420 ****
> multicomp(Baking.aov, focus = "Surfactant",
+ adjust = list(Fat = seq(3)))
95 % simultaneous confidence intervals for specified
linear combinations, by the Sidak method
critical point: 3.2117
response variable: Specific.Vol
intervals excluding 0 are flagged by '****'
644
Summarizing ANOVA Results
1.adj1-2.adj1
1.adj1-3.adj1
2.adj1-3.adj1
1.adj2-2.adj2
1.adj2-3.adj2
2.adj2-3.adj2
1.adj3-2.adj3
1.adj3-3.adj3
2.adj3-3.adj3
Estimate Std.Error Lower Bound Upper Bound
-0.355
0.341
-1.45000
0.740
-0.587
0.344
-1.69000
0.519
-0.232
0.342
-1.33000
0.868
0.314
0.377
-0.89700
1.530
1.020
0.316
0.00922
2.040 ****
0.708
0.363
-0.45700
1.870
-0.571
0.363
-1.74000
0.594
-1.960
0.427
-3.33000
-0.590 ****
-1.390
0.363
-2.55000
-0.225 ****
The levels for both the Fat and Surfactant factors are labeled 1, 2,
and 3, so the rows in the multicomp tables require explanation. For
the first table, the label 1.adj1-2.adj1 refers to the difference
between levels 1 and 2 of Fat (the focus variable) at level 1 of
Surfactant (the adjust variable). For the second table, the label
refers to the difference between levels 1 and 2 of Surfactant at level 1
of Fat. Significant differences are flagged with four stars, ****. As a
result of the Fat x Surfactant interaction, the F test for the
equivalence of the Surfactant marginal means is not significant.
However, there exist significant differences between the mean of
Surfactant levels 1-3 at a Fat level of 2, and also between the means
of Surfactant levels 1-3 and 2-3 at a Fat level of 3.
Estimable
Functions
The Type I and Type III estimable functions for the
overparametrized model show the linear combinations of the model
parameters, tested by each sum of squares. The Type I estimable
functions can be obtained by performing row reductions on the cross
t
products of the overparameterized model matrix X X . The row
t
operations reduce X X to upper triangular form with ones along its
diagonal (SAS Institute, Inc., 1978). The S-PLUS code for this
algorithm, used to compute the matrix TypeI.estim below, is given in
the Appendix. In the following command, we print only four digits of
each entry in TypeI.estim.
> round(TypeI.estim, 4)
(Intercept)
Flour1
Flour2
Flour3
Flour4
Fat1
Fat2
Fat3
Surfactant1
L2
L3
L4
L6
L7
0.0000 0.0000 0.0000 0.0000 0.0000
1.0000 0.0000 0.0000 0.0000 0.0000
0.0000 1.0000 0.0000 0.0000 0.0000
0.0000 0.0000 1.0000 0.0000 0.0000
-1.0000 -1.0000 -1.0000 0.0000 0.0000
0.0667 -0.0833 0.0952 1.0000 0.0000
-0.3000 -0.1250 -0.2143 0.0000 1.0000
0.2333 0.2083 0.1190 -1.0000 -1.0000
0.2333 0.2083 0.1190 0.1152 0.1338
L9
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
1.0000
L10 L12 L13 L15 L16
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
645
Chapter 17 Further Topics in Analysis of Variance
Surfactant2
Surfactant3
Fat1Surfactant1
Fat1Surfactant2
Fat1Surfactant3
Fat2Surfactant1
Fat2Surfactant2
Fat2Surfactant3
Fat3Surfactant1
Fat3Surfactant2
Fat3Surfactant3
-0.1000
-0.1333
0.2000
0.0333
-0.1667
-0.1667
-0.1667
0.0333
0.2000
0.0333
0.0000
-0.2500
0.0417
0.1250
-0.1667
-0.0417
-0.0417
-0.0417
-0.0417
0.1250
-0.0417
0.1250
-0.2143
0.0952
0.1429
-0.0238
-0.0238
-0.0238
-0.1667
-0.0238
0.0000
-0.0238
0.1429
-0.1966
0.0814
0.3531
0.3167
0.3302
-0.0060
0.0049
0.0011
-0.2319
-0.5182
-0.2499
-0.3235
0.1896
0.0359
-0.0060
-0.0299
0.3250
0.2034
0.4716
-0.2271
-0.5209
-0.2520
0.0000
-1.0000
0.3507
-0.0149
-0.3358
0.4242
0.0190
-0.4432
0.2251
-0.0041
-0.2210
1.0000
-1.0000
0.0037
0.3499
-0.3536
0.0760
0.2971
-0.3731
-0.0797
0.3530
-0.2733
0
0
1
0
-1
0
0
0
-1
0
1
0
0
0
1
-1
0
0
0
0
-1
1
0
0
0
0
0
1
0
-1
-1
0
1
0
0
0
0
0
0
1
-1
0
-1
1
The columns labeled L2, L3, and L4 in the above output are for the
Flour hypothesis. Columns L6 and L7 are for the Fat hypothesis, L9
and L10 are for the Surfactant hypothesis, and the last four columns
are for the Fat x Surfactant hypothesis.
The Type III estimable functions can be obtained from the generating
t
t
t
set ( X X )∗ ( X X ) , where ( X X )∗ is the g2 inverse, or generalized inverse
of the cross product matrix (Kennedy & Gentle, 1980). We can then
perform the steps outlined in the SAS/STAT User’s Guide on the
generating set (SAS Institute, Inc., 1990). This algorithm is
implemented in the function print.ssType3, through the option
est.fun=TRUE.
> TypeIII.estim <- print(ssType3(Baking.aov), est.fun = T)
Type III Sum of Squares
Df Sum of Sq
Flour 3
8.69081
Fat 2 10.11785
Surfactant 2
0.99721
Fat:Surfactant 4
5.63876
Residuals 14
2.31586
Mean Sq F Value
Pr(F)
2.896937 17.51280 0.00005181
5.058925 30.58263 0.00000778
0.498605 3.01421 0.08153989
1.409691 8.52198 0.00105692
0.165418
Estimable function coefficients:
Flour : L2, L3, L4
Fat : L6, L7
Surfactant : L9, L10
Fat:Surfactant : L12, L13, L15, L16
. . .
646
Summarizing ANOVA Results
The TypeIII.estim object is a list of lists. We can extract the
overparameterized form of the estimable functions by examining the
names of the list components:
> names(TypeIII.estim)
[1] "ANOVA"
"est.fun"
> names(TypeIII.estim$est.fun)
[1] "gen.form" "over.par" "assign"
The estimable functions we want are located in the over.par
component of est.fun:
> round(TypeIII.estim$est.fun$over.par, 4)
L2 L3 L4
L6
L7
L9
(Intercept) 0 0 0 0.0000 0.0000 0.0000
Flour1 1 0 0 0.0000 0.0000 0.0000
Flour2 0 1 0 0.0000 0.0000 0.0000
Flour3 0 0 1 0.0000 0.0000 0.0000
Flour4 -1 -1 -1 0.0000 0.0000 0.0000
Fat1 0 0 0 1.0000 0.0000 0.0000
Fat2 0 0 0 0.0000 1.0000 0.0000
Fat3 0 0 0 -1.0000 -1.0000 0.0000
Surfactant1 0 0 0 0.0000 0.0000 1.0000
Surfactant2 0 0 0 0.0000 0.0000 0.0000
Surfactant3 0 0 0 0.0000 0.0000 -1.0000
Fat1Surfactant1 0 0 0 0.3333 0.0000 0.3333
Fat1Surfactant2 0 0 0 0.3333 0.0000 0.0000
Fat1Surfactant3 0 0 0 0.3333 0.0000 -0.3333
Fat2Surfactant1 0 0 0 0.0000 0.3333 0.3333
Fat2Surfactant2 0 0 0 0.0000 0.3333 0.0000
Fat2Surfactant3 0 0 0 0.0000 0.3333 -0.3333
Fat3Surfactant1 0 0 0 -0.3333 -0.3333 0.3333
Fat3Surfactant2 0 0 0 -0.3333 -0.3333 0.0000
Fat3Surfactant3 0 0 0 -0.3333 -0.3333 -0.3333
L10 L12 L13 L15 L16
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
0.0000
0
0
0
0
1.0000
0
0
0
0
-1.0000
0
0
0
0
0.0000
1
0
0
0
0.3333
0
1
0
0
-0.3333 -1 -1
0
0
0.0000
0
0
1
0
0.3333
0
0
0
1
-0.3333
0
0 -1 -1
0.0000 -1
0 -1
0
0.3333
0 -1
0 -1
-0.3333
1
1
1
1
Here we see one of the appealing properties of the Type III analysis:
the hypothesis tested by the Type III sum of squares for Flour
involves parameters of the Flour term only, whereas the hypothesis
tested by the Type I sum of squares involves parameters of the Fat,
Surfactant and Fat x Surfactant terms.
As we show in the section Unweighted Means on page 641,
unweighted means can be obtained from multicomp using the
argument comparisons="none". In doing so, we obtain the estimable
functions for the marginal means of the overparametrized model. For
example, the estimable functions for the Fat marginal means are
computed by the following command.
647
Chapter 17 Further Topics in Analysis of Variance
> Fat.mcomp <- multicomp(Baking.aov, focus = "Fat",
+ comparisons = "none")
> round(Fat.mcomp$lmat, 4)
(Intercept)
Flour1
Flour2
Flour3
Flour4
Fat1
Fat2
Fat3
Surfactant1
Surfactant2
Surfactant3
Fat1Surfactant1
Fat1Surfactant2
Fat1Surfactant3
Fat2Surfactant1
Fat2Surfactant2
Fat2Surfactant3
Fat3Surfactant1
Fat3Surfactant2
Fat3Surfactant3
1
1.0000
0.2500
0.2500
0.2500
0.2500
1.0000
0.0000
0.0000
0.3333
0.3333
0.3333
0.3333
0.3333
0.3333
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
2
1.0000
0.2500
0.2500
0.2500
0.2500
0.0000
1.0000
0.0000
0.3333
0.3333
0.3333
0.0000
0.0000
0.0000
0.3333
0.3333
0.3333
0.0000
0.0000
0.0000
3
1.0000
0.2500
0.2500
0.2500
0.2500
0.0000
0.0000
1.0000
0.3333
0.3333
0.3333
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.3333
0.3333
0.3333
The reader can verify that the Type III estimable functions for Fat are
the differences between columns 1 and 3, and between columns 2 and
3. Thus, the L6 column in the over.par component of TypeIII.estim
is the difference between the first and third columns of the
Fat.mcomp$lmat object above. Likewise, the L7 column in the output
from TypeIII.estim is the difference between the second and third
columns of Fat.mcomp$lmat.
Sigma Restricted The function lm reparametrizes a linear model in an attempt to make
Parametrization the model matrix full column rank. In this section, we explore the
analysis of the unweighted means for Fat using the sigma restricted
linear model. In the sigma restricted parameterization, the sum of the
level estimates of each effect is constrained to be zero. That is,
 bi
i
648
=
 fj
j
=
 sk
k
=
 ( fs ) jk
j
=
 ( fs ) jk
k
= 0.
Summarizing ANOVA Results
Therefore, any effect that we sum over in the mean estimate vanishes.
Specifically, we have f 1 + f 2 + f 3 = 0 for the Fat variable in
Baking.aov. We use the sigma restrictions to compute Baking.aov on
page 640, since we specify contr.sum in the contrasts argument to
aov. For clarity, the command is repeated here:
>
+
+
+
Baking.aov <- aov(Specific.Vol ~ Flour + Fat*Surfactant,
data = Baking, contrasts = list(Flour = contr.sum(4),
Fat = contr.sum(3), Surfactant = contr.sum(3)),
na.action = na.exclude)
In this setting, the unweighted means for Fat can be computed with
the estimable functions given in L below.
#
>
+
+
+
+
Define a vector of descriptive row names.
my.rownames <- c("(Intercept)",
"Flour1", "Flour2", "Flour3", "Fat1", "Fat2",
"Surfactant1", "Surfactant2",
"Fat1Surfactant1", "Fat2Surfactant1",
"Fat1Surfactant2", "Fat2Surfactant2")
>
+
+
+
+
L <- as.matrix(data.frame(
Fat.1 = c(1,0,0,0,1,rep(0,7)),
Fat.2 = c(1,0,0,0,0,1,rep(0,6)),
Fat.3 = c(1,0,0,0,-1,-1,rep(0,6)),
row.names = my.rownames))
> L
(Intercept)
Flour1
Flour2
Flour3
Fat1
Fat2
Surfactant1
Surfactant2
Fat1Surfactant1
Fat2Surfactant1
Fat1Surfactant2
Fat2Surfactant2
Fat.1 Fat.2 Fat.3
1
1
1
0
0
0
0
0
0
0
0
0
1
0
-1
0
1
-1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
649
Chapter 17 Further Topics in Analysis of Variance
The intercept in the least squares fit estimates μ . The two coefficients
for the Fat effect (labeled Fat1 and Fat2 in L above) estimate f 1 and
f 2 , respectively, and f 3 = – f 1 – f 2 .
We can check that each function is, in fact, estimable by first ensuring
it is in the row space of the model matrix X , and then computing the
unweighted means. The commands below show this process.
> X <- model.matrix(Baking.aov)
> ls.fit <- lsfit(t(X) %*% X, L, intercept = F)
> apply(abs(ls.fit$residuals), 2, max) < 0.0001
Fat.1 Fat.2 Fat.3
T
T
T
The residuals of ls.fit are small, so the estimable functions are in
the row space of X . The next command uses L and the coefficients
from Baking.aov to compute the unweighted means for Fat. Note
that these are the same values returned by multicomp in the section
Unweighted Means.
> m <- t(L) %*% Baking.aov$coefficients
> m
[,1]
Fat.1 5.850197
Fat.2 6.577131
Fat.3 7.472514
To compute Type III sums of squares, we first use the summary
t
–1
method to obtain ( X X ) and σ̂ . The summary method also helps us
compute the standard errors of the unweighted means, as shown in
the second command below. Again, note that these values are
identical to the ones returned by multicomp.
> Baking.summ <- summary.lm(Baking.aov)
> Baking.summ$sigma *
+ sqrt(diag(t(L) %*% Baking.summ$cov.unscaled %*% L))
[1] 0.1364894 0.1477127 0.1564843
650
Summarizing ANOVA Results
A set of Type III estimable functions for Fat can be obtained using
the orthogonal contrasts generated by contr.helmert. We use these
types of contrasts to test μ .1. = μ .2. and μ .1. + μ .2. = 2μ .3. , which is
equivalent to H Fat .
> contr.helmert(3)
1
2
3
[,1] [,2]
-1
-1
1
-1
0
2
> L.typeIII <- L %*% contr.helmert(3)
> dimnames(L.typeIII)[[2]] = c("Fat.1", "Fat.2")
> L.typeIII
(Intercept)
Flour1
Flour2
Flour3
Fat1
Fat2
Surfactant1
Surfactant2
Fat1Surfactant1
Fat2Surfactant1
Fat1Surfactant2
Fat2Surfactant2
Fat.1 Fat.2
0
0
0
0
0
0
0
0
-1
-3
1
-3
0
0
0
0
0
0
0
0
0
0
0
0
Finally, the Type III sum of squares is computed for Fat. Note that
this is the same value that is returned by anova in the section ANOVA
Tables on page 640.
>
>
+
+
h.m <- t(contr.helmert(3)) %*% m
t(h.m) %*% solve(
t(L.typeIII) %*% Baking.summ$cov.unscaled %*%
L.typeIII) %*% h.m
[,1]
[1,] 10.11785
651
Chapter 17 Further Topics in Analysis of Variance
Alternative computations
Through the sum contrasts provided by contr.sum, we use the sigma
restrictions to compute Baking.aov. Since the Baking data are
complete, we can therefore use drop1 as an alternative way of
obtaining the Type III sum of squares. In general, this fact applies to
any aov model fit with factor coding matrices that are true contrasts;
sum contrasts, Helmert contrasts, and orthogonal polynomials fall
into this category, but treatment contrasts do not. For more details
about true contrasts, see the chapter Specifying Models in Spotfire
S+.
> drop1(Baking.aov, ~.)
Single term deletions
Model:
Specific.Vol ~ Flour + Fat * Surfactant
Df Sum of Sq
RSS F Value
<none>
2.31586
Flour 3
8.69081 11.00667 17.51280
Fat 2 10.11785 12.43371 30.58263
Surfactant 2
0.99721 3.31307 3.01421
Fat:Surfactant 4
5.63876 7.95462 8.52198
Pr(F)
0.00005181
0.00000778
0.08153989
0.00105692
For the sigma restricted model, the hypotheses H Fat and H Surfactant
can also be expressed as
*
H Fat : f 1 = f 2 = 0
*
H Surfactant : s 1 = s 2 = s 3 = 0
The row for Fat in the drop1 ANOVA table is the reduction in sum of
squares due to Fat, given that all other terms are in the model. This
simultaneously tests that the least squares coefficients β Fat1 = f 1 and
β Fat2 = f 2 are zero, and hence f 3 = – ( f 1 + f 2 ) = 0 (Searle, 1987).
The same argument applies to Surfactant. It follows that the
following Type III estimable functions for Fat can be used to test
H∗ (or equivalently H Fat ):
Fat
> L.typeIII <- as.matrix(data.frame(
+ Fat.1 = c(rep(0,4), 1, rep(0,7)),
+ Fat.2 = c(rep(0,5), 1, rep(0,6)),
652
Summarizing ANOVA Results
+ row.names = my.rownames))
> L.typeIII
(Intercept)
Flour1
Flour2
Flour3
Fat1
Fat2
Surfactant1
Surfactant2
Fat1Surfactant1
Fat2Surfactant1
Fat1Surfactant2
Fat2Surfactant2
Fat.1 Fat.2
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
> h.c <- t(L.typeIII) %*% Baking.aov$coef
> t(h.c) %*% solve(t(L.typeIII) %*%
+ Baking.summ$cov.unscaled %*% L.typeIII) %*% h.c
[,1]
[1,] 10.11785
Again, this is the same value for the Type III sum of squares that both
anova and drop1 return.
653
Chapter 17 Further Topics in Analysis of Variance
MULTIVARIATE ANALYSIS OF VARIANCE
Multivariate analysis of variance, known as MANOVA, is the
extension of analysis of variance techniques to multiple responses.
The responses for an observation are considered as one multivariate
observation, rather than as a collection of univariate responses.
If the responses are independent, then it is sensible to just perform
univariate analyses. However, if the responses are correlated, then
MANOVA can be more informative than the univariate analyses as
well as less repetitive.
In S-PLUS the manova function is used to estimate the model. The
formula needs to have a matrix as the response:
> wafer.manova <- manova(cbind(pre.mean, post.mean) ~ .,
+ data = wafer[, c(1:9, 11)])
The manova function creates an object of class "manova". This class of
an object has methods specific to it for a few generic functions. The
most important function is the "manova" method for summary, which
produces a MANOVA table:
> summary(wafer.manova)
maskdim
visc.tem
spinsp
baketime
aperture
exptime
devtime
etchtime
Residuals
Df
1
2
2
2
2
2
2
2
2
Pillai Trace
0.9863
1.00879
1.30002
0.80133
0.96765
1.63457
0.99023
1.26094
approx. F
36.00761
1.01773
1.85724
0.66851
0.93733
4.47305
0.98065
1.70614
num df
2
4
4
4
4
4
4
4
den df
1
4
4
4
4
4
4
4
P-value
0.11703
0.49341
0.28173
0.64704
0.52425
0.08795
0.50733
0.30874
There are four common types of test in MANOVA. The example
above shows the Pillai-Bartlett trace test, which is the default test in SPLUS. The last four columns show an approximate F test (since the
distributions of the four test statistics are not implemented). The other
available tests are Wilks’ Lambda, Hotelling-Lawley trace, and Roy’s
maximum eigenvalue.
654
Multivariate Analysis of Variance
Note
A model with a few residual degrees of freedom as wafer.manova is not likely to produce
informative tests.
You can view the results of another test by using the test argument.
The following command shows you Wilks’ lambda test:
> summary(wafer.manova, test = "wilk")
Below is an example of how to see the results of all four of the
multivariate tests:
>
+
+
>
>
wafer.manova2 <- manova(cbind(pre.mean, post.mean,
log(pre.dev), log(post.dev)) ~
maskdim + visc.tem + spinsp, data = wafer)
wafer.ms2 <- summary(wafer.manova2)
for(i in c("p", "w", "h", "r")) print(wafer.ms2, test=i)
You can also look at the univariate ANOVA tables for each response
with a command like:
> summary(wafer.manova, univariate = T)
Hand and Taylor (1987) provide a nice introduction to MANOVA.
Many books on multivariate statistics contain a chapter on
MANOVA. Examples include Mardia, Kent and Bibby (1979), and
Seber (1984).
655
Chapter 17 Further Topics in Analysis of Variance
SPLIT-PLOT DESIGNS
A split-plot design contains more than one source of error. This can
arise because factors are applied at different scales, as in the guayule
example below.
Split-plots are also encountered because of restrictions on the
randomization. For example, an experiment involving oven
temperature and baking time will probably not randomize the oven
temperature totally, but rather only change the temperature after all
of the runs for that temperature have been made. This type of design
is often mistakenly analyzed as if there were no restrictions on the
randomization (an indication of this can be p-values that are close to
1). See Hicks (1973) and Daniel (1976).
S-PLUS includes the guayule data frame which is also discussed in
Chambers and Hastie (1992). This experiment was on eight varieties
of guayule (a rubber producing shrub) and four treatments on the
seeds. Since a flat (a shallow box for starting seedlings) was not large
enough to contain all 32 combinations of variety and treatment, the
design was to use only a single variety in each flat and to apply each
treatment within each flat. Thus the flats each consist of four subplots. This is a split-plot design since flats are the experimental unit
for varieties, but the sub-plots are the experimental unit for the
treatments. The response is the number of plants that germinated in
each sub-plot.
To analyze a split-plot design like this, put the variable that
corresponds to the whole plot in an Error term in the formula of the
aov call:
> gua.aov1 <- aov(plants ~ variety * treatment +
+ Error(flats), data = guayule)
As usual, you can get an ANOVA table with summary:
> summary(gua.aov1)
Error: flats
Df Sum of Sq Mean Sq F Value
Pr(F)
variety
7
763.156 109.0223 1.232036 0.3420697
Residuals 16 1415.833 88.4896
656
Split-Plot Designs
Error: Within
Df Sum of Sq Mean Sq F Value
Pr(F)
treatment
3 30774.28 10258.09 423.4386 0.00000e+00
variety:treatment 21
2620.14
124.77
5.1502 1.32674e-06
Residuals
48
1162.83
24.23
This shows varieties tested with the error from flats, while treatment
and its interaction with variety are tested with the within-flat error,
which is substantially smaller.
The guayule data actually represent an experiment in which the flats
were grouped into replicates, resulting in three sources of error or a
split-split-plot design. To model this we put more than one term inside
the Error term:
> gua.aov2 <- aov(plants ~ variety * treatment +
+ Error(reps/flats), data = guayule)
> summary(gua.aov2)
Error: reps
Df Sum of Sq Mean Sq F Value Pr(F)
Residuals 2 38.58333 19.29167
Error: flats %in% reps
Df Sum of Sq Mean Sq F Value
Pr(F)
variety
7
763.156 109.0223 1.108232 0.4099625
Residuals 14 1377.250 98.3750
Error: Within
Df Sum of Sq Mean Sq F Value
Pr(F)
treatment
3 30774.28 10258.09 423.4386 0.00000e+00
variety:treatment 21
2620.14
124.77
5.1502 1.32674e-06
Residuals
48
1162.83
24.23
The
Error
term could also have been specified as
Error(reps + Flats).
However,
the
specification
Error(flats + reps) would not give the desired result (the sequence
within the Error term is significant); explicitly stating the nesting is
preferred. Note that only one Error term is allowed.
657
Chapter 17 Further Topics in Analysis of Variance
REPEATED-MEASURES DESIGNS
Repeated-measures designs are those that contain a sequence of
observations on each subject—for example, a medical experiment in
which each patient is given a drug, and observations are taken at zero,
one, two, and three weeks after taking the drug. Although this
description is too simplistic to encompass all repeated-measures
designs, it nevertheless captures the spirit.
Repeated-measures designs are similar to split-plot designs in that
there is more than one source of error (between subjects and within
subjects), but there is correlation in the within-subjects observations.
In the example we expect that the observations in week three will be
more similar to week two observations than to week zero
observations. Because of this, the split-plot analysis (referred to as the
univariate approach) is valid only under certain restrictive conditions.
We will use the artificial data set drug.mult, which has the following
form:
> drug.mult
1
2
3
4
5
6
subject gender Y.1 Y.2 Y.3 Y.4
S1
F 75.9 74.3 80.0 78.9
S2
F 78.3 75.5 79.6 79.2
S3
F 80.3 78.2 80.4 76.2
S4
M 80.7 77.2 82.0 83.8
S5
M 80.3 78.6 81.4 81.5
S6
M 80.1 81.1 81.9 86.4
The data set consists of the two factors subject and gender, and the
matrix Y which contains 4 columns. The first thing to do is stretch this
out into a form suitable for the univariate analysis:
>
>
>
+
+
658
drug.uni <- drug.mult[rep(1:6, rep(4,6)), 1:2]
ymat <- data.matrix(drug.mult[, paste("Y.",1:4, sep="")])
drug.uni <- cbind(drug.uni,
time = ordered(rep(paste("Week", 0:3, sep = ""), 6)),
y = as.vector(t(ymat)))
Repeated-Measures Designs
The univariate analysis treats the data as a split-plot design:
> summary(aov(y ~ gender*time + Error(subject),
+ data = drug.uni))
Error: subject
Df Sum of Sq Mean Sq F Value
Pr(F)
gender
1 60.80167 60.80167 19.32256 0.01173
Residuals 4 12.58667 3.14667
Error: Within
Df Sum of Sq Mean Sq F Value
Pr(F)
time
3 49.10833 16.36944 6.316184 0.0081378
gender:time 3 14.80167 4.93389 1.903751 0.1828514
Residuals
12 31.10000 2.59167
Tests in the Within stratum are valid only if the data satisfy the
circularity property, in addition to the usual conditions. Circularity
means that the variance of the difference of measures at different
times is constant; for example, the variance of the difference between
the measures at week 0 and week 3 should be the same as the
variance of the difference between week 2 and week 3. We also need
the assumption that actual contrasts are used; for example, the
contr.treatment function should not be used. When circularity does
not hold, then the p-values for the tests will be too small.
One approach is to perform tests which are as conservative as
possible. Conservative tests are formed by dividing the degrees of
freedom in both the numerator and denominator of the F test by the
number of repeated measures minus one. In our example there are
four repeated measures on each subject, so we divide by 3. The splitplot and the conservative tests are:
> 1 - pf(6.316184, 3, 12) # usual univariate test
[1] 0.008137789
> 1 - pf(6.316184, 1, 4) # conservative test
[1] 0.06583211
These two tests are telling fairly different tales, so the data analyst
would probably move on to one of two alternatives. A Huynh-Feldt
adjustment of the degrees of freedom provides a middle ground
659
Chapter 17 Further Topics in Analysis of Variance
between the tests above—see Winer, Brown and Michels (1991), for
instance. The multivariate approach, discussed below, substantially
relaxes the assumptions.
The univariate test for time was really a test on three contrasts. In the
multivariate setting we want to do the same thing, so we need to use
contrasts in the response:
> drug.man <- manova(ymat %*% contr.poly(4) ~ gender,
+ data = drug.mult)
> summary(drug.man, intercept = T)
Df Pillai Trace approx. F num df
(Intercept) 1 0.832005
3.301706 3
gender 1 0.694097
1.512671 3
Residuals 4
den df
2
2
P-value
0.241092
0.421731
The line marked (Intercept) corresponds to time in the univariate
approach, and similarly the gender line here corresponds to
gender:time. The p-value of 0.24 is larger than either of the
univariate tests; the price of the multivariate analysis being more
generally valid is that quite a lot of power is lost. Although the
multivariate approach is preferred when the data do not conform to
the required conditions, the univariate approach is preferred when
they do. The trick, of course, is knowing which is which.
Let’s look at the univariate summaries that this MANOVA produces:
> summary(drug.man, intercept = T, univar
Response: .L
Df Sum of Sq Mean Sq F Value
(Intercept) 1
22.188 22.1880 4.327255
gender
1
6.912 6.9120 1.348025
Residuals
4
20.510 5.1275
Response: .Q
Df Sum of Sq Mean Sq F Value
(Intercept) 1 5.415000 5.415000 5.30449
gender
1 4.001667 4.001667 3.92000
Residuals
4 4.083333 1.020833
= T)
Pr(F)
0.1059983
0.3101900
Pr(F)
0.0826524
0.1188153
Response: .C
Df Sum of Sq Mean Sq F Value
Pr(F)
(Intercept) 1 21.50533 21.50533 13.22049 0.0220425
gender
1
3.88800 3.88800 2.39016 0.1969986
Residuals
4
6.50667 1.62667
660
Repeated-Measures Designs
If you add up the respective degrees of freedom and sums of squares,
you will find that the result is the same as the univariate Within
stratum. For this reason, the univariate test is sometimes referred to as
the average F test.
The above discussion has focused on classical inference, which
should not be done before graphical exploration of the data.
Many books discuss repeated measures. Some examples are Hand
and Taylor (1987), Milliken and Johnson (1984), Crowder and Hand
(1990), and Winer, Brown, and Michels (1991).
661
Chapter 17 Further Topics in Analysis of Variance
RANK TESTS FOR ONE-WAY AND TWO-WAY LAYOUTS
This section briefly describes how to use two nonparametric rank
tests for ANOVA: the Kruskal-Wallis rank sum test for a one-way
layout and the Friedman test for unreplicated two-way layout with
(randomized) blocks.
Since these tests are based on ranks, they are robust with regard to the
presence of outliers in the data; that is, they are not affected very
much by outliers. This is not the case for the classical F tests.
You can find detailed discussions of the Kruskal-Wallis and Friedman
rank-based tests in a number of books on nonparametric tests; for
example, Lehmann (1975) and Hettmansperger (1984).
The KruskalWallis Rank
Sum Test
When you have a one-way layout, as in the section Experiments with
One Factor in Chapter 16, you can use the Kruskal-Wallis rank sum test
kruskal.test to test the null hypothesis that all group means are
equal.
We illustrate how to use kruskal.test for the blood coagulation data
of Table 16.1. First you set up your data as for a one-factor experiment
(or one-way layout). You create a vector object coag, arranged by
factor level (or treatment), and you create a factor object diet whose
levels correspond to the factor levels of vector object coag. Then use
kruskal.test:
> kruskal.test(coag, diet)
Kruskal-Wallis rank sum test
data: coag and diet
Kruskal-Wallis chi-square = 17.0154, df = 3,
p-value = 7e-04
alternative hypothesis: two.sided
The p-value of p = 0.0007 is highly significant. This p-value is
computed using an asymptotic chi-squared approximation. See the
online help file for more details.
662
Rank Tests for One-Way and Two-Way Layouts
You may find it helpful to note that kruskal.test and friedman.test
return the results of its computations, and associated information, in
the same style as the functions in Chapter 5, Statistical Inference for
One- and Two-Sample Problems.
The Friedman
Rank Sum Test
When you have a two-way layout with one blocking variable and one
treatment variable, you can use the Friedman rank sum test
friedman.test to test the null hypothesis that there is no treatment
effect.
We illustrate how you use friedman.test for the penicillin yield data
described in Table 16.2 of Chapter 16. The general form of the usage
is
friedman.test(y, groups, blocks)
where y is a numeric vector, groups contains the levels of the
treatment factor and block contains the levels of the blocking factor.
Thus, you can do:
# Make treatment and blend available.
> attach(pen.df, pos = 2)
> friedman.test(yield, treatment, blend)
Friedman rank sum test
data: yield and treatment and blend
Friedman chi-square = 3.4898, df = 3, p-value = 0.3221
alternative hypothesis: two.sided
# Detach the data set.
> detach(2)
The p-value is p = 0.32, which is not significant. This p-value is
computed using an asymptotic chi-squared approximation. For
further details on friedman.test, see the help file.
663
Chapter 17 Further Topics in Analysis of Variance
VARIANCE COMPONENTS MODELS
Variance components models are used when there is interest in the
variability of one or more variables other than the residual error. For
example, manufacturers often run experiments to see which parts of
the manufacturing process contribute most to the variability of the
final product. In this situation variability is undesirable, and attention
is focused on improving those parts of the process that are most
variable. Animal breeding is another area in which variance
components models are routinely used. Some data, from surveys for
example, that have traditionally been analyzed using regression can
more profitably be analyzed using variance component models.
Estimating the
Model
To estimate a variance component model, you first need to use
is.random to state which factors in your data are random. A variable
that is marked as being random will have a variance component in
any models that contain it. Only variables that inherit from class
"factor" can be declared random. Although is.random works on
individual factors, it is often more practical to use it on the columns of
a data frame. You can see if variables are declared random by using
is.random on the data frame:
> is.random(pigment)
Batch Sample Test
F
F
F
Declare variables to be random by using the assignment form of
is.random:
> pigment <- pigment
> is.random(pigment) <- c(T, T, T)
> is.random(pigment)
Batch Sample Test
T
T
T
664
Variance Components Models
Because we want all of the factors to be random, we could have
simply done the following:
> is.random(pigment) <- T
The value on the right is replicated to be the length of the number of
factors in the data frame.
Once you have declared your random variables, you are ready to
estimate the model using the varcomp function. This function takes a
formula and other arguments very much like lm or aov. Because the
pigment data are from a nested design, the call has the following
form:
> pigment.vc <- varcomp(Moisture ~ Batch/Sample,
+ data = pigment)
> pigment.vc
Variances:
Batch Sample %in% Batch Residuals
7.127976
28.53333 0.9166667
Call:
varcomp(formula = Moisture ~ Batch/Sample, data = pigment)
The result of varcomp is an object of class "varcomp". You can use
summary on "varcomp" objects to get more details about the fit, and
you can use plot to get qq-plots for the normal distribution on the
estimated effects for each random term in the model.
Estimation
Methods
The method argument to varcomp allows you to choose the type of
variance component estimator. Maximum likelihood and REML
(restricted maximum likelihood) are two of the choices. REML is very
similar to maximum likelihood but takes the number of fixed effects
into account; the usual unbiased estimate of variance in the onesample model is an REML estimate. See Harville (1977) for more
details on these estimators.
The default method is a MINQUE (minimum norm quadratic
unbiased estimate); this class of estimator is locally best at a particular
spot in the parameter space. The MINQUE option in S-PLUS is
locally best if all of the variance components (except that for the
residuals) are zero. The MINQUE estimate agrees with REML for
balanced data. See Rao (1971) for details. This method was made the
665
Chapter 17 Further Topics in Analysis of Variance
default because it is less computationally intense than the other
methods, however, it can do significantly worse for severely
unbalanced data (Swallow and Monahan (1984)).
You can get robust estimates by using method="winsor". This method
creates new data by moving outlying points or groups of points
toward the rest of the data. One of the standard estimators is then
applied to this possibly revised data. Burns (1992) gives details of the
algorithm along with simulation results. This method uses much
larger amounts of memory than the other methods if there are a large
number of random levels, such as in a deeply nested design.
Random Slope
Example
We now produce a more complicated example in which there are
random slopes and intercepts. The data consist of several pairs of
observations on each of several individuals in the study. An example
might be that the y values represent the score on a test and the x
values are the time at which the test was taken.
Let’s start by creating simulated data of this form. We create data for
30 subjects and 10 observations per subject:
>
>
>
>
>
>
+
>
>
>
subject <- factor(rep(1:30, rep(10,30)))
set.seed(357) # makes these numbers reproducible
trueslope <- rnorm(30, mean = 1)
trueint <- rnorm(30, sd = 0.5)
times <- rchisq(300, 3)
scores <- rep(trueint, rep(10,30)) +
times * rep(trueslope, rep(10,30)) + rnorm(300)
test.df <- data.frame(subject, times, scores)
is.random(test.df) <- T
is.random(test.df)
subject
T
Even though we want to estimate random slopes and random
intercepts, the only variable that is declared random is subject. Our
model for the data has two coefficients: the mean slope (averaged
over subjects) and the mean intercept. It also has three variances: the
variance for the slope, the variance for the intercept, and the residual
variance.
666
Variance Components Models
The following command estimates this model using Maximum
Likelihood, as the default MINQUE is not recommended for this
type of model:
> test.vc <- varcomp(scores ~ times * subject,
+ data = test.df, method = "ml")
This seems very simple. We can see how it works by looking at how
the formula get expanded. The right side of the formula is expanded
into four terms:
scores ~ 1 + times + subject + times:subject
The intercept term in the formula, represented by 1, gives the mean
intercept. The variable times is fixed and produces the mean slope.
The subject variable is random and produces the variance
component for the random intercept. Since any interaction
containing a random variable is considered random, the last term,
times:subject, is also random; this term gives the variance
component for the random slope. Finally, there is always a residual
variance.
Now we can look at the estimates:
> test.vc
Variances:
subject times:subject Residuals
0.3162704
1.161243 0.8801149
Message:
[1] "RELATIVE FUNCTION CONVERGENCE"
Call:
varcomp(formula = scores ~ times*subject, data=test.df,
method = "ml")
This shows the three variance components. The variance of the
intercept, which has true value 0.25, is estimated as 0.32. Next,
labeled times:subject is the variance of the slope, and finally the
residual variance. We can also view the estimates for the coefficients
of the model, which have true values of 0 and 1.
> coef(test.vc)
(Intercept)
times
0.1447211 1.02713
667
Chapter 17 Further Topics in Analysis of Variance
APPENDIX: TYPE I ESTIMABLE FUNCTIONS
In the section Estimable Functions on page 645, we discuss the Type I
estimable functions for the overparameterized model of the Baking
data. This appendix provides the S-PLUS code for the TypeI.estim
object shown in that section. For more details on the algorithm used
to compute Type I estimable functions, see the SAS Technical Report
R-101 (1978).
The commands below are designed to be easily incorporated into a
script or source file, so that they can be modified to suit your
modeling needs. To reproduce TypeI.estim exactly, you must first
define the Baking data and the Baking.aov model in your Spotfire S+
session (see page 638).
# Get crossproduct matrix for overparameterized model.
XtX <- crossprod(.Call("S_ModelMatrix",
model.frame(Baking.aov), F)$X)
n <- as.integer(nrow(XtX))
# Call LAPACK routine for LU decomposition.
LU <- .Fortran("dgetrf", n, n, as.numeric(XtX), n,
integer(n), integer(1))[[3]]
U <- matrix(LU, nrow = n, dimnames = list(
paste("L", seq(n), sep=""), dimnames(XtX)[[1]]))
# Zero out the lower triangular part of U.
U[row(U) > col(U)] <- 0
# Create 1's on the diagonal, as prescribed
# by the SAS technical report.
d <- diag(U)
d[abs(d) < sqrt(.Machine$double.eps)] <- 1
L <- diag(1/d) %*% U
dimnames(L) <- dimnames(U)
L <- t(L)
# Do column operations to produce "pretty" output.
# Flour hypothesis.
L[,2] <- L[,2] - L[3,2]*L[,3]
L[,2] <- L[,2] - L[4,2]*L[,4]
L[,3] <- L[,3] - L[4,3]*L[,4]
668
Appendix: Type I Estimable Functions
# Fat hypothesis.
L[,6] <- L[,6] - L[7,6]*L[,7]
# Surfactant hypothesis.
L[,9] <- L[,9] - L[10,9]*L[,10]
# Fat x Surfactant
L[,12] <- L[,12] L[,12] <- L[,12] L[,12] <- L[,12] L[,13] <- L[,13] L[,13] <- L[,13] L[,15] <- L[,15] -
hypothesis.
L[13,12]*L[,13]
L[15,12]*L[,15]
L[16,12]*L[,16]
L[15,13]*L[,15]
L[16,13]*L[,16]
L[16,15]*L[,16]
# Take only those columns that correspond to a hypothesis.
TypeI.estim <- L[, c("L2", "L3", "L4", "L6", "L7",
"L9", "L10", "L12", "L13", "L15", "L16")]
669
Chapter 17 Further Topics in Analysis of Variance
REFERENCES
Burns, P.J. (1992). Winsorized REML estimates of variance components.
Technical report, Statistical Sciences, Inc.
Chambers, J.M. & Hastie, T.J. (Eds.) (1992). Statistical Models in S.
London: Chapman and Hall.
Crowder, M.J. & Hand, D.J. (1990). Analysis of Repeated Measures.
London: Chapman and Hall.
Daniel, C. (1976). Applications of Statistics to Industrial Experimentation.
New York: John Wiley & Sons, Inc.
Hand, D.J. & Taylor, C.C. (1987). Multivariate Analysis of Variance and
Repeated Measures. London: Chapman and Hall.
Harville, D.A. (1977). Maximum likelihood approaches to variance
component estimation and to related problems (with discussion).
Journal of the American Statistical Association 72:320-340.
Hettmansperger, T.P. (1984). Statistical Inference Based on Ranks. New
York: John Wiley & Sons, Inc.
Hicks, C.R. (1973). Fundamental Concepts in the Design of Experiments.
New York: Holt, Rinehart and Winston.
Kennedy, W.J., Gentle, J.E., (1980), Statistical Computing. New York:
Marcel Dekker, (p. 396).
Lehmann, E.L. (1975). Nonparametrics: Statistical Methods Based on
Ranks. San Francisco: Holden-Day.
Mardia, K.V., Kent, J.T., & Bibby, J.M. (1979). Multivariate Analysis.
London: Academic Press.
Milliken, G.A. & Johnson, D.E., (1984), Analysis of Messy Data Volume
I: Designed Experiments. New York: Van Norstrand Reinhold Co. (p.
473).
Rao, C.R. (1971). Estimation of variance and covariance
components—MINQUE theory. Journal of Multivariate Analysis 1:257275.
SAS Institute, Inc. (1978). Tests of Hypotheses in Fixed-Effects Linear
Models. SAS Technical Report R-101. Cary, NC: SAS Institute, Inc..
670
References
SAS Institute, Inc. (1990). SAS/Stat User’s Guide, Fourth Edition. Cary,
NC: SAS Institute, Inc., (pp. 120-121).
Searle, S.R., (1987), Linear Models for Unbalanced Data. New York:
John Wiley & Sons, (p. 536).
Seber, G.A.F. (1984). Multivariate Observations. New York: John Wiley
& Sons, Inc.
Swallow, W.H. & Monahan, J.F. (1984). Monte Carlo comparison of
ANOVA, MIVQUE, REML, and ML estimators of variance
components. Technometrics 26:47-57.
Winer, B.J., Brown, D.R., & Michels, K.M. (1991). Statistical Principles
in Experimental Design. New York: McGraw-Hill.
671
Chapter 17 Further Topics in Analysis of Variance
672
MULTIPLE COMPARISONS
18
Overview
The fuel.frame Data
Honestly Significant Differences
Rat Growth Hormone Treatments
Upper and Lower Bounds
Calculation of Critical Points
Error Rates for Confidence Intervals
674
674
677
678
681
682
683
Advanced Applications
Adjustment Schemes
Toothaker’s Two-Factor Design
Setting Linear Combinations of Effects
Textbook Parameterization
Overparameterized Models
Multicomp Methods Compared
684
685
686
689
689
691
692
Capabilities and Limits
694
References
696
673
Chapter 18 Multiple Comparisons
OVERVIEW
This chapter describes the use of the function multicomp in the
analysis of multiple comparisons. This particular section describes
simple calls to multicomp for standard comparisons in one-way
layouts. The section Advanced Applications tells how to use
multicomp for nonstandard designs and comparisons. In the section
Capabilities and Limits, the capabilities and limitations of this
function are summarized.
The fuel.frame
Data
When an experiment has been carried out in order to compare effects
of several treatments, a classical analytical approach is to begin with a
test for equality of those effects. Regardless of whether you embrace
this classical strategy, and regardless of the outcome of this test, you
are usually not finished with the analysis until determining where any
differences exist, and how large the differences are (or might be); that
is, until you do multiple comparisons of the treatment effects.
As a simple start, consider the built-in S-PLUS data frame on fuel
consumption of vehicles, fuel.frame. Each row provides the fuel
consumption (Fuel) in 100*gallons/mile for a vehicle model, as well
as the Type group of the model: Compact, Large, Medium, Small,
Sporty, or Van. There is also information available on the Weight and
Displacement of the vehicle. Figure 18.1 shows a box plot of fuel
consumption, the result of the following commands.
> attach(fuel.frame, pos = 2)
> boxplot(split(Fuel, Type))
> detach(2)
674
3.0
3.5
4.0
4.5
5.0
5.5
Overview
Compact
Large
Medium
Small
Sporty
Van
Figure 18.1: Fuel consumption box plot.
Not surprisingly, the plot suggests that there are differences between
vehicle types in terms of mean fuel consumption. This is confirmed
by a one-factor analysis of variance test of equality obtained by a call
to aov.
> aovout.fuel <- aov(Fuel ~ Type, data = fuel.frame)
> anova(aovout.fuel)
Analysis of Variance Table
Response: Fuel
Terms added sequentially (first to last)
Df
Type
5
Residuals 54
Sum of Sq
24.23960
9.61727
Mean Sq
4.847921
0.178098
F Value
27.22058
Pr(F)
1.220135e-13
The box plots show some surprising patterns, and inspire some
questions. Do small cars really have lower mean fuel consumption
than compact cars? If so, by what amount? What about small versus
sporty cars? Vans versus large cars? Answers to these questions are
offered by an analysis of all pairwise differences in mean fuel
consumption, which can be obtained from a call to multicomp.
> mca.fuel <- multicomp(aovout.fuel, focus = "Type")
675
Chapter 18 Multiple Comparisons
> plot(mca.fuel)
> mca.fuel
95 % simultaneous confidence intervals for specified
linear combinations, by the Tukey method
critical point: 2.9545
response variable: Fuel
intervals excluding 0 are flagged by '****'
Estimate
Compact-Large
Compact-Medium
Compact-Small
Compact-Sporty
Compact-Van
Large-Medium
Large-Small
Large-Sporty
Large-Van
Medium-Small
Medium-Sporty
Medium-Van
Small-Sporty
Small-Van
Sporty-Van
Compact-Large
Compact-Medium
Compact-Small
Compact-Sporty
Compact-Van
Large-Medium
Large-Small
Large-Sporty
Large-Van
Medium-Small
Medium-Sporty
Medium-Van
Small-Sporty
Small-Van
Sporty-Van
-3.0
Std.
Error
0.267
0.160
0.160
0.178
0.193
0.270
0.270
0.281
0.291
0.166
0.183
0.198
0.183
0.198
0.213
-0.800
-0.434
0.894
0.210
-1.150
0.366
1.690
1.010
-0.345
1.330
0.644
-0.712
-0.684
-2.040
-1.360
Lower
Bound
-1.590
-0.906
0.422
-0.316
-1.720
-0.432
0.896
0.179
-1.210
0.839
0.103
-1.300
-1.220
-2.620
-1.980
(
Upper
Bound
-0.0116
0.0387
1.3700
0.7360
-0.5750
1.1600
2.4900
1.8400
0.5150
1.8200
1.1800
-0.1270
-0.1440
-1.4600
-0.7270
****
****
****
****
****
****
****
****
****
****
****
)
)
(
(
)
(
(
)
)
(
)
(
)
(
(
)
)
(
)
(
(
)
)
(
(
)
(
-2.5
)
-2.0
)
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
simultaneous 95 % confidence limits, Tukey method
response variable: Fuel
Figure 18.2: Fuel consumption ANOVA.
676
2.0
2.5
Overview
As the output and plot in Figure 18.2 indicate, this default call to
multicomp has resulted in the calculation of simultaneous 95%
confidence intervals for all pairwise differences between vehicle Fuel
means, based on the levels of Type, sometimes referred to as MCA
comparisons (Hsu, 1996). The labeling states that Tukey’s method
(Tukey, 1953) has been used; since group sample sizes are unequal,
this is actually equivalent to what is commonly known as the TukeyKramer (Kramer, 1956) multiple comparison method.
Honestly
Significant
Differences
The output indicates via asterisks the confidence intervals which
exclude zero; in the plot, these can be identified by noting intervals
that do not intersect the vertical reference line at zero. These
identified statistically significant comparisons correspond to pairs of
(long run) means which can be declared different by Tukey’s HSD
(honestly significant difference) method. Not surprisingly, we can assert
that most of the vehicle types have different mean fuel consumption
rates. If we require 95% confidence in all of our statements, we cannot
claim different mean fuel consumption rates between the compact
and medium types, the compact and sporty types, the large and
medium types, and the large and van types.
Note we should not assert that these pairs have equal mean
consumption rates. For example, the interval for Compact-Medium
states that this particular difference in mean fuel consumption is
between -0.906 and 0.0387 units. Hence, the medium vehicle type
may have larger mean fuel consumption than the compact, by as
much as 0.9 units. Only an engineer can judge the importance of a
difference of this size; if it is considered trivial, then using these
intervals we can claim that for all practical purposes these two types
have equal mean consumption rates. If not, there may still be an
important difference between these types, and we would need more
data to resolve the question.
The point to the above discussion is that there is more information in
these simultaneous intervals than is provided by a collection of
significance tests for differences. This is true whether the tests are
reported via conclusions “Reject”/“Do not reject”, or via p-values or
adjusted p-values. This superior level of information using confidence
intervals has been acknowledged by virtually all modern texts on
multiple comparisons (Hsu, 1996; Bechhofer, Santner, and
677
Chapter 18 Multiple Comparisons
Goldsman, 1995; Hochberg and Tamhane, 1987; Toothaker, 1993).
All multiple comparison analyses using multicomp are represented by
using confidence intervals or bounds.
Rat Growth
Hormone
Treatments
If all the intervals are to hold simultaneously with a given confidence
level, it is important to calculate intervals only for those comparisons
which are truly of interest. For example, consider the summary data
in Table 2.5 from Hsu (Hsu, 1996). The data concerns a study by
Juskevich and Guyer (1990) in which rat growth was studied under
several growth-hormone treatments.
In this setting, it may only be necessary to compare each hormone
treatment’s mean growth with that of the placebo (that is, the oral
administration with zero dose). These all-to-one comparisons are
usually referred to as multiple comparisons with a control (MCC)
(Dunnett, 1955). Suppse that the raw data for each rat were available
in a data frame hormone.dfr, with a numeric variable growth and a
factor variable treatment for each rat. The following statements
calculate, print, and plot Dunnett’s intervals for hormone.dfr:
>
+
>
+
aovout.growth <- aov(growth ~ treatment, data =
hormone.dfr)
multicomp(aovout.growth, focus = "treatment",
comparisons = "mcc", control = 1, plot = T)
Table 18.1: Mean weight gain in rats under hormone treatments.
678
Method/Dose
Mean
Growth (g)
Standard
Deviation
Sample
Size
oral, 0
324
39.2
30
inject,1.0
432
60.3
30
oral,0.1
327
39.1
30
oral,0.5
318
53.0
30
oral,5
325
46.3
30
oral,50
328
43.0
30
Overview
The results are shown graphically in Figure 18.3. The intervals clearly
show that only the injection method is distinguishable from the
placebo in terms of long run mean weight gain.
Table 4: MCC for hormone treatments
inject.,1.0-oral,0
oral,0.1-oral,0
oral,0.5-oral,0
oral,5.0-oral,0
oral,50-oral,0
(
(
(
(
)
(
-40
)
)
)
)
-20
0
20
40
60
80
100 120 140
simultaneous 95 % confidence limits, Dunnett method
response variable: growth
Figure 18.3: MCC for rat hormone treatments.
Alternatively, we can compute Dunnett’s intervals directly from the
summary statistics that appear in Table 18.1. This allows us to use
multicomp even when we do not have access to the raw data. To
illustrate this, we first generate the data in Table 18.1 with the
commands below.
>
+
>
>
>
>
method.dose <- c("oral,0", "inject,1.0", "oral,0.1",
"oral,0.5", "oral,5.0", "oral,50")
mean.growth <- c(324,432,327,318,325,328)
names(mean.growth) <- method.dose
std.dev <- c(39.2, 60.3, 39.1, 53.0, 46.3, 43.0)
sample.size <- rep(30,6)
Note that we assigned names to the mean.growth vector. This allows
us to take advantage of the plot labeling in multicomp, as we see
below.
To use multicomp with summary data, we need to specify the x, vmat,
and df.residual arguments. For the default implementation of
multicomp, the x argument is a numeric vector of estimates. This
corresponds to the mean.growth variable in our example. The vmat
argument is the estimated covariance matrix for x, which is diagonal
due to the independence of means in the rat growth hormone
example. To compute the entries of vmat for the data in Table 18.1, we
square the std.dev variable and then divide by 30 (i.e., sample.size)
to obtain variances for the means. The df.residual argument
specifies the number of degrees of freedom for the residuals, and is
679
Chapter 18 Multiple Comparisons
equal to the total number of observations minus the number of
categories. In our example, this is 30 × 6 – 6 = 174 . For more details
on any of these arguments, see the help file for multicomp.default.
The commands below reproduce the plot displayed in Figure 18.3:
>
+
+
>
multicomp(mean.growth, diag(std.dev^2/30),
df.residual = 174, comparisons = "mcc", control = 1,
plot = T, ylabel = "growth")
title("Table 4: MCC for hormone treatments")
95 % simultaneous confidence intervals for specified
linear combinations, by the Dunnett method
critical point: 2.5584
response variable: mean.growth
intervals excluding 0 are flagged by '****'
inject,1.0-oral,0
oral,0.1-oral,0
oral,0.5-oral,0
oral,5.0-oral,0
oral,50-oral,0
inject,1.0-oral,0
oral,0.1-oral,0
oral,0.5-oral,0
oral,5.0-oral,0
oral,50-oral,0
Estimate Std.Error Lower Bound
108
13.1
74.4
3
10.1
-22.9
-6
12.0
-36.8
1
11.1
-27.3
4
10.6
-23.2
Upper Bound
142.0 ****
28.9
24.8
29.3
31.2
Since we assigned names to the mean.growth vector, multicomp
automatically produces labels on the vertical axis of the plot. The
ylabel argument in our call to multicomp fills in the “response
variable” label on the horizontal axis.
More Detail on
multicomp
680
The first and only required argument to multicomp is an aov object (or
equivalent), the results of a fixed-effects linear model fit by aov or a
similar model-fitting function. The focus argument, when specified,
names a factor (a main effect) in the fitted aov model. Comparisons
will then be calculated on (adjusted) means for levels of the focus
factor. The comparisons argument is an optional argument which can
Overview
specify a standard family of comparisons for the levels of the focus
factor. The default is comparisons="mca", which creates all pairwise
comparisons. Setting comparisons="mcc" creates all-to-one
comparisons relative to the level specified by the control argument.
The only other comparisons option available is "none", which states
that the adjusted means themselves are of interest (with no
differencing), in which case the default method for interval
calculation is known as the studentized maximum modulus method.
Other kinds of comparisons and different varieties of adjusted means
can be specified through the lmat and adjust options discussed
below.
Upper and
Lower Bounds
Confidence intervals provide both upper and lower bounds for each
difference or adjusted mean of interest. In some instances, only the
lower bounds, or only the upper bounds, may be of interest.
For instance, in the fuel consumption example earlier, we may only
be interested in determining which types of vehicle clearly have
greater fuel consumption than compacts, and in calculating lower
bounds for the difference. This can be accomplished through lower
mcc bounds:
>
>
+
+
aovout.fuel <- aov(Fuel ~ Type, data = fuel.frame)
multicomp(aovout.fuel, focus = "Type",
comparison = "mcc", bounds = "lower", control = 1,
plot = T)
95 % simultaneous confidence bounds for specified
linear combinations, by the Dunnett method
critical point: 2.3332000000000002
response variable: Fuel
bounds excluding 0 are flagged by '****'
Large-Compact
Medium-Compact
Small-Compact
Sporty-Compact
Van-Compact
Estimate Std.Error Lower Bound
0.800
0.267
0.1770 ****
0.434
0.160
0.0606 ****
-0.894
0.160
-1.2700
-0.210
0.178
-0.6250
1.150
0.193
0.6950 ****
681
Chapter 18 Multiple Comparisons
Figure 18.4: Lower mcc bounds for fuel consumption.
The intervals or bounds computed by multicomp are always of the
form
(estimate) ± (critical point) × (standard error of estimate)
You have probably already noticed that the estimates and standard
errors are supplied in the output table. The critical point used
depends on the specified or implied multiple comparison method.
Calculation of
Critical Points
The multicomp function can calculate critical points for simultaneous
intervals or bounds by the following methods:
•
Tukey (method = "tukey"),
•
Dunnett (method = "dunnett"),
•
Sidak (method = "sidak"),
•
Bonferroni (method = "bon"),
•
Scheffé (method = "scheffe")
•
Simulation-based (method = "sim").
Non-simultaneous intervals use the ordinary Student’s-t critical point,
If a method is specified, the function will check its
validity in view of the model fit and the types of comparisons
requested. For example, method="dunnett" will be invalid if
comparisons="mca". If the specified method does not satisfy the
validity criterion, the function terminates with a message to that
effect. This safety feature can be disabled by specifying the optional
argument valid.check = F. If no method is specified, the function
method="lsd".
682
Overview
uses the smallest critical point among the valid non-simulation-based
methods. If you specify method="best", the function uses the smallest
critical point among all valid methods including simulation; this latter
method may take a few moments of computer time.
The simulation-based method generates a near-exact critical point via
Monte Carlo simulation, as discussed by Edwards and Berry (1987).
For nonstandard families of comparisons or unbalanced designs, this
method will often be substantially more efficient than other valid
methods. The simulation size is set by default to provide a critical
point whose actual error rate is within 10% of the nominal α (with
99% confidence). This amounts to simulation sizes in the tens of
thousands for most choices of α. You may directly specify a
simulation size via the simsize argument to multicomp, but smaller
simulation sizes than the default are not advisable.
It is important to note that if the simulation-based method is used, the
critical point (and hence the intervals) will vary slightly over repeated
calls; recalculating the intervals repeatedly searching for some
desirable outcome will usually be fruitless, and will result in intervals
which do not provide the desired confidence level.
Error Rates for
Confidence
Intervals
Other multicomp arguments of interest are the alpha argument which
specifies the error rate for the intervals or bounds, with default
alpha=0.05. By default, alpha is a familywise error rate, that is, you
may be (1 - alpha) x 100% confident that every calculated bound
holds. If you desire confidence intervals or bounds without
simultaneous coverage, specify
error.type="cwe", meaning
comparisonwise error rate protection; in this case you must also
specify method="lsd". Finally, for those familiar with the Scheffé
(1953) method, the critical point is of the form:
sqrt(Srank * qf(1-alpha, Srank, df.residual))
The numerator degrees of freedom Srank may be directly specified as
an option. If omitted, it is computed based on the specified
comparisons and aov object.
683
Chapter 18 Multiple Comparisons
ADVANCED APPLICATIONS
In the first example, the Fuel consumption differences found between
vehicle types are almost surely attributable to differences in Weight
and/or Displacement. Figure 18.5 shows a plot of Fuel versus Weight
with plotting symbols identifying the various model types:
> plot(Weight, Fuel, type = "n")
> text(Weight, Fuel, abbreviate(as.character(Type)))
5.5
Van
Van
Van Larg
Van
Van
5.0
Sprt
Van
Medm
SprtVan
Medm
Medm
Cmpc Cmpc
Medm
4.5
Medm Medm
Medm
Medm
Medm
Cmpc
Cmpc
Medm
Medm
Larg Medm
Medm
Cmpc Cmpc
Cmpc
Sprt
4.0
Fuel
Cmpc
Larg
Cmpc
Smll
Cmpc
Cmpc
Cmpc Sprt
Cmpc
Cmpc
Sprt Sprt
Smll
Smll
3.5
Smll Sprt
Smll
3.0
Sprt
Smll Smll
SprtSmll
Smll
Smll
Smll
Smll
Smll
2000
2500
3000
3500
Weight
Figure 18.5: Consumption of Fuel versus Weight.
This plot shows a strong, roughly linear relationship between Fuel
consumption and Weight, suggesting the addition of Weight as a
covariate in the model. Though it may be inappropriate to compare
adjusted means for all six vehicle types (see below), for the sake of
example the following calls fit this model and calculate simultaneous
confidence intervals for all pairwise differences of adjusted means,
requesting the best valid method.
684
Advanced Applications
>
+
>
+
lmout.fuel.ancova <- lm(Fuel ~ Type + Weight,
data = fuel.frame)
multicomp(lmout.fuel.ancova, focus = "Type",
method = "best", plot = T)
(
Compact-Large
Compact-Medium
Compact-Small
Compact-Sporty
Compact-Van
Large-Medium
Large-Small
Large-Sporty
Large-Van
Medium-Small
Medium-Sporty
Medium-Van
Small-Sporty
Small-Van
Sporty-Van
)
(
)
(
)
(
)
(
)
(
)
(
(
)
)
(
)
(
)
(
)
(
)
(
)
(
)
)
(
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
simultaneous 95 % confidence limits, simulation-based method
response variable: Fuel
Figure 18.6: Fuel consumption ANCOVA (adjusted for Weight).
The “best” valid method for this particular setting is the simulationbased method; Tukey’s method has not been shown to be valid in the
presence of covariates when there are more than three treatments.
The intervals show that, adjusting for weight, the mean fuel
consumption of the various vehicle types are in most cases within one
unit of each other. The most notable exception is the van type, which
is showing higher mean fuel consumption than the small and sporty
types, and most likely higher than the compact, medium and large
types.
Adjustment
Schemes
When there is more than one term in the lm model, multicomp
calculates standard adjusted means for levels of the focus factor and
then takes differences as specified by the comparisons argument.
Covariates are adjusted to their grand mean value. If there are other
factors in the model, the standard adjusted means for levels of the
focus factor use the average effect over the levels of any other (nonnested) factors. This adjustment scheme can be changed using the
adjust argument, which specifies a list of adjustment levels for nonfocus terms in the model. Any terms excluded from the adjust list
685
Chapter 18 Multiple Comparisons
are adjusted in the standard way. The adjust list may include
multiple adjustment values for each term; a full set of adjusted means
for the focus factor is calculated for each combination of values
specified by the adjust list. Differences (if any) specified by the
comparisons argument are then calculated for each combination of
values specified by the adjust list.
Toothaker’s
Two-Factor
Design
Besides allowing you to specify covariate values for adjustment, the
adjust argument can be used to calculate simple effects comparisons
when factors interact, or (analogously) when covariate slopes are
different. This is best illustrated by an example: Toothaker (1993)
discusses a two-factor design, using the data collected by Frank
(1984). Subjects are female undergraduates, with response the score
on a 20-item multiple choice test over a taped lecture. Factors are
cognitive style (cogstyle, levels FI = Field independent and FD = Field
dependent) and study technique (studytech, levels NN = no notes,
SN = student notes, PO = partial outline supplied, CO = complete
outline). The following code fits the model and performs a standard
two-factor analysis of variance.
>
+
+
+
+
+
+
+
score <- c(13, 13, 10, 16, 14, 11, 13, 13, 11, 16, 15, 16,
10, 15, 19, 19, 17, 19, 17, 20, 17, 18, 17, 18, 18, 19,
19, 18, 17, 19, 17, 19, 17, 19, 17, 15, 18, 17, 15, 15,
19, 16, 17, 19, 15, 20, 16, 19, 16, 19, 19, 18, 11, 14,
11, 10, 15, 10, 16, 16, 17, 11, 16, 11, 10, 12, 16, 16,
17, 16, 16, 16, 14, 14, 16, 15, 15, 15, 18, 15, 15, 14,
15, 18, 19, 18, 18, 16, 16, 18, 16, 18, 19, 15, 16, 19,
18, 19, 19, 18, 17, 16, 17, 15)
>
>
+
+
cogstyle <- factor(c(rep("FI", 52), rep("FD", 52)))
studytec <- factor(c(rep("NN", 13), rep("SN", 13),
rep("PO", 13), rep("CO", 13), rep("NN", 13),
rep("SN", 13), rep("PO",13), rep("CO",13)))
> interaction.plot(cogstyle, studytec, score)
> aovout.students <- aov( score ~ cogstyle * studytec)
> anova(aovout.students)
686
Advanced Applications
F Value
Pr(F)
7.78354 0.00635967
33.21596 0.00000000
2.82793 0.04259714
18
Analysis of Variance Table
Response: score
Terms added sequentially (first to last)
Df
Sum of Sq
Mean Sq
cogstyle
1
25.0096
25.0096
studytec
3
320.1827 106.7276
cogstyle:studytec 3
27.2596
9.0865
Residuals
96
308.4615
3.2131
studytec
16
15
13
14
mean of score
17
SN
CO
PO
NN
FD
FI
cogstyle
Figure 18.7: Two-factor design test scores.
It is apparent from the test for interaction and the profile plot that
there is non-negligible interaction between these factors. In such cases
it is often of interest to follow the tests with an analysis of “simple
effects.” In the following example, a comparison of the four study
techniques is performed separately for each cognitive style group.
The following call calculates simultaneous 95% intervals for these
differences by the best valid method, which is again simulation.
687
Chapter 18 Multiple Comparisons
> mcout.students <- multicomp(aovout.students,
+ focus = "studytec", adjust = list(cogstyle =
+ c("FI", "FD") ), method = "best")
> plot(mcout.students)
> mcout.students
95 % simultaneous confidence intervals for specified
linear combinations, by the simulation-based method
critical point: 2.8526
response variable: score
simulation size= 12616
intervals excluding 0 are flagged by '****'
CO.adj1-NN.adj1
CO.adj1-PO.adj1
CO.adj1-SN.adj1
NN.adj1-PO.adj1
NN.adj1-SN.adj1
PO.adj1-SN.adj1
CO.adj2-NN.adj2
CO.adj2-PO.adj2
CO.adj2-SN.adj2
NN.adj2-PO.adj2
NN.adj2-SN.adj2
PO.adj2-SN.adj2
CO.adj1-NN.adj1
CO.adj1-PO.adj1
CO.adj1-SN.adj1
NN.adj1-PO.adj1
NN.adj1-SN.adj1
PO.adj1-SN.adj1
CO.adj2-NN.adj2
CO.adj2-PO.adj2
CO.adj2-SN.adj2
NN.adj2-PO.adj2
NN.adj2-SN.adj2
PO.adj2-SN.adj2
688
Estimate Std.Error Lower Bound Upper Bound
4.4600
0.703
2.460
6.470
0.7690
0.703
-1.240
2.770
2.1500
0.703
0.148
4.160
-3.6900
0.703
-5.700
-1.690
-2.3100
0.703
-4.310
-0.302
1.3800
0.703
-0.621
3.390
4.3800
0.703
2.380
6.390
0.0769
0.703
-1.930
2.080
-0.3850
0.703
-2.390
1.620
-4.3100
0.703
-6.310
-2.300
-4.7700
0.703
-6.770
-2.760
-0.4620
0.703
-2.470
1.540
****
****
****
****
****
****
****
Advanced Applications
CO.adj1-NN.adj1
CO.adj1-PO.adj1
CO.adj1-SN.adj1
NN.adj1-PO.adj1
NN.adj1-SN.adj1
PO.adj1-SN.adj1
CO.adj2-NN.adj2
CO.adj2-PO.adj2
CO.adj2-SN.adj2
NN.adj2-PO.adj2
NN.adj2-SN.adj2
PO.adj2-SN.adj2
(
(
)
)
(
(
)
)
(
)
(
)
(
(
(
(
-7
)
)
(
)
)
(
)
)
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
simultaneous 95 % confidence limits, simulation-based method
response variable: score
Figure 18.8: Simple effects for study techniques.
Setting Linear
Combinations
of Effects
In many situations, the setting calls for inference on a collection of
comparisons or linear combinations other than those available
through specifications of the focus, adjust, and comparisons
arguments. The lmat argument to multicomp allows you to directly
specify any collection of linear combinations of the model effects for
inference. It is a matrix (or an expression evaluating to a matrix)
whose columns specify linear combinations of the model effects for
which confidence intervals or bounds are desired. Specified linear
combinations are checked for estimability; if inestimable, the function
terminates with a message to that effect. You may disable this safety
feature by specifying the optional argument est.check=F.
Specification of lmat overrides any focus or adjust arguments; at
least one of lmat or focus must be specified. Differences requested or
implied by the comparisons argument are taken over the columns of
lmat. In many instances no such further differencing would be
desired, in which case you should specify comparisons="none".
Textbook
Parameterization
Linear combinations in lmat use the textbook parameterization of the
model. For example, the fuel consumption analysis of covariance
model parameterization has eight parameters: an Intercept, six
coefficients for the factor Type (Compact, Large, Medium, Small,
Sporty, Van) and a coefficient for the covariate Weight. Note that the
levels of the factor object Type are listed in alphabetical order in the
parameter vector.
689
Chapter 18 Multiple Comparisons
In the fuel consumption problem, many would argue that it is not
appropriate to compare, for example, adjusted means of Small
vehicles and Large vehicles, since these two groups’ weights do not
overlap. Inspection of Figure 18.5 shows that, under this
consideration, comparisons are probably only appropriate within two
weight groups: Small, Sporty, and Compact as a small weight group;
Medium, Large, and Van as a large weight group. We can accomplish
comparisons within the two Weight groups using the following matrix,
which is assumed to be in the object lmat.fuel. Note the column
labels, which will be used to identify the intervals in the created figure
and plot.
Table 18.2: The Weight comparison matrix in the file lmat.fuel.
Com-Sma
Com-Spo
Sma-Spo
Lar-Med
Lar-Van
Med-Van
Intercept
0
0
0
0
0
0
Compact
1
1
0
0
0
0
Large
0
0
0
1
1
0
Medium
0
0
0
-1
0
1
Small
-1
0
1
0
0
0
Sporty
0
-1
-1
0
0
0
Van
0
0
0
0
-1
-1
Weight
0
0
0
0
0
0
The code below creates the intervals. If we restrict attention to these
comparisons only, we cannot assert any differences in adjusted mean
fuel consumption.
> multicomp.lm(lmout.fuel.ancova, lmat = lmat.fuel,
+ comparisons = "none", method = "best", plot = T)
690
Advanced Applications
Com.Sma
Com.Spo
Sma.Spo
Lar.Med
Lar.Van
Med.Van.
(
)
(
)
(
(
)
)
(
)
(
-1.2
-1.0
)
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
simultaneous 95 % confidence limits, simulation-based method
response variable: Fuel
Figure 18.9: Using lmat for specialized contrasts.
The textbook parameterization for linear models are created
according to the following algorithm:
1. An intercept parameter is included first, if the model contains
one.
2. For each “main effect” term in the model (terms of order one),
groups of parameters are included in the order the terms are
listed in the model specification. If the term is a factor, a
parameter is included for each level. If the term is numeric, a
parameter is included for each column of its matrix
representation.
3. Parameters for terms of order 2 are created by “multiplying”
the parameters of each main effect in the term, in left-to-right
order. For example, if A has levels A1, A2 and B has levels B1,
B2, B3, the parameters for A:B are A1B1, A1B2, A1B3, A2B1, A2B2,
A2B3.
4. Parameters for higher level terms are created by multiplying
the parameterization of lower level terms two at a time, left to
right. For example, the parameters for A:B:C are those of A:B
multiplied by C.
Overparameterized Models
The textbook parameterization will often be awkwardly
overparameterized. For example, the 2 x 4 factorial model specified
in the student study techniques example has the following
parameters, in order (note the alphabetical rearrangement of the
factor levels).
691
Chapter 18 Multiple Comparisons
•
Intercept
•
FD, FI
•
CO, NN, PO, SN
•
FDCO, FDNN, FDPO, FDSN, FICO, FINN, FIPO, FISN
Clearly, care must be taken in creating an lmat for factorial designs,
especially with crossed and/or nested terms. The flexibility lmat
provides for creating study-specific linear combinations can be
extremely valuable, though. If you are in doubt about the actual
textbook parameterization of a given linear model, it may help to run
a standard analysis and inspect the lmat created, which is part of the
output list of multicomp. For example, for the simple effects analysis
of the student test scores of Figure 18.8, the implied lmat can be seen
using the command:
> mcout.students$lmat
Multicomp
Methods
Compared
The function multicomp.lm, after checking estimability of specified
linear combinations and creating a vector of estimates, a covariance
matrix, and degrees of freedom, calls the base function
multicomp.default. The function multicomp.default will be directly
valuable in many settings. It uses a vector of estimates bvec and
associated covariance matrix vmat as required arguments, with
optional degrees of freedom df.residual (possibly Inf, the default) to
calculate confidence intervals on linear combinations of bvec. These
linear combinations can be specified through an optional lmat
argument and/or comparisons argument; there is neither a focus nor
an adjust argument. Linear combinations of bvec defined by
columns of lmat (if any; the default lmat is an identity matrix) are
calculated, followed by any differences specified or implied by the
comparisons argument. The multicomp.lm options method, bounds,
alpha, error.type, crit.point, sim.size, Srank, valid.check, and
plot are also available in multicomp.default.
The function multicomp.default can be very useful as a means of
calculating intervals based on summary data, or using the results of
some model-fitting program other than lm; bvec must be considered
as a realization of a multivariate normal vector. If the matrix vmat
incorporates any estimate of variance considered to be a realized chisquare variable, the degrees of freedom df.residual must be
specified.
692
Advanced Applications
The rat growth data discussed earlier (Table 18.1) provides a simple
example of the use of multicomp.default. Here, the first few
statements create the vector of estimates bvec and covariance matrix
vmat assuming that a single factor analysis of variance model is
appropriate for the data, followed by the statement that produced the
lower mcc bounds of Figure 18.10:
>
>
>
>
+
>
>
>
+
+
growth <- c(324, 432, 327, 318, 325, 328)
stddev <- c(39.2, 60.3, 39.1, 53.0, 46.3, 43.0)
samp.size <- rep(30, 6)
names(growth) <- c( "oral,0", "inject,1.0", "oral,0.1",
"oral,0.5", "oral,5", "oral,50")
mse <- mean(stddev^2)
vmat <-mse * diag(1/samp.size)
multicomp.default(growth, vmat, df.residual =
sum(samp.size-1), comparisons = "mcc", bounds = "lower",
control = 1, plot = T)
(
inject,1.0-oral,0
oral,0.1-oral,0
oral,0.5-oral,0
oral,5-oral,0
oral,50-oral,0
(
(
(
(
-40
-20
0
20
40
60
80
100
simultaneous 95 % confidence limits, Dunnett method
response variable:
120
Figure 18.10: Lower mcc bounds for rat hormone treatment.
693
Chapter 18 Multiple Comparisons
CAPABILITIES AND LIMITS
In summary, the function multicomp uses the information in a linear
model; that is, a fitted fixed effects linear model. Through some
combination of the focus, adjust, comparisons and lmat arguments,
any collection of estimable linear combinations of the fixed effects
may be estimated, and simultaneous or non-simultaneous intervals or
bounds computed by any of the applicable methods mentioned
above. Specified linear combinations are checked for estimability
unless you specify est.check=F. Specified methods are checked for
validity unless you specify valid.check=F.
The function multicomp.default uses a specified vector of parameter
estimates bvec and a covariance matrix vmat, which will usually have
some associated degrees of freedom df.residual specified. Possibly
through some combination of the comparisons or lmat arguments,
any collection of linear combinations of the parameters may be
estimated, and simultaneous or non-simultaneous intervals or bounds
computed by any of the applicable methods discussed above.
Specified methods are checked for validity unless you specify
valid.check=F.
The output from either procedure is an object of class "multicomp", a
list containing elements table (a matrix of calculated linear
combination estimates, standard errors, and lower and/or upper
bounds), alpha, error.type, method, crit.point, lmat (the final
matrix of linear combinations specified or implied), and other
ancillary information pertaining to the intervals. If the argument
plot=T is specified, the intervals/bounds are plotted on the active
device. If not, the created multicomp object can be used as an
argument to plot (see plot.multicomp).
The critical points for the methods of Tukey and Dunnett are
calculated by numerically using the S-PLUS quantile functions
qtukey, qdunnett, qmvt, and qmvt.sim, which may be directly useful
to advanced users for their own applications.
694
Capabilities and Limits
What the function multicomp does not do:
1. Any stagewise or multiple range test. The simultaneous testing
procedures attributed to Fisher, Tukey, Scheffé, Sidak and
Bonferroni are implied by the use of the corresponding
method and noting which of the calculated intervals excludes
zero. The multiple range tests of Duncan(1955) and NewmanKeuls (Newman, 1939; Keuls, 1952) do not provide
familywise error protection, and are not very efficient for
comparisonwise error protection; modern texts on multiple
comparisons recommend uniformly against these two
multiple range tests (Hsu, 1996; Hochberg and Tamhane,
1987; Bechofer et al., 1996; Toothaker 1993).
2. Multiple comparisons with the “best” treatment (MCB; Hsu,
1996, chapter 4), or any ranking and selection procedure
(Bechofer, et al., 1995) other than selection of treatments
better than a control implied by Dunnett’s one-sided
methods. Users familiar with these methods and reasonably
proficient at Spotfire S+ programming will be able to code
many of these procedures through creative use of multicomp
with the comparisons="mcc" option.
695
Chapter 18 Multiple Comparisons
REFERENCES
Bechhofer R.E., Santner T.J., & Goldsman D.M. (1995). Design and
Analysis of Experiments for Statistical Selection, Screening, and Multiple
Comparisons. New York: John Wiley & Sons, Inc.
Duncan D.B. (1955). Multiple range and multiple F tests. Biometrics
11:1-42.
Dunnett C.W. (1955). A multiple comparison procedure for
comparing several treatments with a control. Journal of the American
Statistical Association 50:1096-1121.
Edwards D. & Berry J.J. (1987). The efficiency of simulation-based
multiple comparisons. Biometrics 43:913-928.
Frank, B.M. (1984). Effect of field independence-dependence and
study technique on learning from a lecture. American Education
Research Journal 21, 669-678.
Hsu J.C. (1996). Multiple Comparisons: Theory and Methods. London:
Chapman and Hall.
Hochberg, Y. & Tamhane, A.C. (1987). Multiple Comparison
Procedures. New York: John Wiley & Sons, Inc.
Juskevich J.C. & Guyer C.G. (1990). Bovine growth hormone: human
food safety evaluation. Science 249:875-884.
Kramer C.Y. (1956). Extension of multiple range tests to group means
with unequal numbers of replications. Biometrics 12:309-310.
Keuls M. (1952). The use of the ‘studentized range’ in connection
with an analysis of variance. Euphytica 1:112-122.
Newman D. (1939). The distribution of the range in samples from a
normal population, expressed in terms of an independent estimate of
standard deviation. Biometrika 31:20-30.
Scheffé H. (1953). A method for judging all contrasts in the analysis of
variance. Biometrika 40:87-104.
Sidak A. (1967). Rectangular confidence regions for the means of
multivariate normal distributions. Journal of the Amererican Statistical
Association 62:626-633.
696
References
Toothaker L.E. (1993). Multiple Comparison Procedures. London: Sage
Publications.
Tukey J.W. (1953). Unpublished report, Princeton University.
697
Chapter 18 Multiple Comparisons
698
Index
INDEX
Symbols
%in% operator
formula 34
* operator
formula 32, 34
formulas 595, 606
+ operator
formulas 595
. operator
formula 36
/ operator
formula 34
: operator
variable interaction 32
^ operator
formulas 32, 606, 609
~ operator 29
Numerics
2k designs
creating design data frame 602
details of ANOVA 613
diagnostic plots 610, 611
EDA 604
estimating effects 605, 607, 609
example of 24 design 602
replicates 607
small order interactions 609
A
ace
algorithm 307
compared to avas 312
example 309
ace function 309
ace goodness-of-fit measure 307
acf function 124, 152
add1 function
generalized linear models 390
add1 function 45
linear models 255
additive models
see generalized additive models
additive predictor
mathematical definition 385
additivity and variance stabilizing
transformation
see avas 312
A estimates of scale 112
AIC
related to Cp statistic 251
air data set 239, 253
algorithms
ace 307
ANOVA 629
avas 312
backfitting 312
correlation coefficient 150
cubic smoothing splines 298
deviance 302
generalized additive models 12
generalized linear models 11
glm function 384, 415
goodness-of-fit measure 307
kernel-type smoothers 295
L1 regression 370
least squares regression 367
least trimmed squares
regression 367
linear models 10
local cross-validation for
variable span smoothers 293
699
Index
locally weighted regression
smoothing 291
Tukey’s one degree of freedom
588
alternating conditional expectations
see ace
alternative hypothesis 126
analysis of deviance tables, see
ANOVA tables
analysis of variance see ANOVA
ANOVA
2k designs 604–614
checking for interaction 594
data type of predictors 10
diagnostic plots 575
diagnostic plots for 584, 595,
611
EDA 572, 580, 593, 604
effects table 577
estimating effects 605, 607, 609
factorial effects 633
fitting functions 8
grand mean plus treatment
effects form 629
interaction 582
one-way layout 574–577
rank sum tests 662
repeated-measures designs 659
robust methods 662
small-order interactions 609
split-plot designs 656
treatment means 577
two-way additive model 583
two-way replicated 594–601
two-way unreplicated 578–590
unbalanced designs 634
variance stabilizing 597, 599,
601
ANOVA, see also MANOVA
anova function
chi-squared test 389, 411
F test 416
generalized additive models 395
generalized linear models 389,
410, 411, 416
700
anova function 519
anova function 9
anova function
additive models 306
ANOVA models
residuals 584, 595
ANOVA tables 9, 595, 606, 609, 626
F statistics 416
generalized additive models 306
logistic regression 389, 395
Poisson regression 410, 411
quasi-likelihood estimation 416
aov.coag data set
created 574
aov.devel.2 data set
created 609
aov.devel.small data set
created 611
aov.devel data set
created 605
aov.pilot data set
created 609
aov function 8
2k model 605
arguments 574
default coefficients returned 613
estimating effects 609
extracting output 606
one-way layout 574, 577
two-way layout 595
two-way layout additive model
583
aov function
repeated-measures designs 659
split-plot designs 656
approx function 564
auto.stats data set 15
autocorrelation function
plot 124, 152
avas
algorithm for population
version 316
avas
algorithm 312
backfitting algorithm 312
Index
compared to ace 312
example 313
key properties 315
avas function 313
B
backfitting 317
Bernoulli trial 69, 71, 84
definition 69
beta distribution 57, 76
beta function 76
binom.test function 184
binomial coefficients 74
definition 70
binomial distribution 57, 69, 182
relation to geometric
distribution 84
relation to hypergeometric
distribution 74
relation to Poisson distribution
71
binomial family 387, 404
inverse link function 421
logit link 382
probit link 382
blocking variable 578
Box-Cox maximum-likelihood
procedure 315
boxplot function
used to compute quartiles 101
boxplots 123, 387, 409, 573, 582,
594
Box-Tidwell procedure 315
breakdown point 365
B-splines 394
B-splines 298
C
cancer study data 196
canonical links 384
catalyst data set 10
catalyst data set 633
categorical data
cross-classification 204
categorical data see also factors
categorical variables 30
interactions 33
Cauchy distribution 57, 79
stable 82
cdf.compare function 160, 161, 170,
175, 178
CDF. See cumulative distribution
functions
Central Limit Theorem 63, 106
central moments
of a probability distribution 55
of a sample 103
C function 41
chisq.gof function 160, 165, 170
cut.points argument 166
distribution argument 166
estimating parameters 175
n.classes argument 166
n.param.est argument 176
warning messages 177
chisq.test function 192
chi-square distribution 57, 64
chi-squared test 192, 195, 206, 389,
411
chi-square goodness of fit test 160
choice of partition 166
comparison with other onesample tests 174
continuous variables 167
distributions 166
large sample theory 177
mathematical definition 165
claims data set 204
classification trees see also treebased models
coag.df data frame
created 571
coagulation data 570
coefficients
converting to treatment effects
629
estimated 606
extracting 8
701
Index
fixing 424
coefficients function
abbreviated coef 8
coef function 424
coef function 8, 23, 606
cognitive style study 686
comp.plot function
defined 590
comparative study 143
comparing means
two samples 226
comparing proportions
two samples 230
comparison values 587
conditioning plots 7, 9
analyzing 443
conditioning panels 441
conditioning values 441
constructing 441
local regression models 453
residuals as response variable
448
conditioning values 441
confidence intervals 120, 191, 564,
681
binomial distribution 185
confidence level 126, 185
correlation coefficient 157
error rate 125
for the sample mean 106, 107
pointwise 272
simultaneous 272
two-sample 188
confint.lm function
defined 273
contingency tables 183, 192, 195
choosing suitable data 209
continuous data 213
creating 204
reading 206
subsetting data 216
continuous data 4
converting to factors 213
cross-tabulating 213
702
continuous random variable 52, 60,
76
continuous response variable 570
continuous variables
interactions 33
contr.helmert function 40
contr.poly function 40
contr.sum function 40
contr.treatment function 39
contrasts
adding to factors 625
creating contrast functions 41
Helmert 39
polynomial 40
specifying 41, 42, 43
sum 40
treatment 39
contrasts function 42
contrasts function 625
coplot function 7, 9
coplots
see conditioning plots
cor.confint function
created 157
cor.test function 154
corelation
serial 120
cor function 156
correlation
example 149
serial 124, 245
shown by scatterplots 120
correlation coefficient 119
algorithm 150
Kendall’s t measure 154, 155
Pearson product-moment 154
p-values
p-values 154
rank-based measure 154, 155
Spearman’s r measure 154, 155
correlation structures 505
correlation structures and variance
functions 507
corStruct classes 280, 507
counts 182
Index
Cp statistic 390
Cp statistic 251, 257
cross-classification 204
crosstabs function 204, 219
arguments 206, 216
return object 206
cross-validation
algorithm 293
cubic smoothing splines 298
algorithm 298
cumulative distribution functions
53, 161
See also probability
distributions
cut function 213
D
data
categorical 4
continuous 4
organizing see data frames
summaries 5
data frames
attaching to search list 247
design data frame 579, 592, 602
degrees of freedom 134, 303
nonparametric 303
parametric 303
smoothing splines 298
density function. See probability
density function
density plot 123
derivatives 548
deriv function 552
design data frames 579, 592, 602
designed experiments
one factor 570–577
randomized blocks 578
replicated 591
two-way layout 578
devel.design data frame
created 602
devel.df data frame
created 604
deviance 418
algorithm 302
deviance residuals 418
D function 551
diagnostic plots
ANOVA 584
linear regression 242
local regression models 436
multiple regression 249
outliers 575
diff.hs data set 151
discrete random variable 52, 69, 84
dispersion parameter 383, 416
obtaining chi-squared estimates
411
distribution functions. See
probability distributions
double exponential distribution
random number generation 87
drop1 function 44
linear models 251
drug.fac data set 195
drug.mult data set 658
drug data set 194
dummy.coef function 630
Dunnett’s intervals 678, 679
durbinWatson function 245
Durbin-Watson statistic 245
dwilcox function 57
E
EDA
see exploratory data analysis
eda.shape
defined 124
eda.ts function 124
EDA functions
interaction.plot 582
plot.design 572, 580, 594
plot.factor 573, 581
empirical distribution function 161
ethanol data set 275
Euclidean norm 365
example functions
703
Index
comp.plot 590
confint.lm 273
cor.confint function 157
eda.shape 124
eda.ts 124
tukey.1 589
examples
2k design of pilot plant data 607
2k design of product
development data 602
ace example with artificial data
set 309
ANOVA of coagulation data
570
ANOVA of gun data 629
ANOVA of penicillin yield data
578
ANOVA of poison data 591
ANOVA table of wafer data
626
avas with artificial data set 313
binomial model of Salk vaccine
trial data 186
binomial test with roulette 184
chi-squared test on propranolol
drug data 196
chi-squared test on Salk vaccine
data 195
coplot of ethanol data 441
correlation of phone and
housing starts data 149
developing a model of auto data
14
Fisher’s exact test on
propranolol drug data 196
goodness of fit tests for the
Michelson data 175
hypothesis testing of lung
cancer data 190
linear model of air pollution
data 239
logistic regression model of
kyphosis data 387
MANOVA of wafer data 654
704
Mantel-Haenszel test on cancer
study data 196
McNemar chi-squared test on
cancer study data 199
multiple regression with
ammonia loss data 247
new family for the negative
binomial distribution 430
new variance function for quasilikelihood estimation 426
one-sample speed of light data
129
paired samples of shoe wear
data 144
parameterization of scores data
619
perspective plot of fitted data
452
Poisson regression model of
solder.balance data 407
probit regression model of
kyphosis data 404
proportions test with roulette
185
quasi-likelihood estimation of
leaf blotch data 426
quasi-likelihood estimation of
solder.balance data 416
repeated-measure design
ANOVA of drug data 658
split-plot design ANOVA of
rubber plant data 656
two-sample weight gain data
137
variance components model of
pigment data 665
weighted regression of course
revenue data 261
expected value 112
of a random variable 54
exploratory data analysis 121
four plot function 124
interaction 582
phone and housing starts data
151
Index
plots 5
serial correlation 124
shoe wear data 145
speed of light data 130
time series function 124
weight gain data 137
exponential distribution 57, 76
random number generation 86
relation to gamma distribution
77
relation to Weibull distribution
77
F
fac.design function 579, 602
factorial effects 633
factors 4
adding contrasts 625
creating from continuous data
213
levels 4
parametrization 39
plotting 387, 409, 582
setting contrasts 42, 43
family functions 383, 425
binomial 382, 387, 404
creating a new family 425
in generalized additive models
386
inverse link function 421
Poisson 383, 407
quasi 383
F distribution 57, 67
first derivatives 548
fisher.test function 192
Fisher’s exact test 193, 196
fitted.values function
abbreviated fitted 575
fitted function 8, 575, 576, 585,
596, 612
fitted values
ANOVA models 585, 596, 599,
611
extracting 8
lm models 242
fitting methods
formulas 37
functions, listed 8
missing data filter functions 47
optional arguments to functions
46
specifiying data frame 46
subsetting rows of data frames
46
weights 46
fitting models 554
fixed coefficients, See offsets
formula function 31
formulas 28–45, 545
automatically generating 249
categorical variables 30, 33, 34
changing terms 44, 45
conditioning plots 441
continuous variables 30, 33, 34
contrasts 39
expressions 30
fitting procedures 37
generating function 31
implications 546
interactions 32, 33, 34
intercept term 30
linear models 239
matrix terms 30
nesting 33, 34, 35
no intercept 424
offsets 424
operators 29, 31, 32, 34, 36
polynomial elements 277
simplifying 546
specifying interactions 595, 606,
609
syntax 31, 36
updating 44, 45
variables 29, 30
friedman.test function 663
Friedman rank sum test 662, 663
F-statistic
linear models 241
F statistics 416
705
Index
F test 416
F-test
local regression models 458
fuel.frame data 674
fuel consumption problem 690
G
gain.high data set 137
gain.low data set 137
gam function 385, 387, 404, 407
available families 386
binomial family 394
family argument 387, 404, 407
gam function 8, 24
gam function
returned object 303
gamma distribution 57, 77
gamma function 66, 67, 77
definition 64
GAMs, see generalized additive
models
Gaussian distribution. See normal
distribution
Gaussian mean
one-sample test of 224
generalized additive models
algorithm 12, 301
analysis of deviance tables 395
ANOVA tables 306
contrasted with generalized
linear models 400
degrees of freedom 303
deviance 418
fitting function 8
link function 385
lo function 386
logistic regression 394
marginal fits 421
mathematical definition 385
plotting 396
prediction 420
residual deviance 302
residuals 418
s function 386
706
smoothing functions 385, 386
summary of fit 394, 395, 397
generalized linear models
adding terms 390
algorithm 11
analysis of deviance tables 416
canonical links 384
composite terms 422
contrasted with generalized
additive models 400
deviance 418
dispersion parameter 383, 411,
416
fitting function 8
fixing coefficients 424
logistic regression 387
logit link function 382
log link function 383
mathematical definition 381
plotting 390, 412
Poisson regression 407
prediction 420
probit link function 382
probit regression 404
quasi-likelihood estimation 383,
415
residuals 418
safe prediction 422
specifying offsets 424
summary of fit 388, 400
using the gam function 385
geometric distribution 57, 84
relation to negative binomial
distribution 84
glm.links data set 425
glm.variances data set 425
glm function 387, 404, 407
algorithm 384, 415
available families 383
binomial family 382
family argument 387, 404, 407
Poisson family 383
quasi family 383
residuals component 418
glm function 8
Index
GLMs, see generalized linear
models
GOF. See goodness of fit tests
goodness-of-fit measure
algorithm 307
goodness of fit tests 160
chi-square 160, 165, 174, 177
comparison of one-sample tests
174
composite 174
conservative tests 175
Kolmogorov-Smirnov 160, 168,
174, 178
one-sample 160, 165, 168, 172
Shapiro-Wilk 160, 172, 174, 175
two-sample 160, 168, 178
gradient attribute 549
groupData class 465
grouped datasets 465
guayule data set 209, 656
gun data set 629, 634
H
half-normal QQ-plots 610
Helmert contrasts 39
hessian attribute 550
hist function 408
hist function 5, 575, 584, 595
histograms 5, 123, 575, 584, 595
horshft argument 528
Hotelling-Lawley trace test 654
Huber psi functions
for M estimates of location 110
Huber rho functions
for tau estimates of scale 113
hypergeometric distribution 57, 74
hypothesis testing 120, 126
goodness of fit 160
one sample proportions 184
p-values 154
three sample proportions 190
two sample proportions 186
I
identify function 20
identifying plotted points 20
I function 398
importance
in ppreg 324
inner covariates 465
interaction.plot function 582,
594
interactions 320
checking for 582, 594
specifying 32, 595, 606
specifying order 609
intercept 30
no-intercept model 424
intercept-only model 255
interquartile range
of a probability distribution 55
of a sample 101
IQR. See interquartile range
is.random function 664
iteratively reweighted least squares
384, 415
score equations 384
K
Kendall’s t measure 154, 155
kernel functions 295, 296
kernel-type smoother
algorithm 295
Kolmogorov-Smirnov goodness of
fit test 160
comparison with other onesample tests 174
distributions 169
hypotheses tested 168
interpretation 168
mathematical definition 168
one-sample 168
two-sample 168, 178
kruskal.test function 662
Kruskal-Wallis rank sum test 662
ks.gof function 160, 176
707
Index
alternative argument 169
distribution argument 169
estimating parameters 175
one-sample 169
two-sample 178
ksmooth function 295
kernels available 295
KS test. See Kolmogorov-Smirnov
goodness of fit test
kurtosis
of a probability distribution 55
of a sample 104
kurtosis function 104
kyphosis data set 387, 404
kyphosis data set 5
kyphosis data set 213
L
l1fit function 370
L1 regression 370
algorithm 370
Laplace distribution. See double
exponential distribution
least absolute deviation regression
see L1 regression
least squares regression 239
algorithm 367
least squares regression,
mathematical representation 276
least squares vs. robust fitted model
objects 340
least trimmed squares regression
algorithm 367
breakdown point 369
leave-one-out residuals 294
level of significance 126
levels
experimental factor 570
likelihood models 544
linear dependency, see correlation
linear mixed-effects models
fitting 479
model definitions 479
linear models
708
adding terms 255
algorithm 10
confidence intervals 272
diagnostic plots 242, 243, 249,
253
dropping terms 251
fitting function 8, 239, 280
intercept-only model 255
mathematical definition 381
modifying 251, 260
pointwise confidence intervals
272
polynomial regression 275
predicted values 270
selecting 251, 257
serial correlation in 245
simultaneous confidence
intervals 272
stepwise selection 257
summary of fitted model 241
updating 260
linear models see also generalized
linear models
linear predictor 385, 420
mathematical definition 381
linear regression 237
link functions
canonical 384
in generalized additive models
385
in generalized linear models
425
log 383
logit 382
mathematical definition 381
probit 382, 425
lme function
advanced fitting 505
arguments 481
lme objects
analysis of variance 486
extracting components 489
ploting 487
predicting values 491
printing 483
Index
summarizing 484
lm function 8, 18, 240
multiple regression 248
subset argument 21
lm function 239, 280
arguments 249
polynomial regression 277
lmRobMM function 335
locally weighted regression
smoothing 290, 434
algorithm 291
local maxima and minima 529
local regression models 12, 434
diagnostic plots 446
diagnostic plots for 436
dropping terms 455
fitting function 8
improving the model 455
multiple predictors 446
one predictor 435
parametric terms 455
plotting 452
predicted values 452
returned values 435
local regression smoothing 394
location.m function 111
loess 290
scatterplot smoother 290
scatterplot smoothing 291
loess.smooth function 291
loess function 8, 435, 436, 453
loess models see local regression
models
loess smoother function 301
lo function 386, 394
lo function 301
logistic distribution 57, 78
logistic regression 387
analysis of deviance tables 389,
395
binary response 402
contrasted with probit
regression 405
Cp statistic 390
factor response 402
logit link function 382
numeric response 402
tabulated response 402
t-tests 389
using the gam function 386, 394
logit link function
mathematical definition 382
log link function
mathematical definition 383
lognormal distribution 57, 80
lprob function 546, 549
ltsreg function 367
lung cancer study 189
M
MAD. See median absolute
deviation
mad function 101
make.family function 425, 430
Mann-Whitney test statistic. See
Wilcoxon test
MANOVA 654
repeated-measures designs 660
test types available 654
manova function 654
Mantel-Haenszel test 193, 196
maximum
of a sample 98, 105
maximum likelihood estimate
for variance components
models 665
maximum likelihood method 479,
486
mcnemar.test function 199
McNemar chi-squared test 193, 199
mean 119
computing median absolute
deviation 100
computing sample moments
103
computing sample variance 99
confidence intervals 107
of a probability distribution 54
of a sample 95, 105, 110
709
Index
of Poisson distribution 72
standard deviation 106
standard error 106, 107
trimmed mean 96
mean absolute deviation
of a random variable 54
mean function 95
trimmed mean 96
median 124
computing median absolute
deviation 100
of a probability distribution 55
of a sample 96, 105, 110
median absolute deviation (MAD)
100
computing A estimates of scale
112
computing M estimates of
location 111
computing tau estimates of scale
113
median function 97
M estimates of location 110
asymptotic variance 112
computing A estimates of scale
112
computing tau estimates of scale
113
M-estimates of regression 372
fitting function 372
Michaelis-Menten relationship 543
mich data set 175
mich data set
created 130
Michelson speed-of-light data 129,
175
minimum
of a sample 98, 105
minimum sum 526
minimum-sum algorithm 544
minimum sum function 534
minimum sum-of-squares 526
missing data
filters 47
mixed-effects model 463
710
MM-estimate 335
mode
of a probability distribution 55
of a sample 97
model
mixed-effects 463
nonlinear mixed-effects 493
model.tables function 577
model.tables function 630
model data frame 579, 592, 604
models 28–45
data format 4
data type of variables 9
development steps 3
example 14
extracting information 8
fitting functions 8
iterative process 14
missing data 47
modifying 9
nesting formulas 33, 34
paradigm for creating 8
parameterization 34
plotting 9
prediction 9
specifying all terms 32
specifying interactions 32
types available in Spotfire S+ 3
models see also fitting methods
moments
of a probability distribution 55
ms function 526, 534
arguments to 554
multicomp
Lmat argument 689
multicomp function
df.residual argument 679
using summary data 679
vmat argument 679
multicomp function 675
alpha argument 683
comparisons argument 680
control argument 681
est.check argument 694
focus argument 680
Index
simsize argument 683
valid.check option 682
multilevel linear mixed-effects
models 479
multiple comparisons 674
from summary data 679
with a control (MCC) 678
multiple regression 247
diagnostic plots 249
multiple R-squared
linear models 241
multivariate analysis of variance
see MANOVA
multivariate normal distribution 57,
82
N
namevec argument 553
negative binomial distribution 57,
84
in generalized linear models
430
nesting formulas 33, 34
nlimb function 530
nlme function
advanced fitting 505
Arguments 494
nlme function 493, ??–520
nlme objects
analysis of variance 501
extractnig components 504
plotting 501
predicting values 502
printing 497
summarizing 499
nlminb function 532
nlregb function 538
nls function 526, 537
arguments to 554
nlsList function 513
nlsList function ??–520
nnls.fit 536
nnls.fit function 535
nonlinear least-squares algorithm
545
nonlinear mixed-effects models
fitting 493
model definition 493
nonlinear models 526
nonnegative least squares problem
535
nonparametric methods 121
nonparametric regression
ace 307
normal (Gaussian) distribution 57,
61
Central Limit Theorem 63, 106
in probit regression 382
lognormal 80
multivariate 57, 82
random number generation 89
stable 82
standard 62
nregb function 536
null hypothesis 126
completely specified
probabilities 186, 187
equal-probabilities 186, 187
null model 255, 390
O
observation weights
in ppreg 326
offset function 424
offsets
in generalized linear models
424
oil.df data set 337
one-sample test
binomial proportion 229
Gaussian mean 224
one-way layout 570, 574
overall mean plus effects form
576
robust methods 662
- operator
formula 32
711
Index
operators
formula 29, 31, 32, 34, 36, 595,
606, 609
optimise function 529
optimization functions 527
options function 43
outer covariates 465
outer function 421
outliers 118
checking for 575, 576, 582
identifying 20
sensitivity to 581
over-dispersion 416
in regression models 415
overparameterized models 691
P
paired comparisons 144
paired t-test 148
pairs function 5, 439
linear models 253
pairs function 247
pairwise scatter plots
see scatterplot matrices
parameter function 547
parametrized data frames 547
param function 547
PDF. See probability density
function
pdMat classes 505
peaks function 529
Pearson product-moment
correlation 154
Pearson residuals 418
pen.design data frame
converted to model data frame
580
created 579
pen.df data frame
created 579
penicillin yield data 578, 579
perspective plots 439
local regression models 452
perspective plots, creating grid 452
712
persp function 421
phone.gain data set 151
phone increase data 149
pigment data 665
pigment data set 665
Pillai-Bartlett trace test 654
pilot.design data frame
created 608
pilot.df data frame
created 609
pilot.yield vector 608
pilot plant data 608
ping-pong example 539, 548, 551,
558
plot.design function 572, 580,
581, 594, 604
plot.factor function 387, 409
plot.factor function 573, 581,
594, 605
plot.gam function 392, 396, 413
plot.glm function 390, 412
ask argument 393
plot function 5, 9
plots
autocorrelation plot 152
boxplots 123, 387, 409, 573,
582, 594
conditioning plots 7, 9, 441
density plot 123
density plots 123
diagnostic 436
for ANOVA 595, 611
diagnostic for ANOVA 575
exploratory data analysis 5, 123
factor plots 387, 409
histograms 5, 123, 575, 584, 595
interactively selecting points 20
normal probability plot 9
perspective 439
qq-plots 123
quantile-quantile 5, 584, 595,
610, 611
quantile-quantile plot 123
quantile-quantile plots 575
scatterplot matrices 5, 439
Index
surface plots 421
plotting
design data frames 580
factors 387, 409, 582
fitted models 9
generalized additive models 396
generalized linear models 390,
412
linear models 243
local regression models 436,
453
residuals in linear models 243
point estimates 156
pointwise confidence intervals
linear models 272
pointwise function 272
poison data 591, 592
poisons.design data set
created 592
poisons.df data frame
created 592
Poisson distribution 57, 71
in Poisson regression 383
mean 72
Poisson family 407
log link function 383
Poisson process 72, 76, 77, 430
Poisson regression 407
analysis of deviance tables 410,
411
log link function 383
using the gam function 386
poly.transform function 277
poly function 277
polynomial contrasts 40
polynomial regression 277
polynomials
formula elements 277
orthogonal form transformed to
simple form 277
polyroot function 528
positive-definite matrices 505
power law 600
ppreg
backward stepwise procedure
324
forward stepwise procedure 322
model selection strategy 324
multivariate response 326
ppreg function 318
examples 320
predict.gam function
safe prediction 423
type argument 420
predict.glm function
type argument 420
predicted response 9
predicted values 452
predict function 9, 25
linear models 270, 272
returned value 270
prediction 25
generalized additive models 420
generalized linear models 420
linear models 270
safe 422
predictor variable 5
probability
definition 51
probability density curves 123
probability density function 52
computing 57
See also probability
distributions
probability distributions 51, 53
beta 76
binomial 69, 182
Cauchy 79
chi-square 57, 64
comparing graphically 161
computing 56
empirical 161
exponential 76, 86
F 67
gamma 77
geometric 84
hypergeometric 74
listed 57
logistic 78
713
Index
lognormal 80
multivariate normal 82
negative binomial 84
normal (Gaussian) 61, 89, 118
Poisson 71
range of standard normals 81
stable 82
t 65
uniform 56, 60
Weibull 77
Wilcoxon rank sum statistic 56,
57, 85
probit link function 425
mathematical definition 382
probit regression 404
contrasted with logistic
regression 405
probit link function 382
using the gam function 386
product development data 602
profile function 561
profile projections 560
profiles for ms 561
profiles for nls 561
profile slices 560
profile t function 561
profiling 560
projection pursuit regression
algorithm 318, 320
prop.test function 185, 186
proportions 182
confidence intervals 185, 188
one sample 184
three or more samples 189
two samples 186
propranolol data 194
puromycin experiment 542
p-values 126, 128
pwilcox function 56
Q
qchisq function 57
qqnorm function 5, 9, 575, 584, 595,
610
714
qqnorm function
linear models 243
qqplot function 178
qq-plots
see quantile-quantile plots
quantile function
used to compute quartiles 101
quantile-quantile plots 5, 123
full 611
half-normal 610
residuals 575, 584, 595, 611
quantiles
computing 57
of a probability distribution 55
quartiles 124
of a probability distribution 55
of a sample 101, 105
quasi family 383
quasi-likelihood estimation 383, 415
defining a new variance
function 426
R
randomized blocks 578
random number generation 56, 86
double exponential (Laplace) 87
exponential 86
normal (Gaussian) 89
random variable 52
continuous 52, 60, 76
discrete 52, 69, 84
range
of a sample 98, 105
of standard normal random
variables 81
range function 98
rat growth-hormone study 678, 693
regression
diagnostic plots 242
generalized additive models 385
generalized linear models 381
least absolute deviation 370
least squares 239
linear models 8, 10
Index
logistic 382, 386, 387
M-estimates 372
multiple predictors 247
one variable 239
overview 237
Poisson 383, 386, 407
polynomial terms 275
probit 382, 386, 404
quasi-likelihood estimation 383,
415
robust techniques 333
simple 239
stepwise model selection 257
updating models 260
weighted 261
regression line 243
confidence intervals 272
regression splines 290
regression trees see also tree-based
models
repeated-measures designs 658
replicated factorial experiments 591
resid function 8, 575, 576, 585,
596, 612
resid function, see residuals
function
residual deviance 302
residuals
ANOVA models 575, 584, 595,
599, 611
definition 239
deviance 418
extracting 8
generalized additive models 418
generalized linear models 418
local regression models 436
normal plots 243
Pearson 418
plotting in linear models 243
response 419
serial correlation in 245
working 418
residuals function 419
type argument 419
residuals function
abbreviated resid 8, 575
response
lm models 242
response residuals 419
response variable 5
logistic regression 402
response weights
in ppreg 326
restricted maximum likelihood
method (REML) 479
robust estimates 96, 100, 111
A estimates of scale 112
interquartile range (IQR) 101
median 96
median absolute deviation 100,
111, 112, 113
M estimates of location 110,
112, 113
mode 97
tau estimates of scale 113
trimmed mean 96
robust methods 121
robust regression 333
least absolute deviation 370
M-estimates 372
Roy’s maximum eigenvalue test 654
rreg function 372
weight functions 374
runif function 56
S
salk.mat data set 193
Salk vaccine trials data 186, 192, 193
sample function 60, 69
sample mean. See mean
sample sum of squares. See sum of
squares
sample variance. See variance
scale.a function 114
scale.tau function 114
scatterplot matrices 5, 247, 253, 439
scatter plots 146
scatterplot smoothers 237, 290
locally weighted regression 291
715
Index
score equations 384
scores.treat data set 619
scores data set 619
second derivatives 550
self-starting function ??–520
biexponential model 514
first-order compartment model
514
four-parameter logistic model
514
logistic model 515
SEM. See standard error
s function 386, 394
s function 301
shapiro.test function 172, 177
allowable sample size 172
Shapiro-Wilk test for normality 160,
175
comparison with other onesample tests 174
interpretation 172
mathematical definition 172
shoe wear data 143
simple effects comparisons 686
simultaneous confidence intervals
273
linear models 272
skewness
of a probability distribution 55
of a sample 103
skewness function 103
smooth.spline function 298
smoothers 237
comparing 299
cubic smoothing spline 290
cubic spline 298
kernel-type 290, 295
locally weighted regression 290
variable span 290, 292
smoothing functions 385
cubic B-splines 394
local regression smoothing 394
solder.balance data set 407
solder data set 209
soybean data 476–520
716
Spearman’s r measure 154, 155
splines
B-splines 298
cubic smoothing splines 298
degrees of freedom 298
regression 290
split-plot designs 656
stable distribution 57, 82
stack.df data set
defined 247
stack.loss data set 247
stack.x data set 247
standard deviation 119
of a probability distribution 54
of a sample 99
of the sample mean 106
standard error
linear models 241
of the sample mean 106, 107
predicted values 270
statistical inference 125
alternative hypothesis 126
assumptions 121
confidence intervals 125
counts and proportions 182
difference of the two sample
means 139
equality of variances 139
hypothesis tests 125
null hypothesis 126
status.fac data set 195
status data set 194
stdev function 99
used to compute standard error
107
step function 257
displaying each step 259
stepwise model selection 257
straight line regression 237
Student’s t-test 127
one-sample 133
paired test 147
two-sample 139
sum contrasts 40
summarizing data 5
Index
summary.gam function 394, 397
summary.glm function 388, 400
disp argument 411
dispersion component 416
summary function 105
generalized additive models
394, 397
generalized linear models 388,
400
summary function 5, 9, 23, 241
ANOVA models 606
sum of squares
of a sample 99, 100
super smoother 312, 317, 323
supersmoother 292
supsm function 292
supsmu
use with ppreg 323
surface plots 421
symbolic differentiation 551
T
t.test function 108
t.test function 133, 139, 147
table function 402
used to compute modes 97
table function 195
tau estimates of scale 113
t distribution 57, 65
computing confidence intervals
108
relation to Cauchy distribution
79
test.vc data set 667
textbook parameterization of the lm
model 689
t measure of correlation 154, 155
Toothaker’s two-factor design 686
transformations
variance stabilizing 312
treatment 570
ANOVA models 574
treatment contrasts 39
tree-based models
fitting function 8
tree function 8
tri-cube weight function 291
trimmed mean 96
t-tests
see Student’s t-test
tukey.1 function 586
defined 589
Tukey’s bisquare functions
for A estimates of scale 112
for M estimates of location 110
Tukey’s method 677
Tukey’s one degree of freedom 586,
588
Tukey-Kramer multiple comparison
method 677
two-way layout
additive model 583
details 600
multiplicative interaction 586
power law 600
replicated 591–601
replicates 594, 596
robust methods 663
unreplicated 578–590
variance stabilizing 597, 599
U
unbiased estimates
sample mean 95
sample variance 99, 100
under-dispersion
in regression models 415
uniform distribution 56, 57, 60
random number generation 86
uniroot function 528
update function 9, 44, 437, 455
linear models 260
updating models 9
linear models 260
local regression models 437,
455
717
Index
V
var.test function 139
varcomp function 8
varcomp function 665
varFunc classes 280, 507
var function 99
computing biased/unbiased
estimates 100
computing the sum of squares
100
SumSquares argument 100
variables
continuous 30
variance 119
biased/unbiased estimates 99
of a probability distribution 54
of a sample 99, 106
variance components models 664
estimation methods 665
maximum likelihood estimate
665
MINQUE estimate 665
random slope example 666
restricted maximum likelihood
(REML) estimate 665
winsorized REML estimates
666
variance functions 505
in generalized additive models
385
in generalized linear models
381, 425
in logistic regression 382
718
in Poisson regression 383
in probit regression 382
variance stabilizing 597, 599
Box-Cox analysis 601
least squares 601
vershft argument 528
W
wafer data 626
wafer data set 626
wave-soldering skips experiment
540
wear.Ascom data set 145
wear.Bscom data set 145
Weibull distribution 57, 77
weighted regression 46, 237, 261
weight gain data 136
wilcox.test 128
wilcox.test function 135, 139, 141,
148
Wilcoxon test 128, 129
one-sample 135
paired test 148
two-sample 85, 141
Wilks’ lambda test 655
working residuals 418
W-statistic. See Shapiro-Wilk test
for normality
Y
yield data set
created 579
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