®
SAS/STAT 13.1 User’s Guide
The LOGISTIC Procedure
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Chapter 58
The LOGISTIC Procedure
Contents
Overview: LOGISTIC Procedure . . . . . . . . . . . . . . . .
Getting Started: LOGISTIC Procedure . . . . . . . . . . . . .
Syntax: LOGISTIC Procedure . . . . . . . . . . . . . . . . .
PROC LOGISTIC Statement . . . . . . . . . . . . . .
BY Statement . . . . . . . . . . . . . . . . . . . . . .
CLASS Statement . . . . . . . . . . . . . . . . . . . .
CODE Statement . . . . . . . . . . . . . . . . . . . . .
CONTRAST Statement . . . . . . . . . . . . . . . . .
EFFECT Statement . . . . . . . . . . . . . . . . . . .
EFFECTPLOT Statement . . . . . . . . . . . . . . . .
ESTIMATE Statement . . . . . . . . . . . . . . . . . .
EXACT Statement . . . . . . . . . . . . . . . . . . . .
EXACTOPTIONS Statement . . . . . . . . . . . . . .
FREQ Statement . . . . . . . . . . . . . . . . . . . . .
ID Statement . . . . . . . . . . . . . . . . . . . . . . .
LSMEANS Statement . . . . . . . . . . . . . . . . . .
LSMESTIMATE Statement . . . . . . . . . . . . . . .
MODEL Statement . . . . . . . . . . . . . . . . . . . .
NLOPTIONS Statement . . . . . . . . . . . . . . . . .
ODDSRATIO Statement . . . . . . . . . . . . . . . . .
OUTPUT Statement . . . . . . . . . . . . . . . . . . .
ROC Statement . . . . . . . . . . . . . . . . . . . . . .
ROCCONTRAST Statement . . . . . . . . . . . . . . .
SCORE Statement . . . . . . . . . . . . . . . . . . . .
SLICE Statement . . . . . . . . . . . . . . . . . . . . .
STORE Statement . . . . . . . . . . . . . . . . . . . .
STRATA Statement . . . . . . . . . . . . . . . . . . .
TEST Statement . . . . . . . . . . . . . . . . . . . . .
UNITS Statement . . . . . . . . . . . . . . . . . . . .
WEIGHT Statement . . . . . . . . . . . . . . . . . . .
Details: LOGISTIC Procedure . . . . . . . . . . . . . . . . .
Missing Values . . . . . . . . . . . . . . . . . . . . . .
Response Level Ordering . . . . . . . . . . . . . . . .
Link Functions and the Corresponding Distributions . .
Determining Observations for Likelihood Contributions
Iterative Algorithms for Model Fitting . . . . . . . . . .
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4485
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4484 F Chapter 58: The LOGISTIC Procedure
Convergence Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Existence of Maximum Likelihood Estimates . . . . . . . . . . . . . . . . . . . . . .
Effect-Selection Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Model Fitting Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Generalized Coefficient of Determination . . . . . . . . . . . . . . . . . . . . . . . .
Score Statistics and Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Confidence Intervals for Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . .
Odds Ratio Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rank Correlation of Observed Responses and Predicted Probabilities . . . . . . . . .
Linear Predictor, Predicted Probability, and Confidence Limits . . . . . . . . . . . . .
Classification Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Overdispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Hosmer-Lemeshow Goodness-of-Fit Test . . . . . . . . . . . . . . . . . . . . .
Receiver Operating Characteristic Curves . . . . . . . . . . . . . . . . . . . . . . . .
Testing Linear Hypotheses about the Regression Coefficients . . . . . . . . . . . . .
Regression Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Scoring Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conditional Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Exact Conditional Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . .
Input and Output Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Computational Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Displayed Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ODS Table Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ODS Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Examples: LOGISTIC Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example 58.1: Stepwise Logistic Regression and Predicted Values . . . . . . . . . .
Example 58.2: Logistic Modeling with Categorical Predictors . . . . . . . . . . . . .
Example 58.3: Ordinal Logistic Regression . . . . . . . . . . . . . . . . . . . . . .
Example 58.4: Nominal Response Data: Generalized Logits Model . . . . . . . . . .
Example 58.5: Stratified Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example 58.6: Logistic Regression Diagnostics . . . . . . . . . . . . . . . . . . . .
Example 58.7: ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, RSquare, and Confidence Limits . . . . . . . . . . . . . . . . . . . . . . . . .
Example 58.8: Comparing Receiver Operating Characteristic Curves . . . . . . . . .
Example 58.9: Goodness-of-Fit Tests and Subpopulations . . . . . . . . . . . . . . .
Example 58.10: Overdispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example 58.11: Conditional Logistic Regression for Matched Pairs Data . . . . . . .
Example 58.12: Firth’s Penalized Likelihood Compared with Other Approaches . . .
Example 58.13: Complementary Log-Log Model for Infection Rates . . . . . . . . .
Example 58.14: Complementary Log-Log Model for Interval-Censored Survival Times
Example 58.15: Scoring Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example 58.16: Using the LSMEANS Statement . . . . . . . . . . . . . . . . . . .
Example 58.17: Partial Proportional Odds Model . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Overview: LOGISTIC Procedure F 4485
Overview: LOGISTIC Procedure
Binary responses (for example, success and failure), ordinal responses (for example, normal, mild, and
severe), and nominal responses (for example, major TV networks viewed at a certain hour) arise in many
fields of study. Logistic regression analysis is often used to investigate the relationship between these discrete
responses and a set of explanatory variables. Texts that discuss logistic regression include Agresti (2002);
Allison (1999); Collett (2003); Cox and Snell (1989); Hosmer and Lemeshow (2000); Stokes, Davis, and
Koch (2012).
For binary response models, the response, Y, of an individual or an experimental unit can take on one of two
possible values, denoted for convenience by 1 and 2 (for example, Y = 1 if a disease is present, otherwise Y =
2). Suppose x is a vector of explanatory variables and D Pr.Y D 1 j x/ is the response probability to be
modeled. The linear logistic model has the form
D ˛ C ˇ0x
logit./ log
1 where ˛ is the intercept parameter and ˇ D .ˇ1 ; : : : ; ˇs /0 is the vector of s slope parameters. Notice that the
LOGISTIC procedure, by default, models the probability of the lower response levels.
The logistic model shares a common feature with a more general class of linear models: a function g D g./
of the mean of the response variable is assumed to be linearly related to the explanatory variables. Since
the mean implicitly depends on the stochastic behavior of the response, and the explanatory variables are
assumed to be fixed, the function g provides the link between the random (stochastic) component and the
systematic (deterministic) component of the response variable Y. For this reason, Nelder and Wedderburn
(1972) refer to g./ as a link function. One advantage of the logit function over other link functions is that
differences on the logistic scale are interpretable regardless of whether the data are sampled prospectively
or retrospectively (McCullagh and Nelder 1989, Chapter 4). Other link functions that are widely used in
practice are the probit function and the complementary log-log function. The LOGISTIC procedure enables
you to choose one of these link functions, resulting in fitting a broader class of binary response models of the
form
g./ D ˛ C ˇ 0 x
For ordinal response models, the response, Y, of an individual or an experimental unit might be restricted to
one of a (usually small) number of ordinal values, denoted for convenience by 1; : : : ; k; k C 1. For example,
the severity of coronary disease can be classified into three response categories as 1=no disease, 2=angina
pectoris, and 3=myocardial infarction. The LOGISTIC procedure fits a common slopes cumulative model,
which is a parallel lines regression model based on the cumulative probabilities of the response categories
rather than on their individual probabilities. The cumulative model has the form
g.Pr.Y i j x// D ˛i C ˇ 0 x;
i D 1; : : : ; k
where ˛1 ; : : : ; ˛k are k intercept parameters, and ˇ is the vector of slope parameters. This model has been
considered by many researchers. Aitchison and Silvey (1957) and Ashford (1959) employ a probit scale
and provide a maximum likelihood analysis; Walker and Duncan (1967) and Cox and Snell (1989) discuss
the use of the log odds scale. For the log odds scale, the cumulative logit model is often referred to as the
proportional odds model.
4486 F Chapter 58: The LOGISTIC Procedure
For nominal response logistic models, where the k C 1 possible responses have no natural ordering, the logit
model can also be extended to a multinomial model known as a generalized or baseline-category logit model,
which has the form
Pr.Y D i j x/
log
D ˛i C ˇi0 x; i D 1; : : : ; k
Pr.Y D k C 1 j x/
where the ˛1 ; : : : ; ˛k are k intercept parameters, and the ˇ1 ; : : : ; ˇk are k vectors of slope parameters. These
models are a special case of the discrete choice or conditional logit models introduced by McFadden (1974).
The LOGISTIC procedure fits linear logistic regression models for discrete response data by the method of
maximum likelihood. It can also perform conditional logistic regression for binary response data and exact
logistic regression for binary and nominal response data. The maximum likelihood estimation is carried out
with either the Fisher scoring algorithm or the Newton-Raphson algorithm, and you can perform the biasreducing penalized likelihood optimization as discussed by Firth (1993) and Heinze and Schemper (2002).
You can specify starting values for the parameter estimates. The logit link function in the logistic regression
models can be replaced by the probit function, the complementary log-log function, or the generalized logit
function.
Any term specified in the model is referred to as an effect. The LOGISTIC procedure enables you to
specify categorical variables (also known as classification or CLASS variables) and continuous variables as
explanatory effects. You can also specify more complex model terms such as interactions and nested terms
in the same way as in the GLM procedure. You can create complex constructed effects with the EFFECT
statement. An effect in the model that is not an interaction or a nested term or a constructed effect is referred
to as a main effect.
The LOGISTIC procedure allows either a full-rank parameterization or a less-than-full-rank parameterization
of the CLASS variables. The full-rank parameterization offers eight coding methods: effect, reference,
ordinal, polynomial, and orthogonalizations of these. The effect coding is the same method that is used in
the CATMOD procedure. The less-than-full-rank parameterization, often called dummy coding, is the same
coding as that used in the GLM procedure.
The LOGISTIC procedure provides four effect selection methods: forward selection, backward elimination,
stepwise selection, and best subset selection. The best subset selection is based on the likelihood score
statistic. This method identifies a specified number of best models containing one, two, three effects, and so
on, up to a single model containing effects for all the explanatory variables.
The LOGISTIC procedure has some additional options to control how to move effects in and out of a model
with the forward selection, backward elimination, or stepwise selection model-building strategies. When there
are no interaction terms, a main effect can enter or leave a model in a single step based on the p-value of the
score or Wald statistic. When there are interaction terms, the selection process also depends on whether you
want to preserve model hierarchy. These additional options enable you to specify whether model hierarchy is
to be preserved, how model hierarchy is applied, and whether a single effect or multiple effects can be moved
in a single step.
Odds ratio estimates are displayed along with parameter estimates. You can also specify the change in the
continuous explanatory main effects for which odds ratio estimates are desired. Confidence intervals for the
regression parameters and odds ratios can be computed based either on the profile-likelihood function or on
the asymptotic normality of the parameter estimators. You can also produce odds ratios for effects that are
involved in interactions or nestings, and for any type of parameterization of the CLASS variables.
Overview: LOGISTIC Procedure F 4487
Various methods to correct for overdispersion are provided, including Williams’ method for grouped binary
response data. The adequacy of the fitted model can be evaluated by various goodness-of-fit tests, including
the Hosmer-Lemeshow test for binary response data.
Like many procedures in SAS/STAT software that enable the specification of CLASS variables, the LOGISTIC procedure provides a CONTRAST statement for specifying customized hypothesis tests concerning
the model parameters. The CONTRAST statement also provides estimation of individual rows of contrasts,
which is particularly useful for obtaining odds ratio estimates for various levels of the CLASS variables.
The LOGISTIC procedure also provides testing capability through the ESTIMATE and TEST statements.
Analyses of LS-means are enabled with the LSMEANS, LSMESTIMATE, and SLICE statements.
You can perform a conditional logistic regression on binary response data by specifying the STRATA
statement. This enables you to perform matched-set and case-control analyses. The number of events and
nonevents can vary across the strata. Many of the features available with the unconditional analysis are also
available with a conditional analysis.
The LOGISTIC procedure enables you to perform exact logistic regression, also known as exact conditional
logistic regression, by specifying one or more EXACT statements. You can test individual parameters or
conduct a joint test for several parameters. The procedure computes two exact tests: the exact conditional
score test and the exact conditional probability test. You can request exact estimation of specific parameters
and corresponding odds ratios where appropriate. Point estimates, standard errors, and confidence intervals
are provided. You can perform stratified exact logistic regression by specifying the STRATA statement.
Further features of the LOGISTIC procedure enable you to do the following:
• control the ordering of the response categories
• compute a generalized R Square measure for the fitted model
• reclassify binary response observations according to their predicted response probabilities
• test linear hypotheses about the regression parameters
• create a data set for producing a receiver operating characteristic (ROC) curve for each fitted model
• specify contrasts to compare several receiver operating characteristic curves
• create a data set containing the estimated response probabilities, residuals, and influence diagnostics
• score a data set by using a previously fitted model
The LOGISTIC procedure uses ODS Graphics to create graphs as part of its output. For general information
about ODS Graphics, see Chapter 21, “Statistical Graphics Using ODS.” For more information about the
plots implemented in PROC LOGISTIC, see the section “ODS Graphics” on page 4616.
The remaining sections of this chapter describe how to use PROC LOGISTIC and discuss the underlying
statistical methodology. The section “Getting Started: LOGISTIC Procedure” on page 4488 introduces
PROC LOGISTIC with an example for binary response data. The section “Syntax: LOGISTIC Procedure” on
page 4496 describes the syntax of the procedure. The section “Details: LOGISTIC Procedure” on page 4559
summarizes the statistical technique employed by PROC LOGISTIC. The section “Examples: LOGISTIC
Procedure” on page 4618 illustrates the use of the LOGISTIC procedure.
For more examples and discussion on the use of PROC LOGISTIC, see Stokes, Davis, and Koch (2012);
Allison (1999); SAS Institute Inc. (1995).
4488 F Chapter 58: The LOGISTIC Procedure
Getting Started: LOGISTIC Procedure
The LOGISTIC procedure is similar in use to the other regression procedures in the SAS System. To
demonstrate the similarity, suppose the response variable y is binary or ordinal, and x1 and x2 are two
explanatory variables of interest. To fit a logistic regression model, you can specify a MODEL statement
similar to that used in the REG procedure. For example:
proc logistic;
model y=x1 x2;
run;
The response variable y can be either character or numeric. PROC LOGISTIC enumerates the total number
of response categories and orders the response levels according to the response variable option ORDER= in
the MODEL statement.
You can also input binary response data that are grouped. In the following statements, n represents the
number of trials and r represents the number of events:
proc logistic;
model r/n=x1 x2;
run;
The following example illustrates the use of PROC LOGISTIC. The data, taken from Cox and Snell (1989, pp.
10–11), consist of the number, r, of ingots not ready for rolling, out of n tested, for a number of combinations
of heating time and soaking time.
data ingots;
input Heat Soak
datalines;
7 1.0 0 10 14 1.0
7 1.7 0 17 14 1.7
7 2.2 0 7 14 2.2
7 2.8 0 12 14 2.8
7 4.0 0 9 14 4.0
;
r n @@;
0
0
2
0
0
31
43
33
31
19
27
27
27
27
27
1.0
1.7
2.2
2.8
4.0
1
4
0
1
1
56
44
21
22
16
51
51
51
51
1.0
1.7
2.2
4.0
3 13
0 1
0 1
0 1
The following invocation of PROC LOGISTIC fits the binary logit model to the grouped data. The continuous
covariates Heat and Soak are specified as predictors, and the bar notation (“|”) includes their interaction,
Heat*Soak. The ODDSRATIO statement produces odds ratios in the presence of interactions, and a graphical
display of the requested odds ratios is produced when ODS Graphics is enabled.
ods graphics on;
proc logistic data=ingots;
model r/n = Heat | Soak;
oddsratio Heat / at(Soak=1 2 3 4);
run;
ods graphics off;
The results of this analysis are shown in the following figures. PROC LOGISTIC first lists background
information in Figure 58.1 about the fitting of the model. Included are the name of the input data set, the
response variable(s) used, the number of observations used, and the link function used.
Getting Started: LOGISTIC Procedure F 4489
Figure 58.1 Binary Logit Model
The LOGISTIC Procedure
Model Information
Data Set
Response Variable (Events)
Response Variable (Trials)
Model
Optimization Technique
Number
Number
Sum of
Sum of
WORK.INGOTS
r
n
binary logit
Fisher's scoring
of Observations Read
of Observations Used
Frequencies Read
Frequencies Used
19
19
387
387
The “Response Profile” table (Figure 58.2) lists the response categories (which are Event and Nonevent when
grouped data are input), their ordered values, and their total frequencies for the given data.
Figure 58.2 Response Profile with Events/Trials Syntax
Response Profile
Ordered
Value
1
2
Binary
Outcome
Total
Frequency
Event
Nonevent
12
375
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
The “Model Fit Statistics” table (Figure 58.3) contains Akaike’s information criterion (AIC), the Schwarz
criterion (SC), and the negative of twice the log likelihood (–2 Log L) for the intercept-only model and the
fitted model. AIC and SC can be used to compare different models, and the ones with smaller values are
preferred. Results of the likelihood ratio test and the efficient score test for testing the joint significance of the
explanatory variables (Soak, Heat, and their interaction) are included in the “Testing Global Null Hypothesis:
BETA=0” table (Figure 58.3); the small p-values reject the hypothesis that all slope parameters are equal to
zero.
4490 F Chapter 58: The LOGISTIC Procedure
Figure 58.3 Fit Statistics and Hypothesis Tests
Model Fit Statistics
Criterion
Intercept
Only
AIC
SC
-2 Log L
Intercept and Covariates
Log
Full Log
Likelihood
Likelihood
108.988
112.947
106.988
103.222
119.056
95.222
35.957
51.791
27.957
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
11.7663
16.5417
13.4588
3
3
3
0.0082
0.0009
0.0037
The “Analysis of Maximum Likelihood Estimates” table in Figure 58.4 lists the parameter estimates, their
standard errors, and the results of the Wald test for individual parameters. Note that the Heat*Soak parameter
is not significantly different from zero (p=0.727), nor is the Soak variable (p=0.6916).
Figure 58.4 Parameter Estimates
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
Heat
Soak
Heat*Soak
1
1
1
1
-5.9901
0.0963
0.2996
-0.00884
1.6666
0.0471
0.7551
0.0253
12.9182
4.1895
0.1574
0.1219
0.0003
0.0407
0.6916
0.7270
The “Association of Predicted Probabilities and Observed Responses” table (Figure 58.5) contains four
measures of association for assessing the predictive ability of a model. They are based on the number of pairs
of observations with different response values, the number of concordant pairs, and the number of discordant
pairs, which are also displayed. Formulas for these statistics are given in the section “Rank Correlation of
Observed Responses and Predicted Probabilities” on page 4575.
Getting Started: LOGISTIC Procedure F 4491
Figure 58.5 Association Table
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
70.9
17.3
11.8
4500
Somers' D
Gamma
Tau-a
c
0.537
0.608
0.032
0.768
The ODDSRATIO statement produces the “Odds Ratio Estimates and Wald Confidence Intervals” table
(Figure 58.6), and a graphical display of these estimates is shown in Figure 58.7. The differences between
the odds ratios are small compared to the variability shown by their confidence intervals, which confirms the
previous conclusion that the Heat*Soak parameter is not significantly different from zero.
Figure 58.6 Odds Ratios of Heat at Several Values of Soak
Odds Ratio Estimates and Wald Confidence Intervals
Odds Ratio
Heat
Heat
Heat
Heat
at
at
at
at
Soak=1
Soak=2
Soak=3
Soak=4
Estimate
1.091
1.082
1.072
1.063
95% Confidence Limits
1.032
1.028
0.986
0.935
1.154
1.139
1.166
1.208
4492 F Chapter 58: The LOGISTIC Procedure
Figure 58.7 Plot of Odds Ratios of Heat at Several Values of Soak
Since the Heat*Soak interaction is nonsignificant, the following statements fit a main-effects model:
proc logistic data=ingots;
model r/n = Heat Soak;
run;
The results of this analysis are shown in the following figures. The model information and response profiles
are the same as those in Figure 58.1 and Figure 58.2 for the saturated model. The “Model Fit Statistics” table
in Figure 58.8 shows that the AIC and SC for the main-effects model are smaller than for the saturated model,
indicating that the main-effects model might be the preferred model. As in the preceding model, the “Testing
Global Null Hypothesis: BETA=0” table indicates that the parameters are significantly different from zero.
Getting Started: LOGISTIC Procedure F 4493
Figure 58.8 Fit Statistics and Hypothesis Tests
The LOGISTIC Procedure
Model Fit Statistics
Intercept and Covariates
Log
Full Log
Likelihood
Likelihood
Intercept
Only
Criterion
AIC
SC
-2 Log L
108.988
112.947
106.988
101.346
113.221
95.346
34.080
45.956
28.080
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
11.6428
15.1091
13.0315
2
2
2
0.0030
0.0005
0.0015
The “Analysis of Maximum Likelihood Estimates” table in Figure 58.9 again shows that the Soak parameter
is not significantly different from zero (p=0.8639). The odds ratio for each effect parameter, estimated
by exponentiating the corresponding parameter estimate, is shown in the “Odds Ratios Estimates” table
(Figure 58.9), along with 95% Wald confidence intervals. The confidence interval for the Soak parameter
contains the value 1, which also indicates that this effect is not significant.
Figure 58.9 Parameter Estimates and Odds Ratios
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
Heat
Soak
1
1
1
-5.5592
0.0820
0.0568
1.1197
0.0237
0.3312
24.6503
11.9454
0.0294
<.0001
0.0005
0.8639
Odds Ratio Estimates
Effect
Heat
Soak
Point
Estimate
1.085
1.058
95% Wald
Confidence Limits
1.036
0.553
1.137
2.026
4494 F Chapter 58: The LOGISTIC Procedure
Figure 58.9 continued
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
64.4
18.4
17.2
4500
Somers' D
Gamma
Tau-a
c
0.460
0.555
0.028
0.730
Using these parameter estimates, you can calculate the estimated logit of as
5:5592 C 0:082 Heat C 0:0568 Soak
For example, if Heat=7 and Soak=1, then logit.b
/ D
b
as follows:
4:9284. Using this logit estimate, you can calculate
b
D 1=.1 C e4:9284 / D 0:0072
This gives the predicted probability of the event (ingot not ready for rolling) for Heat=7 and Soak=1. Note that
PROC LOGISTIC can calculate these statistics for you; use the OUTPUT statement with the PREDICTED=
option, or use the SCORE statement.
To illustrate the use of an alternative form of input data, the following program creates the ingots data set
with the new variables NotReady and Freq instead of n and r. The variable NotReady represents the response
of individual units; it has a value of 1 for units not ready for rolling (event) and a value of 0 for units ready
for rolling (nonevent). The variable Freq represents the frequency of occurrence of each combination of
Heat, Soak, and NotReady. Note that, compared to the previous data set, NotReady=1 implies Freq=r, and
NotReady=0 implies Freq=n–r.
data ingots;
input Heat Soak
datalines;
7 1.0 0 10 14 1.0
7 1.7 0 17 14 1.7
7 2.2 0 7 14 2.2
7 2.8 0 12 14 2.2
7 4.0 0 9 14 2.8
;
NotReady Freq @@;
0
0
1
0
0
31
43
2
31
31
14
27
27
27
27
4.0
1.0
1.0
1.7
1.7
0 19
1 1
0 55
1 4
0 40
27
27
27
27
27
2.2
2.8
2.8
4.0
4.0
0 21
1 1
0 21
1 1
0 15
51
51
51
51
51
1.0
1.0
1.7
2.2
4.0
1 3
0 10
0 1
0 1
0 1
Getting Started: LOGISTIC Procedure F 4495
The following statements invoke PROC LOGISTIC to fit the main-effects model by using the alternative
form of the input data set:
proc logistic data=ingots;
model NotReady(event='1') = Heat Soak;
freq Freq;
run;
Results of this analysis are the same as the preceding single-trial main-effects analysis. The displayed output
for the two runs are identical except for the background information of the model fit and the “Response
Profile” table shown in Figure 58.10.
Figure 58.10 Response Profile with Single-Trial Syntax
The LOGISTIC Procedure
Response Profile
Ordered
Value
NotReady
Total
Frequency
1
2
0
1
375
12
Probability modeled is NotReady=1.
By default, Ordered Values are assigned to the sorted response values in ascending order, and PROC
LOGISTIC models the probability of the response level that corresponds to the Ordered Value 1. There
are several methods to change these defaults; the preceding statements specify the response variable option
EVENT= to model the probability of NotReady=1 as displayed in Figure 58.10. See the section “Response
Level Ordering” on page 4559 for more details.
4496 F Chapter 58: The LOGISTIC Procedure
Syntax: LOGISTIC Procedure
The following statements are available in the LOGISTIC procedure:
PROC LOGISTIC < options > ;
BY variables ;
CLASS variable < (options) > < variable < (options) > . . . > < / options > ;
CODE < options > ;
CONTRAST 'label' effect values< , effect values, . . . > < / options > ;
EFFECT name=effect-type(variables < / options >) ;
EFFECTPLOT < plot-type < (plot-definition-options) > > < / options > ;
ESTIMATE < 'label' > estimate-specification < / options > ;
EXACT < 'label' > < INTERCEPT > < effects > < / options > ;
EXACTOPTIONS options ;
FREQ variable ;
ID variables ;
LSMEANS < model-effects > < / options > ;
LSMESTIMATE model-effect lsmestimate-specification < / options > ;
< label: > MODEL variable < (variable_options) > = < effects > < / options > ;
< label: > MODEL events/trials = < effects > < / options > ;
NLOPTIONS options ;
ODDSRATIO < 'label' > variable < / options > ;
OUTPUT < OUT=SAS-data-set > < keyword=name < keyword=name . . . > > < / option > ;
ROC < 'label' > < specification > < / options > ;
ROCCONTRAST < 'label' > < contrast > < / options > ;
SCORE < options > ;
SLICE model-effect < / options > ;
STORE < OUT= >item-store-name < / LABEL='label' > ;
STRATA effects < / options > ;
< label: > TEST equation1 < ,equation2, . . . > < / option > ;
UNITS < independent1=list1 < independent2=list2 . . . > > < / option > ;
WEIGHT variable < / option > ;
The PROC LOGISTIC and MODEL statements are required. The CLASS and EFFECT statements (if
specified) must precede the MODEL statement, and the CONTRAST, EXACT, and ROC statements (if
specified) must follow the MODEL statement.
The PROC LOGISTIC, MODEL, and ROCCONTRAST statements can be specified at most once. If a FREQ
or WEIGHT statement is specified more than once, the variable specified in the first instance is used. If a BY,
OUTPUT, or UNITS statement is specified more than once, the last instance is used.
The rest of this section provides detailed syntax information for each of the preceding statements, beginning
with the PROC LOGISTIC statement. The remaining statements are covered in alphabetical order. The CODE,
EFFECT, EFFECTPLOT, ESTIMATE, LSMEANS, LSMESTIMATE, SLICE, and STORE statements are
also available in many other procedures. Summary descriptions of functionality and syntax for these
statements are provided, but you can find full documentation on them in the corresponding sections of
Chapter 19, “Shared Concepts and Topics.”
PROC LOGISTIC Statement F 4497
PROC LOGISTIC Statement
PROC LOGISTIC < options > ;
The PROC LOGISTIC statement invokes the LOGISTIC procedure. Optionally, it identifies input and output
data sets, suppresses the display of results, and controls the ordering of the response levels. Table 58.1
summarizes the options available in the PROC LOGISTIC statement.
Table 58.1
Option
PROC LOGISTIC Statement Options
Description
Input/Output Data Set Options
COVOUT
Displays the estimated covariance matrix in the OUTEST= data set
DATA=
Names the input SAS data set
INEST=
Specifies the initial estimates SAS data set
INMODEL=
Specifies the model information SAS data set
NOCOV
Does not save covariance matrix in the OUTMODEL= data set
OUTDESIGN=
Specifies the design matrix output SAS data set
OUTDESIGNONLY Outputs the design matrix only
OUTEST=
Specifies the parameter estimates output SAS data set
OUTMODEL=
Specifies the model output data set for scoring
Response and CLASS Variable Options
DESCENDING
Reverses the sort order of the response variable
NAMELEN=
Specifies the maximum length of effect names
ORDER=
Specifies the sort order of the response variable
TRUNCATE
Truncates class level names
Displayed Output Options
ALPHA=
Specifies the significance level for confidence intervals
NOPRINT
Suppresses all displayed output
PLOTS
Specifies options for plots
SIMPLE
Displays descriptive statistics
Large Data Set Option
MULTIPASS
Does not copy the input SAS data set for internal computations
Control of Other Statement Options
EXACTONLY
Performs exact analysis only
EXACTOPTIONS
Specifies global options for EXACT statements
ROCOPTIONS
Specifies global options for ROC statements
ALPHA=number
specifies the level of significance ˛ for 100.1 ˛/% confidence intervals. The value number must be
between 0 and 1; the default value is 0.05, which results in 95% intervals. This value is used as the
default confidence level for limits computed by the following options:
4498 F Chapter 58: The LOGISTIC Procedure
Statement
Options
CONTRAST
EXACT
MODEL
ODDSRATIO
OUTPUT
PROC LOGISTIC
ROCCONTRAST
SCORE
ESTIMATE=
ESTIMATE=
CLODDS= CLPARM=
CL=
LOWER= UPPER=
PLOTS=EFFECT(CLBAR CLBAND)
ESTIMATE=
CLM
You can override the default in most of these cases by specifying the ALPHA= option in the separate
statements.
COVOUT
adds the estimated covariance matrix to the OUTEST= data set. For the COVOUT option to have
an effect, the OUTEST= option must be specified. See the section “OUTEST= Output Data Set” on
page 4601 for more information.
DATA=SAS-data-set
names the SAS data set containing the data to be analyzed. If you omit the DATA= option, the
procedure uses the most recently created SAS data set. The INMODEL= option cannot be specified
with this option.
DESCENDING
DESC
reverses the sort order for the levels of the response variable. If both the DESCENDING and ORDER=
options are specified, PROC LOGISTIC orders the levels according to the ORDER= option and then
reverses that order. This option has the same effect as the response variable option DESCENDING in
the MODEL statement. See the section “Response Level Ordering” on page 4559 for more detail.
EXACTONLY
requests only the exact analyses. The asymptotic analysis that PROC LOGISTIC usually performs is
suppressed.
EXACTOPTIONS (options)
specifies options that apply to every EXACT statement in the program. The available options are
summarized here, and full descriptions are available in the EXACTOPTIONS statement.
Option
Description
ADDTOBS
BUILDSUBSETS
EPSILON=
MAXTIME=
METHOD=
N=
ONDISK
SEED=
STATUSN=
STATUSTIME=
Adds the observed sufficient statistic to the sampled exact distribution
Builds every distribution for sampling
Specifies the comparison fuzz for partial sums of sufficient statistics
Specifies the maximum time allowed in seconds
Specifies the DIRECT, NETWORK, or NETWORKMC algorithm
Specifies the number of Monte Carlo samples
Uses disk space
Specifies the initial seed for sampling
Specifies the sampling interval for printing a status line
Specifies the time interval for printing a status line
PROC LOGISTIC Statement F 4499
INEST=SAS-data-set
names the SAS data set that contains initial estimates for all the parameters in the model. If BY-group
processing is used, it must be accommodated in setting up the INEST= data set. See the section
“INEST= Input Data Set” on page 4603 for more information.
INMODEL=SAS-data-set
specifies the name of the SAS data set that contains the model information needed for scoring new
data. This INMODEL= data set is the OUTMODEL= data set saved in a previous PROC LOGISTIC
call. The OUTMODEL= data set should not be modified before its use as an INMODEL= data set.
The DATA= option cannot be specified with this option; instead, specify the data sets to be scored
in the SCORE statements. FORMAT statements are not allowed when the INMODEL= data set is
specified; variables in the DATA= and PRIOR= data sets in the SCORE statement should be formatted
within the data sets.
You can specify the BY statement provided that the INMODEL= data set is created under the same
BY-group processing.
The CLASS, EFFECT, EFFECTPLOT, ESTIMATE, EXACT, LSMEANS, LSMESTIMATE, MODEL,
OUTPUT, ROC, ROCCONTRAST, SLICE, STORE, TEST, and UNIT statements are not available
with the INMODEL= option.
MULTIPASS
forces the procedure to reread the DATA= data set as needed rather than require its storage in memory
or in a temporary file on disk. By default, the data set is cleaned up and stored in memory or in a
temporary file. This option can be useful for large data sets. All exact analyses are ignored in the
presence of the MULTIPASS option. If a STRATA statement is specified, then the data set must first
be grouped or sorted by the strata variables.
NAMELEN=n
specifies the maximum length of effect names in tables and output data sets to be n characters, where n
is a value between 20 and 200. The default length is 20 characters.
NOCOV
specifies that the covariance matrix not be saved in the OUTMODEL= data set. The covariance matrix
is needed for computing the confidence intervals for the posterior probabilities in the OUT= data set in
the SCORE statement. Specifying this option will reduce the size of the OUTMODEL= data set.
NOPRINT
suppresses all displayed output. Note that this option temporarily disables the Output Delivery System
(ODS); see Chapter 20, “Using the Output Delivery System,” for more information.
ORDER=DATA | FORMATTED | FREQ | INTERNAL
RORDER=DATA | FORMATTED | INTERNAL
specifies the sort order for the levels of the response variable. See the response variable option
ORDER= in the MODEL statement for more information. For ordering of CLASS variable levels, see
the ORDER= option in the CLASS statement.
OUTDESIGN=SAS-data-set
specifies the name of the data set that contains the design matrix for the model. The data set contains
the same number of observations as the corresponding DATA= data set and includes the response
4500 F Chapter 58: The LOGISTIC Procedure
variable (with the same format as in the DATA= data set), the FREQ variable, the WEIGHT variable,
the OFFSET= variable, and the design variables for the covariates, including the Intercept variable of
constant value 1 unless the NOINT option in the MODEL statement is specified.
OUTDESIGNONLY
suppresses the model fitting and creates only the OUTDESIGN= data set. This option is ignored if the
OUTDESIGN= option is not specified.
OUTEST=SAS-data-set
creates an output SAS data set that contains the final parameter estimates and, optionally, their estimated
covariances (see the preceding COVOUT option). The output data set also includes a variable named
_LNLIKE_, which contains the log likelihood. See the section “OUTEST= Output Data Set” on
page 4601 for more information.
OUTMODEL=SAS-data-set
specifies the name of the SAS data set that contains the information about the fitted model. This data
set contains sufficient information to score new data without having to refit the model. It is solely used
as the input to the INMODEL= option in a subsequent PROC LOGISTIC call. The OUTMODEL=
option is not available with the STRATA statement. Information in this data set is stored in a very
compact form, so you should not modify it manually.
N OTE : The STORE statement can also be used to save your model. See the section “STORE Statement”
on page 4555 for more information.
PLOTS < (global-plot-options) > < =plot-request < (options) > >
PLOTS < (global-plot-options) > =(plot-request < (options) > < ... plot-request < (options) > >)
controls the plots produced through ODS Graphics. When you specify only one plot-request , you can
omit the parentheses from around the plot-request . For example:
PLOTS = ALL
PLOTS = (ROC EFFECT INFLUENCE(UNPACK))
PLOTS(ONLY) = EFFECT(CLBAR SHOWOBS)
ODS Graphics must be enabled before plots can be requested. For example:
ods graphics on;
proc logistic plots=all;
model y=x;
run;
ods graphics off;
For more information about enabling and disabling ODS Graphics, see the section “Enabling and
Disabling ODS Graphics” on page 606 in Chapter 21, “Statistical Graphics Using ODS.”
If the PLOTS option is not specified or is specified with no plot-requests, then graphics are produced
by default in the following situations:
• If the INFLUENCE or IPLOTS option is specified in the MODEL statement, thenthe INFLUENCE plots are produced unless the MAXPOINTS= cutoff is exceeded.
PROC LOGISTIC Statement F 4501
• If you specify the OUTROC= option in the MODEL statement, then ROC curves are produced.
If you also specify a SELECTION= method, then an overlaid plot of all the ROC curves for each
step of the selection process is displayed.
• If the OUTROC= option is specified in a SCORE statement, then the ROC curve for the scored
data set is displayed.
• If you specify ROC statements, then an overlaid plot of the ROC curves for the model (or the
selected model if a SELECTION= method is specified) and for all the ROC statement models is
displayed.
• If an odds ratio table is produced, then a plot of the odds ratios and their confidence limits is
displayed. These plots correspond to the default odds ratio table and the tables produced by
the CLODDS= option in the MODEL statement and the tables produced by the ODDSRATIO
statement.
For general information about ODS Graphics, see Chapter 21, “Statistical Graphics Using ODS.”
The following global-plot-options are available:
LABEL
displays a label on diagnostic plots to aid in identifying the outlying observations. This option
enhances the plots produced by the DFBETAS, DPC, INFLUENCE, LEVERAGE, and PHAT
options. If an ID statement is specified, then the plots are labeled with the ID variables. Otherwise,
the observation number is displayed.
MAXPOINTS=NONE | number
suppresses the plots produced by the DFBETAS, DPC, INFLUENCE, LEVERAGE, and PHAT
options if there are more than number observations. Also, observations are not displayed on the
EFFECT plots when the cutoff is exceeded. The default is MAXPOINTS=5000. The cutoff is
ignored if you specify MAXPOINTS=NONE.
ONLY
specifically requested plot-requests are displayed.
UNPACKPANELS | UNPACK
suppresses paneling. By default, multiple plots can appear in some output panels. Specify
UNPACKPANEL to display each plot separately.
The following plot-requests are available:
ALL
produces all appropriate plots. You can specify other options with ALL. For example, to display
all plots and unpack the DFBETAS plots you can specify plots=(all dfbetas(unpack)).
DFBETAS < (UNPACK) >
displays plots of DFBETAS versus the case (observation) number. This displays the statistics
generated by the DFBETAS=_ALL_ option in the OUTPUT statement. The UNPACK option
displays the plots separately. See Output 58.6.5 for an example of this plot.
4502 F Chapter 58: The LOGISTIC Procedure
DPC< (dpc-options) >
displays plots of DIFCHISQ and DIFDEV versus the predicted event probability, and displays
the markers according to the value of the confidence interval displacement C. See Output 58.6.8
for an example of this plot. You can specify the following dpc-options:
MAXSIZE=Smax
specifies the maximum bubble radius. This option is available only when TYPE=BUBBLE.
By default, Smax =21.
MAXVALUE=Cmax
displays all observations that have CCmax as a bubble, with radius equal to the MAXSIZE=
value. This option is available only when TYPE=BUBBLE. By default, Cmax =maxi .Ci /.
MINSIZE=Smin
specifies the minimum bubble radius. Any observation that maps to a smaller radius is
displayed at this size. This option is available only when TYPE=BUBBLE. By default,
Smin=3.5.
TYPE=BUBBLE | GRADIENT | LABEL
specifies how the C statistic is displayed. TYPE=BUBBLE displays circular markers
whose areas are proportional to C and whose colors are determined by their response.
TYPE=GRADIENT colors the markers according to the value of C; this is the default.
TYPE=LABEL displays the ID variables (if an ID statement is specified) or the observation
number, with colors that are determined by their response and with font sizes that are chosen
by dividing the interval Œ0; maxi .Ci / into 20 bins and setting the font size to the number of
the bin.
UNPACKPANELS | UNPACK
displays the plots separately.
EFFECT< (effect-options) >
displays and enhances the effect plots for the model. For more information about effect plots and
the available effect-options, see the section “PLOTS=EFFECT Plots” on page 4505.
N OTE : The EFFECTPLOT statement provides much of the same functionality and more options
for creating effect plots. See Outputs 58.2.11, 58.3.5, 58.4.8, 58.7.4, and 58.15.4 for examples of
effect plots.
INFLUENCE< (UNPACK | STDRES) >
displays index plots of RESCHI, RESDEV, leverage, confidence interval displacements C and
CBar, DIFCHISQ, and DIFDEV. These plots are produced by default when any plot-request is
specified and the MAXPOINTS= cutoff is not exceeded. The UNPACK option displays the plots
separately. The STDRES option also displays index plots of STDRESCHI, STDRESDEV, and
RESLIK. See Outputs 58.6.3 and 58.6.4 for examples of these plots.
LEVERAGE< (UNPACK) >
displays plots of DIFCHISQ, DIFDEV, confidence interval displacement C, and the predicted
probability versus the leverage. The UNPACK option displays the plots separately. See Output 58.6.7 for an example of this plot.
PROC LOGISTIC Statement F 4503
NONE
suppresses all plots.
ODDSRATIO < (oddsratio-options) >
displays and enhances the odds ratio plots for the model. For more information about odds ratio
plots and the available oddsratio-options, see the section “Odds Ratio Plots” on page 4508. See
Outputs 58.7,58.2.9, 58.3.3, and 58.4.5 for examples of this plot.
PHAT< (UNPACK) >
displays plots of DIFCHISQ, DIFDEV, confidence interval displacement C, and leverage versus the predicted event probability. The UNPACK option displays the plots separately. See
Output 58.6.6 for an example of this plot.
ROC< (ID< =keyword >) >
displays the ROC curve. If you also specify a SELECTION= method, then an overlaid plot
of all the ROC curves for each step of the selection process is displayed. If you specify ROC
statements, then an overlaid plot of the model (or the selected model if a SELECTION= method
is specified) and the ROC statement models is displayed. If the OUTROC= option is specified in
a SCORE statement, then the ROC curve for the scored data set is displayed.
The ID= option labels certain points on the ROC curve. Typically, the labeled points are closest
to the upper-left corner of the plot, and points directly below or to the right of a labeled point are
suppressed. This option is identical to, and has the same keywords as, the ID= suboption of the
ROCOPTIONS option.
See Output 58.7.3 and Example 58.8 for examples of these ROC plots.
ROCOPTIONS (options)
specifies options that apply to every model specified in a ROC statement. Some of these options also
apply to the SCORE statement. The following options are available:
ALPHA=number
sets the significance level for creating confidence limits of the areas and the pairwise differences.
The ALPHA= value specified in the PROC LOGISTIC statement is the default. If neither
ALPHA= value is specified, then ALPHA=0.05 by default.
EPS=value
is an alias for the ROCEPS= option in the MODEL statement. This value is used to determine
which predicted probabilities are equal. The default value is the square root of the machine
epsilon, which is about 1E–8.
ID< =keyword >
displays labels on certain points on the individual ROC curves and also on the SCORE statement’s
ROC curve. This option overrides the ID= suboption of the PLOTS=ROC option. If several
observations lie at the same place on the ROC curve, the value for the last observation is displayed.
If you specify the ID option with no keyword , any variables that are listed in the ID statement
are used. If no ID statement is specified, the observation number is displayed. The following
keywords are available:
4504 F Chapter 58: The LOGISTIC Procedure
PROB
displays the model predicted probability.
OBS
displays the (last) observation number.
SENSIT
displays the true positive fraction (sensitivity).
1MSPEC
displays the false positive fraction (1–specificity).
FALPOS
displays the fraction of nonevents that are predicted as events.
FALNEG
displays the fraction of events that are predicted as nonevents.
POSPRED
displays the positive predictive value (1–FALPOS).
NEGPRED
displays the negative predictive value (1–FALNEG).
MISCLASS
displays the misclassification rate.
ID
displays the ID variables.
The SENSIT, 1MSPEC, FALPOS, and FALNEG statistics are defined in the section “Receiver
Operating Characteristic Curves” on page 4582. The misclassification rate is the number of
events that are predicted as nonevents and the number of nonevents that are predicted as events
as calculated by using the given cutpoint (predicted probability) divided by the number of
observations. If the PEVENT= option is also specified, then FALPOS and FALNEG are computed
using the first PEVENT= value and Bayes’ theorem, as discussed in the section “Predicted
Probability of an Event for Classification” on page 4578.
NODETAILS
suppresses the display of the model fitting information for the models specified in the ROC
statements.
OUT=SAS-data-set-name
is an alias for the OUTROC= option in the MODEL statement.
WEIGHTED
uses frequencyweight in the ROC computations (Izrael et al. 2002) instead of just frequency.
Typically, weights are considered in the fit of the model only, and hence are accounted for in the
parameter estimates. The “Association of Predicted Probabilities and Observed Responses” table
uses frequency (unless the BINWIDTH=0 option is also specified on the MODEL statement), and
is suppressed when ROC comparisons are performed. This option also affects SCORE statement
ROC and area under the ROC curve (AUC) computations.
SIMPLE
displays simple descriptive statistics (mean, standard deviation, minimum and maximum) for each
continuous explanatory variable. For each CLASS variable involved in the modeling, the frequency
counts of the classification levels are displayed. The SIMPLE option generates a breakdown of the
simple descriptive statistics or frequency counts for the entire data set and also for individual response
categories.
TRUNCATE
determines class levels by using no more than the first 16 characters of the formatted values of CLASS,
response, and strata variables. When formatted values are longer than 16 characters, you can use this
option to revert to the levels as determined in releases previous to SAS 9.0. This option invokes the
same option in the CLASS statement.
PROC LOGISTIC Statement F 4505
PLOTS=EFFECT Plots
Only one PLOTS=EFFECT plot is produced by default; you must specify other effect-options to produce
multiple plots. For binary response models, the following plots are produced when an EFFECT option is
specified with no effect-options:
• If you only have continuous covariates in the model, then a plot of the predicted probability versus
the first continuous covariate fixing all other continuous covariates at their means is displayed. See
Output 58.7.4 for an example with one continuous covariate.
• If you only have classification covariates in the model, then a plot of the predicted probability versus
the first CLASS covariate at each level of the second CLASS covariate, if any, holding all other CLASS
covariates at their reference levels is displayed.
• If you have CLASS and continuous covariates, then a plot of the predicted probability versus the first
continuous covariate at up to 10 cross-classifications of the CLASS covariate levels, while fixing all
other continuous covariates at their means and all other CLASS covariates at their reference levels, is
displayed. For example, if your model has four binary covariates, there are 16 cross-classifications of
the CLASS covariate levels. The plot displays the 8 cross-classifications of the levels of the first three
covariates while the fourth covariate is fixed at its reference level.
For polytomous response models, similar plots are produced by default, except that the response levels are
used in place of the CLASS covariate levels. Plots for polytomous response models involving OFFSET=
variables with multiple values are not available.
The following effect-options specify the type of graphic to produce:
AT(variable=value-list | ALL< ...variable=value-list | ALL >)
specifies fixed values for a covariate. For continuous covariates, you can specify one or more numbers
in the value-list . For classification covariates, you can specify one or more formatted levels of the
covariate enclosed in single quotes (for example, A=’cat’ ’dog’), or you can specify the keyword
ALL to select all levels of the classification variable. You can specify a variable at most once in the
AT option. By default, continuous covariates are set to their means when they are not used on an
axis, while classification covariates are set to their reference level when they are not used as an X=,
SLICEBY=, or PLOTBY= effect. For example, for a model that includes a classification variable
A={cat,dog} and a continuous covariate X, specifying AT(A=’cat’ X=7 9) will set A to ‘cat’ when A
does not appear in the plot. When X does not define an axis it first produces plots setting X = 7 and
then produces plots setting X = 9. Note in this example that specifying AT( A=ALL ) is the same as
specifying the PLOTBY=A option.
FITOBSONLY
computes the predicted values only at the observed data. If the FITOBSONLY option is omitted and the
X-axis variable is continuous, the predicted values are computed at a grid of points extending slightly
beyond the range of the data (see the EXTEND= option for more information). If the FITOBSONLY
option is omitted and the X-axis effect is categorical, the predicted values are computed at all possible
categories.
4506 F Chapter 58: The LOGISTIC Procedure
INDIVIDUAL
displays the individual probabilities instead of the cumulative probabilities. This option is available
only with cumulative models, and it is not available with the LINK option.
LINK
displays the linear predictors instead of the probabilities on the Y axis. For example, for a binary
logistic regression, the Y axis will be displayed on the logit scale. The INDIVIDUAL and POLYBAR
options are not available with the LINK option.
PLOTBY=effect
displays an effect plot at each unique level of the PLOTBY= effect. You can specify effect as one
CLASS variable or as an interaction of classification covariates. For polytomous-response models, you
can also specify the response variable as the lone PLOTBY= effect. For nonsingular parameterizations,
the complete cross-classification of the CLASS variables specified in the effect define the different
PLOTBY= levels. When the GLM parameterization is used, the PLOTBY= levels can depend on the
model and the data.
SLICEBY=effect
displays predicted probabilities at each unique level of the SLICEBY= effect. You can specify effect
as one CLASS variable or as an interaction of classification covariates. For polytomous-response
models, you can also specify the response variable as the lone SLICEBY= effect. For nonsingular
parameterizations, the complete cross-classification of the CLASS variables specified in the effect
define the different SLICEBY= levels. When the GLM parameterization is used, the SLICEBY= levels
can depend on the model and the data.
X=effect
X=(effect...effect)
specifies effects to be used on the X axis of the effect plots. You can specify several different X axes:
continuous variables must be specified as main effects, while CLASS variables can be crossed. For
nonsingular parameterizations, the complete cross-classification of the CLASS variables specified in
the effect define the axes. When the GLM parameterization is used, the X= levels can depend on the
model and the data. The response variable is not allowed as an effect .
N OTE : Any variable not specified in a SLICEBY= or PLOTBY= option is available to be displayed on the X
axis. A variable can be specified in at most one of the SLICEBY=, PLOTBY=, and X= options.
The following effect-options enhance the graphical output:
ALPHA=number
specifies the size of the confidence limits. The ALPHA= value specified in the PROC LOGISTIC
statement is the default. If neither ALPHA= value is specified, then ALPHA=0.05 by default.
CLBAND< =YES | NO >
displays confidence limits on the plots. This option is not available with the INDIVIDUAL option.
If you have CLASS covariates on the X axis, then error bars are displayed (see the CLBAR option)
unless you also specify the CONNECT option.
CLBAR
displays the error bars on the plots when you have CLASS covariates on the X axis; if the X axis is
continuous, then this invokes the CLBAND option. For polytomous-response models with CLASS
covariates only and with the POLYBAR option specified, the stacked bar charts are replaced by
side-by-side bar charts with error bars.
PROC LOGISTIC Statement F 4507
CLUSTER< =percent >
displays the levels of the SLICEBY= effect in a side-by-side fashion instead of stacking them. This
option is available when you have CLASS covariates on the X axis. You can specify percent as a
percentage of half the distance between X levels. The percent value must be between 0.1 and 1; the
default percent depends on the number of X levels and the number of SLICEBY= levels. Default
clustering can be removed by specifying the NOCLUSTER option.
CONNECT< =YES | NO >
JOIN< =YES | NO >
connects the predicted values with a line. This option is available when you have CLASS covariates on
the X axis. Default connecting lines can be suppressed by specifying the NOCONNECT option.
EXTEND=value
extends continuous X axes by a factor of value=2 in each direction. By default, EXTEND=0.2.
MAXATLEN=length
specifies the maximum number of characters used to display the levels of all the fixed variables. If
the text is too long, it is truncated and ellipses (“. . . ”) are appended. By default, length is equal to its
maximum allowed value, 256.
NOCLUSTER
prevents clustering of the levels of the SLICEBY= effect. This option is available when you have
CLASS covariates on the X axis.
NOCONNECT
removes the line that connects the predicted values. This option is available when you have CLASS
covariates on the X axis.
POLYBAR
replaces scatter plots of polytomous response models with bar charts. This option has no effect on
binary-response models, and it is overridden by the CONNECT option. By default, the X axis is chosen
to be a crossing of available classification variables so that there are no more than 16 levels; if no such
crossing is possible then the first available classification variable is used. You can override this default
by specifying the X= option.
SHOWOBS< =YES | NO >
displays observations on the plot when the MAXPOINTS= cutoff is not exceeded. For events/trials
notation, the observed proportions are displayed; for single-trial binary-response models, the observed
events are displayed at pO D 1 and the observed nonevents are displayed at pO D 0. For polytomous
response models the predicted probabilities at the observed values of the covariate are computed and
displayed.
YRANGE=(< min >< ,max >)
displays the Y axis as [min,max ]. Note that the axis might extend beyond your specified values. By
default, the entire Y axis, [0,1], is displayed for the predicted probabilities. This option is useful if
your predicted probabilities are all contained in some subset of this range.
4508 F Chapter 58: The LOGISTIC Procedure
Odds Ratio Plots
The odds ratios and confidence limits from the default “Odds Ratio Estimates” table and from the tables
produced by the CLODDS= option or the ODDSRATIO statement can be displayed in a graphic. If you have
many odds ratios, you can produce multiple graphics, or panels, by displaying subsets of the odds ratios.
Odds ratios that have duplicate labels are not displayed. See Outputs 58.2.9 and 58.3.3 for examples of odds
ratio plots.
The following oddsratio-options modify the default odds ratio plot:
CLDISPLAY=SERIF | SERIFARROW | LINE | LINEARROW | BAR< width >
controls the look of the confidence limit error bars. The default CLDISPLAY=SERIF displays the
confidence limits as lines with serifs, and the CLDISPLAY=LINE option removes the serifs from
the error bars. The CLDISPLAY=SERIFARROW and CLDISPLAY=LINEARROW options display
arrowheads on any error bars that are clipped by the RANGE= option; if the entire error bar is cut from
the graphic, then an arrowhead is displayed that points toward the odds ratio. The CLDISPLAY=BAR
< width > option displays the limits along with a bar whose width is equal to the size of the marker.
You can control the width of the bars and the size of the marker by specifying the width value as a
percentage of the distance between the bars, 0 < width 1.
N OTE : Your bar might disappear if you have small values of width.
DOTPLOT
displays dotted gridlines on the plot.
GROUP
displays the odds ratios in panels that are defined by the ODDSRATIO statements. The NPANELPOS=
option is ignored when this option is specified.
LOGBASE=2 | E | 10
displays the odds ratio axis on the specified log scale.
NPANELPOS=n
breaks the plot into multiple graphics that have at most |n| odds ratios per graphic. If n is positive, then
the number of odds ratios per graphic is balanced; if n is negative, then no balancing of the number
of odds ratios takes place. By default, n = 0 and all odds ratios are displayed in a single plot. For
example, suppose you want to display 21 odds ratios. Then specifying NPANELPOS=20 displays two
plots, the first with 11 odds ratios and the second with 10; but specifying NPANELPOS=-20 displays 20
odds ratios in the first plot and only 1 odds ratio in the second plot.
ORDER=ASCENDING | DESCENDING
displays the odds ratios in sorted order. By default the odds ratios are displayed in the order in which
they appear in the corresponding table.
RANGE=(< min >< ,max >) | CLIP
specifies the range of the displayed odds ratio axis. Specifying the RANGE=CLIP option has the same
effect as specifying the minimum odds ratio as min and the maximum odds ratio as max . By default,
all odds ratio confidence intervals are displayed.
BY Statement F 4509
TYPE=HORIZONTAL | HORIZONTALSTAT | VERTICAL | VERTICALBLOCK
controls the look of the graphic. The default TYPE=HORIZONTAL option places the odds ratio values
on the X axis, while the TYPE=HORIZONTALSTAT option also displays the values of the odds ratios
and their confidence limits on the right side of the graphic. The TYPE=VERTICAL option places the
odds ratio values on the Y axis, while the TYPE=VERTICALBLOCK option (available only with the
CLODDS= option) places the odds ratio values on the Y axis and puts boxes around the labels.
BY Statement
BY variables ;
You can specify a BY statement with PROC LOGISTIC to obtain separate analyses of observations in groups
that are defined by the BY variables. When a BY statement appears, the procedure expects the input data
set to be sorted in order of the BY variables. If you specify more than one BY statement, only the last one
specified is used.
If your input data set is not sorted in ascending order, use one of the following alternatives:
• Sort the data by using the SORT procedure with a similar BY statement.
• Specify the NOTSORTED or DESCENDING option in the BY statement for the LOGISTIC procedure.
The NOTSORTED option does not mean that the data are unsorted but rather that the data are arranged
in groups (according to values of the BY variables) and that these groups are not necessarily in
alphabetical or increasing numeric order.
• Create an index on the BY variables by using the DATASETS procedure (in Base SAS software).
If a SCORE statement is specified, then define the training data set to be the DATA= data set or the
INMODEL= data set in the PROC LOGISTIC statement, and define the scoring data set to be the DATA=
data set and PRIOR= data set in the SCORE statement. The training data set contains all of the BY variables,
and the scoring data set must contain either all of them or none of them. If the scoring data set contains all
the BY variables, matching is carried out between the training and scoring data sets. If the scoring data set
does not contain any of the BY variables, the entire scoring data set is used for every BY group in the training
data set and the BY variables are added to the output data sets that are specified in the SCORE statement.
C AUTION : The order of the levels in the response and classification variables is determined from all the data
regardless of BY groups. However, different sets of levels might appear in different BY groups. This might
affect the value of the reference level for these variables, and hence your interpretation of the model and the
parameters.
For more information about BY-group processing, see the discussion in SAS Language Reference: Concepts.
For more information about the DATASETS procedure, see the discussion in the Base SAS Procedures Guide.
4510 F Chapter 58: The LOGISTIC Procedure
CLASS Statement
CLASS variable < (options) > . . . < variable < (options) > > < / global-options > ;
The CLASS statement names the classification variables to be used as explanatory variables in the analysis.
Response variables do not need to be specified in the CLASS statement.
The CLASS statement must precede the MODEL statement. Most options can be specified either as individual
variable options or as global-options. You can specify options for each variable by enclosing the options in
parentheses after the variable name. You can also specify global-options for the CLASS statement by placing
them after a slash (/). Global-options are applied to all the variables specified in the CLASS statement. If you
specify more than one CLASS statement, the global-options specified in any one CLASS statement apply to
all CLASS statements. However, individual CLASS variable options override the global-options. You can
specify the following values for either an option or a global-option:
CPREFIX=n
specifies that, at most, the first n characters of a CLASS variable name be used in creating names for
the corresponding design variables. The default is 32 min.32; max.2; f //, where f is the formatted
length of the CLASS variable.
DESCENDING
DESC
reverses the sort order of the classification variable. If both the DESCENDING and ORDER= options
are specified, PROC LOGISTIC orders the categories according to the ORDER= option and then
reverses that order.
LPREFIX=n
specifies that, at most, the first n characters of a CLASS variable label be used in creating labels for the
corresponding design variables. The default is 256 min.256; max.2; f //, where f is the formatted
length of the CLASS variable.
MISSING
treats missing values (., ._, .A, . . . , .Z for numeric variables and blanks for character variables) as valid
values for the CLASS variable.
ORDER=DATA | FORMATTED | FREQ | INTERNAL
specifies the sort order for the levels of classification variables. This ordering determines which
parameters in the model correspond to each level in the data, so the ORDER= option can be useful when
you use the CONTRAST statement. By default, ORDER=FORMATTED. For ORDER=FORMATTED
and ORDER=INTERNAL, the sort order is machine-dependent. When ORDER=FORMATTED is in
effect for numeric variables for which you have supplied no explicit format, the levels are ordered by
their internal values.
The following table shows how PROC LOGISTIC interprets values of the ORDER= option.
CLASS Statement F 4511
Value of ORDER=
Levels Sorted By
DATA
FORMATTED
Order of appearance in the input data set
External formatted values, except for numeric
variables with no explicit format, which are sorted
by their unformatted (internal) values
Descending frequency count; levels with more
observations come earlier in the order
Unformatted value
FREQ
INTERNAL
For more information about sort order, see the chapter on the SORT procedure in the Base SAS
Procedures Guide and the discussion of BY-group processing in SAS Language Reference: Concepts.
PARAM=keyword
specifies the parameterization method for the classification variable or variables. You can specify any
of the keywords shown in the following table; the default is PARAM=EFFECT. Design matrix columns
are created from CLASS variables according to the corresponding coding schemes:
Value of PARAM=
Coding
EFFECT
Effect coding
GLM
Less-than-full-rank reference cell coding (this
keyword can be used only in a global option)
ORDINAL
THERMOMETER
Cumulative parameterization for an ordinal
CLASS variable
POLYNOMIAL
POLY
Polynomial coding
REFERENCE
REF
Reference cell coding
ORTHEFFECT
Orthogonalizes PARAM=EFFECT coding
ORTHORDINAL
ORTHOTHERM
Orthogonalizes PARAM=ORDINAL coding
ORTHPOLY
Orthogonalizes PARAM=POLYNOMIAL coding
ORTHREF
Orthogonalizes PARAM=REFERENCE coding
All parameterizations are full rank, except for the GLM parameterization. The REF= option in the
CLASS statement determines the reference level for EFFECT and REFERENCE coding and for their
orthogonal parameterizations. It also indirectly determines the reference level for a singular GLM
parameterization through the order of levels.
If PARAM=ORTHPOLY or PARAM=POLY and the classification variable is numeric, then the
ORDER= option in the CLASS statement is ignored, and the internal unformatted values are used. See
the section “Other Parameterizations” on page 391 in Chapter 19, “Shared Concepts and Topics,” for
further details.
4512 F Chapter 58: The LOGISTIC Procedure
REF=’level’ | keyword
specifies the reference level for PARAM=EFFECT, PARAM=REFERENCE, and their orthogonalizations. For PARAM=GLM, the REF= option specifies a level of the classification variable to be put at
the end of the list of levels. This level thus corresponds to the reference level in the usual interpretation
of the linear estimates with a singular parameterization.
For an individual variable REF= option (but not for a global REF= option), you can specify the level
of the variable to use as the reference level. Specify the formatted value of the variable if a format is
assigned. For a global or individual variable REF= option, you can use one of the following keywords.
The default is REF=LAST.
FIRST
designates the first ordered level as reference.
LAST
designates the last ordered level as reference.
TRUNCATE< =n >
specifies the length n of CLASS variable values to use in determining CLASS variable levels. The
default is to use the full formatted length of the CLASS variable. If you specify TRUNCATE without
the length n, the first 16 characters of the formatted values are used. When formatted values are longer
than 16 characters, you can use this option to revert to the levels as determined in releases before SAS
9. The TRUNCATE option is available only as a global option.
Class Variable Naming Convention
Parameter names for a CLASS predictor variable are constructed by concatenating the CLASS variable name
with the CLASS levels. However, for the POLYNOMIAL and orthogonal parameterizations, parameter
names are formed by concatenating the CLASS variable name and keywords that reflect the parameterization.
See the section “Other Parameterizations” on page 391 in Chapter 19, “Shared Concepts and Topics,” for
examples and further details.
Class Variable Parameterization with Unbalanced Designs
PROC LOGISTIC initially parameterizes the CLASS variables by looking at the levels of the variables across
the complete data set. If you have an unbalanced replication of levels across variables or BY groups, then
the design matrix and the parameter interpretation might be different from what you expect. For instance,
suppose you have a model with one CLASS variable A with three levels (1, 2, and 3), and another CLASS
variable B with two levels (1 and 2). If the third level of A occurs only with the first level of B, if you use the
EFFECT parameterization, and if your model contains the effect A(B) and an intercept, then the design for A
within the second level of B is not a differential effect. In particular, the design looks like the following:
B
A
Design Matrix
A(B=1)
A(B=2)
A1 A2
A1 A2
1
1
1
2
2
1
2
3
1
2
1
0
–1
0
0
0
1
–1
0
0
0
0
0
1
0
0
0
0
0
1
CONTRAST Statement F 4513
PROC LOGISTIC detects linear dependency among the last two design variables and sets the parameter for
A2(B=2) to zero, resulting in an interpretation of these parameters as if they were reference- or dummy-coded.
The REFERENCE or GLM parameterization might be more appropriate for such problems.
CODE Statement
CODE < options > ;
The CODE statement writes SAS DATA step code for computing predicted values of the fitted model either
to a file or to a catalog entry. This code can then be included in a DATA step to score new data.
Table 58.2 summarizes the options available in the CODE statement.
Table 58.2 CODE Statement Options
Option
Description
CATALOG=
DUMMIES
ERROR
FILE=
FORMAT=
GROUP=
IMPUTE
Names the catalog entry where the generated code is saved
Retains the dummy variables in the data set
Computes the error function
Names the file where the generated code is saved
Specifies the numeric format for the regression coefficients
Specifies the group identifier for array names and statement labels
Imputes predicted values for observations with missing or invalid
covariates
Specifies the line size of the generated code
Specifies the algorithm for looking up CLASS levels
Computes residuals
LINESIZE=
LOOKUP=
RESIDUAL
For details about the syntax of the CODE statement, see the section “CODE Statement” on page 395 in
Chapter 19, “Shared Concepts and Topics.”
CONTRAST Statement
CONTRAST 'label' row-description< , . . . , row-description > < / options > ;
where a row-description is defined as follows:
effect values< , . . . , effect values >
The CONTRAST statement provides a mechanism for obtaining customized hypothesis tests. It is similar to
the CONTRAST and ESTIMATE statements in other modeling procedures.
The CONTRAST statement enables you to specify a matrix, L, for testing the hypothesis Lˇ D 0, where
ˇ is the vector of intercept and slope parameters. You must be familiar with the details of the model
parameterization that PROC LOGISTIC uses (for more information, see the PARAM= option in the section
“CLASS Statement” on page 4510). Optionally, the CONTRAST statement enables you to estimate each
row, li0 ˇ, of Lˇ and test the hypothesis li0 ˇ D 0. Computed statistics are based on the asymptotic chi-square
distribution of the Wald statistic.
4514 F Chapter 58: The LOGISTIC Procedure
There is no limit to the number of CONTRAST statements that you can specify, but they must appear after
the MODEL statement.
The following parameters are specified in the CONTRAST statement:
label
identifies the contrast in the displayed output. A label is required for every contrast specified, and
it must be enclosed in quotes.
effect
identifies an effect that appears in the MODEL statement. The name INTERCEPT can be used as
an effect when one or more intercepts are included in the model. You do not need to include all
effects that are included in the MODEL statement.
values
are constants that are elements of the L matrix associated with the effect. To correctly specify
your contrast, it is crucial to know the ordering of parameters within each effect and the variable
levels associated with any parameter. The “Class Level Information” table shows the ordering
of levels within variables. The E option, described later in this section, enables you to verify the
proper correspondence of values to parameters. If too many values are specified for an effect, the
extra ones are ignored. If too few values are specified, the remaining ones are set to 0.
Multiple degree-of-freedom hypotheses can be tested by specifying multiple row-descriptions; the rows of
L are specified in order and are separated by commas. The degrees of freedom is the number of linearly
independent constraints implied by the CONTRAST statement—that is, the rank of L.
More details for specifying contrasts involving effects with full-rank parameterizations are given in the
section “Full-Rank Parameterized Effects” on page 4515, while details for less-than-full-rank parameterized
effects are given in the section “Less-Than-Full-Rank Parameterized Effects” on page 4516.
You can specify the following options after a slash (/):
ALPHA=number
specifies the level of significance ˛ for the 100.1 ˛/% confidence interval for each contrast when the
ESTIMATE option is specified. The value of number must be between 0 and 1. By default, number is
equal to the value of the ALPHA= option in the PROC LOGISTIC statement, or 0.05 if that option is
not specified.
E
displays the L matrix.
ESTIMATE=keyword
estimates and tests each individual contrast (that is, each row, li0 ˇ, of Lˇ), exponentiated contrast
0
(e li ˇ ), or predicted probability for the contrast (g 1 .li0 ˇ/). PROC LOGISTIC displays the point
estimate, its standard error, a Wald confidence interval, and a Wald chi-square test. The significance
level of the confidence interval is controlled by the ALPHA= option. You can estimate the individual
contrast, the exponentiated contrast, or the predicted probability for the contrast by specifying one of
the following keywords:
CONTRAST Statement F 4515
PARM
estimates the individual contrast.
EXP
estimates the exponentiated contrast.
BOTH
estimates both the individual contrast and the exponentiated contrast.
PROB
estimates the predicted probability of the contrast.
ALL
estimates the individual contrast, the exponentiated contrast, and the predicted probability of the
contrast.
For details about the computations of the standard errors and confidence limits, see the section “Linear
Predictor, Predicted Probability, and Confidence Limits” on page 4576.
SINGULAR=number
tunes the estimability check. This option is ignored when a full-rank parameterization is specified. If
v is a vector, define ABS.v/ to be the largest absolute value of the elements of v. For a row vector
l 0 of the contrast matrix L, define c D ABS.l / if ABS.l / is greater than 0; otherwise, c = 1. If
ABS.l 0 l 0 T/ is greater than cnumber , then l is declared nonestimable. The T matrix is the Hermite
form matrix I0 I0 , where I0 represents a generalized inverse of the (observed or expected) information
matrix I0 of the null model. The value for number must be between 0 and 1; the default value is 1E–4.
Full-Rank Parameterized Effects
If an effect involving a CLASS variable with a full-rank parameterization does not appear in the CONTRAST
statement, then all of its coefficients in the L matrix are set to 0.
If you use effect coding by default or by specifying PARAM=EFFECT in the CLASS statement, then all
parameters are directly estimable and involve no other parameters. For example, suppose an effect-coded
CLASS variable A has four levels. Then there are three parameters (ˇ1 ; ˇ2 ; ˇ3 ) representing the first three
levels, and the fourth parameter is represented by
ˇ1
ˇ2
ˇ3
To test the first versus the fourth level of A, you would test
ˇ1 D
ˇ1
ˇ2
ˇ3
or, equivalently,
2ˇ1 C ˇ2 C ˇ3 D 0
which, in the form Lˇ D 0, is
2
3
ˇ
1
2 1 1 4 ˇ2 5 D 0
ˇ3
Therefore, you would use the following CONTRAST statement:
4516 F Chapter 58: The LOGISTIC Procedure
contrast '1 vs. 4' A 2 1 1;
To contrast the third level with the average of the first two levels, you would test
ˇ1 C ˇ2
D ˇ3
2
or, equivalently,
ˇ1 C ˇ2
2ˇ3 D 0
Therefore, you would use the following CONTRAST statement:
contrast '1&2 vs. 3' A 1 1 -2;
Other CONTRAST statements are constructed similarly. For example:
contrast
contrast
contrast
contrast
'1 vs. 2
'
'1&2 vs. 4 '
'1&2 vs. 3&4'
'Main Effect'
A
A
A
A
A
A
1 -1
3 3
2 2
1 0
0 1
0 0
0;
2;
0;
0,
0,
1;
Less-Than-Full-Rank Parameterized Effects
When you use the less-than-full-rank parameterization (by specifying PARAM=GLM in the CLASS statement), each row is checked for estimability; see the section “Estimable Functions” on page 59 in Chapter 3,
“Introduction to Statistical Modeling with SAS/STAT Software,” for more information. If PROC LOGISTIC
finds a contrast to be nonestimable, it displays missing values in corresponding rows in the results. PROC
LOGISTIC handles missing level combinations of classification variables in the same manner as PROC GLM:
parameters corresponding to missing level combinations are not included in the model. This convention
can affect the way in which you specify the L matrix in your CONTRAST statement. If the elements of L
are not specified for an effect that contains a specified effect, then the elements of the specified effect are
distributed over the levels of the higher-order effect just as the GLM procedure does for its CONTRAST and
ESTIMATE statements. For example, suppose that the model contains effects A and B and their interaction
A*B. If you specify a CONTRAST statement involving A alone, the L matrix contains nonzero terms for
both A and A*B, since A*B contains A. See rule 4 in the section “Construction of Least Squares Means” on
page 3533 in Chapter 44, “The GLM Procedure,” for more details.
EFFECT Statement
EFFECT name=effect-type (variables < / options >) ;
The EFFECT statement enables you to construct special collections of columns for design matrices. These
collections are referred to as constructed effects to distinguish them from the usual model effects that are
formed from continuous or classification variables, as discussed in the section “GLM Parameterization of
Classification Variables and Effects” on page 387 in Chapter 19, “Shared Concepts and Topics.”
You can specify the following effect-types:
EFFECT Statement F 4517
COLLECTION
is a collection effect that defines one or more variables as a single effect with
multiple degrees of freedom. The variables in a collection are considered as
a unit for estimation and inference.
LAG
is a classification effect in which the level that is used for a given period
corresponds to the level in the preceding period.
MULTIMEMBER | MM
is a multimember classification effect whose levels are determined by one or
more variables that appear in a CLASS statement.
POLYNOMIAL | POLY
is a multivariate polynomial effect in the specified numeric variables.
SPLINE
is a regression spline effect whose columns are univariate spline expansions
of one or more variables. A spline expansion replaces the original variable
with an expanded or larger set of new variables.
Table 58.3 summarizes the options available in the EFFECT statement.
Table 58.3
Option
EFFECT Statement Options
Description
Collection Effects Options
DETAILS
Displays the constituents of the collection effect
Lag Effects Options
DESIGNROLE=
Names a variable that controls to which lag design an observation
is assigned
DETAILS
Displays the lag design of the lag effect
NLAG=
Specifies the number of periods in the lag
PERIOD=
Names the variable that defines the period
WITHIN=
Names the variable or variables that define the group within which
each period is defined
Multimember Effects Options
NOEFFECT
Specifies that observations with all missing levels for the multimember variables should have zero values in the corresponding
design matrix columns
WEIGHT=
Specifies the weight variable for the contributions of each of the
classification effects
Polynomial Effects Options
DEGREE=
Specifies the degree of the polynomial
MDEGREE=
Specifies the maximum degree of any variable in a term of the
polynomial
STANDARDIZE=
Specifies centering and scaling suboptions for the variables that
define the polynomial
4518 F Chapter 58: The LOGISTIC Procedure
Table 58.3 continued
Option
Description
Spline Effects Options
BASIS=
DEGREE=
KNOTMETHOD=
Specifies the type of basis (B-spline basis or truncated power function basis) for the spline effect
Specifies the degree of the spline effect
Specifies how to construct the knots for the spline effect
For more information about the syntax of these effect-types and how columns of constructed effects are
computed, see the section “EFFECT Statement” on page 397 in Chapter 19, “Shared Concepts and Topics.”
EFFECTPLOT Statement
EFFECTPLOT < plot-type < (plot-definition-options) > > < / options > ;
The EFFECTPLOT statement produces a display of the fitted model and provides options for changing and
enhancing the displays. Table 58.4 describes the available plot-types and their plot-definition-options.
Table 58.4 Plot-Types and Plot-Definition-Options
Plot-Type and Description
Plot-Definition-Options
BOX
Displays a box plot of continuous response data at each
level of a CLASS effect, with predicted values
superimposed and connected by a line. This is an
alternative to the INTERACTION plot-type.
PLOTBY= variable or CLASS effect
X= CLASS variable or effect
CONTOUR
Displays a contour plot of predicted values against two
continuous covariates
PLOTBY= variable or CLASS effect
X= continuous variable
Y= continuous variable
FIT
Displays a curve of predicted values versus a
continuous variable
PLOTBY= variable or CLASS effect
X= continuous variable
INTERACTION
Displays a plot of predicted values (possibly with error
bars) versus the levels of a CLASS effect. The
predicted values are connected with lines and can be
grouped by the levels of another CLASS effect.
PLOTBY= variable or CLASS effect
SLICEBY= variable or CLASS effect
X= CLASS variable or effect
MOSAIC
Displays a mosaic plot of predicted values by using up
to three CLASS effects
PLOTBY= variable or CLASS effect
X= CLASS effects
ESTIMATE Statement F 4519
Table 58.4 continued
Plot-Type and Description
Plot-Definition-Options
SLICEFIT
Displays a curve of predicted values versus a
continuous variable, grouped by the levels of a
CLASS effect
PLOTBY= variable or CLASS effect
SLICEBY= variable or CLASS effect
X= continuous variable
For full details about the syntax and options of the EFFECTPLOT statement, see the section “EFFECTPLOT
Statement” on page 416 in Chapter 19, “Shared Concepts and Topics.”
See Outputs 58.2.11, 58.2.12, 58.3.5, 58.4.8, 58.7.4, and 58.15.4 for examples of plots produced by this
statement.
ESTIMATE Statement
ESTIMATE < 'label' > estimate-specification < (divisor =n) >
< , . . . < 'label' > estimate-specification < (divisor =n) > >
< / options > ;
The ESTIMATE statement provides a mechanism for obtaining custom hypothesis tests. Estimates are
formed as linear estimable functions of the form Lˇ. You can perform hypothesis tests for the estimable
functions, construct confidence limits, and obtain specific nonlinear transformations.
Table 58.5 summarizes the options available in the ESTIMATE statement.
Table 58.5 ESTIMATE Statement Options
Option
Description
Construction and Computation of Estimable Functions
DIVISOR=
Specifies a list of values to divide the coefficients
NOFILL
Suppresses the automatic fill-in of coefficients for higher-order
effects
SINGULAR=
Tunes the estimability checking difference
Degrees of Freedom and p-values
ADJUST=
Determines the method for multiple comparison adjustment of
estimates
ALPHA=˛
Determines the confidence level (1 ˛)
LOWER
Performs one-sided, lower-tailed inference
STEPDOWN
Adjusts multiplicity-corrected p-values further in a step-down fashion
TESTVALUE=
Specifies values under the null hypothesis for tests
UPPER
Performs one-sided, upper-tailed inference
4520 F Chapter 58: The LOGISTIC Procedure
Table 58.5 continued
Option
Description
Statistical Output
CL
CORR
COV
E
JOINT
SEED=
Constructs confidence limits
Displays the correlation matrix of estimates
Displays the covariance matrix of estimates
Prints the L matrix
Produces a joint F or chi-square test for the estimable functions
Specifies the seed for computations that depend on random numbers
Generalized Linear Modeling
CATEGORY=
Specifies how to construct estimable functions with multinomial
data
EXP
Exponentiates and displays estimates
ILINK
Computes and displays estimates and standard errors on the inverse
linked scale
For details about the syntax of the ESTIMATE statement, see the section “ESTIMATE Statement” on
page 444 in Chapter 19, “Shared Concepts and Topics.”
EXACT Statement
EXACT < 'label' > < INTERCEPT > < effects > < / options > ;
The EXACT statement performs exact tests of the parameters for the specified effects and optionally estimates
the parameters and outputs the exact conditional distributions. You can specify the keyword INTERCEPT
and any effects in the MODEL statement. Inference on the parameters of the specified effects is performed
by conditioning on the sufficient statistics of all the other model parameters (possibly including the intercept).
You can specify several EXACT statements, but they must follow the MODEL statement. Each statement can
optionally include an identifying label . If several EXACT statements are specified, any statement without
a label is assigned a label of the form “Exactn,” where n indicates the nth EXACT statement. The label is
included in the headers of the displayed exact analysis tables.
If a STRATA statement is also specified, then a stratified exact logistic regression is performed. The model
contains a different intercept for each stratum, and these intercepts are conditioned out of the model along
with any other nuisance parameters (parameters for effects specified in the MODEL statement that are not in
the EXACT statement).
If the LINK=GLOGIT option is specified in the MODEL statement, then the METHOD=DIRECT option is
invoked in the EXACTOPTIONS statement by default and a generalized logit model is fit. Since each effect
specified in the MODEL statement adds k parameters to the model (where k + 1 is the number of response
levels), exact analysis of the generalized logit model by using this method is limited to rather small problems.
The CONTRAST, ESTIMATE, LSMEANS, LSMESTIMATE, ODDSRATIO, OUTPUT, ROC, ROCCONTRAST, SCORE, SLICE, STORE, TEST, and UNITS statements are not available with an exact analysis;
results from these statements are based on the asymptotic results. Exact analyses are not performed when you
EXACT Statement F 4521
specify a WEIGHT statement, a link other than LINK=LOGIT or LINK=GLOGIT, an offset variable, the
NOFIT option, or a model selection method. Exact estimation is not available for ordinal response models.
For classification variables, use of the reference parameterization is recommended.
The following options can be specified in each EXACT statement after a slash (/):
ALPHA=number
specifies the level of significance ˛ for 100.1 ˛/% confidence limits for the parameters or odds
ratios. The value of number must be between 0 and 1. By default, number is equal to the value of the
ALPHA= option in the PROC LOGISTIC statement, or 0.05 if that option is not specified.
CLTYPE=EXACT | MIDP
requests either the exact or mid-p confidence intervals for the parameter estimates. By default, the exact
intervals are produced. The confidence coefficient can be specified with the ALPHA= option. The
mid-p interval can be modified with the MIDPFACTOR= option. See the section “Exact Conditional
Logistic Regression” on page 4597 for details.
ESTIMATE < =keyword >
estimates the individual parameters (conditioned on all other parameters) for the effects specified in the
EXACT statement. For each parameter, a point estimate, a standard error, a confidence interval, and a
p-value for a two-sided test that the parameter is zero are displayed. Note that the two-sided p-value is
twice the one-sided p-value. You can optionally specify one of the following keywords:
PARM
specifies that the parameters be estimated. This is the default.
ODDS
specifies that the odds ratios be estimated. If you have classification variables, then you
must also specify the PARAM=REF option in the CLASS statement.
BOTH
specifies that both the parameters and odds ratios be estimated.
JOINT
performs the joint test that all of the parameters are simultaneously equal to zero, performs individual
hypothesis tests for the parameter of each continuous variable, and performs joint tests for the parameters of each classification variable. The joint test is indicated in the “Conditional Exact Tests” table by
the label “Joint.”
JOINTONLY
performs only the joint test of the parameters. The test is indicated in the “Conditional Exact Tests”
table by the label “Joint.” When this option is specified, individual tests for the parameters of each
continuous variable and joint tests for the parameters of the classification variables are not performed.
MIDPFACTOR=ı1 | (ı1 ; ı2 )
sets the tie factors used to produce the mid-p hypothesis statistics and the mid-p confidence intervals.
ı1 modifies both the hypothesis tests and confidence intervals, while ı2 affects only the hypothesis
tests. By default, ı1 D 0:5 and ı2 D 1:0. See the section “Exact Conditional Logistic Regression” on
page 4597 for details.
ONESIDED
requests one-sided confidence intervals and p-values for the individual parameter estimates and odds
ratios. The one-sided p-value is the smaller of the left- and right-tail probabilities for the observed
sufficient statistic of the parameter under the null hypothesis that the parameter is zero. The two-sided
4522 F Chapter 58: The LOGISTIC Procedure
p-values (default) are twice the one-sided p-values. See the section “Exact Conditional Logistic
Regression” on page 4597 for more details.
OUTDIST=SAS-data-set
names the SAS data set that contains the exact conditional distributions. This data set contains all of
the exact conditional distributions that are required to process the corresponding EXACT statement.
This data set contains the possible sufficient statistics for the parameters of the effects specified
in the EXACT statement, the counts, and, when hypothesis tests are performed on the parameters,
the probability of occurrence and the score value for each sufficient statistic. When you request an
OUTDIST= data set, the observed sufficient statistics are displayed in the “Sufficient Statistics” table.
See the section “OUTDIST= Output Data Set” on page 4604 for more information.
EXACT Statement Examples
In the following example, two exact tests are computed: one for x1 and the other for x2. The test for x1 is
based on the exact conditional distribution of the sufficient statistic for the x1 parameter given the observed
values of the sufficient statistics for the intercept, x2, and x3 parameters; likewise, the test for x2 is conditional
on the observed sufficient statistics for the intercept, x1, and x3.
proc logistic;
model y= x1 x2 x3;
exact x1 x2;
run;
PROC LOGISTIC determines, from all the specified EXACT statements, the distinct conditional distributions
that need to be evaluated. For example, there is only one exact conditional distribution for the following two
EXACT statements:
exact 'One' x1 / estimate=parm;
exact 'Two' x1 / estimate=parm onesided;
For each EXACT statement, individual tests for the parameters of the specified effects are computed unless
the JOINTONLY option is specified. Consider the following EXACT statements:
exact
exact
exact
exact
'E12'
'E1'
'E2'
'J12'
x1 x2 / estimate;
x1
/ estimate;
x2
/ estimate;
x1 x2 / joint;
In the E12 statement, the parameters for x1 and x2 are estimated and tested separately. Specifying the E12
statement is equivalent to specifying both the E1 and E2 statements. In the J12 statement, the joint test for
the parameters of x1 and x2 is computed in addition to the individual tests for x1 and x2.
EXACTOPTIONS Statement
EXACTOPTIONS options ;
The EXACTOPTIONS statement specifies options that apply to every EXACT statement in the program.
The following options are available:
EXACTOPTIONS Statement F 4523
ABSFCONV=value
specifies the absolute function convergence criterion. Convergence requires a small change in the
log-likelihood function in subsequent iterations,
jli
li
1j
< value
where li is the value of the log-likelihood function at iteration i.
By default, ABSFCONV=1E–12. You can also specify the FCONV= and XCONV= criteria; optimizations are terminated as soon as one criterion is satisfied.
ADDTOBS
adds the observed sufficient statistic to the sampled exact distribution if the statistic was not sampled.
This option has no effect unless the METHOD=NETWORKMC option is specified and the ESTIMATE
option is specified in the EXACT statement. If the observed statistic has not been sampled, then the
parameter estimate does not exist; by specifying this option, you can produce (biased) estimates.
BUILDSUBSETS
builds every distribution for sampling. By default, some exact distributions are created by taking a
subset of a previously generated exact distribution. When the METHOD=NETWORKMC option is
invoked, this subsetting behavior has the effect of using fewer than the desired n samples; see the N=
option for more details. Use the BUILDSUBSETS option to suppress this subsetting.
EPSILON=value
controls how the partial sums
value=1E–8.
Pj
i D1 yi xi
are compared. value must be between 0 and 1; by default,
FCONV=value
specifies the relative function convergence criterion. Convergence requires a small relative change in
the log-likelihood function in subsequent iterations,
jli
li 1 j
< value
jli 1 j C 1E–6
where li is the value of the log likelihood at iteration i.
By default, FCONV=1E–8. You can also specify the ABSFCONV= and XCONV= criteria; if more
than one criterion is specified, then optimizations are terminated as soon as one criterion is satisfied.
MAXTIME=seconds
specifies the maximum clock time (in seconds) that PROC LOGISTIC can use to calculate the exact
distributions. If the limit is exceeded, the procedure halts all computations and prints a note to the
LOG. The default maximum clock time is seven days.
METHOD=keyword
specifies which exact conditional algorithm to use for every EXACT statement specified. You can
specify one of the following keywords:
DIRECT
invokes the multivariate shift algorithm of Hirji, Mehta, and Patel (1987). This method
directly builds the exact distribution, but it can require an excessive amount of memory in its
intermediate stages. METHOD=DIRECT is invoked by default when you are conditioning
out at most the intercept, or when the LINK=GLOGIT option is specified in the MODEL
statement.
4524 F Chapter 58: The LOGISTIC Procedure
invokes an algorithm described in Mehta, Patel, and Senchaudhuri (1992). This method
builds a network for each parameter that you are conditioning out, combines the networks,
then uses the multivariate shift algorithm to create the exact distribution. The NETWORK
method can be faster and require less memory than the DIRECT method. The NETWORK
method is invoked by default for most analyses.
NETWORK
invokes the hybrid network and Monte Carlo algorithm of Mehta, Patel, and Senchaudhuri (1992). This method creates a network, then samples from that network; this
method does not reject any of the samples at the cost of using a large amount of memory
to create the network. METHOD=NETWORKMC is most useful for producing parameter
estimates for problems that are too large for the DIRECT and NETWORK methods to
handle and for which asymptotic methods are invalid—for example, for sparse data on a
large grid.
NETWORKMC
N=n
specifies the number of Monte Carlo samples to take when the METHOD=NETWORKMC option is
specified. By default, n = 10,000. If the procedure cannot obtain n samples due to a lack of memory,
then a note is printed in the SAS log (the number of valid samples is also reported in the listing) and
the analysis continues.
The number of samples used to produce any particular statistic might be smaller than n. For example,
let X1 and X2 be continuous variables, denote their joint distribution by f (X1,X2), and let f (X1 | X2 =
x2) denote the marginal distribution of X1 conditioned on the observed value of X2. If you request
the JOINT test of X1 and X2, then n samples are used to generate the estimate fO(X1,X2) of f (X1,X2),
from which the test is computed. However, the parameter estimate for X1 is computed from the subset
of fO(X1,X2) that has X2 = x2, and this subset need not contain n samples. Similarly, the distribution
for each level of a classification variable is created by extracting the appropriate subset from the joint
distribution for the CLASS variable.
In some cases, the marginal sample size can be too small to admit accurate estimation of a particular
statistic; a note is printed in the SAS log when a marginal sample size is less than 100. Increasing n
increases the number of samples used in a marginal distribution; however, if you want to control the
sample size exactly, you can either specify the BUILDSUBSETS option or do both of the following:
• Remove the JOINT option from the EXACT statement.
• Create dummy variables in a DATA step to represent the levels of a CLASS variable, and specify
them as independent variables in the MODEL statement.
NOLOGSCALE
specifies that computations for the exact conditional models be computed by using normal scaling.
Log scaling can handle numerically larger problems than normal scaling; however, computations in the
log scale are slower than computations in normal scale.
ONDISK
uses disk space instead of random access memory to build the exact conditional distribution. Use this
option to handle larger problems at the cost of slower processing.
FREQ Statement F 4525
SEED=seed
specifies the initial seed for the random number generator used to take the Monte Carlo samples when
the METHOD=NETWORKMC option is specified. The value of the SEED= option must be an integer.
If you do not specify a seed, or if you specify a value less than or equal to zero, then PROC LOGISTIC
uses the time of day from the computer’s clock to generate an initial seed.
STATUSN=number
prints a status line in the SAS log after every number of Monte Carlo samples when the
METHOD=NETWORKMC option is specified. The number of samples taken and the current exact
p-value for testing the significance of the model are displayed. You can use this status line to track the
progress of the computation of the exact conditional distributions.
STATUSTIME=seconds
specifies the time interval (in seconds) for printing a status line in the LOG. You can use this status line
to track the progress of the computation of the exact conditional distributions. The time interval you
specify is approximate; the actual time interval varies. By default, no status reports are produced.
XCONV=value
specifies the relative parameter convergence criterion. Convergence requires a small relative parameter
change in subsequent iterations,
.i /
max jıj j < value
j
where
.i /
ıj
D
8 .i /
< ˇj
.i/
ˇj
:
.i 1/
ˇj
.i 1/
ˇj
.i 1/
ˇj
.i 1/
jˇj
j < 0:01
otherwise
.i /
and ˇj is the estimate of the jth parameter at iteration i.
By default, XCONV=1E–4. You can also specify the ABSFCONV= and FCONV= criteria; if more
than one criterion is specified, then optimizations are terminated as soon as one criterion is satisfied.
FREQ Statement
FREQ variable ;
The FREQ statement identifies a variable that contains the frequency of occurrence of each observation.
PROC LOGISTIC treats each observation as if it appears n times, where n is the value of the FREQ variable
for the observation. If it is not an integer, the frequency value is truncated to an integer. If the frequency
value is less than 1 or missing, the observation is not used in the model fitting. When the FREQ statement is
not specified, each observation is assigned a frequency of 1. If you specify more than one FREQ statement,
then the first statement is used.
If a SCORE statement is specified, then the FREQ variable is used for computing fit statistics and the ROC
curve, but they are not required for scoring. If the DATA= data set in the SCORE statement does not contain
the FREQ variable, the frequency values are assumed to be 1 and a warning message is issued in the LOG.
If you fit a model and perform the scoring in the same run, the same FREQ variable is used for fitting and
4526 F Chapter 58: The LOGISTIC Procedure
scoring. If you fit a model in a previous run and input it with the INMODEL= option in the current run, then
the FREQ variable can be different from the one used in the previous run. However, if a FREQ variable was
not specified in the previous run, you can still specify a FREQ variable in the current run.
ID Statement
ID variable< variable,... > ;
The ID statement specifies variables in the DATA= data set that are used for labeling ROC curves and influence
diagnostic plots. If more than one ID variable is specified, then the plots are labeled by concatenating the ID
variable values together. See the PLOTS(LABEL) and ROCOPTIONS(ID) options in the PROC LOGISTIC
statement for more details.
LSMEANS Statement
LSMEANS < model-effects > < / options > ;
The LSMEANS statement computes and compares least squares means (LS-means) of fixed effects. LS-means
are predicted population margins—that is, they estimate the marginal means over a balanced population. In a
sense, LS-means are to unbalanced designs as class and subclass arithmetic means are to balanced designs.
Table 58.6 summarizes the options available in the LSMEANS statement.
Table 58.6
Option
LSMEANS Statement Options
Description
Construction and Computation of LS-Means
AT
Modifies the covariate value in computing LS-means
BYLEVEL
Computes separate margins
DIFF
Requests differences of LS-means
OM=
Specifies the weighting scheme for LS-means computation as determined by the input data set
SINGULAR=
Tunes estimability checking
Degrees of Freedom and p-values
ADJUST=
Determines the method for multiple-comparison adjustment of LSmeans differences
ALPHA=˛
Determines the confidence level (1 ˛)
Adjusts multiple-comparison p-values further in a step-down
STEPDOWN
fashion
LSMESTIMATE Statement F 4527
Table 58.6 continued
Option
Description
Statistical Output
CL
CORR
COV
E
LINES
MEANS
PLOTS=
SEED=
Constructs confidence limits for means and mean differences
Displays the correlation matrix of LS-means
Displays the covariance matrix of LS-means
Prints the L matrix
Produces a “Lines” display for pairwise LS-means differences
Prints the LS-means
Requests graphs of means and mean comparisons
Specifies the seed for computations that depend on random numbers
Generalized Linear Modeling
EXP
Exponentiates and displays estimates of LS-means or LS-means
differences
ILINK
Computes and displays estimates and standard errors of LS-means
(but not differences) on the inverse linked scale
ODDSRATIO
Reports (simple) differences of least squares means in terms of
odds ratios if permitted by the link function
For details about the syntax of the LSMEANS statement, see the section “LSMEANS Statement” on page 460
in Chapter 19, “Shared Concepts and Topics.”
N OTE : If you have classification variables in your model, then the LSMEANS statement is allowed only if
you also specify the PARAM=GLM option.
LSMESTIMATE Statement
LSMESTIMATE model-effect < 'label' > values < divisor =n >
< , . . . < 'label' > values < divisor =n > >
< / options > ;
The LSMESTIMATE statement provides a mechanism for obtaining custom hypothesis tests among least
squares means.
Table 58.7 summarizes the options available in the LSMESTIMATE statement.
4528 F Chapter 58: The LOGISTIC Procedure
Table 58.7 LSMESTIMATE Statement Options
Option
Description
Construction and Computation of LS-Means
AT
Modifies covariate values in computing LS-means
BYLEVEL
Computes separate margins
DIVISOR=
Specifies a list of values to divide the coefficients
OM=
Specifies the weighting scheme for LS-means computation as determined by a data set
SINGULAR=
Tunes estimability checking
Degrees of Freedom and p-values
ADJUST=
Determines the method for multiple-comparison adjustment of LSmeans differences
ALPHA=˛
Determines the confidence level (1 ˛)
LOWER
Performs one-sided, lower-tailed inference
STEPDOWN
Adjusts multiple-comparison p-values further in a step-down fashion
TESTVALUE=
Specifies values under the null hypothesis for tests
UPPER
Performs one-sided, upper-tailed inference
Statistical Output
CL
CORR
COV
E
ELSM
JOINT
SEED=
Constructs confidence limits for means and mean differences
Displays the correlation matrix of LS-means
Displays the covariance matrix of LS-means
Prints the L matrix
Prints the K matrix
Produces a joint F or chi-square test for the LS-means and LSmeans differences
Specifies the seed for computations that depend on random numbers
Generalized Linear Modeling
CATEGORY=
Specifies how to construct estimable functions with multinomial
data
EXP
Exponentiates and displays LS-means estimates
ILINK
Computes and displays estimates and standard errors of LS-means
(but not differences) on the inverse linked scale
For details about the syntax of the LSMESTIMATE statement, see the section “LSMESTIMATE Statement”
on page 476 in Chapter 19, “Shared Concepts and Topics.”
N OTE : If you have classification variables in your model, then the LSMESTIMATE statement is allowed
only if you also specify the PARAM=GLM option.
MODEL Statement F 4529
MODEL Statement
< label: > MODEL variable< (variable_options) > = < effects > < / options > ;
< label: > MODEL events/trials = < effects > < / options > ;
The MODEL statement names the response variable and the explanatory effects, including covariates, main
effects, interactions, and nested effects; see the section “Specification of Effects” on page 3495 in Chapter 44,
“The GLM Procedure,” for more information. If you omit the explanatory effects, the procedure fits an
intercept-only model. You must specify exactly one MODEL statement.
Two forms of the MODEL statement can be specified. The first form, referred to as single-trial syntax, is
applicable to binary, ordinal, and nominal response data. The second form, referred to as events/trials syntax,
is restricted to the case of binary response data. The single-trial syntax is used when each observation in
the DATA= data set contains information about only a single trial, such as a single subject in an experiment.
When each observation contains information about multiple binary-response trials, such as the counts of the
number of subjects observed and the number responding, then events/trials syntax can be used.
In the events/trials syntax, you specify two variables that contain count data for a binomial experiment. These
two variables are separated by a slash. The value of the first variable, events, is the number of positive
responses (or events). The value of the second variable, trials, is the number of trials. The values of both
events and (trials–events) must be nonnegative and the value of trials must be positive for the response to be
valid.
In the single-trial syntax, you specify one variable (on the left side of the equal sign) as the response variable.
This variable can be character or numeric. Variable_options specific to the response variable can be specified
immediately after the response variable with parentheses around them.
For both forms of the MODEL statement, explanatory effects follow the equal sign. Variables can be either
continuous or classification variables. Classification variables can be character or numeric, and they must be
declared in the CLASS statement. When an effect is a classification variable, the procedure inserts a set of
coded columns into the design matrix instead of directly entering a single column containing the values of
the variable.
Response Variable Options
DESCENDING | DESC
reverses the order of the response categories. If both the DESCENDING and ORDER= options are
specified, PROC LOGISTIC orders the response categories according to the ORDER= option and then
reverses that order. See the section “Response Level Ordering” on page 4559 for more detail.
EVENT=’category ’ | keyword
specifies the event category for the binary response model. PROC LOGISTIC models the probability
of the event category. The EVENT= option has no effect when there are more than two response
categories. You can specify the value (formatted if a format is applied) of the event category in quotes,
or you can specify one of the following keywords. The default is EVENT=FIRST.
4530 F Chapter 58: The LOGISTIC Procedure
FIRST
designates the first ordered category as the event.
LAST
designates the last ordered category as the event.
One of the most common sets of response levels is {0,1}, with 1 representing the event for which the
probability is to be modeled. Consider the example where Y takes the values 1 and 0 for event and
nonevent, respectively, and Exposure is the explanatory variable. To specify the value 1 as the event
category, use the following MODEL statement:
model Y(event='1') = Exposure;
ORDER= DATA | FORMATTED | FREQ | INTERNAL
specifies the sort order for the levels of the response variable. The following table displays the available
ORDER= options:
ORDER=
Levels Sorted By
DATA
FORMATTED
order of appearance in the input data set
external formatted value, except for numeric
variables with no explicit format, which are sorted
by their unformatted (internal) value
descending frequency count; levels with the most
observations come first in the order
unformatted value
FREQ
INTERNAL
By default, ORDER=FORMATTED. For ORDER=FORMATTED and ORDER=INTERNAL, the
sort order is machine dependent. When ORDER=FORMATTED is in effect for numeric variables for
which you have supplied no explicit format, the levels are ordered by their internal values.
For more information about sort order, see the chapter on the SORT procedure in the Base SAS
Procedures Guide and the discussion of BY-group processing in SAS Language Reference: Concepts.
REFERENCE=’category ’ | keyword
REF=’category ’ | keyword
specifies the reference category for the generalized logit model and the binary response model. For the
generalized logit model, each logit contrasts a nonreference category with the reference category. For
the binary response model, specifying one response category as the reference is the same as specifying
the other response category as the event category. You can specify the value (formatted if a format is
applied) of the reference category in quotes, or you can specify one of the following keywords:
FIRST
designates the first ordered category as the reference.
LAST
designates the last ordered category as the reference. This is the default.
MODEL Statement F 4531
Model Options
Table 58.8 summarizes the options available in the MODEL statement. These options can be specified after a
slash (/).
Table 58.8 Model Statement Options
Option
Description
Model Specification Options
LINK=
Specifies the link function
NOFIT
Suppresses model fitting
NOINT
Suppresses the intercept
OFFSET=
Specifies the offset variable
SELECTION=
Specifies the effect selection method
UNEQUALSLOPES Specifies cumulative partial proportional odds models
Effect Selection Options
BEST=
Controls the number of models displayed for SCORE
selection
DETAILS
Requests detailed results at each step
FAST
Uses the fast elimination method
HIERARCHY=
Specifies whether and how hierarchy is maintained and
whether a single effect or multiple effects are allowed to
enter or leave the model per step
INCLUDE=
Specifies the number of effects included in every model
MAXSTEP=
Specifies the maximum number of steps for STEPWISE
selection
SEQUENTIAL
Adds or deletes effects in sequential order
SLENTRY=
Specifies the significance level for entering effects
SLSTAY=
Specifies the significance level for removing effects
START=
Specifies the number of variables in the first model
STOP=
Specifies the number of variables in the final model
STOPRES
Adds or deletes variables by the residual chi-square
criterion
Model-Fitting Specification Options
ABSFCONV=
Specifies the absolute function convergence criterion
FCONV=
Specifies the relative function convergence criterion
FIRTH
Specifies Firth’s penalized likelihood method
GCONV=
Specifies the relative gradient convergence criterion
MAXFUNCTION=
Specifies the maximum number of function calls for the
conditional analysis
MAXITER=
Specifies the maximum number of iterations
NOCHECK
Suppresses checking for infinite parameters
RIDGING=
Specifies the technique used to improve the log-likelihood
function when its value is worse than that of the previous
step
SINGULAR=
Specifies the tolerance for testing singularity
TECHNIQUE=
Specifies the iterative algorithm for maximization
XCONV=
Specifies the relative parameter convergence criterion
4532 F Chapter 58: The LOGISTIC Procedure
Table 58.8 continued
Option
Description
Confidence Interval Options
ALPHA=
Specifies ˛ for the 100.1 ˛/% confidence intervals
CLODDS=
Computes confidence intervals for odds ratios
CLPARM=
Computes confidence intervals for parameters
PLCONV=
Specifies the profile-likelihood convergence criterion
Classification Options
CTABLE
Displays the classification table
PEVENT=
Specifies prior event probabilities
PPROB=
Specifies probability cutpoints for classification
Overdispersion and Goodness-of-Fit Test Options
AGGREGATE=
Determines subpopulations for Pearson chi-square and
deviance
LACKFIT
Requests the Hosmer and Lemeshow goodness-of-fit test
SCALE=
Specifies the method to correct overdispersion
ROC Curve Options
OUTROC=
Names the output ROC data set
ROCEPS=
Specifies the probability grouping criterion
Regression Diagnostics Options
INFLUENCE
Displays influence statistics
IPLOTS
Displays influence statistics
Display Options
CORRB
Displays the correlation matrix
COVB
Displays the covariance matrix
EXPB
Displays exponentiated values of the estimates
ITPRINT
Displays the iteration history
NODUMMYPRINT Suppresses the “Class Level Information” table
NOODDSRATIO
Suppresses the default “Odds Ratio” table
PARMLABEL
Displays parameter labels
PCORR
Displays the partial correlation statistic
RSQUARE
Displays the generalized R Square
STB
Displays standardized estimates
Computational Options
BINWIDTH=
Specifies the bin size for estimating association statistics
NOLOGSCALE
Performs calculations by using normal scaling
The following list describes these options.
ABSFCONV=value
specifies the absolute function convergence criterion. Convergence requires a small change in the
log-likelihood function in subsequent iterations,
jli
li
1j
< value
where li is the value of the log-likelihood function at iteration i. See the section “Convergence Criteria”
on page 4565 for more information.
MODEL Statement F 4533
AGGREGATE< =(variable-list) >
specifies the subpopulations on which the Pearson chi-square test statistic and the likelihood ratio
chi-square test statistic (deviance) are calculated. Observations with common values in the given
list of variables are regarded as coming from the same subpopulation. Variables in the list can be
any variables in the input data set. Specifying the AGGREGATE option is equivalent to specifying
the AGGREGATE= option with a variable list that includes all explanatory variables in the MODEL
statement. The deviance and Pearson goodness-of-fit statistics are calculated only when the SCALE=
option is specified. Thus, the AGGREGATE (or AGGREGATE=) option has no effect if the SCALE=
option is not specified.
See the section “Rescaling the Covariance Matrix” on page 4579 for more information.
ALPHA=number
sets the level of significance ˛ for 100.1 ˛/% confidence intervals for regression parameters or odds
ratios. The value of number must be between 0 and 1. By default, number is equal to the value of
the ALPHA= option in the PROC LOGISTIC statement, or 0.05 if the option is not specified. This
option has no effect unless confidence limits for the parameters (CLPARM= option) or odds ratios
(CLODDS= option or ODDSRATIO statement) are requested.
BEST=n
specifies that n models with the highest score chi-square statistics are to be displayed for each model
size. It is used exclusively with the SCORE model selection method. If the BEST= option is omitted
and there are no more than 10 explanatory variables, then all possible models are listed for each model
size. If the option is omitted and there are more than 10 explanatory variables, then the number of
models selected for each model size is, at most, equal to the number of explanatory variables listed in
the MODEL statement.
BINWIDTH=width
specifies the size of the bins used for estimating the association statistics. See the section “Rank
Correlation of Observed Responses and Predicted Probabilities” on page 4575 for details. Valid values
are 0 width < 1 (for polytomous response models, 0 < width < 1). The default width is 0.002.
If the width does not evenly divide the unit interval, it is reduced to a valid value and a message is
displayed in the SAS log. The width is also constrained by the amount of memory available on your
machine; if you specify a width that is too small, it is adjusted to a value for which memory can be
allocated and a note is displayed in the SAS log.
If you have a binary response and specify BINWIDTH=0, then no binning is performed and the exact
values of the statistics are computed; this method is a bit slower and might require more memory than
the binning approach.
The BINWIDTH= option is ignored and no binning is performed when a ROC statement is specified,
when ROC graphics are produced, or when the SCORE statement computes an ROC area.
CLODDS=PL | WALD | BOTH
produces confidence intervals for odds ratios of main effects not involved in interactions or nestings.
Computation of these confidence intervals is based on the profile likelihood (CLODDS=PL) or based on
individual Wald tests (CLODDS=WALD). By specifying CLODDS=BOTH, the procedure computes
two sets of confidence intervals for the odds ratios, one based on the profile likelihood and the other
based on the Wald tests. The confidence coefficient can be specified with the ALPHA= option. The
CLODDS=PL option is not available with the STRATA statement. Classification main effects that use
parameterizations other than REF, EFFECT, or GLM are ignored. Specifying the CLODDS option
4534 F Chapter 58: The LOGISTIC Procedure
suppresses the display of the default odds ratio table. If you need to compute odds ratios for an effect
involved in interactions or nestings, or using some other parameterization, then you should specify an
ODDSRATIO statement for that effect.
CLPARM=PL | WALD | BOTH
requests confidence intervals for the parameters. Computation of these confidence intervals is based
on the profile likelihood (CLPARM=PL) or individual Wald tests (CLPARM=WALD). If you specify
CLPARM=BOTH, the procedure computes two sets of confidence intervals for the parameters, one
based on the profile likelihood and the other based on individual Wald tests. The confidence coefficient
can be specified with the ALPHA= option. The CLPARM=PL option is not available with the STRATA
statement.
See the section “Confidence Intervals for Parameters” on page 4571 for more information.
CORRB
displays the correlation matrix of the parameter estimates.
COVB
displays the covariance matrix of the parameter estimates.
CTABLE
classifies the input binary response observations according to whether the predicted event probabilities
are above or below some cutpoint value z in the range .0; 1/. An observation is predicted as an event if
the predicted event probability exceeds or equals z. You can supply a list of cutpoints other than the
default list by specifying the PPROB= option (page 4539). Also, false positive and negative rates can
be computed as posterior probabilities by using Bayes’ theorem. You can use the PEVENT= option to
specify prior probabilities for computing these rates. The CTABLE option is ignored if the data have
more than two response levels. The CTABLE option is not available with the STRATA statement.
For more information, see the section “Classification Table” on page 4577.
DETAILS
produces a summary of computational details for each step of the effect selection process. It produces
the “Analysis of Effects Eligible for Entry” table before displaying the effect selected for entry for
forward or stepwise selection. For each model fitted, it produces the “Type 3 Analysis of Effects” table
if the fitted model involves CLASS variables, the “Analysis of Maximum Likelihood Estimates” table,
and measures of association between predicted probabilities and observed responses. For the statistics
included in these tables, see the section “Displayed Output” on page 4609. The DETAILS option has
no effect when SELECTION=NONE.
EXPB
EXPEST
displays the exponentiated values (eb̌i ) of the parameter estimates b
ˇ i in the “Analysis of Maximum
Likelihood Estimates” table for the logit model. These exponentiated values are the estimated odds
ratios for parameters corresponding to the continuous explanatory variables, and for CLASS effects
that use reference or GLM parameterizations.
FAST
uses a computational algorithm of Lawless and Singhal (1978) to compute a first-order approximation
to the remaining slope estimates for each subsequent elimination of a variable from the model. Variables
are removed from the model based on these approximate estimates. The FAST option is extremely
MODEL Statement F 4535
efficient because the model is not refitted for every variable removed. The FAST option is used when
SELECTION=BACKWARD and in the backward elimination steps when SELECTION=STEPWISE.
The FAST option is ignored when SELECTION=FORWARD or SELECTION=NONE.
FCONV=value
specifies the relative function convergence criterion. Convergence requires a small relative change in
the log-likelihood function in subsequent iterations,
jli
li 1 j
< value
jli 1 j C 1E–6
where li is the value of the log likelihood at iteration i. See the section “Convergence Criteria” on
page 4565 for more information.
FIRTH
performs Firth’s penalized maximum likelihood estimation to reduce bias in the parameter estimates
(Heinze and Schemper 2002; Firth 1993). This method is useful in cases of separability, as often occurs
when the event is rare, and is an alternative to performing an exact logistic regression. See the section
“Firth’s Bias-Reducing Penalized Likelihood” on page 4564 for more information.
N OTE : The intercept-only log likelihood is modified by using the full-model Hessian, computed with
the slope parameters equal to zero. When fitting a model and scoring a data set in the same PROC
LOGISTIC step, the model is fit using Firth’s penalty for parameter estimation purposes, but the
penalty is not applied to the scored log likelihood.
GCONV=value
specifies the relative gradient convergence criterion. Convergence requires that the normalized prediction function reduction is small,
gi0 Ii 1 gi
< value
jli j C 1E–6
where li is the value of the log-likelihood function, gi is the gradient vector, and Ii is the negative
(expected) Hessian matrix, all at iteration i. This is the default convergence criterion, and the default
value is 1E–8. See the section “Convergence Criteria” on page 4565 for more information.
HIERARCHY=keyword
HIER=keyword
specifies whether and how the model hierarchy requirement is applied and whether a single effect or
multiple effects are allowed to enter or leave the model in one step. You can specify that only CLASS effects, or both CLASS and interval effects, be subject to the hierarchy requirement. The HIERARCHY=
option is ignored unless you also specify one of the following options: SELECTION=FORWARD,
SELECTION=BACKWARD, or SELECTION=STEPWISE.
Model hierarchy refers to the requirement that, for any term to be in the model, all effects contained
in the term must be present in the model. For example, in order for the interaction A*B to enter the
model, the main effects A and B must be in the model. Likewise, neither effect A nor B can leave the
model while the interaction A*B is in the model.
The keywords you can specify in the HIERARCHY= option are as follows:
4536 F Chapter 58: The LOGISTIC Procedure
NONE
indicates that the model hierarchy is not maintained. Any single effect can enter or leave
the model at any given step of the selection process.
SINGLE
indicates that only one effect can enter or leave the model at one time, subject to the model
hierarchy requirement. For example, suppose that you specify the main effects A and B
and the interaction A*B in the model. In the first step of the selection process, either A
or B can enter the model. In the second step, the other main effect can enter the model.
The interaction effect can enter the model only when both main effects have already been
entered. Also, before A or B can be removed from the model, the A*B interaction must first
be removed. All effects (CLASS and interval) are subject to the hierarchy requirement.
is the same as HIERARCHY=SINGLE except that only CLASS effects are subject
to the hierarchy requirement.
SINGLECLASS
indicates that more than one effect can enter or leave the model at one time, subject
to the model hierarchy requirement. In a forward selection step, a single main effect can
enter the model, or an interaction can enter the model together with all the effects that are
contained in the interaction. In a backward elimination step, an interaction itself, or the
interaction together with all the effects that the interaction contains, can be removed. All
effects (CLASS and continuous) are subject to the hierarchy requirement.
MULTIPLE
is the same as HIERARCHY=MULTIPLE except that only CLASS effects are
subject to the hierarchy requirement.
MULTIPLECLASS
The default value is HIERARCHY=SINGLE, which means that model hierarchy is to be maintained
for all effects (that is, both CLASS and continuous effects) and that only a single effect can enter or
leave the model at each step.
INCLUDE=n
includes the first n effects in the MODEL statement in every model. By default, INCLUDE=0. The
INCLUDE= option has no effect when SELECTION=NONE.
Note that the INCLUDE= and START= options perform different tasks: the INCLUDE= option
includes the first n effects variables in every model, whereas the START= option requires only that the
first n effects appear in the first model.
INFLUENCE< (STDRES) >
displays diagnostic measures for identifying influential observations in the case of a binary response
model. For each observation, the INFLUENCE option displays the case number (which is the sequence
number of the observation), the values of the explanatory variables included in the final model, and
the regression diagnostic measures developed by Pregibon (1981). The STDRES option includes
standardized and likelihood residuals in the display.
For a discussion of these diagnostic measures, see the section “Regression Diagnostics” on page 4586.
When a STRATA statement is specified, the diagnostics are computed following Storer and Crowley
(1985); see the section “Regression Diagnostic Details” on page 4595 for details.
IPLOTS
produces an index plot for the regression diagnostic statistics developed by Pregibon (1981). An index
plot is a scatter plot with the regression diagnostic statistic represented on the Y axis and the case
number on the X axis. See Example 58.6 for an illustration.
MODEL Statement F 4537
ITPRINT
displays the iteration history of the maximum-likelihood model fitting. The ITPRINT option also
displays the last evaluation of the gradient vector and the final change in the –2 Log Likelihood.
LACKFIT< (n) >
performs the Hosmer and Lemeshow goodness-of-fit test (Hosmer and Lemeshow 2000) for the case of
a binary response model. The subjects are divided into approximately 10 groups of roughly the same
size based on the percentiles of the estimated probabilities. The discrepancies between the observed
and expected number of observations in these groups are summarized by the Pearson chi-square
statistic, which is then compared to a chi-square distribution with t degrees of freedom, where t is the
number of groups minus n. By default, n = 2. A small p-value suggests that the fitted model is not an
adequate model. The LACKFIT option is not available with the STRATA statement. See the section
“The Hosmer-Lemeshow Goodness-of-Fit Test” on page 4581 for more information.
LINK=keyword
L=keyword
specifies the link function linking the response probabilities to the linear predictors. You can specify
one of the following keywords. The default is LINK=LOGIT.
is the complementary log-log function. PROC LOGISTIC fits the binary complementary
log-log model when there are two response categories and fits the cumulative complementary log-log model when there are more than two response categories. The aliases are
CCLOGLOG, CCLL, and CUMCLOGLOG.
CLOGLOG
GLOGIT
is the generalized logit function. PROC LOGISTIC fits the generalized logit model where
each nonreference category is contrasted with the reference category. You can use the
response variable option REF= to specify the reference category.
LOGIT
is the log odds function. PROC LOGISTIC fits the binary logit model when there are
two response categories and fits the cumulative logit model when there are more than two
response categories. The aliases are CLOGIT and CUMLOGIT.
PROBIT
is the inverse standard normal distribution function. PROC LOGISTIC fits the binary probit
model when there are two response categories and fits the cumulative probit model when
there are more than two response categories. The aliases are NORMIT, CPROBIT, and
CUMPROBIT.
The LINK= option is not available with the STRATA statement.
See the section “Link Functions and the Corresponding Distributions” on page 4561 for more details.
MAXFUNCTION=number
specifies the maximum number of function calls to perform when maximizing the conditional likelihood.
This option is valid only when a STRATA statement or the UNEQUALSLOPES option is specified.
The default values are as follows:
• 125 when the number of parameters p < 40
• 500 when 40 p < 400
• 1000 when p 400
Since the optimization is terminated only after completing a full iteration, the number of function calls
that are actually performed can exceed number . If convergence is not attained, the displayed output
4538 F Chapter 58: The LOGISTIC Procedure
and all output data sets created by the procedure contain results based on the last maximum likelihood
iteration.
MAXITER=number
specifies the maximum number of iterations to perform. By default, MAXITER=25. If convergence is
not attained in number iterations, the displayed output and all output data sets created by the procedure
contain results that are based on the last maximum likelihood iteration.
MAXSTEP=n
specifies the maximum number of times any explanatory variable is added to or removed from the
model when SELECTION=STEPWISE. The default number is twice the number of explanatory
variables in the MODEL statement. When the MAXSTEP= limit is reached, the stepwise selection
process is terminated. All statistics displayed by the procedure (and included in output data sets) are
based on the last model fitted. The MAXSTEP= option has no effect when SELECTION=NONE,
FORWARD, or BACKWARD.
NOCHECK
disables the checking process to determine whether maximum likelihood estimates of the regression
parameters exist. If you are sure that the estimates are finite, this option can reduce the execution time
if the estimation takes more than eight iterations. For more information, see the section “Existence of
Maximum Likelihood Estimates” on page 4565.
NODUMMYPRINT
NODESIGNPRINT
NODP
suppresses the “Class Level Information” table, which shows how the design matrix columns for the
CLASS variables are coded.
NOINT
suppresses the intercept for the binary response model, the first intercept for the ordinal response model
(which forces all intercepts to be nonnegative), or all intercepts for the generalized logit model. This
can be particularly useful in conditional logistic analysis; see Example 58.11.
NOFIT
performs the global score test without fitting the model. The global score test evaluates the joint
significance of the effects in the MODEL statement. No further analyses are performed. If the NOFIT
option is specified along with other MODEL statement options, NOFIT takes effect and all other
options except FIRTH, LINK=, NOINT, OFFSET=, ROC, and TECHNIQUE= are ignored. The
NOFIT option is not available with the STRATA statement.
NOLOGSCALE
specifies that computations for the conditional and exact logistic regression models should be computed
by using normal scaling. Log scaling can handle numerically larger problems than normal scaling;
however, computations in the log scale are slower than computations in normal scale.
NOODDSRATIO
NOOR
suppresses the default “Odds Ratio” table.
MODEL Statement F 4539
OFFSET=name
names the offset variable. The regression coefficient for this variable will be fixed at 1. For an example
that uses this option, see Example 58.13. You can also use the OFFSET= option to restrict parameters to
a fixed value. For example, if you want to restrict the parameter for variable X1 to 1 and the parameter
for X2 to 2, compute RestrictD X1 C 2 X 2 in a DATA step, specify the option offset=Restrict,
and leave X1 and X2 out of the model.
OUTROC=SAS-data-set
OUTR=SAS-data-set
creates, for binary response models, an output SAS data set that contains the data necessary to produce
the receiver operating characteristic (ROC) curve. The OUTROC= option is not available with the
STRATA statement. See the section “OUTROC= Output Data Set” on page 4606 for the list of variables
in this data set.
PARMLABEL
displays the labels of the parameters in the “Analysis of Maximum Likelihood Estimates” table.
PCORR
r
computes the partial correlation statistic sign.ˇi /
2i 2
2 log L0
for each parameter i, where 2i is the
Wald chi-square statistic for the parameter and log L0 is the log-likelihood of the intercept-only model
(Hilbe 2009, p. 101). If 2i < 2 then the partial correlation is set to 0. The partial correlation for the
intercept terms is set to missing.
PEVENT=value| (list)
specifies one prior probability or a list of prior probabilities for the event of interest. The false positive
and false negative rates are then computed as posterior probabilities by Bayes’ theorem. The prior
probability is also used in computing the rate of correct prediction. For each prior probability in the
given list, a classification table of all observations is computed. By default, the prior probability is
the total sample proportion of events. The PEVENT= option is useful for stratified samples. It has no
effect if the CTABLE option is not specified. For more information, see the section “False Positive,
False Negative, and Correct Classification Rates Using Bayes’ Theorem” on page 4578. Also see the
PPROB= option for information about how the list is specified.
PLCL
is the same as specifying CLPARM=PL.
PLCONV=value
controls the convergence criterion for confidence intervals based on the profile-likelihood function.
The quantity value must be a positive number, with a default value of 1E–4. The PLCONV= option
has no effect if profile-likelihood confidence intervals (CLPARM=PL) are not requested.
PLRL
is the same as specifying CLODDS=PL.
PPROB=value | (list)
specifies one critical probability value (or cutpoint) or a list of critical probability values for classifying
observations with the CTABLE option. Each value must be between 0 and 1. A response that has a
cross validated predicted probability greater than or equal to the current PPROB= value is classified as
an event response. The PPROB= option is ignored if the CTABLE option is not specified.
4540 F Chapter 58: The LOGISTIC Procedure
A classification table for each of several cutpoints can be requested by specifying a list. For example,
the following statement requests a classification of the observations for each of the cutpoints 0.3, 0.5,
0.6, 0.7, and 0.8:
pprob= (0.3, 0.5 to 0.8 by 0.1)
If the PPROB= option is not specified, the default is to display the classification for a range of
probabilities from the smallest estimated probability (rounded down to the nearest 0.02) to the highest
estimated probability (rounded up to the nearest 0.02) with 0.02 increments.
RIDGING=ABSOLUTE | RELATIVE | NONE
specifies the technique used to improve the log-likelihood function when its value in the current
iteration is less than that in the previous iteration. If you specify the RIDGING=ABSOLUTE option,
the diagonal elements of the negative (expected) Hessian are inflated by adding the ridge value. If you
specify the RIDGING=RELATIVE option, the diagonal elements are inflated by a factor of 1 plus the
ridge value. If you specify the RIDGING=NONE option, the crude line search method of taking half a
step is used instead of ridging. By default, RIDGING=RELATIVE.
RISKLIMITS
RL
WALDRL
is the same as specifying CLODDS=WALD.
ROCEPS=number
specifies a criterion for the ROC curve used for grouping estimated event probabilities that are close
to each other. In each group, the difference between the largest and the smallest estimated event
probabilities does not exceed the given value. The value for number must be between 0 and 1; the
default value is the square root of the machine epsilon, which is about 1E–8 (in releases prior to
9.2, the default was 1E–4). The smallest estimated probability in each group serves as a cutpoint for
predicting an event response. The ROCEPS= option has no effect unless the OUTROC= option, the
BINWIDTH=0 option, or a ROC statement is specified.
RSQUARE
RSQ
requests a generalized R Square measure for the fitted model. For more information, see the section
“Generalized Coefficient of Determination” on page 4568.
SCALE=scale
enables you to supply the value of the dispersion parameter or to specify the method for estimating
the dispersion parameter. It also enables you to display the “Deviance and Pearson Goodness-of-Fit
Statistics” table. To correct for overdispersion or underdispersion, the covariance matrix is multiplied
by the estimate of the dispersion parameter. Valid values for scale are as follows:
specifies that the dispersion parameter be estimated by the deviance divided by its
degrees of freedom.
D | DEVIANCE
specifies that the dispersion parameter be estimated by the Pearson chi-square
statistic divided by its degrees of freedom.
P | PEARSON
MODEL Statement F 4541
WILLIAMS < (constant ) >
specifies that Williams’ method be used to model overdispersion. This
option can be used only with the events/trials syntax. An optional constant can be specified
as the scale parameter; otherwise, a scale parameter is estimated under the full model. A
set of weights is created based on this scale parameter estimate. These weights can then be
used in fitting subsequent models of fewer terms than the full model. When fitting these
submodels, specify the computed scale parameter as constant . See Example 58.10 for an
illustration.
specifies that no correction is needed for the dispersion parameter; that is, the dispersion
parameter remains as 1. This specification is used for requesting the deviance and the
Pearson chi-square statistic without adjusting for overdispersion.
N | NONE
constant
sets the estimate of the dispersion parameter to be the square of the given constant . For
example, SCALE=2 sets the dispersion parameter to 4. The value constant must be a
positive number.
You can use the AGGREGATE (or AGGREGATE=) option to define the subpopulations for calculating the Pearson chi-square statistic and the deviance. In the absence of the AGGREGATE (or
AGGREGATE=) option, each observation is regarded as coming from a different subpopulation. For
the events/trials syntax, each observation consists of n Bernoulli trials, where n is the value of the trials
variable. For single-trial syntax, each observation consists of a single response, and for this setting it is
not appropriate to carry out the Pearson or deviance goodness-of-fit analysis. Thus, PROC LOGISTIC
ignores specifications SCALE=P, SCALE=D, and SCALE=N when single-trial syntax is specified
without the AGGREGATE (or AGGREGATE=) option.
The “Deviance and Pearson Goodness-of-Fit Statistics” table includes the Pearson chi-square statistic,
the deviance, the degrees of freedom, the ratio of each statistic divided by its degrees of freedom, and
the corresponding p-value. The SCALE= option is not available with the STRATA statement. For
more information, see the section “Overdispersion” on page 4579.
SELECTION=BACKWARD | B
| FORWARD | F
| NONE | N
| STEPWISE | S
| SCORE
specifies the method used to select the variables in the model. BACKWARD requests backward
elimination, FORWARD requests forward selection, NONE fits the complete model specified in the
MODEL statement, and STEPWISE requests stepwise selection. SCORE requests best subset selection.
By default, SELECTION=NONE.
For more information, see the section “Effect-Selection Methods” on page 4566.
SEQUENTIAL
SEQ
forces effects to be added to the model in the order specified in the MODEL statement or eliminated
from the model in the reverse order of that specified in the MODEL statement. The model-building
process continues until the next effect to be added has an insignificant adjusted chi-square statistic or
until the next effect to be deleted has a significant Wald chi-square statistic. The SEQUENTIAL option
has no effect when SELECTION=NONE.
4542 F Chapter 58: The LOGISTIC Procedure
SINGULAR=value
specifies the tolerance for testing the singularity of the Hessian matrix (Newton-Raphson algorithm) or
the expected value of the Hessian matrix (Fisher scoring algorithm). The Hessian matrix is the matrix
of second partial derivatives of the log-likelihood function. The test requires that a pivot for sweeping
this matrix be at least this number times a norm of the matrix. Values of the SINGULAR= option must
be numeric. By default, value is the machine epsilon times 1E7, which is approximately 1E–9.
SLENTRY=value
SLE=value
specifies the significance level of the score chi-square for entering an effect into the model in the FORWARD or STEPWISE method. Values of the SLENTRY= option should be between 0 and 1, inclusive.
By default, SLENTRY=0.05. The SLENTRY= option has no effect when SELECTION=NONE,
SELECTION=BACKWARD, or SELECTION=SCORE.
SLSTAY=value
SLS=value
specifies the significance level of the Wald chi-square for an effect to stay in the model in a backward elimination step. Values of the SLSTAY= option should be between 0 and 1, inclusive. By
default, SLSTAY=0.05. The SLSTAY= option has no effect when SELECTION=NONE, SELECTION=FORWARD, or SELECTION=SCORE.
START=n
begins the FORWARD, BACKWARD, or STEPWISE effect selection process with the first n effects
listed in the MODEL statement. The value of n ranges from 0 to s, where s is the total number of
effects in the MODEL statement. The default value of n is s for the BACKWARD method and 0 for the
FORWARD and STEPWISE methods. Note that START=n specifies only that the first n effects appear
in the first model, while INCLUDE=n requires that the first n effects be included in every model. For
the SCORE method, START=n specifies that the smallest models contain n effects, where n ranges
from 1 to s; the default value is 1. The START= option has no effect when SELECTION=NONE.
STB
displays the standardized estimates for the parameters in the “Analysis of Maximum Likelihood
Estimates” table. The standardized estimate of ˇi is given by b
ˇ i =.s=si /, where si is the total sample
standard deviation for the ith explanatory variable and
p
8
< = 3 Logistic
1 p Normal
sD
:
= 6 Extreme-value
The sample standard deviations for parameters associated with CLASS and EFFECT variables are
computed using their codings. For the intercept parameters, the standardized estimates are set to
missing.
STOP=n
specifies the maximum (SELECTION=FORWARD) or minimum (SELECTION=BACKWARD)
number of effects to be included in the final model. The effect selection process is stopped when n
effects are found. The value of n ranges from 0 to s, where s is the total number of effects in the MODEL
statement. The default value of n is s for the FORWARD method and 0 for the BACKWARD method.
MODEL Statement F 4543
For the SCORE method, STOP=n specifies that the largest models contain n effects, where n ranges
from 1 to s; the default value of n is s. The STOP= option has no effect when SELECTION=NONE or
STEPWISE.
STOPRES
SR
specifies that the removal or entry of effects be based on the value of the residual chi-square. If
SELECTION=FORWARD, then the STOPRES option adds the effects into the model one at a time
until the residual chi-square becomes insignificant (until the p-value of the residual chi-square exceeds
the SLENTRY= value). If SELECTION=BACKWARD, then the STOPRES option removes effects
from the model one at a time until the residual chi-square becomes significant (until the p-value of the
residual chi-square becomes less than the SLSTAY= value). The STOPRES option has no effect when
SELECTION=NONE or SELECTION=STEPWISE.
TECHNIQUE=FISHER | NEWTON
TECH=FISHER | NEWTON
specifies the optimization technique for estimating the regression parameters. NEWTON (or NR) is
the Newton-Raphson algorithm and FISHER (or FS) is the Fisher scoring algorithm. Both techniques
yield the same estimates, but the estimated covariance matrices are slightly different except for the case
when the LOGIT link is specified for binary response data. The default is TECHNIQUE=FISHER.
If the LINK=GLOGIT option is specified, then Newton-Raphson is the default and only available
method. The TECHNIQUE= option is not applied to conditional and exact conditional analyses. This
option is not available when the UNEQUALSLOPES option is specified. See the section “Iterative
Algorithms for Model Fitting” on page 4563 for more details.
UNEQUALSLOPES< =effect | (effect-list) >
specifies one or more effects in a cumulative response model that have a different set of parameters for
each response function. If you specify more than one effect, enclose the effects in parentheses. The
effects must be explanatory effects that are specified in the MODEL statement.
If you do not specify this option, the cumulative response models make the parallel lines assumption,
F .Pr.Y < i // D ˛i C x0 ˇ, where each response function has the same slope parameters ˇ. If
you specify this option without an effect or effect-list , all slope parameters vary across the response
functions, resulting in the model F .Pr.Y < i // D ˛i C x0 ˇi . Specifying an effect or effect-list enables
you to choose which effects have different parameters across the response functions. If you select the
first x1 parameters to have equal slopes and the remaining x2 parameters to have unequal slopes, the
model can be written as F .Pr.Y < i // D ˛i C x01 ˇ1 C x02 ˇ2i . A model that uses the CLOGIT link is
called a partial proportional odds model (Peterson and Harrell 1990).
For more information, see Example 58.17. The following statements are not available with this option:
EFFECTPLOT, ESTIMATE, EXACT, LSMEANS, LSMESTIMATE, ROC, ROCCONTRAST, SLICE,
STORE, and STRATA. The following options are not available with this option: FIRTH, RIDGING=,
TECHNIQUE=, CTABLE, PEVENT=, PPROB=, OUTROC=, and ROCEPS=.
WALDCL
CL
is the same as specifying CLPARM=WALD.
4544 F Chapter 58: The LOGISTIC Procedure
XCONV=value
specifies the relative parameter convergence criterion. Convergence requires a small relative parameter
change in subsequent iterations,
.i /
max jıj j < value
j
where
.i /
ıj
D
8 .i /
< ˇj
.i 1/
ˇj
.i/
ˇj
:
.i 1/
ˇj
.i 1/
ˇj
.i 1/
jˇj
j < 0:01
otherwise
.i /
and ˇj is the estimate of the jth parameter at iteration i. See the section “Convergence Criteria” on
page 4565 for more information.
NLOPTIONS Statement
NLOPTIONS < options > ;
The NLOPTIONS statement controls the optimization process for conditional analyses (which result from
specifying a STRATA statement) and for partial parallel slope models (which result from specifying the
UNEQUALSLOPES option in the MODEL statement). An option specified in the NLOPTIONS statement
takes precedence over the same option specified in the MODEL statement.
The default optimization techniques are chosen according to the number of parameters, p, as follows:
• Newton-Raphson with ridging when p < 40
• quasi-Newton when 40 p < 400
• conjugate gradient when p 400
The available options are described in the section “NLOPTIONS Statement” on page 488 in Chapter 19,
“Shared Concepts and Topics.”
ODDSRATIO Statement
ODDSRATIO < 'label' > variable < / options > ;
The ODDSRATIO statement produces odds ratios for variable even when the variable is involved in interactions with other covariates, and for classification variables that use any parameterization. You can also
specify variables on which constructed effects are based, in addition to the names of COLLECTION or
MULTIMEMBER effects. You can specify several ODDSRATIO statements.
If variable is continuous, then the odds ratios honor any values specified in the UNITS statement. If variable
is a classification variable, then odds ratios comparing each pairwise difference between the levels of variable
are produced. If variable interacts with a continuous variable, then the odds ratios are produced at the mean
of the interacting covariate by default. If variable interacts with a classification variable, then the odds ratios
ODDSRATIO Statement F 4545
are produced at each level of the interacting covariate by default. The computed odds ratios are independent
of the parameterization of any classification variable.
The odds ratios are uniquely labeled by concatenating the following terms to variable:
1. If this is a polytomous response model, then prefix the response variable and the level describing the
logit followed by a colon; for example, “Y 0:”.
2. If variable is continuous and the UNITS statement provides a value that is not equal to 1, then append
“Units=value”; otherwise, if variable is a classification variable, then append the levels being contrasted;
for example, “cat vs dog”.
3. Append all interacting covariates preceded by “At”; for example, “At X=1.2 A=cat”.
If you are also creating odds ratio plots, then this label is displayed on the plots (see the PLOTS option for
more information). If you specify a 'label' in the ODDSRATIO statement, then the odds ratios produced by
this statement are also labeled: 'label', 'label 2', 'label 3',. . . , and these are the labels used in the plots. If there
are any duplicated labels across all ODDSRATIO statements, then the corresponding odds ratios are not
displayed on the plots.
The following options are available:
AT(covariate=value-list | REF | ALL< ...covariate=value-list | REF | ALL >)
specifies fixed levels of the interacting covariates. If a specified covariate does not interact with the
variable, then its AT list is ignored.
For continuous interacting covariates, you can specify one or more numbers in the value-list . For
classification covariates, you can specify one or more formatted levels of the covariate enclosed in
single quotes (for example, A=’cat’ ’dog’), you can specify the keyword REF to select the referencelevel, or you can specify the keyword ALL to select all levels of the classification variable. By default,
continuous covariates are set to their means, while CLASS covariates are set to ALL. For a model that
includes a classification variable A={cat,dog} and a continuous covariate X, specifying AT(A=’cat’
X=7 9) will set A to ’cat’, and X to 7 and then 9.
CL=WALD | PL | BOTH
specifies whether to create Wald or profile-likelihood confidence limits, or both. By default, Wald
confidence limits are produced.
DIFF=REF | ALL
specifies whether the odds ratios for a classification variable are computed against the reference level,
or all pairs of variable are compared. By default, DIFF=ALL. The DIFF= option is ignored when
variable is continuous.
PLCONV=value
controls the convergence criterion for confidence intervals based on the profile-likelihood function.
The quantity value must be a positive number, with a default value of 1E–4. The PLCONV= option
has no effect if profile-likelihood confidence intervals (CL=PL) are not requested.
PLMAXITER=n
specifies the maximum number of iterations to perform. By default, PLMAXITER=25. If convergence is not attained in n iterations, the odds ratio or the confidence limits are set to missing.
The PLMAXITER= option has no effect if profile-likelihood confidence intervals (CL=PL) are not
requested.
4546 F Chapter 58: The LOGISTIC Procedure
PLSINGULAR=value
specifies the tolerance for testing the singularity of the Hessian matrix (Newton-Raphson algorithm) or
the expected value of the Hessian matrix (Fisher scoring algorithm). The test requires that a pivot for
sweeping this matrix be at least this number times a norm of the matrix. Values of the PLSINGULAR=
option must be numeric. By default, value is the machine epsilon times 1E7, which is approximately
1E–9. The PLSINGULAR= option has no effect if profile-likelihood confidence intervals (CL=PL) are
not requested.
OUTPUT Statement
OUTPUT < OUT=SAS-data-set > < options > ;
The OUTPUT statement creates a new SAS data set that contains all the variables in the input data set and,
optionally, the estimated linear predictors and their standard error estimates, the estimates of the cumulative
or individual response probabilities, and the confidence limits for the cumulative probabilities. Regression
diagnostic statistics and estimates of cross validated response probabilities are also available for binary
response models. If you specify more than one OUTPUT statement, only the last one is used. Formulas for
the statistics are given in the sections “Linear Predictor, Predicted Probability, and Confidence Limits” on
page 4576 and “Regression Diagnostics” on page 4586, and, for conditional logistic regression, in the section
“Conditional Logistic Regression” on page 4594.
If you use the single-trial syntax, the data set also contains a variable named _LEVEL_, which indicates the
level of the response that the given row of output is referring to. For instance, the value of the cumulative
probability variable is the probability that the response variable is as large as the corresponding value of
_LEVEL_. For details, see the section “OUT= Output Data Set in the OUTPUT Statement” on page 4603.
The estimated linear predictor, its standard error estimate, all predicted probabilities, and the confidence
limits for the cumulative probabilities are computed for all observations in which the explanatory variables
have no missing values, even if the response is missing. By adding observations with missing response values
to the input data set, you can compute these statistics for new observations or for settings of the explanatory
variables not present in the data without affecting the model fit. Alternatively, the SCORE statement can be
used to compute predicted probabilities and confidence intervals for new observations.
Table 58.9 summarizes the options available in the OUTPUT statement. These options can be specified
after a slash (/). The statistic and diagnostic options specify the statistics to be included in the output data
set and name the new variables that contain the statistics. If a STRATA statement is specified, only the
PREDICTED=, RESCHI=, STDRESCHI=, DFBETAS=, and H= options are available; see the section
“Regression Diagnostic Details” on page 4595 for details.
Table 58.9
OUTPUT Statement Options
Option
Description
ALPHA=
OUT=
Specifies ˛ for the 100.1 ˛/% confidence intervals
Names the output data set
OUTPUT Statement F 4547
Table 58.9 continued
Option
Description
Statistic Options
LOWER=
Names the lower confidence limit
PREDICTED= Names the predicted probabilities
PREDPROBS= Requests the individual, cumulative, or cross validated predicted probabilities
STDXBETA=
Names the standard error estimate of the linear predictor
UPPER=
Names the upper confidence limit
XBETA=
Names the linear predictor
Diagnostic Options for Binary Response
C=
Names the confidence interval displacement
CBAR=
Names the confidence interval displacement
DFBETAS=
Names the standardized deletion parameter differences
DIFCHISQ=
Names the deletion chi-square goodness-of-fit change
DIFDEV=
Names the deletion deviance change
H=
Names the leverage
RESCHI=
Names the Pearson chi-square residual
RESDEV=
Names the deviance residual
RESLIK=
Names the likelihood residual
STDRESCHI= Names the standardized Pearson chi-square residual
STDRESDEV= Names the standardized deviance residual
The following list describes these options.
ALPHA=number
sets the level of significance ˛ for 100.1 ˛/% confidence limits for the appropriate response
probabilities. The value of number must be between 0 and 1. By default, number is equal to the value
of the ALPHA= option in the PROC LOGISTIC statement, or 0.05 if that option is not specified.
C=name
specifies the confidence interval displacement diagnostic that measures the influence of individual
observations on the regression estimates.
CBAR=name
specifies the confidence interval displacement diagnostic that measures the overall change in the global
regression estimates due to deleting an individual observation.
DFBETAS=_ALL_ | var-list
specifies the standardized differences in the regression estimates for assessing the effects of individual
observations on the estimated regression parameters in the fitted model. You can specify a list of up
to s C 1 variable names, where s is the number of explanatory variables in the MODEL statement,
or you can specify just the keyword _ALL_. In the former specification, the first variable contains
the standardized differences in the intercept estimate, the second variable contains the standardized
differences in the parameter estimate for the first explanatory variable in the MODEL statement,
and so on. In the latter specification, the DFBETAS statistics are named DFBETA_xxx, where xxx
is the name of the regression parameter. For example, if the model contains two variables X1 and
4548 F Chapter 58: The LOGISTIC Procedure
X2, the specification DFBETAS=_ALL_ produces three DFBETAS statistics: DFBETA_Intercept,
DFBETA_X1, and DFBETA_X2. If an explanatory variable is not included in the final model, the
corresponding output variable named in DFBETAS=var-list contains missing values.
DIFCHISQ=name
specifies the change in the chi-square goodness-of-fit statistic attributable to deleting the individual
observation.
DIFDEV=name
specifies the change in the deviance attributable to deleting the individual observation.
H=name
specifies the diagonal element of the hat matrix for detecting extreme points in the design space.
LOWER=name
L=name
names the variable containing the lower confidence limits for , where is the probability of the event
response if events/trials syntax or single-trial syntax with binary response is specified; for a cumulative
model, is the cumulative probability (that is, the probability that the response is less than or equal
to the value of _LEVEL_); for the generalized logit model, it is the individual probability (that is, the
probability that the response category is represented by the value of _LEVEL_). See the ALPHA=
option to set the confidence level.
OUT=SAS-data-set
names the output data set. If you omit the OUT= option, the output data set is created and given a
default name by using the DATAn convention.
PREDICTED=name
PRED=name
PROB=name
P=name
names the variable containing the predicted probabilities. For the events/trials syntax or single-trial
syntax with binary response, it is the predicted event probability. For a cumulative model, it is the
predicted cumulative probability (that is, the probability that the response variable is less than or equal
to the value of _LEVEL_); and for the generalized logit model, it is the predicted individual probability
(that is, the probability of the response category represented by the value of _LEVEL_).
PREDPROBS=(keywords)
requests individual, cumulative, or cross validated predicted probabilities. Descriptions of the keywords
are as follows.
requests the predicted probability of each response level. For a response variable Y
with three levels, 1, 2, and 3, the individual probabilities are Pr(Y=1), Pr(Y=2), and Pr(Y=3).
INDIVIDUAL | I
requests the cumulative predicted probability of each response level. For a
response variable Y with three levels, 1, 2, and 3, the cumulative probabilities are Pr(Y1),
Pr(Y2), and Pr(Y3). The cumulative probability for the last response level always has
the constant value of 1. For generalized logit models, the cumulative predicted probabilities
are not computed and are set to missing.
CUMULATIVE | C
OUTPUT Statement F 4549
requests the cross validated individual predicted probability
of each response level. These probabilities are derived from the leave-one-out principle—
that is, dropping the data of one subject and reestimating the parameter estimates. PROC
LOGISTIC uses a less expensive one-step approximation to compute the parameter estimates.
This option is valid only for binary response models; for nominal and ordinal models, the
cross validated probabilities are not computed and are set to missing.
CROSSVALIDATE | XVALIDATE | X
See the section “Details of the PREDPROBS= Option” on page 4549 at the end of this section for
further details.
RESCHI=name
specifies the Pearson (chi-square) residual for identifying observations that are poorly accounted for by
the model.
RESDEV=name
specifies the deviance residual for identifying poorly fitted observations.
RESLIK=name
specifies the likelihood residual for identifying poorly fitted observations.
STDRESCHI=name
specifies the standardized Pearson (chi-square) residual for identifying observations that are poorly
accounted for by the model.
STDRESDEV=name
specifies the standardized deviance residual for identifying poorly fitted observations.
STDXBETA=name
names the variable containing the standard error estimates of XBETA. See the section “Linear Predictor,
Predicted Probability, and Confidence Limits” on page 4576 for details.
UPPER=name
U=name
names the variable containing the upper confidence limits for , where is the probability of the event
response if events/trials syntax or single-trial syntax with binary response is specified; for a cumulative
model, is cumulative probability (that is, the probability that the response is less than or equal to
the value of _LEVEL_); for the generalized logit model, it is the individual probability (that is, the
probability that the response category is represented by the value of _LEVEL_). See the ALPHA=
option to set the confidence level.
XBETA=name
names the variable containing the estimates of the linear predictor ˛i Cˇ 0 x, where i is the corresponding
ordered value of _LEVEL_.
Details of the PREDPROBS= Option
You can request any of the three types of predicted probabilities. For example, you can request both the
individual predicted probabilities and the cross validated probabilities by specifying PREDPROBS=(I X).
When you specify the PREDPROBS= option, two automatic variables, _FROM_ and _INTO_, are included
for the single-trial syntax and only one variable, _INTO_, is included for the events/trials syntax. The variable
4550 F Chapter 58: The LOGISTIC Procedure
_FROM_ contains the formatted value of the observed response. The variable _INTO_ contains the formatted
value of the response level with the largest individual predicted probability.
If you specify PREDPROBS=INDIVIDUAL, the OUT= data set contains k additional variables representing
the individual probabilities, one for each response level, where k is the maximum number of response levels
across all BY groups. The names of these variables have the form IP_xxx, where xxx represents the particular
level. The representation depends on the following situations:
• If you specify events/trials syntax, xxx is either ‘Event’ or ‘Nonevent’. Thus, the variable containing
the event probabilities is named IP_Event and the variable containing the nonevent probabilities is
named IP_Nonevent.
• If you specify the single-trial syntax with more than one BY group, xxx is 1 for the first ordered level
of the response, 2 for the second ordered level of the response, and so forth, as given in the “Response
Profile” table. The variable containing the predicted probabilities Pr(Y=1) is named IP_1, where Y is
the response variable. Similarly, IP_2 is the name of the variable containing the predicted probabilities
Pr(Y=2), and so on.
• If you specify the single-trial syntax with no BY-group processing, xxx is the left-justified formatted
value of the response level (the value might be truncated so that IP_xxx does not exceed 32 characters).
For example, if Y is the response variable with response levels ‘None’, ‘Mild’, and ‘Severe’, the
variables representing individual probabilities Pr(Y=’None’), P(Y=’Mild’), and P(Y=’Severe’) are
named IP_None, IP_Mild, and IP_Severe, respectively.
If you specify PREDPROBS=CUMULATIVE, the OUT= data set contains k additional variables representing
the cumulative probabilities, one for each response level, where k is the maximum number of response
levels across all BY groups. The names of these variables have the form CP_xxx, where xxx represents the
particular response level. The naming convention is similar to that given by PREDPROBS=INDIVIDUAL.
The PREDPROBS=CUMULATIVE values are the same as those output by the PREDICT= option, but are
arranged in variables on each output observation rather than in multiple output observations.
If you specify PREDPROBS=CROSSVALIDATE, the OUT= data set contains k additional variables representing the cross validated predicted probabilities of the k response levels, where k is the maximum number
of response levels across all BY groups. The names of these variables have the form XP_xxx, where xxx
represents the particular level. The representation is the same as that given by PREDPROBS=INDIVIDUAL
except that for the events/trials syntax there are four variables for the cross validated predicted probabilities
instead of two:
XP_EVENT_R1E
is the cross validated predicted probability of an event when a single event is removed
from the current observation.
is the cross validated predicted probability of a nonevent when a single event is
removed from the current observation.
XP_NONEVENT_R1E
XP_EVENT_R1N
is the cross validated predicted probability of an event when a single nonevent is removed
from the current observation.
is the cross validated predicted probability of a nonevent when a single nonevent is
removed from the current observation.
XP_NONEVENT_R1N
The cross validated predicted probabilities are precisely those used in the CTABLE option. See the section
“Predicted Probability of an Event for Classification” on page 4578 for details of the computation.
ROC Statement F 4551
ROC Statement
ROC < 'label' > < specification > < / options > ;
The ROC statements specify models to be used in the ROC comparisons. You can specify more than one
ROC statement. ROC statements are identified by their label —if you do not specify a label , the ith ROC
statement is labeled “ROCi”. Additionally, the specified or selected model is labeled with the MODEL
statement label or “Model” if the MODEL label is not present. The specification can be either a list of effects
that have previously been specified in the MODEL statement, or PRED=variable, where the variable does not
have to be specified in the MODEL statement. The PRED= option enables you to input a criterion produced
outside PROC LOGISTIC; for example, you can fit a random-intercept model by using PROC GLIMMIX
or use survey weights in PROC SURVEYLOGISTIC, then use the predicted values from those models to
produce an ROC curve for the comparisons. If you do not make a specification, then an intercept-only model
is fit to the data, resulting in a noninformative ROC curve that can be used for comparing the area under
another ROC curve to 0.5.
You can specify a ROCCONTRAST statement and a ROCOPTIONS option in the PROC LOGISTIC
statement to control how the models are compared, while the PLOTS=ROC option controls the ODS Graphics
displays. See Example 58.8 for an example that uses the ROC statement.
If you specify any options, then a “ROC Model Information” table summarizing the new ROC model is
displayed. The options are ignored for the PRED= specification. The following options are available:
NOOFFSET
does not include an offset variable if the OFFSET= option is specified in the MODEL statement.
A constant offset has no effect on the ROC curve, although the cutpoints might be different, but a
nonconstant offset can affect the parameter estimates and hence the ROC curve.
LINK=keyword
specifies the link function to be used in the model. The available keywords are LOGIT, NORMIT, and
CLOGLOG. The logit link is the default. Note that the LINK= option in the MODEL statement is
ignored.
ROCCONTRAST Statement
ROCCONTRAST < 'label' > < contrast > < / options > ;
The ROCCONTRAST statement compares the different ROC models. You can specify only one ROCCONTRAST statement. The ROCOPTIONS options in the PROC LOGISTIC statement control how the models
are compared. You can specify one of the following contrast specifications:
REFERENCE< (MODEL | ’roc-label’) >
produces a contrast matrix of differences between each ROC curve and a reference curve. The MODEL
keyword specifies that the reference curve is that produced from the MODEL statement; the roc-label
specifies the label of the ROC curve that is to be used as the reference curve. If neither the MODEL
keyword nor the roc-label label is specified, then the reference ROC curve is either the curve produced
from the MODEL statement, the selected model if a selection method is specified, or the model from
the first ROC statement if the NOFIT option is specified.
4552 F Chapter 58: The LOGISTIC Procedure
ADJACENTPAIRS
produces a contrast matrix of each ROC curve minus the succeeding curve.
matrix
specifies the contrast in the form row1,row2,..., where each row contains the coefficients used to
compare the ROC curves. Each row must contain the same number of entries as there are ROC curves
being compared. The elements of each row refer to the ROC statements in the order in which they are
specified. However, the first element of each row refers either to the fitted model, the selected model
if a SELECTION= method is specified, or the first specified ROC statement if the NOFIT option is
specified.
If no contrast is specified, then the REFERENCE contrast with the default reference curve is used. See the
section “Comparing ROC Curves” on page 4583 for more information about comparing ROC curves, and see
Example 58.8 for an example.
The following options are available:
E
displays the contrast.
ESTIMATE < = ROWS | ALLPAIRS >
produces estimates of each row of the contrast when ESTIMATE or ESTIMATE=ROWS is specified.
If the ESTIMATE=ALLPAIRS option is specified, then estimates of every pairwise difference of ROC
curves are produced.
The row contrasts are labeled “ModelLabel1 – ModelLabel2”, where the model labels are as described
in the ROC statement; in particular, for the REFERENCE contrast, ModelLabel2 is the reference model
label. If you specify your own contrast matrix, then the ith contrast row estimate is labeled “Rowi”.
COV
displays covariance matrices used in the computations.
SCORE Statement
SCORE < options > ;
The SCORE statement creates a data set that contains all the data in the DATA= data set together with
posterior probabilities and, optionally, prediction confidence intervals. Fit statistics are displayed on request.
If you have binary response data, the SCORE statement can be used to create a data set containing data for
the ROC curve. You can specify several SCORE statements. FREQ, WEIGHT, and BY statements can be
used with the SCORE statements. Weights do not affect the computation of predicted probabilities, their
confidence limits, or the predicted response level. Weights affect some fit statistics as described in “Fit
Statistics for Scored Data Sets” on page 4590. The SCORE statement is not available with the STRATA
statement.
If a SCORE statement is specified in the same run as fitting the model, FORMAT statements should be
specified after the SCORE statement in order for the formats to apply to all the DATA= and PRIOR= data
sets in the SCORE statement.
See the section “Scoring Data Sets” on page 4589 for more information, and see Example 58.15 for an
illustration of how to use this statement.
SCORE Statement F 4553
Table 58.10 summarizes the options available in the SCORE statement.
Table 58.10 SCORE Statement Options
Option
Description
ALPHA=
CLM
CUMULATIVE
DATA=
FITSTAT
OUT=
OUTROC=
PRIOR=
PRIOREVENT=
ROCEPS=
Specifies the significance level
Outputs the Wald-test-based confidence limits
Outputs the cumulative predicted probabilities
Names the SAS data that you want to score
Displays fit statistics
Names the SAS data set that contains the predicted information
Names the SAS data set that contains the ROC curve
Names the SAS data set that contains the priors of the response categories
Specifies the prior event probability
Specifies the criterion for grouping estimated event probabilities
You can specify the following options:
ALPHA=number
specifies the significance level ˛ for 100.1 ˛/% confidence intervals. By default, the value of
number is equal to the ALPHA= option in the PROC LOGISTIC statement, or 0.05 if that option is
not specified. This option has no effect unless the CLM option in the SCORE statement is requested.
CLM
outputs the Wald-test-based confidence limits for the predicted probabilities. This option is not available
when the INMODEL= data set is created with the NOCOV option.
CUMULATIVE
outputs the cumulative predicted probabilities Pr.Y i /; i D 1; : : : ; k C 1, to the OUT= data set.
This option is valid only when you have more than two response levels; otherwise, the option is ignored
and a note is printed in the SAS log. These probabilities are named CP_level_i, where level_i is the ith
response level.
If the CLM option is also specified in the SCORE statement, then the Wald-based confidence limits for
the cumulative predicted probabilities are also output. The confidence limits are named CLCL_level_i
and CUCL_level_i. In particular, for the lowest response level, the cumulative values (CP, CLCL,
CUCL) should be identical to the individual values (P, LCL, UCL), and for the highest response level
CP=CLCL=CUCL=1.
DATA=SAS-data-set
names the SAS data set that you want to score. If you omit the DATA= option in the SCORE statement,
then scoring is performed on the DATA= input data set in the PROC LOGISTIC statement, if specified;
otherwise, the DATA=_LAST_ data set is used.
It is not necessary for the DATA= data set in the SCORE statement to contain the response variable
unless you are specifying the FITSTAT or OUTROC= option.
Only those variables involved in the fitted model effects are required in the DATA= data set in the
SCORE statement. For example, the following statements use forward selection to select effects:
4554 F Chapter 58: The LOGISTIC Procedure
proc logistic data=Neuralgia outmodel=sasuser.Model;
class Treatment Sex;
model Pain(event='Yes')= Treatment|Sex Age
/ selection=forward sle=.01;
run;
Suppose Treatment and Age are the effects selected for the final model. You can score a data set that
does not contain the variable Sex since the effect Sex is not in the model that the scoring is based on.
For example, the following statements score the Neuralgia data set after dropping the Sex variable:
proc logistic inmodel=sasuser.Model;
score data=Neuralgia(drop=Sex);
run;
FITSTAT
displays fit statistics for the data set you are scoring. The data set must contain the response variable.
See the section “Fit Statistics for Scored Data Sets” on page 4590 for details.
OUT=SAS-data-set
names the SAS data set that contains the predicted information. If you omit the OUT= option, the
output data set is created and given a default name by using the DATAn convention.
OUTROC=SAS-data-set
names the SAS data set that contains the ROC curve for the DATA= data set. The ROC curve is
computed only for binary response data. See the section “OUTROC= Output Data Set” on page 4606
for the list of variables in this data set.
PRIOR=SAS-data-set
names the SAS data set that contains the priors of the response categories. The priors can be values
proportional to the prior probabilities; thus, they do not necessarily sum to one. This data set should
include a variable named _PRIOR_ that contains the prior probabilities. For events/trials MODEL
statement syntax, this data set should also include an _OUTCOME_ variable that contains the values
EVENT and NONEVENT; for single-trial syntax, this data set should include the response variable
that contains the unformatted response categories. See Example 58.15 for an example.
PRIOREVENT=value
specifies the prior event probability for a binary response model. If both PRIOR= and PRIOREVENT=
options are specified, the PRIOR= option takes precedence.
ROCEPS=value
specifies the criterion for grouping estimated event probabilities that are close to each other for the ROC
curve. In each group, the difference between the largest and the smallest estimated event probability
does not exceed the given value. The value must be between 0 and 1; the default value is the square
root of the machine epsilon, which is about 1E–8 (in releases prior to 9.2, the default was 1E–4). The
smallest estimated probability in each group serves as a cutpoint for predicting an event response. The
ROCEPS= option has no effect if the OUTROC= option is not specified in the SCORE statement.
SLICE Statement F 4555
SLICE Statement
SLICE model-effect < / options > ;
The SLICE statement provides a general mechanism for performing a partitioned analysis of the LS-means
for an interaction. This analysis is also known as an analysis of simple effects.
The SLICE statement uses the same options as the LSMEANS statement, which are summarized in Table 19.21. For details about the syntax of the SLICE statement, see the section “SLICE Statement” on
page 505 in Chapter 19, “Shared Concepts and Topics.”
N OTE : If you have classification variables in your model, then the SLICE statement is allowed only if you
also specify the PARAM=GLM option.
STORE Statement
STORE < OUT= >item-store-name < / LABEL='label' > ;
The STORE statement requests that the procedure save the context and results of the statistical analysis. The
resulting item store has a binary file format that cannot be modified. The contents of the item store can be
processed with the PLM procedure.
For details about the syntax of the STORE statement, see the section “STORE Statement” on page 508 in
Chapter 19, “Shared Concepts and Topics.”
STRATA Statement
STRATA variable < (option) > . . . < variable < (option) > > < / options > ;
The STRATA statement names the variables that define strata or matched sets to use in stratified logistic
regression of binary response data.
Observations that have the same variable values are in the same matched set. For a stratified logistic model,
you can analyze 1W 1, 1W n, mW n, and general mi W ni matched sets where the number of cases and controls
varies across strata. At least one variable must be specified to invoke the stratified analysis, and the usual
unconditional asymptotic analysis is not performed. The stratified logistic model has the form
logit.hi / D ˛h C x0hi ˇ
where hi is the event probability for the ith observation in stratum h with covariates xhi and where the
stratum-specific intercepts ˛h are the nuisance parameters that are to be conditioned out.
STRATA variables can also be specified in the MODEL statement as classification or continuous covariates;
however, the effects are nondegenerate only when crossed with a nonstratification variable. Specifying several
STRATA statements is the same as specifying one STRATA statement that contains all the strata variables.
The STRATA variables can be either character or numeric, and the formatted values of the STRATA variables
determine the levels. Thus, you can also use formats to group values into levels; see the discussion of the
FORMAT procedure in the Base SAS Procedures Guide.
4556 F Chapter 58: The LOGISTIC Procedure
The “Strata Summary” table is displayed by default. For an exact logistic regression, it displays the number
of strata that have a specific number of events and non-events. For example, if you are analyzing a 1W 5
matched study, this table enables you to verify that every stratum in the analysis has exactly one event and
five non-events. Strata that contain only events or only non-events are reported in this table, but such strata
are uninformative and are not used in the analysis.
If an EXACT statement is also specified, then a stratified exact logistic regression is performed.
The EFFECTPLOT, SCORE, and WEIGHT statements are not available with a STRATA statement. The
following MODEL options are also not supported with a STRATA statement: CLPARM=PL, CLODDS=PL,
CTABLE, FIRTH, LACKFIT, LINK=, NOFIT, OUTMODEL=, OUTROC=, ROC, and SCALE=.
The following option can be specified for a stratification variable by enclosing the option in parentheses after
the variable name, or it can be specified globally for all STRATA variables after a slash (/).
MISSING
treats missing values (‘.’, ‘._’, ‘.A’, . . . , ‘.Z’ for numeric variables and blanks for character variables)
as valid STRATA variable values.
The following strata options are also available after the slash:
CHECKDEPENDENCY | CHECK=keyword
specifies which variables are to be tested for dependency before the analysis is performed. The available
keywords are as follows:
NONE
performs no dependence checking. Typically, a message about a singular information matrix
is displayed if you have dependent variables. Dependent variables can be identified after the
analysis by noting any missing parameter estimates.
checks dependence between covariates and an added intercept. Dependent covariates
are removed from the analysis. However, covariates that are linear functions of the strata
variable might not be removed, which results in a singular information matrix message
being displayed in the SAS log. This is the default.
COVARIATES
ALL
checks dependence between all the strata and covariates. This option can adversely affect
performance if you have a large number of strata.
NOSUMMARY
suppresses the display of the “Strata Summary” table.
INFO
displays the “Strata Information” table, which includes the stratum number, levels of the STRATA
variables that define the stratum, the number of events, the number of non-events, and the total
frequency for each stratum. Since the number of strata can be very large, this table is displayed only
by request.
TEST Statement F 4557
TEST Statement
< label: > TEST equation1 < , equation2, . . . > < / option > ;
The TEST statement tests linear hypotheses about the regression coefficients. The Wald test is used to
perform a joint test of the null hypotheses H0 W Lˇ D c specified in a single TEST statement, where ˇ is the
vector of intercept and slope parameters. When c D 0 you should specify a CONTRAST statement instead.
Each equation specifies a linear hypothesis (a row of the L matrix and the corresponding element of the c
vector). Multiple equations are separated by commas. The label , which must be a valid SAS name, is used
to identify the resulting output and should always be included. You can submit multiple TEST statements.
The form of an equation is as follows:
term< ˙ term : : : > < D ˙term < ˙term: : : > >
where term is a parameter of the model, or a constant, or a constant times a parameter. Intercept and
CLASS variable parameter names should be specified as described in the section “Parameter Names in the
OUTEST= Data Set” on page 4602. Note for generalized logit models that this enables you to construct tests
of parameters from specific logits. When no equal sign appears, the expression is set to 0. The following
statements illustrate possible uses of the TEST statement:
proc logistic;
model y= a1
test1: test
test2: test
test3: test
test4: test
run;
a2 a3 a4;
intercept + .5 * a2 = 0;
intercept + .5 * a2;
a1=a2=a3;
a1=a2, a2=a3;
Note that the first and second TEST statements are equivalent, as are the third and fourth TEST statements.
You can specify the following option in the TEST statement after a slash(/):
PRINT
displays intermediate calculations in the testing of the null hypothesis H0 W Lˇ D c. This includes
Lb
V.b̌/L0 bordered by .Lb̌ c/ and ŒLb
V.b̌/L0 1 bordered by ŒLb
V.b̌/L0 1 .Lb̌ c/, where b̌ is the
maximum likelihood estimator of ˇ and b
V.b̌/ is the estimated covariance matrix of b̌.
For more information, see the section “Testing Linear Hypotheses about the Regression Coefficients”
on page 4585.
UNITS Statement
UNITS < independent1=list1 < independent2=list2: : : > > < / option > ;
The UNITS statement enables you to specify units of change for the continuous explanatory variables so
that customized odds ratios can be estimated. If you specify more than one UNITS statement, only the last
one is used. An estimate of the corresponding odds ratio is produced for each unit of change specified for
an explanatory variable. The UNITS statement is ignored for CLASS variables. Odds ratios are computed
only for main effects that are not involved in interactions or nestings, unless an ODDSRATIO statement is
4558 F Chapter 58: The LOGISTIC Procedure
also specified. If the CLODDS= option is specified in the MODEL statement, the corresponding confidence
limits for the odds ratios are also displayed, as are odds ratios and confidence limits for any CLASS main
effects that are not involved in interactions or nestings. The CLASS effects must use the GLM, reference, or
effect coding.
The UNITS statement also enables you to customize the odds ratios for effects specified in ODDSRATIO
statements, in which case interactions and nestings are allowed, and CLASS variables can be specified with
any parameterization.
The term independent is the name of an explanatory variable and list represents a list of units of change,
separated by spaces, that are of interest for that variable. Each unit of change in a list has one of the following
forms:
• number
• SD or –SD
• number * SD
where number is any nonzero number, and SD is the sample standard deviation of the corresponding
independent variable. For example, X D 2 requests an odds ratio that represents the change in the odds
when the variable X is decreased by two units. X D 2SD requests an estimate of the change in the odds
when X is increased by two sample standard deviations.
You can specify the following option in the UNITS statement after a slash(/):
DEFAULT=list
gives a list of units of change for all explanatory variables that are not specified in the UNITS statement.
Each unit of change can be in any of the forms described previously. If the DEFAULT= option is not
specified, PROC LOGISTIC does not produce customized odds ratio estimates for any continuous
explanatory variable that is not listed in the UNITS statement.
For more information, see the section “Odds Ratio Estimation” on page 4572.
WEIGHT Statement
WEIGHT variable < / option > ;
When a WEIGHT statement appears, each observation in the input data set is weighted by the value of the
WEIGHT variable. Unlike a FREQ variable, the values of the WEIGHT variable can be nonintegral and are
not truncated. Observations with negative, zero, or missing values for the WEIGHT variable are not used
in the model fitting. When the WEIGHT statement is not specified, each observation is assigned a weight
of 1. The WEIGHT statement is not available with the STRATA statement. If you specify more than one
WEIGHT statement, then the first WEIGHT variable is used.
If a SCORE statement is specified, then the WEIGHT variable is used for computing fit statistics (see the
section “Fit Statistics for Scored Data Sets” on page 4590) and the ROC curve (see the WEIGHTED option
of the ROCOPTIONS option), but it is not required for scoring. Weights do not affect the computation of
predicted probabilities, their confidence limits, or the predicted response level. If the DATA= data set in the
SCORE statement does not contain the WEIGHT variable, the weights are assumed to be 1 and a warning
message is issued in the SAS log. If you fit a model and perform the scoring in the same run, the same
Details: LOGISTIC Procedure F 4559
WEIGHT variable is used for fitting and scoring. If you fit a model in a previous run and input it with the
INMODEL= option in the current run, then the WEIGHT variable can be different from the one used in the
previous run; however, if a WEIGHT variable was not specified in the previous run, you can still specify a
WEIGHT variable in the current run.
C AUTION : PROC LOGISTIC does not compute the proper variance estimators if you are analyzing survey
data and specifying the sampling weights through the WEIGHT statement. The SURVEYLOGISTIC
procedure is designed to perform the necessary, and correct, computations.
The following option can be added to the WEIGHT statement after a slash (/):
NORMALIZE
NORM
causes the weights specified by the WEIGHT variable to be normalized so that they add up to the
actual sample size. Weights wi are normalized by multiplying them by Pn n w , where n is the sample
iD1 i
size. With this option, the estimated covariance matrix of the parameter estimators is invariant to the
scale of the WEIGHT variable.
Details: LOGISTIC Procedure
Missing Values
Any observation with missing values for the response, offset, strata, or explanatory variables is excluded
from the analysis; however, missing values are valid for variables specified with the MISSING option in the
CLASS or STRATA statement. Observations with a nonpositive or missing weight or with a frequency less
than 1 are also excluded. The estimated linear predictor and its standard error estimate, the fitted probabilities
and confidence limits, and the regression diagnostic statistics are not computed for any observation with
missing offset or explanatory variable values. However, if only the response value is missing, the linear
predictor, its standard error, the fitted individual and cumulative probabilities, and confidence limits for the
cumulative probabilities can be computed and output to a data set by using the OUTPUT statement.
Response Level Ordering
Response level ordering is important because, by default, PROC LOGISTIC models the probability of
response levels with lower Ordered Value. Ordered Values are assigned to response levels in ascending sorted
order (that is, the lowest response level is assigned Ordered Value 1, the next lowest is assigned Ordered
Value 2, and so on) and are displayed in the “Response Profiles” table. If your response variable Y takes
values in f1; : : : ; k C 1g, then, by default, the functions modeled with the binary or cumulative model are
logit.Pr.Y i jx//;
i D 1; : : : ; k
and for the generalized logit model the functions modeled are
Pr.Y D i jx/
log
; i D 1; : : : ; k
Pr.Y D k C 1jx/
4560 F Chapter 58: The LOGISTIC Procedure
where the highest Ordered Value Y D k C 1 is the reference level. You can change which probabilities are
modeled by specifying the EVENT=, REF=, DESCENDING, or ORDER= response variable options in the
MODEL statement.
For binary response data with event and nonevent categories, if your event category has a higher Ordered
Value, then by default the nonevent is modeled. Since the default response function modeled is
logit./ D log
1 where is the probability of the response level assigned Ordered Value 1, and since
logit./ D
logit.1
/
the effect of modeling the nonevent is to change the signs of ˛ and ˇ in the model for the event, logit./ D
˛ C ˇ 0 x.
For example, suppose the binary response variable Y takes the values 1 and 0 for event and nonevent,
respectively, and Exposure is the explanatory variable. By default, PROC LOGISTIC assigns Ordered Value
1 to response level Y=0, and Ordered Value 2 to response level Y=1. As a result, PROC LOGISTIC models
the probability of the nonevent (Ordered Value=1) category, and your parameter estimates have the opposite
sign from those in the model for the event. To model the event without using a DATA step to change the
values of the variable Y, you can control the ordering of the response levels or select the event or reference
level, as shown in the following list:
• Explicitly state which response level is to be modeled by using the response variable option EVENT=
in the MODEL statement:
model Y(event='1') = Exposure;
• Specify the nonevent category for the response variable in the response variable option REF= in the
MODEL statement. This option is most useful for generalized logit models where the EVENT= option
cannot be used.
model Y(ref='0') = Exposure;
• Specify the response variable option DESCENDING in the MODEL statement to assign the lowest
Ordered Value to Y=1:
model Y(descending)=Exposure;
• Assign a format to Y such that the first formatted value (when the formatted values are put in sorted
order) corresponds to the event. In the following example, Y=1 is assigned the formatted value ‘event’
and Y=0 is assigned the formatted value ‘nonevent’. Since ORDER=FORMATTED by default, Ordered
Value 1 is assigned to response level Y=1, so the procedure models the event.
Link Functions and the Corresponding Distributions F 4561
proc format;
value Disease 1='event' 0='nonevent';
run;
proc logistic;
format Y Disease.;
model Y=Exposure;
run;
Link Functions and the Corresponding Distributions
Four link functions are available in the LOGISTIC procedure. The logit function is the default. To specify
a different link function, use the LINK= option in the MODEL statement. The link functions and the
corresponding distributions are as follows:
• The logit function
g.p/ D log.p=.1
p//
is the inverse of the cumulative logistic distribution function, which is
F .x/ D 1=.1 C exp. x// D exp.x/=.1 C exp.x//
• The probit (or normit) function
g.p/ D ˆ
1
.p/
is the inverse of the cumulative standard normal distribution function, which is
Z x
1=2
F .x/ D ˆ.x/ D .2/
exp. z 2 =2/dz
1
Traditionally, the probit function contains the additive constant 5, but throughout PROC LOGISTIC,
the terms probit and normit are used interchangeably.
• The complementary log-log function
g.p/ D log. log.1
p//
is the inverse of the cumulative extreme-value function (also called the Gompertz distribution), which
is
F .x/ D 1
exp. exp.x//
• The generalized logit function extends the binary logit link to a vector of levels .p1 ; : : : ; pkC1 / by
contrasting each level with a fixed level
g.pi / D log.pi =pkC1 /
i D 1; : : : ; k
4562 F Chapter 58: The LOGISTIC Procedure
The variances of the normal, logistic, and extreme-value distributions are not the same. Their respective
means and variances are shown in the following table:
Distribution
Normal
Logistic
Extreme-value
Mean
Variance
0
0
2 =3
1
2 =6
Here is the Euler constant. In comparing parameter estimates from different link functions, you need to
take into account the different scalings of the corresponding distributions and, for the complementary log-log
function, a possible shift in location. For example, if the fitted probabilities are inpthe neighborhood of 0.1 to
0.9, then the parameter estimates from the logit link function should be about = 3 larger than the estimates
from the probit link function.
Determining Observations for Likelihood Contributions
If you use events/trials MODEL statement syntax, split each observation into two observations. One has
response value 1 with a frequency equal to the frequency of the original observation (which is 1 if the FREQ
statement is not used) times the value of the events variable. The other observation has response value 2 and
a frequency equal to the frequency of the original observation times the value of (trials–events). These two
observations will have the same explanatory variable values and the same FREQ and WEIGHT values as the
original observation.
For either single-trial or events/trials syntax, let j index all observations. In other words, for single-trial syntax,
j indexes the actual observations. And, for events/trials syntax, j indexes the observations after splitting (as
described in the preceding paragraph). If your data set has 30 observations and you use single-trial syntax, j
has values from 1 to 30; if you use events/trials syntax, j has values from 1 to 60.
Suppose the response variable in a cumulative response model can take on the ordered values 1; : : : ; k; k C 1,
where k is an integer 1. The likelihood for the jth observation with ordered response value yj and
explanatory variables vector xj is given by
8
< F .˛1 C ˇ 0 xj /
F .˛i C ˇ 0 xj / F .˛i
Lj D
:
1 F .˛k C ˇ 0 xj /
yj D 1
0x / 1 < y D i k
C
ˇ
1
j
j
yj D k C 1
where F ./ is the logistic, normal, or extreme-value distribution function, ˛1 ; : : : ; ˛k are ordered intercept
parameters, and ˇ is the common slope parameter vector.
For the generalized logit model, letting the k C 1st level be the reference level, the intercepts ˛1 ; : : : ; ˛k are
unordered and the slope vector ˇi varies with each logit. The likelihood for the jth observation with response
value yj and explanatory variables vector xj is given by
8
0
e ˛i Cxj ˇi
ˆ
ˆ
ˆ
1 yj D i k
Pk
<
˛m Cx0j ˇm
1
C
e
mD1
Lj D Pr.Y D yj jxj / D
ˆ
1
ˆ
ˆ
yj D k C 1
:
Pk
0
1 C mD1 e ˛m Cxj ˇm
Iterative Algorithms for Model Fitting F 4563
Iterative Algorithms for Model Fitting
Two iterative maximum likelihood algorithms are available in PROC LOGISTIC for fitting an unconditional
logistic regression, and these two methods are discussed in this section. For conditional logistic regression and
models with the UNEQUALSLOPES specification, see the section “NLOPTIONS Statement” on page 4544
for details about available optimization techniques. Exact logistic regression uses a special algorithm
described in the section “Exact Conditional Logistic Regression” on page 4597.
The default maximum likelihood algorithm is the Fisher scoring method, which is equivalent to fitting
by iteratively reweighted least squares. The alternative algorithm is the Newton-Raphson method. Both
algorithms give the same parameter estimates; however, the estimated covariance matrix of the parameter
estimators can differ slightly. This is due to the fact that Fisher scoring is based on the expected information
matrix while the Newton-Raphson method is based on the observed information matrix. In the case of
a binary logit model, the observed and expected information matrices are identical, resulting in identical
estimated covariance matrices for both algorithms. You can specify the TECHNIQUE= option to select a
fitting algorithm, and specify the FIRTH option to perform a bias-reducing penalized maximum likelihood fit.
Note for generalized logit models that only the Newton-Raphson technique is available.
Iteratively Reweighted Least Squares Algorithm (Fisher Scoring)
Consider the multinomial variable Zj D .Z1j ; : : : ; ZkC1;j /0 such that
Zij D
1 if Yj D i
0 otherwise
With ij denoting the probability that the jth observation has response value i, the expected value of Zj
Pk
is j D .1j ; : : : ; kC1;j /0 where kC1;j D 1
i D1 ij . The covariance matrix of Zj is Vj , which is
the covariance matrix of a multinomial random variable for one trial with parameter vector j . Let ˇ be
the vector of regression parameters; in other words, ˇ D .˛1 ; : : : ; ˛k ; ˇ1 ; : : : ; ˇs /0 . Let Dj be the matrix of
partial derivatives of j with respect to ˇ. The estimating equation for the regression parameters is
X
D0j Wj .Zj j / D 0
j
where Wj D wj fj Vj , wj and fj are the weight and frequency of the jth observation, and Vj is a
generalized inverse of Vj . PROC LOGISTIC chooses Vj as the inverse of the diagonal matrix with j as
the diagonal.
With a starting value of ˇ .0/ , the maximum likelihood estimate of ˇ is obtained iteratively as
X
X
ˇ .mC1/ D ˇ .m/ C .
D0j Wj Dj / 1
D0j Wj .Zj j /
j
j
where Dj , Wj , and j are evaluated at ˇ .m/ . The expression after the plus sign is the step size. If the
likelihood evaluated at ˇ .mC1/ is less than that evaluated at ˇ .m/ , then ˇ .mC1/ is recomputed by step-halving
or ridging as determined by the value of the RIDGING= option. The iterative scheme continues until
convergence is obtained—that is, until ˇ .mC1/ is sufficiently close to ˇ .m/ . Then the maximum likelihood
estimate of ˇ is b̌ D ˇ .mC1/ .
4564 F Chapter 58: The LOGISTIC Procedure
The covariance matrix of b̌ is estimated by
X
b
bjb
Dj / 1 D b
I 1
Cov.b̌/ D .
D0j W
b
j
b j are, respectively, Dj and Wj evaluated at b̌. b
Dj and W
I is the information matrix, or the negative
where b
expected Hessian matrix, evaluated at b̌.
By default, starting values are zero for the slope parameters, and for the intercept parameters, starting
values are the observed cumulative logits (that is, logits of the observed cumulative proportions of response).
Alternatively, the starting values can be specified with the INEST= option.
Newton-Raphson Algorithm
For cumulative models, let the parameter vector be ˇ D .˛1 ; : : : ; ˛k ; ˇ1 ; : : : ; ˇs /0 , and for the generalized
logit model let ˇ D .˛1 ; : : : ; ˛k ; ˇ10 ; : : : ; ˇk0 /0 . The gradient vector and the Hessian matrix are given,
respectively, by
g D
X
H D
X
wj fj
@lj
@ˇ
wj fj
@2 lj
@ˇ 2
j
j
where lj D log Lj is the log likelihood for the jth observation. With a starting value of ˇ .0/ , the maximum
likelihood estimate b̌ of ˇ is obtained iteratively until convergence is obtained:
ˇ .mC1/ D ˇ .m/
H
1
g
where H and g are evaluated at ˇ .m/ . If the likelihood evaluated at ˇ .mC1/ is less than that evaluated at ˇ .m/ ,
then ˇ .mC1/ is recomputed by step-halving or ridging.
The covariance matrix of b̌ is estimated by
b
Cov.b̌/ D b
I
1
where the observed information matrix b
ID
b is computed by evaluating H at b̌.
H
Firth’s Bias-Reducing Penalized Likelihood
Firth’s method is currently available only for binary logistic models. It replaces the usual score (gradient)
equation
g.ˇj / D
n
X
.yi
i /xij D 0
.j D 1; : : : ; p/
i D1
where p is the number of parameters in the model, with the modified score equation
g.ˇj / D
n
X
i D1
fyi
i C hi .0:5
i /gxij D 0
.j D 1; : : : ; p/
Convergence Criteria F 4565
where the hi s are the ith diagonal elements of the hat matrix W1=2 X.X0 WX/ 1 X0 W1=2 and W D
diagfi .1 i /g. The Hessian matrix is not modified by this penalty, and the optimization method is
performed in the usual manner.
Convergence Criteria
Four convergence criteria are available: ABSFCONV=, FCONV=, GCONV=, and XCONV=. If you specify
more than one convergence criterion, the optimization is terminated as soon as one of the criteria is satisfied.
If none of the criteria is specified, the default is GCONV=1E–8.
If you specify a STRATA statement or the UNEQUALSLOPES option in the MODEL statement, all
unspecified (or nondefault) criteria are also compared to zero. For example, specifying only the criterion
XCONV=1E–8 but attaining FCONV=0 terminates the optimization even if the XCONV= criterion is not
satisfied, because the log likelihood has reached its maximum. More convergence criteria are also available;
see the section “NLOPTIONS Statement” on page 4544 for details.
Existence of Maximum Likelihood Estimates
The likelihood equation for a logistic regression model does not always have a finite solution. Sometimes
there is a nonunique maximum on the boundary of the parameter space, at infinity. The existence, finiteness,
and uniqueness of maximum likelihood estimates for the logistic regression model depend on the patterns of
data points in the observation space (Albert and Anderson 1984; Santner and Duffy 1986). Existence checks
are not performed for conditional logistic regression.
Consider a binary response model. Let Yj be the response of the jth subject, and let xj be the vector of
explanatory variables (including the constant 1 associated with the intercept). There are three mutually
exclusive and exhaustive types of data configurations: complete separation, quasi-complete separation, and
overlap.
Complete Separation There is a complete separation of data points if there exists a vector b that correctly
allocates all observations to their response groups; that is,
0
b xj > 0 Yj D 1
b0 xj < 0 Yj D 2
This configuration gives nonunique infinite estimates. If the iterative process of maximizing the
likelihood function is allowed to continue, the log likelihood diminishes to zero, and the dispersion
matrix becomes unbounded.
Quasi-complete Separation The data are not completely separable, but there is a vector b such that
0
b xj 0 Yj D 1
b0 xj 0 Yj D 2
and equality holds for at least one subject in each response group. This configuration also yields
nonunique infinite estimates. If the iterative process of maximizing the likelihood function is
allowed to continue, the dispersion matrix becomes unbounded and the log likelihood diminishes
to a nonzero constant.
4566 F Chapter 58: The LOGISTIC Procedure
Overlap If neither complete nor quasi-complete separation exists in the sample points, there is an overlap
of sample points. In this configuration, the maximum likelihood estimates exist and are unique.
Complete separation and quasi-complete separation are problems typically encountered with small data sets.
Although complete separation can occur with any type of data, quasi-complete separation is not likely with
truly continuous explanatory variables.
The LOGISTIC procedure uses a simple empirical approach to recognize the data configurations that lead
to infinite parameter estimates. The basis of this approach is that any convergence method of maximizing
the log likelihood must yield a solution giving complete separation, if such a solution exists. In maximizing
the log likelihood, there is no checking for complete or quasi-complete separation if convergence is attained
in eight or fewer iterations. Subsequent to the eighth iteration, the probability of the observed response is
computed for each observation. If the predicted response equals the observed response for every observation,
there is a complete separation of data points and the iteration process is stopped. If the complete separation of
data has not been determined and an observation is identified to have an extremely large probability (0.95)
of predicting the observed response, there are two possible situations. First, there is overlap in the data set,
and the observation is an atypical observation of its own group. The iterative process, if allowed to continue,
will stop when a maximum is reached. Second, there is quasi-complete separation in the data set, and the
asymptotic dispersion matrix is unbounded. If any of the diagonal elements of the dispersion matrix for the
standardized observations vectors (all explanatory variables standardized to zero mean and unit variance)
exceeds 5000, quasi-complete separation is declared and the iterative process is stopped. If either complete
separation or quasi-complete separation is detected, a warning message is displayed in the procedure output.
Checking for quasi-complete separation is less foolproof than checking for complete separation. The
NOCHECK option in the MODEL statement turns off the process of checking for infinite parameter
estimates. In cases of complete or quasi-complete separation, turning off the checking process typically
results in the procedure failing to converge. The presence of a WEIGHT statement also turns off the checking
process.
To address the separation issue, you can change your model, specify the FIRTH option to use Firth’s penalized
likelihood method, or for small data sets specify an EXACT statement to perform an exact logistic regression.
Effect-Selection Methods
Five effect-selection methods are available by specifying the SELECTION= option in the MODEL statement.
The simplest method (and the default) is SELECTION=NONE, for which PROC LOGISTIC fits the complete
model as specified in the MODEL statement. The other four methods are FORWARD for forward selection,
BACKWARD for backward elimination, STEPWISE for stepwise selection, and SCORE for best subsets
selection. Intercept parameters are forced to stay in the model unless the NOINT option is specified.
When SELECTION=FORWARD, PROC LOGISTIC first estimates parameters for effects forced into the
model. These effects are the intercepts and the first n explanatory effects in the MODEL statement, where n
is the number specified by the START= or INCLUDE= option in the MODEL statement (n is zero by default).
Next, the procedure computes the score chi-square statistic for each effect not in the model and examines the
largest of these statistics. If it is significant at the SLENTRY= level, the corresponding effect is added to the
model. Once an effect is entered in the model, it is never removed from the model. The process is repeated
until none of the remaining effects meet the specified level for entry or until the STOP= value is reached.
When SELECTION=BACKWARD, parameters for the complete model as specified in the MODEL statement
are estimated unless the START= option is specified. In that case, only the parameters for the intercepts and
Model Fitting Information F 4567
the first n explanatory effects in the MODEL statement are estimated, where n is the number specified by
the START= option. Results of the Wald test for individual parameters are examined. The least significant
effect that does not meet the SLSTAY= level for staying in the model is removed. Once an effect is removed
from the model, it remains excluded. The process is repeated until no other effect in the model meets the
specified level for removal or until the STOP= value is reached. Backward selection is often less successful
than forward or stepwise selection because the full model fit in the first step is the model most likely to
result in a complete or quasi-complete separation of response values as described in the section “Existence of
Maximum Likelihood Estimates” on page 4565.
The SELECTION=STEPWISE option is similar to the SELECTION=FORWARD option except that effects
already in the model do not necessarily remain. Effects are entered into and removed from the model in such
a way that each forward selection step can be followed by one or more backward elimination steps. The
stepwise selection process terminates if no further effect can be added to the model or if the current model is
identical to a previously visited model.
For SELECTION=SCORE, PROC LOGISTIC uses the branch-and-bound algorithm of Furnival and Wilson
(1974) adapted to find a specified number of models with the highest likelihood score (chi-square) statistic
for all possible model sizes, from 1, 2, 3 effect models, and so on, up to the single model containing all of the
explanatory effects. The number of models displayed for each model size is controlled by the BEST= option.
You can use the START= option to impose a minimum model size, and you can use the STOP= option to
impose a maximum model size. For instance, with BEST=3, START=2, and STOP=5, the SCORE selection
method displays the best three models (that is, the three models with the highest score chi-squares) containing
2, 3, 4, and 5 effects. The SELECTION=SCORE option is not available for models with CLASS variables.
The options FAST, SEQUENTIAL, and STOPRES can alter the default criteria for entering or removing
effects from the model when they are used with the FORWARD, BACKWARD, or STEPWISE selection
method.
Model Fitting Information
For the jth observation, let b
j be the estimated probability of the observed response. The three criteria
displayed by the LOGISTIC procedure are calculated as follows:
• –2 log likelihood:
2 Log L D
2
X wj
j
2
fj log.b
j /
where wj and fj are the weight and frequency values of the jth observation, and 2 is the dispersion
parameter, which equals 1 unless the SCALE= option is specified. For binary response models that use
events/trials MODEL statement syntax, this is
!
X wj
nj
2 Log L D 2
fj Œlog
C rj log.b
j / C .nj rj / log.1 b
j /
2
rj
j
where rj is the number of events, nj is the number of trials, b
j is the estimated event probability, and
the statistic is reported both with and without the constant term.
4568 F Chapter 58: The LOGISTIC Procedure
• Akaike’s information criterion:
AIC D
2 Log L C 2p
where p is the number of parameters in the model. For cumulative response models, p D k C s, where
k is the total number of response levels minus one and s is the number of explanatory effects. For the
generalized logit model, p D k.s C 1/.
• Schwarz (Bayesian information) criterion:
X
SC D 2 Log L C p log.
fj nj /
j
where p is the number of parameters in the model, nj is the number of trials when events/trials syntax
is specified, and nj D 1 with single-trial syntax.
The AIC and SC statistics give two different ways of adjusting the –2 Log L statistic for the number of terms
in the model and the number of observations used. These statistics can be used when comparing different
models for the same data (for example, when you use the SELECTION=STEPWISE option in the MODEL
statement). The models being compared do not have to be nested; lower values of the statistics indicate a
more desirable model.
The difference in the –2 Log L statistics between the intercepts-only model and the specified model has a
p k degree-of-freedom chi-square distribution under the null hypothesis that all the explanatory effects
in the model are zero, where p is the number of parameters in the specified model and k is the number of
intercepts. The likelihood ratio test in the “Testing Global Null Hypothesis: BETA=0” table displays this
difference and the associated p-value for this statistic. The score and Wald tests in that table test the same
hypothesis and are asymptotically equivalent; see the sections “Residual Chi-Square” on page 4569 and
“Testing Linear Hypotheses about the Regression Coefficients” on page 4585 for details.
Generalized Coefficient of Determination
Cox and Snell (1989, pp. 208–209) propose the following generalization of the coefficient of determination
to a more general linear model:
2
R D1
L.0/
L.b̌/
n2
b̌
where
PL.0/ is the likelihood of the intercept-only model, L. / is the likelihood of the specified model,
n D j fj nj is the sample size, fj is the frequency of the jth observation, and nj is the number of trials
when events/trials syntax is specified or nj D 1 with single-trial syntax.
The quantity R2 achieves a maximum of less than one for discrete models, where the maximum is given by
2
Rmax
D1
2
fL.0/g n
Nagelkerke (1991) proposes the following adjusted coefficient, which can achieve a maximum value of one:
R2
RQ 2 D 2
Rmax
Score Statistics and Tests F 4569
Specifying the NORMALIZE option in the WEIGHT statement makes these coefficients invariant to the
scale of the weights.
Like the AIC and SC statistics described in the section “Model Fitting Information” on page 4567, R2 and
RQ 2 are most useful for comparing competing models that are not necessarily nested—larger values indicate
better models. More properties and interpretation of R2 and RQ 2 are provided in Nagelkerke (1991). In
the “Testing Global Null Hypothesis: BETA=0” table, R2 is labeled as “RSquare” and RQ 2 is labeled as
“Max-rescaled RSquare.” Use the RSQUARE option to request R2 and RQ 2 .
Score Statistics and Tests
To understand the general form of the score statistics, let g.ˇ/ be the vector of first partial derivatives of the
log likelihood with respect to the parameter vector ˇ, and let H.ˇ/ be the matrix of second partial derivatives
of the log likelihood with respect to ˇ. That is, g.ˇ/ is the gradient vector, and H.ˇ/ is the Hessian matrix.
Let I.ˇ/ be either H.ˇ/ or the expected value of H.ˇ/. Consider a null hypothesis H0 . Let b̌H0 be the
MLE of ˇ under H0 . The chi-square score statistic for testing H0 is defined by
g0 .b̌H0 /I
1
.b̌H0 /g.b̌H0 /
and it has an asymptotic 2 distribution with r degrees of freedom under H0 , where r is the number of
restrictions imposed on ˇ by H0 .
Residual Chi-Square
When you use SELECTION=FORWARD, BACKWARD, or STEPWISE, the procedure calculates a residual
chi-square score statistic and reports the statistic, its degrees of freedom, and the p-value. This section
describes how the statistic is calculated.
Suppose there are s explanatory effects of interest. The full cumulative response model has a parameter
vector
ˇ D .˛1 ; : : : ; ˛k ; ˇ1 ; : : : ; ˇs /0
where ˛1 ; : : : ; ˛k are intercept parameters, and ˇ1 ; : : : ; ˇs are the common slope parameters for the s
explanatory effects. The full generalized logit model has a parameter vector
ˇ D .˛1 ; : : : ; ˛k ; ˇ10 ; : : : ; ˇk0 /0
ˇi0
D .ˇi1 ; : : : ; ˇi s /;
with
i D 1; : : : ; k
where ˇij is the slope parameter for the jth effect in the ith logit.
Consider the null hypothesis H0 W ˇt C1 D D ˇs D 0, where t < s for the cumulative response model, and
H0 W ˇi;t C1 D D ˇi s D 0; t < s; i D 1; : : : ; k, for the generalized logit model. For the reduced model
with t explanatory effects, let b
˛1; : : : ; b
˛ k be the MLEs of the unknown intercept parameters, let b
ˇ1; : : : ; b
ˇt
0
b
b
b̌
be the MLEs of the unknown slope parameters, and let i.t / D .ˇ i1 ; : : : ; ˇ i t /; i D 1; : : : ; k, be those for the
generalized logit model. The residual chi-square is the chi-square score statistic testing the null hypothesis
H0 ; that is, the residual chi-square is
g0 .b̌H0 /I
1
.b̌H0 /g.b̌H0 /
4570 F Chapter 58: The LOGISTIC Procedure
˛1; : : : ; b
˛k ; b
ˇ1; : : : ; b
ˇ t ; 0; : : : ; 0/0 , and for the generalized
where for the cumulative response model b̌H0 D .b
0
0
logit model b̌H0 D .b
˛1; : : : ; b
˛ k ; b̌1.t / ; 00.s t / ; : : : b̌k.t / ; 00.s t / /0 , where 0.s t / denotes a vector of s t
zeros.
The residual chi-square has an asymptotic chi-square distribution with s t degrees of freedom (k.s t / for
the generalized logit model). A special case is the global score chi-square, where the reduced model consists
of the k intercepts and no explanatory effects. The global score statistic is displayed in the “Testing Global
Null Hypothesis: BETA=0” table. The table is not produced when the NOFIT option is used, but the global
score statistic is displayed.
Testing Individual Effects Not in the Model
These tests are performed when you specify SELECTION=FORWARD or STEPWISE, and are displayed
when the DETAILS option is specified. In the displayed output, the tests are labeled “Score Chi-Square” in
the “Analysis of Effects Eligible for Entry” table and in the “Summary of Stepwise (Forward) Selection”
table. This section describes how the tests are calculated.
Suppose that k intercepts and t explanatory variables (say v1 ; : : : ; vt ) have been fit to a model and that vt C1
is another explanatory variable of interest. Consider a full model with the k intercepts and t C 1 explanatory
variables (v1 ; : : : ; vt ; vt C1 ) and a reduced model with vt C1 excluded. The significance of vt C1 adjusted
for v1 ; : : : ; vt can be determined by comparing the corresponding residual chi-square with a chi-square
distribution with one degree of freedom (k degrees of freedom for the generalized logit model).
Testing the Parallel Lines Assumption
For an ordinal response, PROC LOGISTIC performs a test of the parallel lines assumption. In the displayed
output, this test is labeled “Score Test for the Equal Slopes Assumption” when the LINK= option is NORMIT
or CLOGLOG. When LINK=LOGIT, the test is labeled as “Score Test for the Proportional Odds Assumption”
in the output. For small sample sizes, this test might be too liberal (Stokes, Davis, and Koch 2000, p. 249).
This section describes the methods used to calculate the test.
For this test the number of response levels, k C 1, is assumed to be strictly greater than 2. Let Y be the
response variable taking values 1; : : : ; k; k C 1. Suppose there are s explanatory variables. Consider the
general cumulative model without making the parallel lines assumption
g.Pr.Y i j x// D .1; x0 /ˇi ;
1i k
where g./ is the link function, and ˇi D .˛i ; ˇi1 ; : : : ; ˇi s /0 is a vector of unknown parameters consisting of
an intercept ˛i and s slope parameters ˇi1 ; : : : ; ˇi s . The parameter vector for this general cumulative model
is
ˇ D .ˇ10 ; : : : ; ˇk0 /0
Under the null hypothesis of parallelism H0 W ˇ1m D ˇ2m D D ˇkm ; 1 m s, there is a single
common slope parameter for each of the s explanatory variables. Let ˇ1 ; : : : ; ˇs be the common slope
parameters. Let b
˛1; : : : ; b
˛ k and b
ˇ1; : : : ; b
ˇ s be the MLEs of the intercept parameters and the common slope
parameters. Then, under H0 , the MLE of ˇ is
Confidence Intervals for Parameters F 4571
b̌H D .b̌01 ; : : : ; b̌0k /0
0
with b̌i D .b
˛i ; b
ˇ1; : : : ; b
ˇ s /0
1i k
and the chi-square score statistic g0 .b̌H0 /I 1 .b̌H0 /g.b̌H0 / has an asymptotic chi-square distribution with
s.k 1/ degrees of freedom. This tests the parallel lines assumption by testing the equality of separate slope
parameters simultaneously for all explanatory variables.
Confidence Intervals for Parameters
There are two methods of computing confidence intervals for the regression parameters. One is based on the
profile-likelihood function, and the other is based on the asymptotic normality of the parameter estimators.
The latter is not as time-consuming as the former, since it does not involve an iterative scheme; however, it is
not thought to be as accurate as the former, especially with small sample size. You use the CLPARM= option
to request confidence intervals for the parameters.
Likelihood Ratio-Based Confidence Intervals
The likelihood ratio-based confidence interval is also known as the profile-likelihood confidence interval.
The construction of this interval is derived from the asymptotic 2 distribution of the generalized likelihood
ratio test (Venzon and Moolgavkar 1988). Suppose that the parameter vector is ˇ D .ˇ0 ; ˇ1 ; : : : ; ˇs /0 and
you want to compute a confidence interval for ˇj . The profile-likelihood function for ˇj D is defined as
lj ./ D max l.ˇ/
ˇ2Bj . /
where Bj ./ is the set of all ˇ with the jth element fixed at , and l.ˇ/ is the log-likelihood function for ˇ.
If lmax D l.b̌/ is the log likelihood evaluated at the maximum likelihood estimate b̌, then 2.lmax lj .ˇj //
has a limiting chi-square distribution with one degree of freedom if ˇj is the true parameter value. Let
l0 D lmax 0:521 .1 ˛/, where 21 .1 ˛/ is the 100.1 ˛/ percentile of the chi-square distribution with
one degree of freedom. A 100.1 ˛/% confidence interval for ˇj is
f W lj . / l0 g
The endpoints of the confidence interval are found by solving numerically for values of ˇj that satisfy
equality in the preceding relation. To obtain an iterative algorithm for computing the confidence limits, the
log-likelihood function in a neighborhood of ˇ is approximated by the quadratic function
Q C ı/ D l.ˇ/ C ı 0 g C 1 ı 0 Vı
l.ˇ
2
where g D g.ˇ/ is the gradient vector and V D V.ˇ/ is the Hessian matrix. The increment ı for the next
iteration is obtained by solving the likelihood equations
d Q
fl.ˇ C ı/ C .e0j ı
dı
/g D 0
where is the Lagrange multiplier, ej is the jth unit vector, and is an unknown constant. The solution is
ıD
V
1
.g C ej /
4572 F Chapter 58: The LOGISTIC Procedure
Q C ı/ D l0 , you can estimate as
By substituting this ı into the equation l.ˇ
l.ˇ/ C 12 g0 V
2.l0
D˙
e0j V
1
1
g/
21
ej
The upper confidence limit for ˇj is computed by starting at the maximum likelihood estimate of ˇ and
iterating with positive values of until convergence is attained. The process is repeated for the lower
confidence limit by using negative values of .
Convergence is controlled by the value specified with the PLCONV= option in the MODEL statement (the
default value of is 1E–4). Convergence is declared on the current iteration if the following two conditions
are satisfied:
jl.ˇ/
l0 j and
.g C ej /0 V
1
.g C ej / Wald Confidence Intervals
Wald confidence intervals are sometimes called the normal confidence intervals. They are based on the
asymptotic normality of the parameter estimators. The 100.1 ˛/% Wald confidence interval for ˇj is given
by
b
ˇ j ˙ z1
j
˛=2b
where zp is the 100p percentile of the standard normal distribution, b
ˇ j is the maximum likelihood estimate
of ˇj , and b
j is the standard error estimate of b
ˇ .
j
Odds Ratio Estimation
Consider a dichotomous response variable with outcomes event and nonevent. Consider a dichotomous risk
factor variable X that takes the value 1 if the risk factor is present and 0 if the risk factor is absent. According
to the logistic model, the log odds function, logit.X /, is given by
Pr.event j X /
logit.X/ log
D ˛ C Xˇ
Pr.nonevent j X /
The odds ratio is defined as the ratio of the odds for those with the risk factor (X = 1) to the odds for those
without the risk factor (X = 0). The log of the odds ratio is given by
log. / log. .X D 1; X D 0// D logit.X D 1/
logit.X D 0/ D .˛ C 1 ˇ/
.˛ C 0 ˇ/ D ˇ
In general, the odds ratio can be computed by exponentiating the difference of the logits between any two
population profiles. This is the approach taken by the ODDSRATIO statement, so the computations are
available regardless of parameterization, interactions, and nestings. However, as shown in the preceding
Odds Ratio Estimation F 4573
equation for log. /, odds ratios of main effects can be computed as functions of the parameter estimates,
and the remainder of this section is concerned with this methodology.
The parameter, ˇ, associated with X represents the change in the log odds from X D 0 to X D 1. So the
odds ratio is obtained by simply exponentiating the value of the parameter associated with the risk factor.
The odds ratio indicates how the odds of the event change as you change X from 0 to 1. For instance, D 2
means that the odds of an event when X = 1 are twice the odds of an event when X = 0. You can also express
this as follows: the percent change in the odds of an event from X = 0 to X = 1 is .
1/100% D 100%.
Suppose the values of the dichotomous risk factor are coded as constants a and b instead of 0 and 1. The
odds when X D a become exp.˛ C aˇ/, and the odds when X D b become exp.˛ C bˇ/. The odds ratio
corresponding to an increase in X from a to b is
D expŒ.b
a/ˇ D Œexp.ˇ/b
a
Œexp.ˇ/c
Note that for any a and b such that c D b a D 1; D exp.ˇ/. So the odds ratio can be interpreted as the
change in the odds for any increase of one unit in the corresponding risk factor. However, the change in
odds for some amount other than one unit is often of greater interest. For example, a change of one pound in
body weight might be too small to be considered important, while a change of 10 pounds might be more
meaningful. The odds ratio for a change in X from a to b is estimated by raising the odds ratio estimate for a
unit change in X to the power of c D b a as shown previously.
For a polytomous risk factor, the computation of odds ratios depends on how the risk factor is parameterized.
For illustration, suppose that Race is a risk factor with four categories: White, Black, Hispanic, and Other.
For the effect parameterization scheme (PARAM=EFFECT) with White as the reference group
(REF=’White’), the design variables for Race are as follows:
Race
Black
Hispanic
Other
White
Design Variables
X1
X2
X3
1
0
0
–1
0
1
0
–1
0
0
1
–1
The log odds for Black is
logit.Black/ D ˛ C .X1 D 1/ˇ1 C .X2 D 0/ˇ2 C .X3 D 0/ˇ3
D ˛ C ˇ1
The log odds for White is
logit.White/ D ˛ C .X1 D
D ˛
ˇ1
ˇ2
1/ˇ1 C .X2 D
1/ˇ2 C .X3 D
ˇ3
Therefore, the log odds ratio of Black versus White becomes
log. .Black; White// D logit.Black/
logit.White/
D 2ˇ1 C ˇ2 C ˇ3
1/ˇ3
4574 F Chapter 58: The LOGISTIC Procedure
For the reference cell parameterization scheme (PARAM=REF) with White as the reference cell, the design
variables for race are as follows:
Race
Black
Hispanic
Other
White
Design Variables
X1
X2
X3
1
0
0
0
0
1
0
0
0
0
1
0
The log odds ratio of Black versus White is given by
log. .Black; White// D logit.Black/
logit.White/
D .˛ C .X1 D 1/ˇ1 C .X2 D 0/ˇ2 C .X3 D 0/ˇ3 /
.˛ C .X1 D 0/ˇ1 C .X2 D 0/ˇ2 C .X3 D 0/ˇ3 /
D ˇ1
For the GLM parameterization scheme (PARAM=GLM), the design variables are as follows:
Race
Black
Hispanic
Other
White
Design Variables
X1 X2 X3 X4
1
0
0
0
0
1
0
0
0
0
1
0
0
0
0
1
The log odds ratio of Black versus White is
log. .Black; White// D logit.Black/
logit.White/
D .˛ C .X1 D 1/ˇ1 C .X2 D 0/ˇ2 C .X3 D 0/ˇ3 C .X4 D 0/ˇ4 /
.˛ C .X1 D 0/ˇ1 C .X2 D 0/ˇ2 C .X3 D 0/ˇ3 C .X4 D 1/ˇ4 /
D ˇ1
ˇ4
Consider the hypothetical example of heart disease among race in Hosmer and Lemeshow (2000, p. 56). The
entries in the following contingency table represent counts:
Disease Status
White
Present
Absent
5
20
Race
Black Hispanic
20
10
15
10
Other
10
10
The computation of odds ratio of Black versus White for various parameterization schemes is tabulated in
Table 58.11.
Rank Correlation of Observed Responses and Predicted Probabilities F 4575
Table 58.11 Odds Ratio of Heart Disease Comparing Black to White
PARAM=
EFFECT
REF
GLM
b
ˇ1
Parameter Estimates
b
b
b
ˇ2
ˇ3
ˇ4
0.7651
2.0794
2.0794
0.4774
1.7917
1.7917
0.0719
1.3863
1.3863
0.0000
Odds Ratio Estimates
exp.2 0:7651 C 0:4774 C 0:0719/ D 8
exp.2:0794/ D 8
exp.2:0794/ D 8
Since the log odds ratio (log. /) is a linear function of the parameters, the Wald confidence interval for
log. / can be derived from the parameter estimates and the estimated covariance matrix. Confidence
intervals for the odds ratios are obtained by exponentiating the corresponding confidence limits for the log
odd ratios. In the displayed output of PROC LOGISTIC, the “Odds Ratio Estimates” table contains the odds
ratio estimates and the corresponding 95% Wald confidence intervals. For continuous explanatory variables,
these odds ratios correspond to a unit increase in the risk factors.
To customize odds ratios for specific units of change for a continuous risk factor, you can use the UNITS
statement to specify a list of relevant units for each explanatory variable in the model. Estimates of these
customized odds ratios are given in a separate table. Let .Lj ; Uj / be a confidence interval for log. /. The
corresponding lower and upper confidence limits for the customized odds ratio exp.cˇj / are exp.cLj /
and exp.cUj /, respectively (for c > 0), or exp.cUj / and exp.cLj /, respectively (for c < 0). You use the
CLODDS= option or ODDSRATIO statement to request the confidence intervals for the odds ratios.
For a generalized logit model, odds ratios are computed similarly, except k odds ratios are computed for each
effect, corresponding to the k logits in the model.
Rank Correlation of Observed Responses and Predicted Probabilities
The predicted mean score of an observation is the sum of the Ordered Values (shown in the “Response
Profile” table) minus one, weighted by the corresponding predicted probabilities for that observation; that is,
P
the predicted means score D kC1
1/b
i , where k C 1 is the number of response levels and b
i is the
i D1 .i
predicted probability of the ith (ordered) response.
A pair of observations with different observed responses is said to be concordant if the observation with
the lower ordered response value has a lower predicted mean score than the observation with the higher
ordered response value. If the observation with the lower ordered response value has a higher predicted mean
score than the observation with the higher ordered response value, then the pair is discordant. If the pair is
neither concordant nor discordant, it is a tie. Enumeration of the total numbers of concordant and discordant
pairs is carried out by categorizing the predicted mean score into intervals of length k=500 and accumulating
the corresponding frequencies of observations. Note that the length of these intervals can be modified by
specification of the BINWIDTH= option in the MODEL statement.
Let N be the sum of observation frequencies in the data. Suppose there are a total of t pairs with different
responses: nc of them are concordant, nd of them are discordant, and t nc nd of them are tied. PROC
4576 F Chapter 58: The LOGISTIC Procedure
LOGISTIC computes the following four indices of rank correlation for assessing the predictive ability of a
model:
c
Somers’ D (Gini coefficient)
Goodman-Kruskal Gamma
Kendall’s Tau-a
D
D
D
D
.nc C 0:5.t nc nd //=t
.nc nd /=t
.nc nd /=.nc C nd /
.nc nd /=.0:5N.N 1//
If there are no ties, then Somers’ D (Gini’s coefficient) D 2c 1. Note that the concordance index, c, also
gives an estimate of the area under the receiver operating characteristic (ROC) curve when the response is
binary (Hanley and McNeil 1982). See the section “ROC Computations” on page 4584 for more information
about this area.
For binary responses, the predicted mean score is equal to the predicted probability for Ordered Value 2. As
such, the preceding definition of concordance is consistent with the definition used in previous releases for
the binary response model.
These statistics are not available when the STRATA statement is specified.
Linear Predictor, Predicted Probability, and Confidence Limits
This section describes how predicted probabilities and confidence limits are calculated by using the maximum
likelihood estimates (MLEs) obtained from PROC LOGISTIC. For a specific example, see the section
“Getting Started: LOGISTIC Procedure” on page 4488. Predicted probabilities and confidence limits can be
output to a data set with the OUTPUT statement.
Binary and Cumulative Response Models
For a vector of explanatory variables x, the linear predictor
i D g.Pr.Y i j x// D ˛i C x0 ˇ
1i k
is estimated by
O i D b
˛ i C x0b̌
where b
˛ i and b̌ are the MLEs of ˛i and ˇ. The estimated standard error of i is O .O i /, which can be
computed as the square root of the quadratic form .1; x0 /b
Vb .1; x0 /0 , where b
Vb is the estimated covariance
matrix of the parameter estimates. The asymptotic 100.1 ˛/% confidence interval for i is given by
O i ˙ z˛=2 O .O i /
where z˛=2 is the 100.1
˛=2/ percentile point of a standard normal distribution.
The predicted probability and the 100.1 ˛/% confidence limits for i D Pr.Y i j x/ are obtained by
back-transforming the corresponding measures for the linear predictor, as shown in the following table:
Link
Predicted Probability
100(1–˛)% Confidence Limits
LOGIT
PROBIT
CLOGLOG
1=.1 C exp. O i //
ˆ.O i /
1 exp. exp.O i //
1=.1 C exp. O i ˙ z˛=2 O .O i ///
ˆ.O i ˙ z˛=2 O .O i //
1 exp. exp.O i ˙ z˛=2 O .O i ///
Classification Table F 4577
The CONTRAST statement also enables you to estimate the exponentiated contrast, e O i . The corresponding
standard error is e Oi O .O i /, and the confidence limits are computed by exponentiating those for the linear
predictor: expfO i ˙ z˛=2 O .O i /g.
Generalized Logit Model
For a vector of explanatory variables x, define the linear predictors i D ˛i C x0 ˇi , and let i denote the
probability of obtaining the response value i:
8
1i k
< kC1 e i
1
i D
i DkC1
P
:
1 C kj D1 e j
By the delta method,
@i 0
@i
2
.i / D
V.ˇ/
@ˇ
@ˇ
A 100(1 ˛)% confidence level for i is given by
b
i ˙ z˛=2 O .b
i /
where b
i is the estimated expected probability of response i, and O .b
i / is obtained by evaluating .i / at
b̌
ˇD .
Note that the contrast O i and exponentiated contrast e O i , their standard errors, and their confidence intervals
are computed in the same fashion as for the cumulative response models, replacing ˇ with ˇi .
Classification Table
For binary response data, the response is either an event or a nonevent. In PROC LOGISTIC, the response
with Ordered Value 1 is regarded as the event, and the response with Ordered Value 2 is the nonevent.
PROC LOGISTIC models the probability of the event. From the fitted model, a predicted event probability
can be computed for each observation. A method to compute a reduced-bias estimate of the predicted
probability is given in the section “Predicted Probability of an Event for Classification” on page 4578. If the
predicted event probability exceeds or equals some cutpoint value z 2 Œ0; 1, the observation is predicted to
be an event observation; otherwise, it is predicted as a nonevent. A 2 2 frequency table can be obtained
by cross-classifying the observed and predicted responses. The CTABLE option produces this table, and
the PPROB= option selects one or more cutpoints. Each cutpoint generates a classification table. If the
PEVENT= option is also specified, a classification table is produced for each combination of PEVENT= and
PPROB= values.
The accuracy of the classification is measured by its sensitivity (the ability to predict an event correctly) and
specificity (the ability to predict a nonevent correctly). Sensitivity is the proportion of event responses that
were predicted to be events. Specificity is the proportion of nonevent responses that were predicted to be
nonevents. PROC LOGISTIC also computes three other conditional probabilities: false positive rate, false
negative rate, and rate of correct classification. The false positive rate is the proportion of predicted event
responses that were observed as nonevents. The false negative rate is the proportion of predicted nonevent
responses that were observed as events. Given prior probabilities specified with the PEVENT= option, these
conditional probabilities can be computed as posterior probabilities by using Bayes’ theorem.
4578 F Chapter 58: The LOGISTIC Procedure
Predicted Probability of an Event for Classification
When you classify a set of binary data, if the same observations used to fit the model are also used to estimate
the classification error, the resulting error-count estimate is biased. One way of reducing the bias is to
remove the binary observation to be classified from the data, reestimate the parameters of the model, and
then classify the observation based on the new parameter estimates. However, it would be costly to fit the
model by leaving out each observation one at a time. The LOGISTIC procedure provides a less expensive
one-step approximation to the preceding parameter estimates. Let b̌ be the MLE of the parameter vector
.˛; ˇ1 ; : : : ; ˇs /0 based on all observations. Let b̌.j / denote the MLE computed without the jth observation.
The one-step estimate of b̌.j / is given by
b̌1 D b̌
.j /
wj .yj b
j / b b̌
V. /
1 hj
1
xj
where
yj
is 1 for an observed event response and 0 otherwise
wj
is the weight of the observation
b
j
is the predicted event probability based on b̌
hj
is the hat diagonal element (defined on page 4587) with nj D 1 and rj D yj
b
V.b̌/
is the estimated covariance matrix of b̌
False Positive, False Negative, and Correct Classification Rates Using Bayes’ Theorem
Suppose n1 of n individuals experience an event, such as a disease. Let this group be denoted by C1 , and let
the group of the remaining n2 D n n1 individuals who do not have the disease be denoted by C2 . The jth
individual is classified as giving a positive response if the predicted probability of disease (b
.j / ) is large.
The probability b
.j / is the reduced-bias estimate based on the one-step approximation given in the preceding
section. For a given cutpoint z, the jth individual is predicted to give a positive response if b
.j / z.
Let B denote the event that a subject has the disease, and let BN denote the event of not having the disease. Let
A denote the event that the subject responds positively, and let AN denote the event of responding negatively.
N where
Results of the classification are represented by two conditional probabilities, Pr.AjB/ and Pr.AjB/,
N
Pr.AjB/ is the sensitivity and Pr.AjB/ is one minus the specificity.
These probabilities are given by
P
Pr.AjB/ D
j 2C1
I.b
.j / z/
n1
.j / z/
j 2C2 I.b
P
N D
Pr.AjB/
n2
where I./ is the indicator function.
Bayes’ theorem is used to compute several rates of the classification. For a given prior probability Pr.B/ of
the disease, the false positive rate PF C , the false negative rate PF , and the correct classification rate PC
Overdispersion F 4579
are given by Fleiss (1981, pp. 4–5) as follows:
N
Pr.AjB/Œ1
Pr.B/
N C Pr.B/ŒPr.AjB/ Pr.AjB/
N
Pr.AjB/
Œ1 Pr.AjB/Pr.B/
N D
PF D Pr.BjA/
N
N
1 Pr.AjB/
Pr.B/ŒPr.AjB/ Pr.AjB/
N
N
N
N
PC D Pr.BjA/ C Pr.BjA/ D Pr.AjB/Pr.B/ C Pr.AjB/Œ1 Pr.B/
N
PF C D Pr.BjA/
D
The prior probability Pr.B/ can be specified by the PEVENT= option. If the PEVENT= option is not
specified, the sample proportion of diseased individuals is used; that is, Pr.B/ D n1 =n. In such a case, the
false positive rate and the false negative rate reduce to
P
PF C D
P
j 2C1
PF
D
I.b
.j /
P
I.b
.j / < z/
P
< z/ C j 2C2 I.b
.j / < z/
P
z/ C j 2C2 I.b
.j / < z/
j 2C1
P
I.b
.j /
P
I.b
.j /
j 2C1
PC
I.b
.j / z/
P
z/ C j 2C2 I.b
.j / z/
j 2C2
D
j 2C1
n
Note that for a stratified sampling situation in which n1 and n2 are chosen a priori, n1 =n is not a desirable
estimate of Pr.B/. For such situations, the PEVENT= option should be specified.
Overdispersion
For a correctly specified model, the Pearson chi-square statistic and the deviance, divided by their degrees of
freedom, should be approximately equal to one. When their values are much larger than one, the assumption
of binomial variability might not be valid and the data are said to exhibit overdispersion. Underdispersion,
which results in the ratios being less than one, occurs less often in practice.
When fitting a model, there are several problems that can cause the goodness-of-fit statistics to exceed their
degrees of freedom. Among these are such problems as outliers in the data, using the wrong link function,
omitting important terms from the model, and needing to transform some predictors. These problems should
be eliminated before proceeding to use the following methods to correct for overdispersion.
Rescaling the Covariance Matrix
One way of correcting overdispersion is to multiply the covariance matrix by a dispersion parameter. This
method assumes that the sample sizes in each subpopulation are approximately equal. You can supply the
value of the dispersion parameter directly, or you can estimate the dispersion parameter based on either the
Pearson chi-square statistic or the deviance for the fitted model.
4580 F Chapter 58: The LOGISTIC Procedure
The Pearson chi-square statistic 2P and the deviance 2D are given by
2P
D
m kC1
X
X .rij
i D1 j D1
2D
D 2
m kC1
X
X
i D1 j D1
ni b
ij /2
ni b
ij
rij
rij log
ni b
ij
where m is the number of subpopulation profiles, k C 1 is the number of response levels, rij is the total
weight (sum of the product of the frequencies and the weights) associated with jth level responses in the
P
ith profile, ni D kC1
ij is the fitted probability for the jth level at the ith profile. Each of these
j D1 rij , and b
chi-square statistics has mk p degrees of freedom, where p is the number of parameters estimated. The
dispersion parameter is estimated by
8 2
< P =.mk p/ SCALE=PEARSON
c2 D
2 =.mk p/ SCALE=DEVIANCE
: D
.constant/2
SCALE=constant
In order for the Pearson statistic and the deviance to be distributed as chi-square, there must be sufficient
replication within the subpopulations. When this is not true, the data are sparse, and the p-values for these
statistics are not valid and should be ignored. Similarly, these statistics, divided by their degrees of freedom,
cannot serve as indicators of overdispersion. A large difference between the Pearson statistic and the deviance
provides some evidence that the data are too sparse to use either statistic.
You can use the AGGREGATE (or AGGREGATE=) option to define the subpopulation profiles. If you do not
specify this option, each observation is regarded as coming from a separate subpopulation. For events/trials
syntax, each observation represents n Bernoulli trials, where n is the value of the trials variable; for single-trial
syntax, each observation represents a single trial. Without the AGGREGATE (or AGGREGATE=) option,
the Pearson chi-square statistic and the deviance are calculated only for events/trials syntax.
Note that the parameter estimates are not changed by this method. However, their standard errors are adjusted
for overdispersion, affecting their significance tests.
Williams’ Method
Suppose that the data consist of n binomial observations. For the ith observation, let ri =ni be the observed
proportion and let xi be the associated vector of explanatory variables. Suppose that the response probability
for the ith observation is a random variable Pi with mean and variance
E.Pi / D i
and
V .Pi / D i .1
i /
where pi is the probability of the event, and is a nonnegative but otherwise unknown scale parameter. Then
the mean and variance of ri are
E.ri / D ni i
and V .ri / D ni i .1
i /Œ1 C .ni
1/
Williams (1982) estimates the unknown parameter by equating the value of Pearson’s chi-square statistic
for the full model to its approximate expected value. Suppose wi is the weight associated with the ith
The Hosmer-Lemeshow Goodness-of-Fit Test F 4581
observation. The Pearson chi-square statistic is given by
2
D
n
X
w .ri
ni b
i /2
ni b
i .1 b
i /
i
i D1
Let g 0 ./ be the first derivative of the link function g./. The approximate expected value of 2 is
E2 D
n
X
wi .1
wi vi di /Œ1 C .ni
1/
i D1
where vi D ni =.i .1 i /Œg 0 .i /2 / and di is the variance of the linear predictor b
˛ i C x0i b̌. The scale
parameter is estimated by the following iterative procedure.
At the start, let wi D 1 and let i be approximated by ri =ni , i D 1; 2; : : : ; n. If you apply these weights
and approximated probabilities to 2 and E2 and then equate them, an initial estimate of is
2
O 0 D P
i .ni
.n p/
1/.1 vi di /
where p is the total number of parameters. The initial estimates of the weights become wO i0 D Œ1 C .ni
˛ i and b̌ are recalculated, and so is 2 . Then a revised
1/O 0 1 . After a weighted fit of the model, the b
estimate of is given by
P 2
wi vi di /
i wi .1
O 1 D wi .ni 1/.1 wi vi di /
The iterative procedure is repeated until 2 is very close to its degrees of freedom.
O
Once has been estimated by O under the full model, weights of .1 C .ni 1//
that have fewer terms than the full model. See Example 58.10 for an illustration.
1
can be used to fit models
N OTE : If the WEIGHT statement is specified with the NORMALIZE option, then the initial wi values are
set to the normalized weights, and the weights resulting from Williams’ method will not add up to the actual
sample size. However, the estimated covariance matrix of the parameter estimates remains invariant to the
scale of the WEIGHT variable.
The Hosmer-Lemeshow Goodness-of-Fit Test
Sufficient replication within subpopulations is required to make the Pearson and deviance goodness-of-fit
tests valid. When there are one or more continuous predictors in the model, the data are often too sparse to
use these statistics. Hosmer and Lemeshow (2000) proposed a statistic that they show, through simulation, is
distributed as chi-square when there is no replication in any of the subpopulations. This test is available only
for binary response models.
First, the observations are sorted in increasing order of their estimated event probability. The event is the
response level specified in the response variable option EVENT=, or the response level that is not specified in
the REF= option, or, if neither of these options was specified, then the event is the response level identified in
the “Response Profiles” table as “Ordered Value 1”. The observations are then divided into approximately 10
4582 F Chapter 58: The LOGISTIC Procedure
groups according to the following scheme. Let N be the total number of subjects. Let M be the target number
of subjects for each group given by
M D Œ0:1 N C 0:5
where Œx represents the integral value of x. If the single-trial syntax is used, blocks of subjects are formed
of observations with identical values of the explanatory variables. Blocks of subjects are not divided when
being placed into groups.
Suppose there are n1 subjects in the first block and n2 subjects in the second block. The first block of subjects
is placed in the first group. Subjects in the second block are added to the first group if
n1 < M
and
n1 C Œ0:5 n2 M
Otherwise, they are placed in the second group. In general, suppose subjects of the (j – 1) block have been
placed in the kth group. Let c be the total number of subjects currently in the kth group. Subjects for the jth
block (containing nj subjects) are also placed in the kth group if
c<M
and c C Œ0:5 nj M
Otherwise, the nj subjects are put into the next group. In addition, if the number of subjects in the last group
does not exceed Œ0:05 N (half the target group size), the last two groups are collapsed to form only one
group.
Note that the number of groups, g, can be smaller than 10 if there are fewer than 10 patterns of explanatory
variables. There must be at least three groups in order for the Hosmer-Lemeshow statistic to be computed.
The Hosmer-Lemeshow goodness-of-fit statistic is obtained by calculating the Pearson chi-square statistic
from the 2 g table of observed and expected frequencies, where g is the number of groups. The statistic is
written
2HL
D
g
X
.Oi
i D1
Ni N i /2
Ni N i .1 N i /
where Ni is the total frequency of subjects in the ith group, Oi is the total frequency of event outcomes in the
ith group, and N i is the average estimated predicted probability of an event outcome for the ith group. (Note
that the predicted probabilities are computed as shown in the section “Linear Predictor, Predicted Probability,
and Confidence Limits” on page 4576 and are not the cross validated estimates discussed in the section
“Classification Table” on page 4577.) The Hosmer-Lemeshow statistic is then compared to a chi-square
distribution with .g n/ degrees of freedom, where the value of n can be specified in the LACKFIT option
in the MODEL statement. The default is n = 2. Large values of 2HL (and small p-values) indicate a lack of
fit of the model.
Receiver Operating Characteristic Curves
ROC curves are used to evaluate and compare the performance of diagnostic tests; they can also be used to
evaluate model fit. An ROC curve is just a plot of the proportion of true positives (events predicted to be
events) versus the proportion of false positives (nonevents predicted to be events).
In a sample of n individuals, suppose n1 individuals are observed to have a certain condition or event. Let
this group be denoted by C1 , and let the group of the remaining n2 D n n1 individuals who do not have the
Receiver Operating Characteristic Curves F 4583
condition be denoted by C2 . Risk factors are identified for the sample, and a logistic regression model is fitted
to the data. For the jth individual, an estimated probability b
j of the event of interest is calculated. Note that
the b
j are computed as shown in the section “Linear Predictor, Predicted Probability, and Confidence Limits”
on page 4576 and are not the cross validated estimates discussed in the section “Classification Table” on
page 4577.
Suppose the n individuals undergo a test for predicting the event and the test is based on the estimated
probability of the event. Higher values of this estimated probability are assumed to be associated with the
event. A receiver operating characteristic (ROC) curve can be constructed by varying the cutpoint that
determines which estimated event probabilities are considered to predict the event. For each cutpoint z, the
following measures can be output to a data set by specifying the OUTROC= option in the MODEL statement
or the OUTROC= option in the SCORE statement:
_POS_.z/ D
X
I.b
i z/
i 2C1
_NEG_.z/ D
X
I.b
i < z/
i 2C2
_FALPOS_.z/ D
X
I.b
i z/
i 2C2
_FALNEG_.z/ D
X
I.b
i < z/
i 2C1
_SENSIT_.z/ D
_1MSPEC_.z/ D
_POS_.z/
n1
_FALPOS_.z/
n2
where I./ is the indicator function.
Note that _POS_(z) is the number of correctly predicted event responses, _NEG_(z) is the number of
correctly predicted nonevent responses, _FALPOS_(z) is the number of falsely predicted event responses,
_FALNEG_(z) is the number of falsely predicted nonevent responses, _SENSIT_(z) is the sensitivity of the
test, and _1MSPEC_(z) is one minus the specificity of the test.
The ROC curve is a plot of sensitivity (_SENSIT_) against 1–specificity (_1MSPEC_). The plot can be
produced by using the PLOTS option or by using the GPLOT or SGPLOT procedure with the OUTROC= data
set. See Example 58.7 for an illustration. The area under the ROC curve, as determined by the trapezoidal
rule, is estimated by the concordance index, c, in the “Association of Predicted Probabilities and Observed
Responses” table.
Comparing ROC Curves
ROC curves can be created from each model fit in a selection routine, from the specified model in the MODEL
statement, from specified models in ROC statements, or from input variables which act as b
in the preceding
discussion. Association statistics are computed for these models, and the models are compared when the
ROCCONTRAST statement is specified. The ROC comparisons are performed by using a contrast matrix to
take differences of the areas under the empirical ROC curves (DeLong, DeLong, and Clarke-Pearson 1988).
4584 F Chapter 58: The LOGISTIC Procedure
For example, if you have three curves and the second curve is the reference, the contrast used for the overall
test is
0 l1
1
1 0
L1 D
D
l20
0
1 1
and you can optionally estimate and test each row of this contrast, in order to test the difference between
the reference curve and each of the other curves. If you do not want to use a reference curve, the global test
optionally uses the following contrast:
0 l1
1
1
0
L2 D
D
l20
0
1
1
You can also specify your own contrast matrix. Instead of estimating the rows of these contrasts, you can
request that the difference between every pair of ROC curves be estimated and tested. Demler, Pencina, and
D’Agostino (2012) caution that testing the difference in the AUC between two nested models is not a valid
approach if the added predictor is not significantly associated with the response; in any case, if you use this
approach, you are more likely to fail to reject the null.
By default for the reference contrast, the specified or selected model is used as the reference unless the
NOFIT option is specified in the MODEL statement, in which case the first ROC model is the reference.
In order to label the contrasts, a name is attached to every model. The name for the specified or selected
model is the MODEL statement label, or “Model” if the MODEL label is not present. The ROC statement
models are named with their labels, or as “ROCi” for the ith ROC statement if a label is not specified.
The contrast L1 is labeled as “Reference = ModelName”, where ModelName is the reference model name,
while L2 is labeled “Adjacent Pairwise Differences”. The estimated rows of the contrast matrix are labeled
“ModelName1 – ModelName2”. In particular, for the rows of L1 , ModelName2 is the reference model name.
If you specify your own contrast matrix, then the contrast is labeled “Specified” and the ith contrast row
estimates are labeled “Rowi”.
If ODS Graphics is enabled, then all ROC curves are displayed individually and are also overlaid in a final
display. If a selection method is specified, then the curves produced in each step of the model selection
process are overlaid onto a single plot and are labeled “Stepi”, and the selected model is displayed on a
separate plot and on a plot with curves from specified ROC statements. See Example 58.8 for an example.
ROC Computations
The trapezoidal area under an empirical ROC curve is equal to the Mann-Whitney two-sample rank measure
of association statistic (a generalized U-statistic) applied to two samples, fXi g; i D 1; : : : ; n1 , in C1 and
fYi g; i D 1; : : : ; n2 , in C2 . PROC LOGISTIC uses the predicted probabilities in place of X and Y; however,
in general any criterion could be used. Denote the frequency of observation i in Ck as fki , and denote the
total frequency in Ck as Fk . The WEIGHTED option replaces fki with fki wki , where wki is the weight of
observation i in group Ck . The trapezoidal area under the curve is computed as
n1 X
n2
1 X
.Xi ; Yj /f1i f2j
F1 F2
i D1 j D1
8
< 1 Y <X
1
.X; Y / D
Y DX
: 2
0 Y >X
cO D
Testing Linear Hypotheses about the Regression Coefficients F 4585
so that E.c/
O D Pr.Y < X / C 12 Pr.Y D X /. Note that the concordance index, c, in the “Association of
Predicted Probabilities and Observed Responses” table does not use weights unless both the WEIGHTED
and BINWIDTH=0 options are specified. Also, in this table, c is computed by creating 500 bins and binning
the Xi and Yj ; this results in more ties than the preceding method (unless the BINWIDTH=0 or ROCEPS=0
option is specified), so c is not necessarily equal to E.c/.
O
To compare K empirical ROC curves, first compute the trapezoidal areas. Asymptotic normality of the
estimated area follows from U-statistic theory, and a covariance matrix S can be computed; see DeLong,
DeLong, and Clarke-Pearson (1988) for details. A Wald confidence interval for the rth area, 1 r K, can
be constructed as
cOr ˙ z1
˛
2
sr;r
where sr;r is the rth diagonal of S.
For a contrast of ROC curve areas, Lc, the statistic
1
.Oc c/0 L0 LSL0
L.Oc c/
has a chi-square distribution with df=rank(LSL0 ). For a row of the contrast, l 0 c,
l 0 cO
l 0c
Œl 0 Sl 1=2
has a standard normal distribution. The corresponding confidence interval is
l 0 cO ˙ z1
˛
2
0 1=2
l Sl
Testing Linear Hypotheses about the Regression Coefficients
Linear hypotheses for ˇ are expressed in matrix form as
H0 W Lˇ D c
where L is a matrix of coefficients for the linear hypotheses, and c is a vector of constants. The vector of
regression coefficients ˇ includes slope parameters as well as intercept parameters. The Wald chi-square
statistic for testing H0 is computed as
2W D .Lb̌
c/0 ŒLb
V.b̌/L0
1
.Lb̌
c/
where b
V.b̌/ is the estimated covariance matrix. Under H0 , 2W has an asymptotic chi-square distribution
with r degrees of freedom, where r is the rank of L.
Type 3 Tests
For models that use the less-than-full-rank parameterization (as specified by the PARAM=GLM option in
the CLASS statement), a Type 3 test of an effect of interest is a test of the Type III estimable functions that
are defined for that effect. When there are no missing cells, the Type 3 test of a main effect corresponds to
testing the hypotheses of equal marginal means. For more information about Type III estimable functions,
4586 F Chapter 58: The LOGISTIC Procedure
see Chapter 44, “The GLM Procedure,” and Chapter 15, “The Four Types of Estimable Functions.” Also see
Littell, Freund, and Spector (1991).
For models that use a full-rank parameterization, all parameters are estimable when there are no missing
cells; so it is unnecessary to define estimable functions. The Type 3 test of an effect of interest is the joint test
that the parameters associated with that effect are zero. For a model that uses effects parameterization (as
specified by the PARAM=EFFECT option in the CLASS statement), testing a main effect is equivalent to
testing the equality of marginal means. For a model that uses reference parameterization (as specified by the
PARAM=REF option in the CLASS statement), the Type 3 test is a test of the equality of cell means at the
reference level of the other model effects. For more information about the coding scheme and the associated
interpretation of results, see Muller and Fetterman (2002, Chapter 14).
For a model without an interaction term, the Type 3 tests of main effects are the same regardless of the type
of parameterization that is used. For a model that contains an interaction term and no missing cells, the
Type 3 test of a component main effect is the same under GLM parameterization and effect parameterization,
because both test the equality of cell means. But this differs from reference parameterization, which tests the
equality of cell means at the reference level of the other component main effect. If some cells are missing,
you can obtain meaningful Type 3 tests only by testing a Type III estimable function, so in this case you
should use GLM parameterization.
The results of a Type 3 analysis do not depend on the order in which the terms are specified in the MODEL
statement.
Regression Diagnostics
For binary response data, regression diagnostics developed by Pregibon (1981) can be requested by specifying
the INFLUENCE option. For diagnostics available with conditional logistic regression, see the section
“Regression Diagnostic Details” on page 4595. These diagnostics can also be obtained from the OUTPUT
statement.
This section uses the following notation:
rj ; nj
rj is the number of event responses out of nj trials for the jth observation. If events/trials syntax
is used, rj is the value of events and nj is the value of trials. For single-trial syntax, nj D 1, and
rj D 1 if the ordered response is 1, and rj D 0 if the ordered response is 2.
wj
is the weight of the jth observation.
j
is the probability of an event response for the jth observation given by j D F .˛ C ˇ 0 xj /, where
F ./ is the inverse link function defined on page 4561.
b
V.b̌/
is the maximum likelihood estimate (MLE) of .˛; ˇ1 ; : : : ; ˇs /0 .
is the estimated covariance matrix of b̌.
pOj ; qO j
pOj is the estimate of j evaluated at b̌, and qO j D 1
b̌
pOj .
Pregibon (1981) suggests using the index plots of several diagnostic statistics to identify influential observations and to quantify the effects on various aspects of the maximum likelihood fit. In an index plot,
the diagnostic statistic is plotted against the observation number. In general, the distributions of these
diagnostic statistics are not known, so cutoff values cannot be given for determining when the values are
large. However, the IPLOTS and INFLUENCE options in the MODEL statement and the PLOTS option in
Regression Diagnostics F 4587
the PROC LOGISTIC statement provide displays of the diagnostic values, allowing visual inspection and
comparison of the values across observations. In these plots, if the model is correctly specified and fits all
observations well, then no extreme points should appear.
The next five sections give formulas for these diagnostic statistics.
Hat Matrix Diagonal (Leverage)
The diagonal elements of the hat matrix are useful in detecting extreme points in the design space where they
tend to have larger values. The jth diagonal element is
(
hj D
w
ej .1; x0j /b
V.b̌/.1; x0j /0 Fisher scoring
w
bj .1; x0j /b
V.b̌/.1; x0j /0 Newton-Raphson
where
w
ej
w
bj
wj nj
pOj qO j Œg 0 .pOj /2
wj .rj nj pOj /ŒpOj qO j g 00 .pOj / C .qO j
D w
ej C
.pOj qO j /2 Œg 0 .pOj /3
D
pOj /g 0 .pOj /
and g 0 ./ and g 00 ./ are the first and second derivatives of the link function g./, respectively.
For a binary response logit model, the hat matrix diagonal elements are
1
0 b b̌
hj D wj nj pOj qO j .1; xj /V. /
xj
If the estimated probability is extreme (less than 0.1 and greater than 0.9, approximately), then the hat
diagonal might be greatly reduced in value. Consequently, when an observation has a very large or very small
estimated probability, its hat diagonal value is not a good indicator of the observation’s distance from the
design space (Hosmer and Lemeshow 2000, p. 171).
Residuals
Residuals are useful in identifying observations that are not explained well by the model. Pearson residuals
are components of the Pearson chi-square statistic and deviance residuals are components of the deviance.
The Pearson residual for the jth observation is
p
wj .rj nj pOj /
j D
p
nj pOj qO j
The Pearson chi-square statistic is the sum of squares of the Pearson residuals.
The deviance residual for the jth observation is
p
q 2wj nj log.qO j /
r
˙ 2wj Œrj log. n jpO / C .nj
dj D
j j
ˆ
p
:
2wj nj log.pOj /
8
ˆ
<
if rj D 0
n r
rj / log. nj qO j
j j
/ if 0 < rj < nj
if rj D nj
4588 F Chapter 58: The LOGISTIC Procedure
where the plus (minus) in ˙ is used if rj =nj is greater (less) than pOj . The deviance is the sum of squares of
the deviance residuals.
The STDRES option in the INFLUENCE and PLOTS=INFLUENCE options computes three more residuals
(Collett 2003). The Pearson and deviance residuals are standardized to have approximately unit variance:
j
e pj
D
p
edj
D
1 hj
dj
p
1
hj
The likelihood residuals, which estimate components of a likelihood ratio test of deleting an individual
observation, are a weighted combination of the standardized Pearson and deviance residuals
q
elj D sign.rj nj pOj / hj ep2 j C .1 hj /ed2
j
DFBETAS
For each parameter estimate, the procedure calculates a DFBETAS diagnostic for each observation. The
DFBETAS diagnostic for an observation is the standardized difference in the parameter estimate due to
deleting the observation, and it can be used to assess the effect of an individual observation on each estimated
parameter of the fitted model. Instead of reestimating the parameter every time an observation is deleted,
PROC LOGISTIC uses the one-step estimate. See the section “Predicted Probability of an Event for
Classification” on page 4578. For the jth observation, the DFBETAS are given by
DFBETASij D i b̌1 =O i
j
where i D 0; 1; : : : ; s; O i is the standard error of the ith component of b̌, and i b̌1j is the ith component of
the one-step difference
wj .rj nj pOj / b b̌
1
1
b̌
j D
V. /
xj
1 hj
b̌1j is the approximate change (b̌ b̌1j ) in the vector of parameter estimates due to the omission of the jth
observation. The DFBETAS are useful in detecting observations that are causing instability in the selected
coefficients.
C and CBAR
C and CBAR are confidence interval displacement diagnostics that provide scalar measures of the influence
of individual observations on b̌. These diagnostics are based on the same idea as the Cook distance in
linear regression theory (Cook and Weisberg 1982), but use the one-step estimate. C and CBAR for the jth
observation are computed as
Cj D 2j hj =.1
hj /2
C j D 2j hj =.1
hj /
and
respectively.
Typically, to use these statistics, you plot them against an index and look for outliers.
Scoring Data Sets F 4589
DIFDEV and DIFCHISQ
DIFDEV and DIFCHISQ are diagnostics for detecting ill-fitted observations; in other words, observations
that contribute heavily to the disagreement between the data and the predicted values of the fitted model.
DIFDEV is the change in the deviance due to deleting an individual observation while DIFCHISQ is the
change in the Pearson chi-square statistic for the same deletion. By using the one-step estimate, DIFDEV
and DIFCHISQ for the jth observation are computed as
DIFDEV D dj2 C C j
and
DIFCHISQ D C j = hj
Scoring Data Sets
Scoring a data set, which is especially important for predictive modeling, means applying a previously fitted
model to a new data set in order to compute the conditional, or posterior, probabilities of each response
category given the values of the explanatory variables in each observation.
The SCORE statement enables you to score new data sets and output the scored values and, optionally, the
corresponding confidence limits into a SAS data set. If the response variable is included in the new data set,
then you can request fit statistics for the data, which is especially useful for test or validation data. If the
response is binary, you can also create a SAS data set containing the receiver operating characteristic (ROC)
curve. You can specify multiple SCORE statements in the same invocation of PROC LOGISTIC.
By default, the posterior probabilities are based on implicit prior probabilities that are proportional to the
frequencies of the response categories in the training data (the data used to fit the model). Explicit prior
probabilities should be specified with the PRIOR= or PRIOREVENT= option when the sample proportions
of the response categories in the training data differ substantially from the operational data to be scored. For
example, to detect a rare category, it is common practice to use a training set in which the rare categories
are overrepresented; without prior probabilities that reflect the true incidence rate, the predicted posterior
probabilities for the rare category will be too high. By specifying the correct priors, the posterior probabilities
are adjusted appropriately.
The model fit to the DATA= data set in the PROC LOGISTIC statement is the default model used for the
scoring. Alternatively, you can save a model fit in one run of PROC LOGISTIC and use it to score new
data in a subsequent run. The OUTMODEL= option in the PROC LOGISTIC statement saves the model
information in a SAS data set. Specifying this data set in the INMODEL= option of a new PROC LOGISTIC
run will score the DATA= data set in the SCORE statement without refitting the model.
The STORE statement can also be used to save your model. The PLM procedure can use this model to score
new data sets; see Chapter 73, “The PLM Procedure,” for more information. You cannot specify priors in
PROC PLM.
4590 F Chapter 58: The LOGISTIC Procedure
Fit Statistics for Scored Data Sets
Specifying the FITSTAT option displays the following fit statistics when the data set being scored includes
the response variable:
Statistic
Total frequency
Total weight
Log likelihood
Full log likelihood
Misclassification (error) rate
AIC
AICC
BIC
SC
R-square
Maximum-rescaled R-square
AUC
Brier score (polytomous response)
Brier score (binary response)
Brier reliability (events/trials syntax)
Description
P
F D Pi fi ni
W D iP
fi wi ni
log L D i fi wi log.b
i /
log
P Lf D constant C log L
i 1fF_Yi ¤ I_Yi gfi ni
F
2 log Lf C 2p
2pn
2 log Lf C
n p 1
2 log Lf C p log.n/
2 log Lf C p log.F /
2=F
L0
2
R D1
L
R2
2=F
1 L0
Area under the ROC curve
P
1 P
b
ij /2
j .yij
W Pi fi wi
1
b
i /2 C .ni
W Pi fi wi .ri .1
1
b
i /2
i fi wi .ri =ni
W
ri /b
2i /
In the preceding table, fi is the
Pfrequency of the ith observation in the data set being scored, wi is the weight
of the observation, and n D i fi . The number of trials when events/trials syntax is specified is ni , and
with single-trial syntax ni D 1. The values F_Yi and I_Yi are described in the section “OUT= Output Data
Set in a SCORE Statement” on page 4604. The indicator function 1fAg is 1 if A is true and 0 otherwise. The
likelihood of the model is L, and L0 denotes the likelihood of the intercept-only model. For polytomous
response models, yi is the observed polytomous response level, b
ij is the predicted probability of the jth
response level for observation i, and yij D 1fyi D j g. For binary response models, b
i is the predicted
probability of the observation, ri is the number of events when you specify events/trials syntax, and ri D yi
when you specify single-trial syntax.
The log likelihood, Akaike’s information criterion (AIC), and Schwarz criterion (SC) are described in the
section “Model Fitting Information” on page 4567. The full log likelihood is displayed for models specified
with events/trials syntax, and the constant term is described in the section “Model Fitting Information”
on page 4567. The AICC is a small-sample bias-corrected version of the AIC (Hurvich and Tsai 1993;
Burnham and Anderson 1998). The Bayesian information criterion (BIC) is the same as the SC except when
events/trials syntax is specified. The area under the ROC curve for binary response models is defined in
the section “ROC Computations” on page 4584. The R-square and maximum-rescaled R-square statistics,
defined in “Generalized Coefficient of Determination” on page 4568, are not computed when you specify
both an OFFSET= variable and the INMODEL= data set. The Brier score (Brier 1950) is the weighted
squared difference between the predicted probabilities and their observed response levels. For events/trials
syntax, the Brier reliability is the weighted squared difference between the predicted probabilities and the
observed proportions (Murphy 1973).
Scoring Data Sets F 4591
Posterior Probabilities and Confidence Limits
Let F be the inverse link function. That is,
8
<
1
1Cexp. t /
logistic
F .t/ D
ˆ.t /
normal
:
1 exp. exp.t // complementary log-log
The first derivative of F is given by
0
F .t/ D
8
ˆ
<
exp. t /
.1Cexp. t //2
logistic
.t /
normal
ˆ
: exp.t / exp. exp.t // complementary log-log
Suppose there are k C 1 response categories. Let Y be the response variable with levels 1; : : : ; k C 1. Let
x D .x0 ; x1 ; : : : ; xs /0 be a .s C 1/-vector of covariates, with x0 1. Let ˇ be the vector of intercept and
slope regression parameters.
Posterior probabilities are given by
p .Y Di /
po .Y D i jx/ pe
o .Y Di /
p.Y D i jx/ D P
p .Y Dj /
p .Y D j jx/ e
j
o
i D 1; : : : ; k C 1
po .Y Dj /
where the old posterior probabilities (po .Y D i jx/; i D 1; : : : ; k C 1) are the conditional probabilities of
the response categories given x, the old priors (po .Y D i /; i D 1; : : : ; k C 1) are the sample proportions of
response categories of the training data, and the new priors (e
p .Y D i /; i D 1; : : : ; k C 1) are specified in the
PRIOR= or PRIOREVENT= option. To simplify notation, absorb the old priors into the new priors; that is
p.Y D i / D
e
p .Y D i /
po .Y D i /
i D 1; : : : ; k C 1
Note if the PRIOR= and PRIOREVENT= options are not specified, then p.Y D i / D 1.
The posterior probabilities are functions of ˇ and their estimates are obtained by substituting ˇ by its MLE
b̌. The variances of the estimated posterior probabilities are given by the delta method as follows:
@p.Y D ijx/
@p.Y D i jx/ 0
Var.b̌/
Var.b
p .Y D i jx// D
@ˇ
@ˇ
where
@po .Y Di jx/
p.Y D i /
@p.Y D i jx/
@ˇ
P
D
@ˇ
j po .Y D j jx/p.Y D j /
P
po .Y D i jx/p.Y D i / j @po [email protected]ˇDj jx/ p.Y D j /
P
Πj po .Y D j jx/p.Y D j /2
and the old posterior probabilities po .Y D i jx/ are described in the following sections.
˛)% confidence interval for p.Y D i jx/ is
q
c p .Y D i jx//
b
p .Y D i jx/ ˙ z1 ˛=2 Var.b
A 100(1
where z is the upper 100 percentile of the standard normal distribution.
4592 F Chapter 58: The LOGISTIC Procedure
Binary and Cumulative Response Models
Let ˛1 ; : : : ; ˛k be the intercept parameters and let ˇs be the vector of slope parameters. Denote ˇ D
.˛1 ; : : : ; ˛k ; ˇs0 /0 . Let
i D i .ˇ/ D ˛i C x0 ˇs ; i D 1; : : : ; k
Estimates of 1 ; : : : ; k are obtained by substituting the maximum likelihood estimate b̌ for ˇ.
The predicted probabilities of the responses are
8
< F .O 1 /
b
F .O i / F .O i
p
co .Y D i jx/ D Pr.Y D i / D
:
1 F .O k /
i D1
1 / i D 2; : : : ; k
i DkC1
For i D 1; : : : ; k, let ıi .x/ be a (k + 1) column vector with ith entry equal to 1, k + 1 entry equal to x, and all
other entries 0. The derivative of po .Y D i jx/ with respect to ˇ are
8 0
< F .˛1 C x0 ˇs /ı1 .x/
@po .Y D i jx/
F 0 .˛i C x0 ˇs /ıi .x/ F 0 .˛i
D
:
@ˇ
F 0 .˛k C x0 ˇs /ık .x/
0
1 C x ˇs /ıi
i D1
.x/
i
D 2; : : : ; k
1
i DkC1
The cumulative posterior probabilities are
Pi
j D1 po .Y
D j jx/p.Y D j /
j D1 po .Y
D j jx/p.Y D j /
p.Y i jx/ D PkC1
D
i
X
p.Y D j jx/
i D 1; : : : ; k C 1
j D1
Their derivatives are
i
X
@p.Y i jx/
@p.Y D j jx/
D
@ˇ
@ˇ
i D 1; : : : ; k C 1
j D1
In the delta-method equation for the variance, replace p.Y D jx/ with p.Y jx/.
Finally, for the cumulative response model, use
p
co .Y i jx/ D F .O i /
i D 1; : : : ; k
p
co .Y k C 1jx/ D 1
@po .Y i jx/
D F 0 .˛i C x0 ˇs /ıi .x/
@ˇ
@po .Y k C 1jx/
D 0
@ˇ
i D 1; : : : ; k
Scoring Data Sets F 4593
Generalized Logit Model
Consider the last response level (Y=k+1) as the reference. Let ˇ1 ; : : : ; ˇk be the (intercept and slope)
parameter vectors for the first k logits, respectively. Denote ˇ D .ˇ10 ; : : : ; ˇk0 /0 . Let D .1 ; : : : ; k /0 with
i D i .ˇ/ D x0 ˇi
i D 1; : : : ; k
Estimates of 1 ; : : : ; k are obtained by substituting the maximum likelihood estimate b̌ for ˇ.
The predicted probabilities are
p
co .Y D k C 1jx/ Pr.Y D k C 1jx/ D
1
Pk
1 C lD1 exp.O l /
p
co .Y D i jx/ Pr.Y D i jx/ D p
co .Y D k C 1jx/ exp.i /; i D 1; : : : ; k
The derivative of po .Y D ijx/ with respect to ˇ are
@po .Y D ijx/
@ˇ
@ @po .Y D i jx/
@ˇ
@
@po .Y D i jx/
@po .Y D i jx/ 0
D .Ik ˝ x/
; ;
@1
@k
D
where
@po .Y D ijx/
D
@j
po .Y D i jx/.1 po .Y D i jx// j D i
po .Y D i jx/po .Y D j jx/
otherwise
Special Case of Binary Response Model with No Priors
Let ˇ be the vector of regression parameters. Let
D .ˇ/ D x0 ˇ
The variance of O is given by
Var./
O D x0 Var.b̌/x
˛) percent confidence interval for is
q
c /
O
O ˙ z1 ˛=2 Var.
A 100(1
Estimates of po .Y D 1jx/ and confidence intervals for the po .Y D 1jx/ are obtained by back-transforming
O and the confidence intervals for , respectively. That is,
p
co .Y D 1jx/ D F ./
O
and the confidence intervals are
q
c
F O ˙ z1 ˛=2 Var./
O
4594 F Chapter 58: The LOGISTIC Procedure
Conditional Logistic Regression
The method of maximum likelihood described in the preceding sections relies on large-sample asymptotic
normality for the validity of estimates and especially of their standard errors. When you do not have a large
sample size compared to the number of parameters, this approach might be inappropriate and might result
in biased inferences. This situation typically arises when your data are stratified and you fit intercepts to
each stratum so that the number of parameters is of the same order as the sample size. For example, in
a 1W 1 matched pairs study with n pairs and p covariates, you would estimate n 1 intercept parameters
and p slope parameters. Taking the stratification into account by “conditioning out” (and not estimating)
the stratum-specific intercepts gives consistent and asymptotically normal MLEs for the slope coefficients.
See Breslow and Day (1980) and Stokes, Davis, and Koch (2012) for more information. If your nuisance
parameters are not just stratum-specific intercepts, you can perform an exact conditional logistic regression.
Computational Details
For each stratum h, h D 1; : : : ; H , number the observations as i D 1; : : : ; nh so that hi indexes the ith
observation in stratum h. Denote the p covariates for the hith observation as xhi and its binary response
as yhi , and let y D .y11 ; : : : ; y1n1 ; : : : ; yH1 ; : : : ; yH nH /0 , Xh D .xh1 : : : xhnh /0 , and X D .X01 : : : X0H /0 .
Let the dummy variables zh ; h D 1; : : : ; H , be indicator functions for the strata (zh D 1 if the observation
is in stratum h), and denote zhi D .z1 ; : : : ; zH / for the hith observation, Zh D .zh1 : : : zhnh /0 , and Z D
.Z01 : : : Z0H /0 . Denote X D .ZjX) and xhi D .z0hi jx0hi /0 . Arrange the observations in each stratum h so
that yhi D 1 for i D 1; : : : ; mh , and yhi D 0 for i D mhC1 ; : : : ; nh . Suppose all observations have unit
frequency.
Consider the binary logistic regression model on page 4485 written as
logit./ D X where the parameter vector D .˛0 ; ˇ 0 /0 consists of ˛ D .˛1 ; : : : ; ˛H /0 , ˛h is the intercept for stratum
h; h D 1; : : : ; H , and ˇ is the parameter vector for the p covariates.
From the section “Determining Observations for Likelihood Contributions” on page 4562, you can write the
likelihood contribution of observation hi; i D 1; : : : ; nh ; h D 1; : : : ; H; as
0
Lhi ./ D
where yhi
e yhi xhi
0
1 C e xhi D 1 when the response takes Ordered Value 1, and yhi D 0 otherwise.
The full likelihood is
nh
H Y
Y
ey X L./ D
Lhi ./ D Q
0
H Qnh
x
hi 1
C
e
i
D1
hD1
hD1
i D1
0
Unconditional likelihood inference is based on maximizing this likelihood function.
When your nuisance parameters are the stratum-specific intercepts .˛1 ; : : : ; ˛H /0 , and the slopes ˇ are
your parameters of interest, “conditioning out” the nuisance parameters produces the conditional likelihood
(Lachin 2000)
Qmh
H
H
0
Y
Y
i D1 exp.xhi ˇ/
L.ˇ/ D
Lh .ˇ/ D
P Qjmh
0
hD1
hD1
j Dj1 exp.xhj ˇ/
Conditional Logistic Regression F 4595
nh where the summation is over all m
subsets fj1 ; : : : ; jmh g of mh observations chosen from the nh observah
tions in stratum h. Note that the nuisance parameters have been factored out of this equation.
For conditional asymptotic inference, maximum likelihood estimates b̌ of the regression parameters are
obtained by maximizing the conditional likelihood, and asymptotic results are applied to the conditional
likelihood function and the maximum likelihood estimators. A relatively fast method of computing this
conditional likelihood and its derivatives is given by Gail, Lubin, and Rubinstein (1981) and Howard (1972).
The optimization techniques can be controlled by specifying the NLOPTIONS statement.
Sometimes the log likelihood converges but the estimates diverge. This condition is flagged by having
inordinately large standard errors for some of your parameter estimates, and can be monitored by specifying
the ITPRINT option. Unfortunately, broad existence criteria such as those discussed in the section “Existence
of Maximum Likelihood Estimates” on page 4565 do not exist for this model. It might be possible to
circumvent such a problem by standardizing your independent variables before fitting the model.
Regression Diagnostic Details
Diagnostics are used to indicate observations that might have undue influence on the model fit or that might
be outliers. Further investigation should be performed before removing such an observation from the data set.
The derivations in this section use an augmentation method described by Storer and Crowley (1985), which
provides an estimate of the “one-step” DFBETAS estimates advocated by Pregibon (1984). The method
also provides estimates of conditional stratum-specific predicted values, residuals, and leverage for each
observation. The augmentation method can take a lot of time and memory.
Following Storer and Crowley (1985), the log-likelihood contribution can be written as
lh D log.Lh / D yh0 h a.h / where
2
3
jmh
X Y
a.h / D log 4
exp.hj /5
j Dj1
and the h subscript on matrices indicates the submatrix for the stratum, h D .h1 ; : : : ; hnh /0 , and hi D
x0hi ˇ. Then the gradient and information matrix are
g.ˇ/ D
ƒ.ˇ/ D
@lh
@ˇ
H
@2 lh
@ˇ 2
D X0 .y
hD1
H
hD1
/
D X0 diag.U1 ; : : : ; UH /X
4596 F Chapter 58: The LOGISTIC Procedure
where
D
hi
@a.h /
D
@hi
P
j.i /
Qjmh
j Dj1
P Qjmh
j Dj1
exp.hj /
exp.hj /
h D .h1 ; : : : ; hnh /
2
@2 a.h /
@ a.h /
Uh D
D faij g
D
@hi @hj
@h2
P
Qkmh
exp.hk / @a.h / @a.h /
k.i;j /
kDk1
aij D
D hij
P Qkmh
@hi @hj
exp. /
kDk1
hi hj
hk
and where hi is the conditional stratum-specific probability that subject i in stratum h is a case, the
summation on j.i / is over all subsets from f1; : : : ; nh g of size mh that contain the index i, and the summation
on k.i; j / is over all subsets from f1; : : : ; nh g of size mh that contain the indices i and j.
1
To produce the true one-step estimate ˇhi
, start at the MLE b̌, delete the hith observation, and use this
reduced data set to compute the next Newton-Raphson step. Note that if there is only one event or one
nonevent in a stratum, deletion of that single observation is equivalent to deletion of the entire stratum. The
augmentation method does not take this into account.
The augmented model is
logit.Pr.yhi D 1jxhi // D x0hi ˇ C z0hi 0
where zhi D .0; : : : ; 0; 1; 0; : : : ; 0/0 has a 1 in the hith coordinate, and use ˇ 0 D .b̌ ; 0/0 as the initial estimate
for .ˇ 0 ; /0 . The gradient and information matrix before the step are
0
g.ˇ / D
ƒ.ˇ 0 / D
X0
z0hi
X0
z0hi
.y
/ D
0
yhi hi
ƒ.ˇ/ X0 Uzhi
U ŒX zhi D
z0hi UX z0hi Uzhi
Inserting the ˇ 0 and .X0 ; z0hi /0 into the Gail, Lubin, and Rubinstein (1981) algorithm provides the appropriate
b
estimates of g.ˇ 0 / and ƒ.ˇ 0 /. Indicate these estimates with b
D .b̌/, b
U D U.b̌/, b
g, and ƒ.
DFBETA is computed from the information matrix as
1
hi ˇ D ˇ 0 ˇhi
b 1 .ˇ 0 /b
D
ƒ
g.ˇ 0 /
b 1 .b̌/.X0 b
D
ƒ
Uzhi /M
1 0
zhi .y
b
/
where
M D .z0hi b
Uzhi /
b
.z0hi b
UX/ƒ
1
.b̌/.X0 b
Uzhi /
For each observation in the data set, a DFBETA statistic is computed for each parameter ˇj , 1 j p, and
standardized by the standard error of ˇj from the full data set to produce the estimate of DFBETAS.
Exact Conditional Logistic Regression F 4597
The estimated leverage is defined as
hhi D
b
UX/ƒ
tracef.z0hi b
1 .b̌/.X0 b
Uzhi /g
Uzhi g
tracefz0hi b
This definition of leverage produces different values from those defined by Pregibon (1984); Moolgavkar,
Lustbader, and Venzon (1985); Hosmer and Lemeshow (2000); however, it has the advantage that no extra
computations beyond those for the DFBETAS are required.
The estimated residuals ehi D yhi b
hi are obtained from b
g.ˇ 0 /, and the weights, or predicted probabilities,
are then b
hi D yhi ehi . The residuals are standardized and reported as (estimated) Pearson residuals:
rhi
p
nhi b
hi
nhi b
hi .1
b
hi /
where rhi is the number of events in the observation and nhi is the number of trials.
The STDRES option in the INFLUENCE option computes the standardized Pearson residual:
ehi
es;hi D p
1 hhi
For events/trials MODEL statement syntax, treat each observation as two observations (the first for the
nonevents and the second for the events) with frequencies fh;2i 1 D nhi rhi and fh;2i D rhi , and augment
the model with a matrix Zhi D Œzh;2i 1 zh;2i instead of a single zhi vector. Writing hi D x0hi ˇfhi in the
preceding section results in the following gradient and information matrix:
2
0
3
g.ˇ 0 / D 4 fh;2i 1 .yh;2i 1 h;2i 1 / 5
fh;2i .yh;2i h;2i /
ƒ.ˇ/
X0 diag.f /Udiag.f /Zhi
ƒ.ˇ 0 / D
Z0hi diag.f /Udiag.f /X Z0hi diag.f /Udiag.f /Zhi
The predicted probabilities are then b
hi D yh;2i eh;2i =rh;2i , while the leverage and the DFBETAS are
produced from ƒ.ˇ 0 / in a fashion similar to that for the preceding single-trial equations.
Exact Conditional Logistic Regression
The theory of exact logistic regression, also known as exact conditional logistic regression, was originally
laid out by Cox (1970), and the computational methods employed in PROC LOGISTIC are described in Hirji,
Mehta, and Patel (1987); Hirji (1992); Mehta, Patel, and Senchaudhuri (1992). Other useful references for
the derivations include Cox and Snell (1989); Agresti (1990); Mehta and Patel (1995).
Exact conditional inference is based on generating the conditional distribution for the sufficient statistics of
the parameters of interest. This distribution is called the permutation or exact conditional distribution. Using
the notation in the section “Computational Details” on page 4594, follow Mehta and Patel (1995) and first
note that the sufficient statistics T D .T1 ; : : : ; Tp / for the parameter vector of intercepts and slopes, ˇ, are
Tj D
n
X
i D1
yi xij ;
j D 1; : : : ; p
4598 F Chapter 58: The LOGISTIC Procedure
Denote a vector of observable sufficient statistics as t D .t1 ; : : : ; tp /0 .
The probability density function (PDF) for T can be created by summing over all binary sequences y that
generate an observable t and letting C.t/ D jjfy W y0 X D t0 gjj denote the number of sequences y that
generate t
C.t/ exp.t0 ˇ/
0
i D1 Œ1 C exp.xi ˇ/
Pr.T D t/ D Qn
0
In order to condition out the nuisance parameters, partition the parameter vector ˇ D .ˇN
; ˇI0 /0 , where ˇN is
a pN 1 vector of the nuisance parameters, and ˇI is the parameter vector for the remaining pI D p pN
parameters of interest. Likewise, partition X into XN and XI , T into TN and TI , and t into tN and tI . The
nuisance parameters can be removed from the analysis by conditioning on their sufficient statistics to create
the conditional likelihood of TI given TN D tN ,
Pr.T D t/
Pr.TN D tN /
C.tN ; tI / exp.t0I ˇI /
D fˇI .tI jtN / D P
0
u C.tN ; u/ exp.u ˇI /
Pr.TI D tI jTN D tN / D
where C.tN ; u/ is the number of vectors y such that y0 XN D tN and y0 XI D u. Note that the nuisance
parameters have factored out of this equation, and that C.tN ; tI / is a constant.
The goal of the exact conditional analysis is to determine how likely the observed response y0 is with respect
to all 2n possible responses y D .y1 ; : : : ; yn /0 . One way to proceed is to generate every y vector for which
y0 XN D tN , and count the number of vectors y for which y0 XI is equal to each unique tI . Generating the
conditional distribution from complete enumeration of the joint distribution is conceptually simple; however,
this method becomes computationally infeasible very quickly. For example, if you had only 30 observations,
you would have to scan through 230 different y vectors.
Several algorithms are available in PROC LOGISTIC to generate the exact distribution. All of the algorithms
are based on the following observation. Given any y D .y1 ; : : : ; yn /0 and a design X D .x1 ; : : : ; xn /0 , let
y.i / D .y1 ; : : : ; yi /0 and X.i / D .x1 ; : : : ; xi /0 be the first i rows of each matrix. Write the sufficient statistic
0
based on these i rows as t0.i / D y.i
X . A recursion relation results: t.i C1/ D t.i / C yi C1 xi C1 .
/ .i /
The following methods are available:
• The multivariate shift algorithm developed by Hirji, Mehta, and Patel (1987), which steps through the
recursion relation by adding one observation at a time and building an intermediate distribution at each
step. If it determines that t.i / for the nuisance parameters could eventually equal t, then t.i / is added
to the intermediate distribution.
• An extension of the multivariate shift algorithm to generalized logit models by Hirji (1992). Since
the generalized logit model fits a new set of parameters to each logit, the number of parameters in the
model can easily get too large for this algorithm to handle. Note for these models that the hypothesis
tests for each effect are computed across the logit functions, while individual parameters are estimated
for each logit function.
• A network algorithm described in Mehta, Patel, and Senchaudhuri (1992), which builds a network for
each parameter that you are conditioning out in order to identify feasible yi for the y vector. These
Exact Conditional Logistic Regression F 4599
networks are combined and the set of feasible yi is further reduced, and then the multivariate shift
algorithm uses this knowledge to build the exact distribution without adding as many intermediate
t.iC1/ as the multivariate shift algorithm does.
• A hybrid Monte Carlo and network algorithm described by Mehta, Patel, and Senchaudhuri (2000),
which extends their 1992 algorithm by sampling from the combined network to build the exact
distribution.
The bulk of the computation time and memory for these algorithms is consumed by the creation of the
networks and the exact joint distribution. After the joint distribution for a set of effects is created, the
computational effort required to produce hypothesis tests and parameter estimates for any subset of the effects
is (relatively) trivial. See the section “Computational Resources for Exact Logistic Regression” on page 4607
for more computational notes about exact analyses.
N OTE : An alternative to using these exact conditional methods is to perform Firth’s bias-reducing penalized
likelihood method (see the FIRTH option in the MODEL statement); this method has the advantage of being
much faster and less memory intensive than exact algorithms, but it might not converge to a solution.
Hypothesis Tests
Consider testing the null hypothesis H0 W ˇI D 0 against the alternative HA W ˇI ¤ 0, conditional on TN D tN .
Under the null hypothesis, the test statistic for the exact probability test is just fˇI D0 .tI jtN /, while the
corresponding p-value is the probability of getting a less likely (more extreme) statistic,
X
p.tI jtN / D
f0 .ujtN /
u2p
where p D fuW there exist y with y0 XI D u, y0 XN D tN , and f0 .ujtN / f0 .tI jtN /g.
For the exact conditional scores test, the conditional mean I and variance matrix †I of the TI (conditional
on TN D tN ) are calculated, and the score statistic for the observed value,
s D .tI
I /0 †I 1 .tI
I /
is compared to the score for each member of the distribution
S.TI / D .TI
I /0 †I 1 .TI
I /
The resulting p-value is
p.tI jtN / D Pr.S s/ D
X
f0 .ujtN /
u2s
where s D fuW there exist y with y0 XI D u, y0 XN D tN , and S.u/ sg.
The mid-p statistic, defined as
1
f0 .tI jtN /
2
was proposed by Lancaster (1961) to compensate for the discreteness of a distribution. See Agresti (1992) for
more information. However, to allow for more flexibility in handling ties, you can write the mid-p statistic as
(based on a suggestion by Lamotte (2002) and generalizing Vollset, Hirji, and Afifi (1991))
X
X
f0 .ujtN / C ı1 f0 .tI jtN / C ı2
f0 .ujtN /
p.tI jtN /
u2<
u2D
4600 F Chapter 58: The LOGISTIC Procedure
where, for i 2 fp; sg, < is i using strict inequalities, and D is i using equalities with the added
restriction that u ¤ tI . Letting .ı1 ; ı2 / D .0:5; 1:0/ yields Lancaster’s mid-p.
C AUTION : When the exact distribution has ties and METHOD=NETWORKMC is specified, the Monte
Carlo algorithm estimates p.tjtN / with error, and hence it cannot determine precisely which values contribute
to the reported p-values. For example, if the exact distribution has densities f0:2; 0:2; 0:2; 0:4g and if the
observed statistic has probability 0.2, then the exact probability p-value is exactly 0.6. Under Monte Carlo
sampling, if the densities after N samples are f0:18; 0:21; 0:23; 0:38g and the observed probability is 0.21,
then the resulting p-value is 0.39. Therefore, the exact probability test p-value for this example fluctuates
between 0.2, 0.4, and 0.6, and the reported p-values are actually lower bounds for the true p-values. If you
need more precise values, you can specify the OUTDIST= option, determine appropriate cutoff values for
the observed probability and score, and then construct the true p-value estimates from the OUTDIST= data
set and display them in the SAS log by using the following statements:
data _null_;
set outdist end=end;
retain pvalueProb 0 pvalueScore 0;
if prob < ProbCutOff then pvalueProb+prob;
if score > ScoreCutOff then pvalueScore+prob;
if end then put pvalueProb= pvalueScore=;
run;
Inference for a Single Parameter
Exact parameter estimates are derived for a single parameter ˇi by regarding all the other parameters
ˇN D .ˇ1 ; : : : ; ˇi 1 ; ˇi C1 ; : : : ; ˇpN CpI /0 as nuisance parameters. The appropriate sufficient statistics are
TI D Ti and TN D .T1 ; : : : ; Ti 1 ; Ti C1 ; : : : ; TpN CpI /0 , with their observed values denoted by the lowercase
t. Hence, the conditional PDF used to create the parameter estimate for ˇi is
C.tN ; ti / exp.ti ˇi /
u2 C.tN ; u/ exp.uˇi /
fˇi .ti jtN / D P
for D fuW there exist y with Ti D u and TN D tN g.
ˇ i , which maximizes the conditional
The maximum exact conditional likelihood estimate is the quantity b
PDF. A Newton-Raphson algorithm is used to perform this search. However, if the observed ti attains
either its maximum or minimum value in the exact distribution (that is, either ti D minfu W u 2 g or
ti D maxfu W u 2 g), then the conditional PDF is monotonically increasing in ˇi and cannot be maximized.
In this case, a median unbiased estimate (Hirji, Tsiatis, and Mehta 1989) b
ˇ i is produced that satisfies
fb̌ .ti jtN / D 0:5, and a Newton-Raphson algorithm is used to perform the search.
i
The standard error of the exact conditional likelihood estimate is just the negative of the inverse of the second
derivative of the exact conditional log likelihood (Agresti 2002).
Likelihood ratio tests based on the conditional PDF are used to test the null H0 W ˇi D 0 against the alternative
HA W ˇi > 0. The critical region for this UMP test consists of the upper tail of values for Ti in the exact
distribution. Thus, the one-sided significance level pC .ti I 0/ is
X
pC .ti I 0/ D
f0 .ujtN /
uti
Similarly, the one-sided significance level p .ti I 0/ against HA W ˇi < 0 is
X
p .ti I 0/ D
f0 .ujtN /
uti
Input and Output Data Sets F 4601
The two-sided significance level p.ti I 0/ against HA W ˇi ¤ 0 is calculated as
p.ti I 0/ D 2 minŒp .ti I 0/; pC .ti I 0/
An upper 100.1 2/% exact confidence limit for b
ˇ i corresponding to the observed ti is the solution ˇU .ti /
of D p .ti ; ˇU .ti //, while the lower exact confidence limit is the solution ˇL .ti / of D pC .ti ; ˇL .ti //.
Again, a Newton-Raphson procedure is used to search for the solutions. Note that one of the confidence
limits for a median unbiased estimate is set to infinity, but the other is still computed at . This results in the
display of a one-sided 100.1 /% confidence interval; if you want the 2 limit instead, you can specify the
ONESIDED option.
Specifying the ONESIDED option displays only one p-value and one confidence interval, because small
values of pC .ti I 0/ and p .ti I 0/ support different alternative hypotheses and only one of these p-values can
be less than 0.50.
The mid-p confidence limits are the solutions to minfp .ti ; ˇ.ti //; pC .ti ; ˇ.ti //g .1 ı1 /fˇ .ti / .ti jtN / D for D ˛=2; 1 ˛=2 (Vollset, Hirji, and Afifi 1991). ı1 D 1 produces the usual exact (or max-p) confidence
interval, ı1 D 0:5 yields the mid-p interval, and ı1 D 0 gives the min-p interval. The mean of the endpoints
of the max-p and min-p intervals provides the mean-p interval as defined by Hirji, Mehta, and Patel (1988).
Estimates and confidence intervals for the odds ratios are produced by exponentiating the estimates and
interval endpoints for the parameters.
Notes about Exact p-Values
In the “Conditional Exact Tests” table, the exact probability test is not necessarily a sum of tail areas and can
be inflated if the distribution is skewed. The more robust exact conditional scores test is a sum of tail areas
and is generally preferred over the exact probability test.
The p-value reported for a single parameter in the “Exact Parameter Estimates” table is twice the one-sided
tail area of a likelihood ratio test against the null hypothesis of the parameter equaling zero.
Input and Output Data Sets
OUTEST= Output Data Set
The OUTEST= data set contains one observation for each BY group containing the maximum likelihood
estimates of the regression coefficients. If you also use the COVOUT option in the PROC LOGISTIC
statement, there are additional observations containing the rows of the estimated covariance matrix. If you
specify SELECTION=FORWARD, BACKWARD, or STEPWISE, only the estimates of the parameters and
covariance matrix for the final model are output to the OUTEST= data set.
Variables in the OUTEST= Data Set
The OUTEST= data set contains the following variables:
• any BY variables specified
• _LINK_, a character variable of length 8 with four possible values: CLOGLOG for the complementary
log-log function, LOGIT for the logit function, NORMIT for the probit (alias normit) function, and
GLOGIT for the generalized logit function
4602 F Chapter 58: The LOGISTIC Procedure
• _TYPE_, a character variable of length 8 with two possible values: PARMS for parameter estimates
or COV for covariance estimates. If an EXACT statement is also specified, then two other values are
possible: EPARMMLE for the exact maximum likelihood estimates and EPARMMUE for the exact
median unbiased estimates.
• _NAME_, a character variable containing the name of the response variable when _TYPE_=PARMS,
EPARMMLE, and EPARMMUE, or the name of a model parameter when _TYPE_=COV
• _STATUS_, a character variable that indicates whether the estimates have converged
• one variable for each intercept parameter
• one variable for each slope parameter and one variable for the offset variable if the OFFSET= option if
specified. If an effect is not included in the final model in a model building process, the corresponding
parameter estimates and covariances are set to missing values.
• _LNLIKE_, the log likelihood
Parameter Names in the OUTEST= Data Set
If there are only two response categories in the entire data set, the intercept parameter is named Intercept.
If there are more than two response categories in the entire data set, the intercept parameters are named
Intercept_xxx, where xxx is the value (formatted if a format is applied) of the corresponding response
category.
For continuous explanatory variables, the names of the parameters are the same as the corresponding variables.
For CLASS variables, the parameter names are obtained by concatenating the corresponding CLASS variable
name with the CLASS category; see the section “Class Variable Naming Convention” on page 4512 for more
details. For interaction and nested effects, the parameter names are created by concatenating the names of
each effect.
For the generalized logit model, names of parameters corresponding to each nonreference category contain
_xxx as the suffix, where xxx is the value (formatted if a format is applied) of the corresponding nonreference
category. For example, suppose the variable Net3 represents the television network (ABC, CBS, and NBC)
viewed at a certain time. The following statements fit a generalized logit model with Age and Gender (a
CLASS variable with values Female and Male) as explanatory variables:
proc logistic;
class Gender;
model Net3 = Age Gender / link=glogit;
run;
There are two logit functions, one contrasting ABC with NBC and the other contrasting CBS with NBC.
For each logit, there are three parameters: an intercept parameter, a slope parameter for Age, and a slope
parameter for Gender (since there are only two gender levels and the EFFECT parameterization is used by
default). The names of the parameters and their descriptions are as follows:
Intercept_ABC
intercept parameter for the logit contrasting ABC with NBC
Intercept_CBS
intercept parameter for the logit contrasting CBS with NBC
Age_ABC
Age slope parameter for the logit contrasting ABC with NBC
Age_CBS
Age slope parameter for the logit contrasting CBS with NBC
Input and Output Data Sets F 4603
GenderFemale_ABC
Gender=Female slope parameter for the logit contrasting ABC with NBC
GenderFemale_CBS
Gender=Female slope parameter for the logit contrasting CBS with NBC
INEST= Input Data Set
You can specify starting values for the iterative algorithm in the INEST= data set. The INEST= data set has
the same structure as the OUTEST= data set but is not required to have all the variables or observations that
appear in the OUTEST= data set. A previous OUTEST= data set can be used as, or modified for use as, an
INEST= data set.
The INEST= data set must contain the intercept variables (named Intercept for binary response models and
Intercept, Intercept_2, Intercept_3, and so forth, for ordinal and nominal response models) and all explanatory
variables in the MODEL statement. If BY processing is used, the INEST= data set should also include the
BY variables, and there must be one observation for each BY group. If the INEST= data set also contains the
_TYPE_ variable, only observations with _TYPE_ value ’PARMS’ are used as starting values.
OUT= Output Data Set in the OUTPUT Statement
The OUT= data set in the OUTPUT statement contains all the variables in the input data set along with
statistics you request by specifying keyword=name options or the PREDPROBS= option in the OUTPUT
statement. In addition, if you use the single-trial syntax and you request any of the XBETA=, STDXBETA=,
PREDICTED=, LCL=, and UCL= options, the OUT= data set contains the automatic variable _LEVEL_.
The value of _LEVEL_ identifies the response category upon which the computed values of XBETA=,
STDXBETA=, PREDICTED=, LCL=, and UCL= are based.
When there are more than two response levels, only variables named by the XBETA=, STDXBETA=,
PREDICTED=, LOWER=, and UPPER= options and the variables given by PREDPROBS=(INDIVIDUAL
CUMULATIVE) have their values computed; the other variables have missing values. If you fit a generalized
logit model, the cumulative predicted probabilities are not computed.
When there are only two response categories, each input observation produces one observation in the OUT=
data set.
If there are more than two response categories and you specify only the PREDPROBS= option, then each
input observation produces one observation in the OUT= data set. However, if you fit an ordinal (cumulative)
model and specify options other than the PREDPROBS= options, each input observation generates as many
output observations as one fewer than the number of response levels, and the predicted probabilities and their
confidence limits correspond to the cumulative predicted probabilities. If you fit a generalized logit model
and specify options other than the PREDPROBS= options, each input observation generates as many output
observations as the number of response categories; the predicted probabilities and their confidence limits
correspond to the probabilities of individual response categories.
For observations in which only the response variable is missing, values of the XBETA=, STDXBETA=,
PREDICTED=, UPPER=, LOWER=, and the PREDPROBS= options are computed even though these
observations do not affect the model fit. This enables, for instance, predicted probabilities to be computed for
new observations.
4604 F Chapter 58: The LOGISTIC Procedure
OUT= Output Data Set in a SCORE Statement
The OUT= data set in a SCORE statement contains all the variables in the data set being scored. The data
set being scored can be either the input DATA= data set in the PROC LOGISTIC statement or the DATA=
data set in the SCORE statement. The DATA= data set in the SCORE statement does not need to contain the
response variable.
If the data set being scored contains the response variable, then denote the normalized levels (left-justified,
formatted values of 16 characters or less) of your response variable Y by Y1 ; : : : ; YkC1 . For each response
level, the OUT= data set also contains the following:
• F_Y, the normalized levels of the response variable Y in the data set being scored. If the events/trials
syntax is used, the F_Y variable is not created.
• I_Y, the normalized levels that the observations are classified into. Note that an observation is classified
into the level with the largest probability. If the events/trials syntax is used, the _INTO_ variable is
created instead, and it contains the values EVENT and NONEVENT.
• P_Yi , the posterior probabilities of the normalized response level Yi
• If the CLM option is specified in the SCORE statement, the OUT= data set also includes the following:
– LCL_Yi , the lower 100(1
˛)% confidence limits for P_Yi
– UCL_Yi , the upper 100(1
˛)% confidence limits for P_Yi
OUTDIST= Output Data Set
The OUTDIST= data set contains every exact conditional distribution necessary to process the corresponding
EXACT statement. For example, the following statements create one distribution for the x1 parameter and
another for the x2 parameters, and produce the data set dist shown in Table 58.12:
data test;
input y x1 x2 count;
datalines;
0 0 0 1
1 0 0 1
0 1 1 2
1 1 1 1
1 0 2 3
1 1 2 1
1 2 0 3
1 2 1 2
1 2 2 1
;
proc logistic data=test exactonly;
class x2 / param=ref;
model y=x1 x2;
exact x1 x2/ outdist=dist;
run;
proc print data=dist;
run;
Input and Output Data Sets F 4605
Table 58.12 OUTDIST= Data Set
Obs
x1
x20
x21
1
2
3
4
5
6
7
8
9
.
.
.
.
.
.
.
.
.
0
0
0
1
1
1
2
2
3
0
1
2
0
1
2
0
1
0
10
11
12
13
14
2
3
4
5
6
.
.
.
.
.
.
.
.
.
.
Count
Score
Prob
3
15
9
15
18
6
19
2
3
5.81151
1.66031
3.12728
1.46523
0.21675
4.58644
1.61869
3.27293
6.27189
0.03333
0.16667
0.10000
0.16667
0.20000
0.06667
0.21111
0.02222
0.03333
6
12
11
18
3
3.03030
0.75758
0.00000
0.75758
3.03030
0.12000
0.24000
0.22000
0.36000
0.06000
The first nine observations in the dist data set contain an exact distribution for the parameters of the x2
effect (hence the values for the x1 parameter are missing), and the remaining five observations are for the
x1 parameter. If a joint distribution was created, there would be observations with values for both the x1
and x2 parameters. For CLASS variables, the corresponding parameters in the dist data set are identified by
concatenating the variable name with the appropriate classification level.
The data set contains the possible sufficient statistics of the parameters for the effects specified in the EXACT
statement, and the Count variable contains the number of different responses that yield these statistics. In
particular, there are six possible response vectors y for which the dot product y0 x1 was equal to 2, and for
which y0 x20, y0 x21, and y0 1 were equal to their actual observed values (displayed in the “Sufficient Statistics”
table).
When hypothesis tests are performed on the parameters, the Prob variable contains the probability of obtaining
that statistic (which is just the count divided by the total count), and the Score variable contains the score for
that statistic.
The OUTDIST= data set can contain a different exact conditional distribution for each specified EXACT
statement. For example, consider the following EXACT statements:
exact
exact
exact
exact
'O1'
'OJ12'
'OA12'
'OE12'
x1
/
x1 x2 / jointonly
x1 x2 / joint
x1 x2 / estimate
outdist=o1;
outdist=oj12;
outdist=oa12;
outdist=oe12;
The O1 statement outputs a single exact conditional distribution. The OJ12 statement outputs only the joint
distribution for x1 and x2. The OA12 statement outputs three conditional distributions: one for x1, one for x2,
and one jointly for x1 and x2. The OE12 statement outputs two conditional distributions: one for x1 and the
other for x2. Data set oe12 contains both the x1 and x2 variables; the distribution for x1 has missing values
in the x2 column while the distribution for x2 has missing values in the x1 column.
4606 F Chapter 58: The LOGISTIC Procedure
OUTROC= Output Data Set
The OUTROC= data set contains data necessary for producing the ROC curve, and can be created by
specifying the OUTROC= option in the MODEL statement or the OUTROC= option in the SCORE statement:
It has the following variables:
• any BY variables specified
• _STEP_, the model step number. This variable is not included if model selection is not requested.
• _PROB_, the estimated probability of an event. These estimated probabilities serve as cutpoints for
predicting the response. Any observation with an estimated event probability that exceeds or equals
_PROB_ is predicted to be an event; otherwise, it is predicted to be a nonevent. Predicted probabilities
that are close to each other are grouped together, with the maximum allowable difference between the
largest and smallest values less than a constant that is specified by the ROCEPS= option. The smallest
estimated probability is used to represent the group.
• _POS_, the number of correctly predicted event responses
• _NEG_, the number of correctly predicted nonevent responses
• _FALPOS_, the number of falsely predicted event responses
• _FALNEG_, the number of falsely predicted nonevent responses
• _SENSIT_, the sensitivity, which is the proportion of event observations that were predicted to have an
event response
• _1MSPEC_, one minus specificity, which is the proportion of nonevent observations that were predicted
to have an event response
Note that none of these statistics are affected by the bias-correction method discussed in the section “Classification Table” on page 4577. An ROC curve is obtained by plotting _SENSIT_ against _1MSPEC_.
For more information, see the section “Receiver Operating Characteristic Curves” on page 4582.
Computational Resources
The memory needed to fit an unconditional model is approximately 8n.p C 2/ C 24.p C 2/2 bytes, where p
is the number of parameters estimated and n is the number of observations in the data set. For cumulative
response models with more than two response levels, a test of the parallel lines assumption requires an
additional memory of approximately 4k 2 .m C 1/2 C 24.m C 2/2 bytes, where k is the number of response
levels and m is the number of slope parameters. However, if this additional memory is not available, the
procedure skips the test and finishes the other computations. You might need more memory if you use the
SELECTION= option for model building.
The data that consist of relevant variables (including the design variables for model effects) and observations
for fitting the model are stored in a temporary utility file. If sufficient memory is available, such data will also
be kept in memory; otherwise, the data are reread from the utility file for each evaluation of the likelihood
function and its derivatives, with the resulting execution time of the procedure substantially increased.
Computational Resources F 4607
Specifying the MULTIPASS option in the MODEL statement avoids creating this utility file and also does
not store the data in memory; instead, the DATA= data set is reread when needed. This saves approximately
8n.p C 2/ bytes of memory but increases the execution time.
If a conditional logistic regression is performed, then approximately 4.m2 C m C 4/ maxh .mh / C .8sH C
36/H C 12sH additional bytes of memory are needed, where mh is the number of events in stratum
h, H is the total number of strata, and sH is the number of variables used to define the strata. If the
CHECKDEPENDENCY=ALL option is specified in the STRATA statement, then an extra 4.m C H /.m C
H C 1/ bytes are required, and the resulting execution time of the procedure might be substantially increased.
Computational Resources for Exact Logistic Regression
Many problems require a prohibitive amount of time and memory for exact computations, depending on the
speed and memory available on your computer. For such problems, consider whether exact methods are
really necessary. Stokes, Davis, and Koch (2012) suggest looking at exact p-values when the sample size is
small and the approximate p-values from the unconditional analysis are less than 0.10, and they provide rules
of thumb for determining when various models are valid.
A formula does not exist that can predict the amount of time and memory necessary to generate the exact
conditional distributions for a particular problem. The time and memory required depends on several
factors, including the total sample size, the number of parameters of interest, the number of nuisance
parameters, and the order in which the parameters are processed. To provide a feel for how these factors affect
performance, 19 data sets containing Nobs 2 f10; : : : ; 500g observations consisting of up to 10 independent
uniform binary covariates (X1,. . . ,XN ) and a binary response variable (Y), are generated, and the following
statements create exact conditional distributions for X1 conditional on the other covariates by using the
default METHOD=NETWORK. Figure 58.11 displays results obtained on a 400Mhz PC with 768MB RAM
running Microsoft Windows NT.
data one;
do obs=1 to HalfNobs;
do Y=0 to 1;
X1=round(ranuni(0));
...
XN =round(ranuni(0));
output;
end;
end;
options fullstimer;
proc logistic exactonly;
exactoptions method=network maxtime=1200;
class X1...XN / param=ref;
model Y=X1...XN ;
exact X1 / outdist=dist;
run;
4608 F Chapter 58: The LOGISTIC Procedure
Figure 58.11 Mean Time and Memory Required
At any time while PROC LOGISTIC is deriving the distributions, you can terminate the computations by
pressing the system interrupt key sequence (see the SAS Companion for your system) and choosing to stop
computations. If you run out of memory, see the SAS Companion for your system to see how to allocate
more.
You can use the EXACTOPTIONS option MAXTIME= to limit the total amount of time PROC LOGISTIC
uses to derive all of the exact distributions. If PROC LOGISTIC does not finish within that time, the procedure
terminates.
Calculation of frequencies are performed in the log scale by default. This reduces the need to check for
excessively large frequencies but can be slower than not scaling. You can turn off the log scaling by specifying
the NOLOGSCALE option in the EXACTOPTIONS statement. If a frequency in the exact distribution is
larger than the largest integer that can be held in double precision, a warning is printed to the SAS log. But
since inaccuracies due to adding small numbers to these large frequencies might have little or no effect on the
statistics, the exact computations continue.
You can monitor the progress of the procedure by submitting your program with the EXACTOPTIONS option
STATUSTIME=. If the procedure is too slow, you can try another method by specifying the EXACTOPTIONS
option METHOD=, you can try reordering the variables in the MODEL statement (note that CLASS variables
are always processed before continuous covariates), or you can try reparameterizing your classification
variables as in the following statement:
class class-variables / param=ref ref=first order=freq;
If you condition out CLASS variables that use reference or GLM coding but you are not using the STRATA
statement, then you can speed up the analysis by specifying one of the nuisance CLASS variables in the
Displayed Output F 4609
STRATA statement. This performance gain occurs because STRATA variables partition your data set into
smaller pieces. However, moving two (or more) nuisance CLASS variables into the STRATA statement
results in a different model, because the sufficient statistics for the second CLASS variable are actually
computed across the levels of the first CLASS variable.
Displayed Output
If you use the NOPRINT option in the PROC LOGISTIC statement, the procedure does not display any
output. Otherwise, the tables displayed by the LOGISTIC procedure are discussed in the following section
in the order in which they appear in the output. Some of the tables appear only in conjunction with certain
options or statements; see the section “ODS Table Names” on page 4614 for details.
N OTE : The EFFECT, ESTIMATE, LSMEANS, LSMESTIMATE, and SLICE statements also create tables,
which are not listed in this section. For information about these tables, see the corresponding sections of
Chapter 19, “Shared Concepts and Topics.”
Table Summary
Model Information and the Number of Observations
See the section “Missing Values” on page 4559 for information about missing-value handling, and the
sections “FREQ Statement” on page 4525 and “WEIGHT Statement” on page 4558 for information about
valid frequencies and weights.
Response Profile
Displays the Ordered Value assigned to each response level. See the section “Response Level Ordering” on
page 4559 for details.
Class Level Information
Displays the design values for each CLASS explanatory variable. See the section “Other Parameterizations”
on page 391 in Chapter 19, “Shared Concepts and Topics,” for details.
Simple Statistics Tables
The following tables are displayed if you specify the SIMPLE option in the PROC LOGISTIC statement:
• Descriptive Statistics for Continuous Explanatory Variables
• Frequency Distribution of Class Variables
• Weight Distribution of Class Variables
Displays if you also specify a WEIGHT statement.
Strata Tables for (Exact) Conditional Logistic Regression
The following tables are displayed if you specify a STRATA statement:
• Strata Summary
Shows the pattern of the number of events and the number of nonevents in a stratum. See the section
“STRATA Statement” on page 4555 for more information.
4610 F Chapter 58: The LOGISTIC Procedure
• Strata Information
Displays if you specify the INFO option in a STRATA statement.
Maximum Likelihood Iteration History
Displays if you specify the ITPRINT option in the MODEL statement. See the sections “Iterative Algorithms
for Model Fitting” on page 4563, “Convergence Criteria” on page 4565, and “Existence of Maximum
Likelihood Estimates” on page 4565 for details.
Deviance and Pearson Goodness-of-Fit Statistics
Displays if you specify the SCALE= option in the MODEL statement. Small p-values reject the null
hypothesis that the fitted model is adequate. See the section “Overdispersion” on page 4579 for details.
Score Test for the Equal Slopes (Proportional Odds) Assumption
Tests the parallel lines assumption if you fit an ordinal response model with the LINK=CLOGLOG or
LINK=PROBIT options. If you specify LINK=LOGIT, this is called the “Proportional Odds” assumption.
The table is not displayed if you specify the UNEQUALSLOPES option in the MODEL statement. Small
p-values reject the null hypothesis that the slope parameters for each explanatory variable are constant across
all the response functions. See the section “Testing the Parallel Lines Assumption” on page 4570 for details.
Model Fit Statistics
Computes various fit criteria based on a model with intercepts only and a model with intercepts and
explanatory variables. If you specify the NOINT option in the MODEL statement, these statistics are
calculated without considering the intercept parameters. See the section “Model Fitting Information” on
page 4567 for details.
Testing Global Null Hypothesis: BETA=0
Tests the joint effect of the explanatory variables included in the model. Small p-values reject the null
hypothesis that all slope parameters are equal to zero, H0 W ˇ D 0. See the sections “Model Fitting Information”
on page 4567, “Residual Chi-Square” on page 4569, and “Testing Linear Hypotheses about the Regression
Coefficients” on page 4585 for details. If you also specify the RSQUARE option in the MODEL statement,
two generalized R Square measures are included; see the section “Generalized Coefficient of Determination”
on page 4568 for details.
Score Test for Global Null Hypothesis
Displays instead of the “Testing Global Null Hypothesis: BETA=0” table if the NOFIT option is specified in
the MODEL statement. The global score test evaluates the joint significance of the effects in the MODEL
statement. Small p-values reject the null hypothesis that all slope parameters are equal to zero, H0 W ˇ D 0.
See the section “Residual Chi-Square” on page 4569 for details.
Model Selection Tables
The tables in this section are produced when the SELECTION= option is specified in the MODEL statement.
See the section “Effect-Selection Methods” on page 4566 for more information.
• Residual Chi-Square Test
Displays if you specify SELECTION=FORWARD, BACKWARD, or STEPWISE in the MODEL
statement. Small p-values reject the null hypothesis that the reduced model is adequate. See the section
“Residual Chi-Square” on page 4569 for details.
Displayed Output F 4611
• Analysis of Effects Eligible for Entry
Displays if you specify the DETAILS option and the SELECTION=FORWARD or STEPWISE option
in the MODEL statement. Small p-values reject H0 W ˇi ¤ 0. The score chi-square is used to determine
entry; see the section “Testing Individual Effects Not in the Model” on page 4570 for details.
• Analysis of Effects Eligible for Removal
Displays if you specify the SELECTION=BACKWARD or STEPWISE option in the MODEL statement. Small p-values reject H0 W ˇi D 0. The Wald chi-square is used to determine removal; see the
section “Testing Linear Hypotheses about the Regression Coefficients” on page 4585 for details.
• Analysis of Effects Removed by Fast Backward Elimination
Displays if you specify the FAST option and the SELECTION=BACKWARD or STEPWISE option in
the MODEL statement. This table gives the approximate chi-square statistic for the variable removed,
the corresponding p-value with respect to a chi-square distribution with one degree of freedom, the
residual chi-square statistic for testing the joint significance of the variable and the preceding ones, the
degrees of freedom, and the p-value of the residual chi-square with respect to a chi-square distribution
with the corresponding degrees of freedom.
• Summary of Forward, Backward, and Stepwise Selection
Displays if you specify SELECTION=FORWARD, BACKWARD, or STEPWISE in the MODEL
statement. The score chi-square is used to determine entry; see the section “Testing Individual Effects
Not in the Model” on page 4570 for details. The Wald chi-square is used to determine removal; see the
section “Testing Linear Hypotheses about the Regression Coefficients” on page 4585 for details.
• Regression Models Selected by Score Criterion
Displays the score chi-square for all models if you specify the SELECTION=SCORE option in the
MODEL statement. Small p-values reject the null hypothesis that the fitted model is adequate. See the
section “Effect-Selection Methods” on page 4566 for details.
Type 3 Analysis of Effect
Displays if the model contains a CLASS variable. Performs Wald chi-square tests of the joint effect of
the parameters for each CLASS variable in the model. Small p-values reject H0 W ˇi D 0. See the section
“Testing Linear Hypotheses about the Regression Coefficients” on page 4585 for details.
Analysis of Maximum Likelihood Estimates
CLASS effects are identified by their (nonreference) level. For generalized logit models, a response variable
column displays the nonreference level of the logit. The table includes the following:
• the estimated standard error of the parameter estimate, computed as the square root of the corresponding
diagonal element of the estimated covariance matrix
• the Wald chi-square statistic, computed by squaring the ratio of the parameter estimate divided by its
standard error estimate. See the section “Testing Linear Hypotheses about the Regression Coefficients”
on page 4585 for details.
• the p-value tests the null hypothesis H0 W ˇi D 0; small values reject the null.
• the standardized estimate for the slope parameter, if you specify the STB option in the MODEL
statement. See the STB option on page 4542 for details.
4612 F Chapter 58: The LOGISTIC Procedure
• exponentiated values of the estimates of the slope parameters, if you specify the EXPB option in the
MODEL statement. See the EXPB option on page 4534 for details.
• the label of the variable, if you specify the PARMLABEL option in the MODEL statement and if
space permits. Due to constraints on the line size, the variable label might be suppressed in order to
display the table in one panel. Use the SAS system option LINESIZE= to specify a larger line size to
accommodate variable labels. A shorter line size can break the table into two panels allowing labels to
be displayed.
Odds Ratio Estimates
Displays the odds ratio estimates and the corresponding 95% Wald confidence intervals for variables that are
not involved in nestings or interactions. For continuous explanatory variables, these odds ratios correspond to
a unit increase in the risk factors. See the section “Odds Ratio Estimation” on page 4572 for details.
Association of Predicted Probabilities and Observed Responses
See the section “Rank Correlation of Observed Responses and Predicted Probabilities” on page 4575 for
details.
Parameter Estimates and Profile-Likelihood or Wald Confidence Intervals
Displays if you specify the CLPARM= option in the MODEL statement. See the section “Confidence
Intervals for Parameters” on page 4571 for details.
Odds Ratio Estimates and Profile-Likelihood or Wald Confidence Intervals
Displays if you specify the ODDSRATIO statement for any effects with any class parameterizations. Also
displays if you specify the CLODDS= option in the MODEL statement, except odds ratios are computed
only for main effects not involved in interactions or nestings, and if the main effect is a CLASS variable, the
parameterization must be EFFECT, REFERENCE, or GLM. See the section “Odds Ratio Estimation” on
page 4572 for details.
Estimated Covariance or Correlation Matrix
Displays if you specify the COVB or CORRB option in the MODEL statement. See the section “Iterative
Algorithms for Model Fitting” on page 4563 for details.
Contrast Test Results
Displays the Wald test for each specified CONTRAST statement. Small p-values reject H0 W Lˇ D 0. The
“Coefficients of Contrast” table displays the contrast matrix if you specify the E option, and the “Contrast
Estimation and Testing Results by Row” table displays estimates and Wald tests for each row of the contrast
matrix if you specify the ESTIMATE= option. See the sections “CONTRAST Statement” on page 4513,
“Testing Linear Hypotheses about the Regression Coefficients” on page 4585, and “Linear Predictor, Predicted
Probability, and Confidence Limits” on page 4576 for details.
Linear Hypotheses Testing Results
Displays the Wald test for each specified TEST statement. See the sections “Testing Linear Hypotheses about
the Regression Coefficients” on page 4585 and “TEST Statement” on page 4557 for details.
Displayed Output F 4613
Hosmer and Lemeshow Goodness-of-Fit Test
Displays if you specify the LACKFIT option in the MODEL statement. Small p-values reject the null
hypothesis that the fitted model is adequate. The “Partition for the Hosmer and Lemeshow Test” table
displays the grouping used in the test. See the section “The Hosmer-Lemeshow Goodness-of-Fit Test” on
page 4581 for details.
Classification Table
Displays if you use the CTABLE option in the MODEL statement. If you specify a list of cutpoints with the
PPROB= option, then the cutpoints are displayed in the Prob Level column. If you specify the prior event
probabilities with the PEVENT= option, then the probabilities are displayed in the Prob Event column. The
Correct column displays the number of correctly classified events and nonevents, the Incorrect Event column
displays the number of nonevents incorrectly classified as events, and the Incorrect Nonevent column gives
the number of events incorrectly classified as nonevents. See the section “Classification Table” on page 4577
for more details.
Regression Diagnostics
Displays if you specify the INFLUENCE option in the MODEL statement. See the section “Regression
Diagnostics” on page 4586 for more information about diagnostics from an unconditional analysis, and the
section “Regression Diagnostic Details” on page 4595 for information about diagnostics from a conditional
analysis.
Fit Statistics for SCORE Data
Displays if you specify the FITSTAT option in the SCORE statement. See the section “Scoring Data Sets” on
page 4589 for details.
ROC Association Statistic and Contrast Tables
Displayed if a ROC statement or a ROCCONTRAST statement is specified. See the section “ROC Computations” on page 4584 for details about the Mann-Whitney statistics and the test and estimation computations,
and see the section “Rank Correlation of Observed Responses and Predicted Probabilities” on page 4575 for
details about the other statistics.
Exact Conditional Logistic Regression Tables
The tables in this section are produced when the EXACT statement is specified.
If the
METHOD=NETWORKMC option is specified,p
the test and estimate tables are renamed “Monte Carlo”
tables and a Monte Carlo standard error column ( p.1 p/=n) is displayed.
• Sufficient Statistics
Displays if you request an OUTDIST= data set in an EXACT statement. The table lists the parameters
and their observed sufficient statistics.
• (Monte Carlo) Conditional Exact Tests
See the section “Hypothesis Tests” on page 4599 for details.
• (Monte Carlo) Exact Parameter Estimates
Displays if you specify the ESTIMATE option in the EXACT statement. This table gives individual
parameter estimates for each variable (conditional on the values of all the other parameters in the
model), confidence limits, and a two-sided p-value (twice the one-sided p-value) for testing that the
parameter is zero. See the section “Inference for a Single Parameter” on page 4600 for details.
4614 F Chapter 58: The LOGISTIC Procedure
• (Monte Carlo) Exact Odds Ratios
Displays if you specify the ESTIMATE=ODDS or ESTIMATE=BOTH option in the EXACT statement.
See the section “Inference for a Single Parameter” on page 4600 for details.
ODS Table Names
PROC LOGISTIC assigns a name to each table it creates. You can use these names to reference the table
when using the Output Delivery System (ODS) to select tables and create output data sets. These names are
listed in Table 58.13. For more information about ODS, see Chapter 20, “Using the Output Delivery System.”
The EFFECT, ESTIMATE, LSMEANS, LSMESTIMATE, and SLICE statements also create tables, which
are not listed in Table 58.13. For information about these tables, see the corresponding sections of Chapter 19,
“Shared Concepts and Topics.”
Table 58.13
ODS Tables Produced by PROC LOGISTIC
ODS Table Name
Description
Statement
Option
Association
Association of predicted
probabilities and observed
responses
Best subset selection
Frequency breakdown of
CLASS variables
CLASS variable levels and
design variables
Classification table
Weight breakdown of
CLASS variables
Odds ratio estimates and
profile-likelihood confidence
intervals
Odds ratio estimates and
Wald confidence intervals
Parameter estimates and
profile-likelihood confidence
intervals
Parameter estimates and
Wald confidence intervals
L matrix from CONTRAST
Estimates from CONTRAST
Wald test for CONTRAST
Convergence status
Estimated correlation matrix
of parameter estimators
Estimated covariance matrix
of parameter estimators
MODEL
(without STRATA)
Default
MODEL
PROC
MODEL
SELECTION=SCORE
Simple
(with CLASS vars)
Default
(with CLASS vars)
CTABLE
Simple
(with CLASS vars)
CLODDS=PL
MODEL
CLODDS=WALD
MODEL
CLPARM=PL
MODEL
CLPARM=WALD
CONTRAST
CONTRAST
CONTRAST
MODEL
MODEL
E
ESTIMATE=
Default
Default
CORRB
MODEL
COVB
BestSubsets
ClassFreq
ClassLevelInfo
Classification
ClassWgt
CLOddsPL
CLOddsWald
CLParmPL
CLParmWald
ContrastCoeff
ContrastEstimate
ContrastTest
ConvergenceStatus
CorrB
CovB
MODEL
MODEL
PROC, WEIGHT
ODS Table Names F 4615
Table 58.13 continued
ODS Table Name
Description
Statement
Option
CumulativeModelTest
MODEL
(Ordinal response)
EffectNotInModel
ExactOddsRatio
Test of the cumulative model
assumption
Test for effects not in model
Exact odds ratios
MODEL
EXACT
ExactParmEst
Parameter estimates
EXACT
ExactTests
FastElimination
FitStatistics
GlobalScore
GlobalTests
Conditional exact tests
Fast backward elimination
Model fit statistics
Global score test
Test for global null
hypothesis
Pearson and deviance
goodness-of-fit tests
Batch capture of the index
plots
Regression diagnostics
Iteration history
Hosmer-Lemeshow
chi-square test results
Partition for the HosmerLemeshow test
Last evaluation of gradient
Linear combination
Final change in the log
likelihood
Summary of model building
Model information
Number of observations
Adjusted odds ratios
Odds ratio estimates
Odds ratio estimates and
Wald confidence intervals
Odds ratio estimates and PL
confidence intervals
Maximum likelihood
estimates of model
parameters
R-square
Residual chi-square
Response profile
Association table for ROC
models
EXACT
MODEL
MODEL
MODEL
MODEL
SELECTION=S|F
ESTIMATE=ODDS,
ESTIMATE=BOTH
ESTIMATE,
ESTIMATE=PARM,
ESTIMATE=BOTH
Default
SELECTION=B,FAST
Default
NOFIT
Default
MODEL
SCALE
MODEL
IPLOTS
MODEL
MODEL
MODEL
INFLUENCE
ITPRINT
LACKFIT
MODEL
LACKFIT
MODEL
PROC
MODEL
ITPRINT
Default
ITPRINT
MODEL
PROC
PROC
UNITS
MODEL
ODDSRATIOS
SELECTION=B|F|S
Default
Default
Default
Default
CL=WALD
ODDSRATIOS
CL=PL
MODEL
Default
MODEL
MODEL
PROC
ROC
RSQUARE
SELECTION=F|B
Default
Default
GoodnessOfFit
IndexPlots
Influence
IterHistory
LackFitChiSq
LackFitPartition
LastGradient
Linear
LogLikeChange
ModelBuildingSummary
ModelInfo
NObs
OddsEst
OddsRatios
OddsRatiosWald
OddsRatiosPL
ParameterEstimates
RSquare
ResidualChiSq
ResponseProfile
ROCAssociation
4616 F Chapter 58: The LOGISTIC Procedure
Table 58.13 continued
ODS Table Name
Description
Statement
Option
ROCContrastCoeff
L matrix from
ROCCONTRAST
Covariance of
ROCCONTRAST rows
Estimates from
ROCCONTRAST
Wald test from
ROCCONTRAST
Covariance between ROC
curves
Fit statistics for Scored data
Summary statistics for
explanatory variables
Number of strata with
specific response frequencies
Event and nonevent
frequencies for each stratum
Sufficient statistics
L[Cov(b)]L’ and Lb-c
Ginv(L[Cov(b)]L’) and
Ginv(L[Cov(b)]L’)(Lb-c)
Linear hypotheses testing
results
Type 3 tests of effects
ROCCONTRAST
E
ROCCONTRAST
COV
ROCCONTRAST
ESTIMATE=
ROCCONTRAST
Default
ROCCONTRAST
COV
SCORE
PROC
FITSTAT
SIMPLE
STRATA
Default
STRATA
INFO
EXACT
TEST
TEST
OUTDIST=
PRINT
PRINT
TEST
Default
MODEL
Default
(with CLASS variables)
ROCContrastCov
ROCContrastEstimate
ROCContrastTest
ROCCov
ScoreFitStat
SimpleStatistics
StrataSummary
StrataInfo
SuffStats
TestPrint1
TestPrint2
TestStmts
Type3
ODS Graphics
Statistical procedures use ODS Graphics to create graphs as part of their output. ODS Graphics is described
in detail in Chapter 21, “Statistical Graphics Using ODS.”
Before you create graphs, ODS Graphics must be enabled (for example, by specifying the ODS GRAPHICS ON statement). For more information about enabling and disabling ODS Graphics, see the section
“Enabling and Disabling ODS Graphics” on page 606 in Chapter 21, “Statistical Graphics Using ODS.”
The overall appearance of graphs is controlled by ODS styles. Styles and other aspects of using ODS
Graphics are discussed in the section “A Primer on ODS Statistical Graphics” on page 605 in Chapter 21,
“Statistical Graphics Using ODS.”
You must also specify the options in the PROC LOGISTIC statement that are indicated in Table 58.14.
When ODS Graphics is enabled, then the EFFECT, EFFECTPLOT, ESTIMATE, LSMEANS, LSMESTIMATE, and SLICE statements can produce plots that are associated with their analyses. For information
about these plots, see the corresponding sections of Chapter 19, “Shared Concepts and Topics.”
ODS Graphics F 4617
PROC LOGISTIC assigns a name to each graph it creates using ODS. You can use these names to reference
the graphs when using ODS. The names are listed in Table 58.14.
Table 58.14 Graphs Produced by PROC LOGISTIC
ODS Graph Name
Plot Description
Statement or Option
DfBetasPlot
Panel of dfbetas by case number
PLOTS=DFBETAS
or MODEL / INFLUENCE or IPLOTS
PLOTS=DFBETAS(UNPACK)
PLOTS=DPC
DPCPlot
EffectPlot
InfluencePlots
CBarPlot
CPlot
DevianceResidualPlot
DifChisqPlot
DifDeviancePlot
LeveragePlot
LikelihoodResidualPlot
PearsonResidualPlot
StdDevianceResidualPlot
StdPearsonResidualPlot
LeveragePlots
LeverageCPlot
LeverageDifChisqPlot
LeverageDifDevPlot
LeveragePhatPlot
ORPlot
PhatPlots
PhatCPlot
PhatDifChisqPlot
PhatDifDevPlot
PhatLeveragePlot
Effect dfbetas by case number
Difchisq and/or difdev by predicted
probability by CI displacement C
Predicted probability
Panel of influence statistics by case
number
CI displacement Cbar by case
number
CI displacement C by case number
Deviance residual by case number
Difchisq by case number
Difdev by case number
Hat diagonal by case number
Likelihood residual by case number
Pearson chi-square residual by case
number
Standardized deviance residual by
case number
Standardized Pearson chi-square
residual by case number
Panel of influence statistics by
leverage
CI displacement C by leverage
Difchisq by leverage
Difdev by leverage
Predicted probability by leverage
Odds ratios
Panel of influence by predicted
probability
CI displacement C by predicted
probability
Difchisq by predicted probability
Difdev by predicted probability
Leverage by predicted probability
PLOTS=EFFECT
PLOTS=INFLUENCE
or MODEL / INFLUENCE or IPLOTS
PLOTS=INFLUENCE(UNPACK)
PLOTS=INFLUENCE(UNPACK)
PLOTS=INFLUENCE(UNPACK)
PLOTS=INFLUENCE(UNPACK)
PLOTS=INFLUENCE(UNPACK)
PLOTS=INFLUENCE(UNPACK)
PLOTS=INFLUENCE(UNPACK
STDRES)
PLOTS=INFLUENCE(UNPACK)
PLOTS=INFLUENCE(UNPACK
STDRES)
PLOTS=INFLUENCE(UNPACK
STDRES)
PLOTS=LEVERAGE
PLOTS=LEVERAGE(UNPACK)
PLOTS=LEVERAGE(UNPACK)
PLOTS=LEVERAGE(UNPACK)
PLOTS=LEVERAGE(UNPACK)
Default or
PLOTS=ODDSRATIO and
MODEL / CLODDS=
or ODDSRATIO
PLOTS=PHAT
PLOTS=PHAT(UNPACK)
PLOTS=PHAT(UNPACK)
PLOTS=PHAT(UNPACK)
PLOTS=PHAT(UNPACK)
4618 F Chapter 58: The LOGISTIC Procedure
Table 58.14 continued
ODS Graph Name
Plot Description
Statement or Option
ROCCurve
Receiver operating characteristics
curve
ROCOverlay
ROC curves for comparisons
PLOTS=ROC
or MODEL / OUTROC=
or SCORE OUTROC=
or ROC
PLOTS=ROC and
MODEL / SELECTION=
or ROC
Examples: LOGISTIC Procedure
Example 58.1: Stepwise Logistic Regression and Predicted Values
Consider a study on cancer remission (Lee 1974). The data consist of patient characteristics and whether or
not cancer remission occurred. The following DATA step creates the data set Remission containing seven
variables. The variable remiss is the cancer remission indicator variable with a value of 1 for remission and
a value of 0 for nonremission. The other six variables are the risk factors thought to be related to cancer
remission.
data Remission;
input remiss cell smear infil li blast temp;
label remiss='Complete Remission';
datalines;
1
.8
.83 .66 1.9 1.1
.996
1
.9
.36 .32 1.4
.74
.992
0
.8
.88 .7
.8
.176
.982
0 1
.87 .87
.7 1.053
.986
1
.9
.75 .68 1.3
.519
.98
0 1
.65 .65
.6
.519
.982
1
.95 .97 .92 1
1.23
.992
0
.95 .87 .83 1.9 1.354 1.02
0 1
.45 .45
.8
.322
.999
0
.95 .36 .34
.5 0
1.038
0
.85 .39 .33
.7
.279
.988
0
.7
.76 .53 1.2
.146
.982
0
.8
.46 .37
.4
.38
1.006
0
.2
.39 .08
.8
.114
.99
0 1
.9
.9
1.1 1.037
.99
1 1
.84 .84 1.9 2.064 1.02
0
.65 .42 .27
.5
.114 1.014
0 1
.75 .75 1
1.322 1.004
0
.5
.44 .22
.6
.114
.99
1 1
.63 .63 1.1 1.072
.986
0 1
.33 .33
.4
.176 1.01
Example 58.1: Stepwise Logistic Regression and Predicted Values F 4619
0
1
0
1
1
0
;
.9
1
.95
1
1
1
.93
.58
.32
.6
.69
.73
.84
.58
.3
.6
.69
.73
.6
1
1.6
1.7
.9
.7
1.591
.531
.886
.964
.398
.398
1.02
1.002
.988
.99
.986
.986
The following invocation of PROC LOGISTIC illustrates the use of stepwise selection to identify the
prognostic factors for cancer remission. A significance level of 0.3 is required to allow a variable into
the model (SLENTRY=0.3), and a significance level of 0.35 is required for a variable to stay in the model
(SLSTAY=0.35). A detailed account of the variable selection process is requested by specifying the DETAILS
option. The Hosmer and Lemeshow goodness-of-fit test for the final selected model is requested by specifying
the LACKFIT option. The OUTEST= and COVOUT options in the PROC LOGISTIC statement create a
data set that contains parameter estimates and their covariances for the final selected model. The response
variable option EVENT= chooses remiss=1 (remission) as the event so that the probability of remission is
modeled. The OUTPUT statement creates a data set that contains the cumulative predicted probabilities and
the corresponding confidence limits, and the individual and cross validated predicted probabilities for each
observation.
title 'Stepwise Regression on Cancer Remission Data';
proc logistic data=Remission outest=betas covout;
model remiss(event='1')=cell smear infil li blast temp
/ selection=stepwise
slentry=0.3
slstay=0.35
details
lackfit;
output out=pred p=phat lower=lcl upper=ucl
predprob=(individual crossvalidate);
run;
proc print data=betas;
title2 'Parameter Estimates and Covariance Matrix';
run;
proc print data=pred;
title2 'Predicted Probabilities and 95% Confidence Limits';
run;
In stepwise selection, an attempt is made to remove any insignificant variables from the model before adding
a significant variable to the model. Each addition or deletion of a variable to or from a model is listed as a
separate step in the displayed output, and at each step a new model is fitted. Details of the model selection
steps are shown in Outputs 58.1.1 through 58.1.5.
Prior to the first step, the intercept-only model is fit and individual score statistics for the potential variables
are evaluated (Output 58.1.1).
4620 F Chapter 58: The LOGISTIC Procedure
Output 58.1.1 Startup Model
Stepwise Regression on Cancer Remission Data
The LOGISTIC Procedure
Step
0. Intercept entered:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
-2 Log L = 34.372
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
1
-0.6931
0.4082
2.8827
0.0895
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
9.4609
6
0.1493
Analysis of Effects Eligible for Entry
Effect
cell
smear
infil
li
blast
temp
DF
Score
Chi-Square
Pr > ChiSq
1
1
1
1
1
1
1.8893
1.0745
1.8817
7.9311
3.5258
0.6591
0.1693
0.2999
0.1701
0.0049
0.0604
0.4169
In Step 1 (Output 58.1.2), the variable li is selected into the model since it is the most significant variable
among those to be chosen (p D 0:0049 < 0:3). The intermediate model that contains an intercept and li is
then fitted. li remains significant (p D 0:0146 < 0:35) and is not removed.
Example 58.1: Stepwise Logistic Regression and Predicted Values F 4621
Output 58.1.2 Step 1 of the Stepwise Analysis
Step
1. Effect li entered:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
36.372
37.668
34.372
30.073
32.665
26.073
AIC
SC
-2 Log L
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
8.2988
7.9311
5.9594
1
1
1
0.0040
0.0049
0.0146
Likelihood Ratio
Score
Wald
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
li
1
1
-3.7771
2.8973
1.3786
1.1868
7.5064
5.9594
0.0061
0.0146
Odds Ratio Estimates
Effect
li
Point
Estimate
95% Wald
Confidence Limits
18.124
1.770
185.563
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
84.0
13.0
3.1
162
Somers' D
Gamma
Tau-a
c
0.710
0.732
0.328
0.855
4622 F Chapter 58: The LOGISTIC Procedure
Output 58.1.2 continued
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
3.1174
5
0.6819
Analysis of Effects Eligible for Removal
Effect
li
DF
Wald
Chi-Square
Pr > ChiSq
1
5.9594
0.0146
NOTE: No effects for the model in Step 1 are removed.
Analysis of Effects Eligible for Entry
Effect
cell
smear
infil
blast
temp
DF
Score
Chi-Square
Pr > ChiSq
1
1
1
1
1
1.1183
0.1369
0.5715
0.0932
1.2591
0.2903
0.7114
0.4497
0.7601
0.2618
In Step 2 (Output 58.1.3), the variable temp is added to the model. The model then contains an intercept and
the variables li and temp. Both li and temp remain significant at 0.35 level; therefore, neither li nor temp is
removed from the model.
Output 58.1.3 Step 2 of the Stepwise Analysis
Step
2. Effect temp entered:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
36.372
37.668
34.372
30.648
34.535
24.648
Example 58.1: Stepwise Logistic Regression and Predicted Values F 4623
Output 58.1.3 continued
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
9.7239
8.3648
5.9052
2
2
2
0.0077
0.0153
0.0522
Likelihood Ratio
Score
Wald
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
li
temp
1
1
1
47.8448
3.3017
-52.4214
46.4381
1.3593
47.4897
1.0615
5.9002
1.2185
0.3029
0.0151
0.2697
Odds Ratio Estimates
Effect
li
temp
Point
Estimate
95% Wald
Confidence Limits
27.158
<0.001
1.892
<0.001
389.856
>999.999
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
87.0
12.3
0.6
162
Somers' D
Gamma
Tau-a
c
0.747
0.752
0.345
0.873
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
2.1429
4
0.7095
Analysis of Effects Eligible for Removal
Effect
li
temp
DF
Wald
Chi-Square
Pr > ChiSq
1
1
5.9002
1.2185
0.0151
0.2697
4624 F Chapter 58: The LOGISTIC Procedure
Output 58.1.3 continued
NOTE: No effects for the model in Step 2 are removed.
Analysis of Effects Eligible for Entry
Effect
cell
smear
infil
blast
DF
Score
Chi-Square
Pr > ChiSq
1
1
1
1
1.4700
0.1730
0.8274
1.1013
0.2254
0.6775
0.3630
0.2940
In Step 3 (Output 58.1.4), the variable cell is added to the model. The model then contains an intercept and
the variables li, temp, and cell. None of these variables are removed from the model since all are significant
at the 0.35 level.
Output 58.1.4 Step 3 of the Stepwise Analysis
Step
3. Effect cell entered:
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
36.372
37.668
34.372
29.953
35.137
21.953
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
12.4184
9.2502
4.8281
3
3
3
0.0061
0.0261
0.1848
Example 58.1: Stepwise Logistic Regression and Predicted Values F 4625
Output 58.1.4 continued
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
cell
li
temp
1
1
1
1
67.6339
9.6521
3.8671
-82.0737
56.8875
7.7511
1.7783
61.7124
1.4135
1.5507
4.7290
1.7687
0.2345
0.2130
0.0297
0.1835
Odds Ratio Estimates
Effect
Point
Estimate
95% Wald
Confidence Limits
cell
li
temp
>999.999
47.804
<0.001
0.004
1.465
<0.001
>999.999
>999.999
>999.999
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
88.9
11.1
0.0
162
Somers' D
Gamma
Tau-a
c
0.778
0.778
0.359
0.889
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
0.1831
3
0.9803
Analysis of Effects Eligible for Removal
Effect
cell
li
temp
DF
Wald
Chi-Square
Pr > ChiSq
1
1
1
1.5507
4.7290
1.7687
0.2130
0.0297
0.1835
4626 F Chapter 58: The LOGISTIC Procedure
Output 58.1.4 continued
NOTE: No effects for the model in Step 3 are removed.
Analysis of Effects Eligible for Entry
Effect
smear
infil
blast
DF
Score
Chi-Square
Pr > ChiSq
1
1
1
0.0956
0.0844
0.0208
0.7572
0.7714
0.8852
Finally, none of the remaining variables outside the model meet the entry criterion, and the stepwise selection
is terminated. A summary of the stepwise selection is displayed in Output 58.1.5.
Output 58.1.5 Summary of the Stepwise Selection
Summary of Stepwise Selection
Step
1
2
3
Effect
Entered Removed
li
temp
cell
DF
Number
In
Score
Chi-Square
1
1
1
1
2
3
7.9311
1.2591
1.4700
Wald
Chi-Square
Pr > ChiSq
0.0049
0.2618
0.2254
Results of the Hosmer and Lemeshow test are shown in Output 58.1.6. There is no evidence of a lack of fit in
the selected model .p D 0:5054/.
Output 58.1.6 Display of the LACKFIT Option
Partition for the Hosmer and Lemeshow Test
Group
Total
1
2
3
4
5
6
7
8
9
3
3
3
3
4
3
3
3
2
remiss = 1
Observed
Expected
0
0
0
0
1
2
2
3
1
0.00
0.01
0.19
0.56
1.09
1.35
1.84
2.15
1.80
remiss = 0
Observed
Expected
3
3
3
3
3
1
1
0
1
3.00
2.99
2.81
2.44
2.91
1.65
1.16
0.85
0.20
Example 58.1: Stepwise Logistic Regression and Predicted Values F 4627
Output 58.1.6 continued
Hosmer and Lemeshow Goodness-of-Fit Test
Chi-Square
DF
Pr > ChiSq
6.2983
7
0.5054
The data set betas created by the OUTEST= and COVOUT options is displayed in Output 58.1.7. The
data set contains parameter estimates and the covariance matrix for the final selected model. Note that all
explanatory variables listed in the MODEL statement are included in this data set; however, variables that are
not included in the final model have all missing values.
Output 58.1.7 Data Set of Estimates and Covariances
Stepwise Regression on Cancer Remission Data
Parameter Estimates and Covariance Matrix
Obs
_LINK_
_TYPE_
_STATUS_
1
2
3
4
5
6
7
8
LOGIT
LOGIT
LOGIT
LOGIT
LOGIT
LOGIT
LOGIT
LOGIT
PARMS
COV
COV
COV
COV
COV
COV
COV
Obs
smear
infil
li
blast
1
2
3
4
5
6
7
8
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3.8671
64.5726
6.9454
.
.
3.1623
.
-75.3513
.
.
.
.
.
.
.
.
0
0
0
0
0
0
0
0
_NAME_
Converged
Converged
Converged
Converged
Converged
Converged
Converged
Converged
remiss
Intercept
cell
smear
infil
li
blast
temp
Intercept
cell
67.63
3236.19
157.10
.
.
64.57
.
-3483.23
9.652
157.097
60.079
.
.
6.945
.
-223.669
temp
_LNLIKE_
_ESTTYPE_
-82.07
-3483.23
-223.67
.
.
-75.35
.
3808.42
-10.9767
-10.9767
-10.9767
-10.9767
-10.9767
-10.9767
-10.9767
-10.9767
MLE
MLE
MLE
MLE
MLE
MLE
MLE
MLE
The data set pred created by the OUTPUT statement is displayed in Output 58.1.8. It contains all the variables
in the input data set, the variable phat for the (cumulative) predicted probability, the variables lcl and ucl
for the lower and upper confidence limits for the probability, and four other variables (IP_1, IP_0, XP_1,
and XP_0) for the PREDPROBS= option. The data set also contains the variable _LEVEL_, indicating
the response value to which phat, lcl, and ucl refer. For instance, for the first row of the OUTPUT data
set, the values of _LEVEL_ and phat, lcl, and ucl are 1, 0.72265, 0.16892, and 0.97093, respectively; this
means that the estimated probability that remiss=1 is 0.723 for the given explanatory variable values, and
the corresponding 95% confidence interval is (0.16892, 0.97093). The variables IP_1 and IP_0 contain the
predicted probabilities that remiss=1 and remiss=0, respectively. Note that values of phat and IP_1 are
identical since they both contain the probabilities that remiss=1. The variables XP_1 and XP_0 contain the
4628 F Chapter 58: The LOGISTIC Procedure
cross validated predicted probabilities that remiss=1 and remiss=0, respectively.
Output 58.1.8 Predicted Probabilities and Confidence Intervals
Stepwise Regression on Cancer Remission Data
Predicted Probabilities and 95% Confidence Limits
Obs
1
2
3
4
5
6
7
8
9
10
11
12
Obs
1
2
3
4
5
6
7
8
9
10
11
12
remiss
1
1
0
0
1
0
1
0
0
0
0
0
cell
smear
infil
li
blast
temp
_FROM_
_INTO_
IP_0
0.80
0.90
0.80
1.00
0.90
1.00
0.95
0.95
1.00
0.95
0.85
0.70
0.83
0.36
0.88
0.87
0.75
0.65
0.97
0.87
0.45
0.36
0.39
0.76
0.66
0.32
0.70
0.87
0.68
0.65
0.92
0.83
0.45
0.34
0.33
0.53
1.9
1.4
0.8
0.7
1.3
0.6
1.0
1.9
0.8
0.5
0.7
1.2
1.100
0.740
0.176
1.053
0.519
0.519
1.230
1.354
0.322
0.000
0.279
0.146
0.996
0.992
0.982
0.986
0.980
0.982
0.992
1.020
0.999
1.038
0.988
0.982
1
1
0
0
1
0
1
0
0
0
0
0
1
1
0
0
1
0
0
1
0
0
0
0
0.27735
0.42126
0.89540
0.71742
0.28582
0.72911
0.67844
0.39277
0.83368
0.99843
0.92715
0.82714
IP_1
XP_0
XP_1
_LEVEL_
phat
lcl
ucl
0.72265
0.57874
0.10460
0.28258
0.71418
0.27089
0.32156
0.60723
0.16632
0.00157
0.07285
0.17286
0.43873
0.47461
0.87060
0.67259
0.36901
0.67269
0.72923
0.09906
0.80864
0.99840
0.91723
0.63838
0.56127
0.52539
0.12940
0.32741
0.63099
0.32731
0.27077
0.90094
0.19136
0.00160
0.08277
0.36162
1
1
1
1
1
1
1
1
1
1
1
1
0.72265
0.57874
0.10460
0.28258
0.71418
0.27089
0.32156
0.60723
0.16632
0.00157
0.07285
0.17286
0.16892
0.26788
0.00781
0.07498
0.25218
0.05852
0.13255
0.10572
0.03018
0.00000
0.00614
0.00637
0.97093
0.83762
0.63419
0.65683
0.94876
0.68951
0.59516
0.95287
0.56123
0.68962
0.49982
0.87206
Example 58.1: Stepwise Logistic Regression and Predicted Values F 4629
Output 58.1.8 continued
Stepwise Regression on Cancer Remission Data
Predicted Probabilities and 95% Confidence Limits
Obs
13
14
15
16
17
18
19
20
21
22
23
24
Obs
13
14
15
16
17
18
19
20
21
22
23
24
remiss
0
0
0
1
0
0
0
1
0
0
1
0
cell
smear
infil
li
blast
temp
_FROM_
_INTO_
IP_0
0.80
0.20
1.00
1.00
0.65
1.00
0.50
1.00
1.00
0.90
1.00
0.95
0.46
0.39
0.90
0.84
0.42
0.75
0.44
0.63
0.33
0.93
0.58
0.32
0.37
0.08
0.90
0.84
0.27
0.75
0.22
0.63
0.33
0.84
0.58
0.30
0.4
0.8
1.1
1.9
0.5
1.0
0.6
1.1
0.4
0.6
1.0
1.6
0.380
0.114
1.037
2.064
0.114
1.322
0.114
1.072
0.176
1.591
0.531
0.886
1.006
0.990
0.990
1.020
1.014
1.004
0.990
0.986
1.010
1.020
1.002
0.988
0
0
0
1
0
0
0
1
0
0
1
0
0
0
1
1
0
0
0
1
0
0
0
1
0.99654
0.99982
0.42878
0.28530
0.99938
0.77711
0.99846
0.35089
0.98307
0.99378
0.74739
0.12989
IP_1
XP_0
XP_1
_LEVEL_
phat
lcl
ucl
0.00346
0.00018
0.57122
0.71470
0.00062
0.22289
0.00154
0.64911
0.01693
0.00622
0.25261
0.87011
0.99644
0.99981
0.35354
0.47213
0.99937
0.73612
0.99842
0.42053
0.98170
0.99348
0.84423
0.03637
0.00356
0.00019
0.64646
0.52787
0.00063
0.26388
0.00158
0.57947
0.01830
0.00652
0.15577
0.96363
1
1
1
1
1
1
1
1
1
1
1
1
0.00346
0.00018
0.57122
0.71470
0.00062
0.22289
0.00154
0.64911
0.01693
0.00622
0.25261
0.87011
0.00001
0.00000
0.25303
0.15362
0.00000
0.04483
0.00000
0.26305
0.00029
0.00003
0.06137
0.40910
0.46530
0.96482
0.83973
0.97189
0.62665
0.63670
0.79644
0.90555
0.50475
0.56062
0.63597
0.98481
Stepwise Regression on Cancer Remission Data
Predicted Probabilities and 95% Confidence Limits
Obs
25
26
27
Obs
25
26
27
remiss
1
1
0
cell
smear
infil
li
blast
temp
_FROM_
_INTO_
IP_0
1.00
1.00
1.00
0.60
0.69
0.73
0.60
0.69
0.73
1.7
0.9
0.7
0.964
0.398
0.398
0.990
0.986
0.986
1
1
0
1
0
0
0.06868
0.53949
0.71742
IP_1
XP_0
XP_1
_LEVEL_
phat
lcl
ucl
0.93132
0.46051
0.28258
0.08017
0.62312
0.67259
0.91983
0.37688
0.32741
1
1
1
0.93132
0.46051
0.28258
0.44114
0.16612
0.07498
0.99573
0.78529
0.65683
Next, a different variable selection method is used to select prognostic factors for cancer remission, and an
efficient algorithm is employed to eliminate insignificant variables from a model. The following statements
invoke PROC LOGISTIC to perform the backward elimination analysis:
4630 F Chapter 58: The LOGISTIC Procedure
title 'Backward Elimination on Cancer Remission Data';
proc logistic data=Remission;
model remiss(event='1')=temp cell li smear blast
/ selection=backward fast slstay=0.2 ctable;
run;
The backward elimination analysis (SELECTION=BACKWARD) starts with a model that contains all
explanatory variables given in the MODEL statement. By specifying the FAST option, PROC LOGISTIC
eliminates insignificant variables without refitting the model repeatedly. This analysis uses a significance level
of 0.2 to retain variables in the model (SLSTAY=0.2), which is different from the previous stepwise analysis
where SLSTAY=.35. The CTABLE option is specified to produce classifications of input observations based
on the final selected model.
Results of the fast elimination analysis are shown in Output 58.1.9 and Output 58.1.10. Initially, a full model
containing all six risk factors is fit to the data (Output 58.1.9). In the next step (Output 58.1.10), PROC
LOGISTIC removes blast, smear, cell, and temp from the model all at once. This leaves li and the intercept
as the only variables in the final model. Note that in this analysis, only parameter estimates for the final
model are displayed because the DETAILS option has not been specified.
Output 58.1.9 Initial Step in Backward Elimination
Backward Elimination on Cancer Remission Data
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.REMISSION
remiss
2
binary logit
Fisher's scoring
Complete Remission
Number of Observations Read
Number of Observations Used
27
27
Response Profile
Ordered
Value
remiss
Total
Frequency
1
2
0
1
18
9
Probability modeled is remiss=1.
Example 58.1: Stepwise Logistic Regression and Predicted Values F 4631
Output 58.1.9 continued
Backward Elimination Procedure
Step
0. The following effects were entered:
Intercept
temp
cell
li
smear
blast
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
36.372
37.668
34.372
33.857
41.632
21.857
AIC
SC
-2 Log L
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
12.5146
9.3295
4.7284
5
5
5
0.0284
0.0966
0.4499
Likelihood Ratio
Score
Wald
Output 58.1.10 Fast Elimination Step
Step
1. Fast Backward Elimination:
Analysis of Effects Removed by Fast Backward Elimination
Effect
Removed
blast
smear
cell
temp
Chi-Square
DF
Pr > ChiSq
Residual
Chi-Square
0.0008
0.0951
1.5134
0.6535
1
1
1
1
0.9768
0.7578
0.2186
0.4189
0.0008
0.0959
1.6094
2.2628
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
DF
Pr >
Residual
ChiSq
1
2
3
4
0.9768
0.9532
0.6573
0.6875
4632 F Chapter 58: The LOGISTIC Procedure
Output 58.1.10 continued
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
36.372
37.668
34.372
30.073
32.665
26.073
AIC
SC
-2 Log L
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
8.2988
7.9311
5.9594
1
1
1
0.0040
0.0049
0.0146
Likelihood Ratio
Score
Wald
Residual Chi-Square Test
Chi-Square
DF
Pr > ChiSq
2.8530
4
0.5827
Summary of Backward Elimination
Step
1
1
1
1
Effect
Removed
blast
smear
cell
temp
DF
Number
In
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
4
3
2
1
0.0008
0.0951
1.5134
0.6535
0.9768
0.7578
0.2186
0.4189
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
li
1
1
-3.7771
2.8973
1.3786
1.1868
7.5064
5.9594
0.0061
0.0146
Odds Ratio Estimates
Effect
li
Point
Estimate
18.124
95% Wald
Confidence Limits
1.770
185.563
Example 58.1: Stepwise Logistic Regression and Predicted Values F 4633
Output 58.1.10 continued
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
84.0
13.0
3.1
162
Somers' D
Gamma
Tau-a
c
0.710
0.732
0.328
0.855
Note that you can also use the FAST option when SELECTION=STEPWISE. However, the FAST option
operates only on backward elimination steps. In this example, the stepwise process only adds variables, so
the FAST option would not be useful.
Results of the CTABLE option are shown in Output 58.1.11.
4634 F Chapter 58: The LOGISTIC Procedure
Output 58.1.11 Classifying Input Observations
Classification Table
Prob
Level
0.060
0.080
0.100
0.120
0.140
0.160
0.180
0.200
0.220
0.240
0.260
0.280
0.300
0.320
0.340
0.360
0.380
0.400
0.420
0.440
0.460
0.480
0.500
0.520
0.540
0.560
0.580
0.600
0.620
0.640
0.660
0.680
0.700
0.720
0.740
0.760
0.780
0.800
0.820
0.840
0.860
0.880
0.900
0.920
0.940
0.960
Correct
NonEvent Event
9
9
9
9
9
9
9
8
8
8
6
6
6
6
5
5
5
5
5
5
4
4
4
4
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
0
0
0
0
0
0
0
0
2
4
4
7
10
10
13
13
13
13
13
13
14
14
14
15
15
15
15
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
17
17
17
17
17
17
17
17
18
Incorrect
NonEvent Event
18
16
14
14
11
8
8
5
5
5
5
5
5
4
4
4
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
1
1
1
3
3
3
3
4
4
4
4
4
4
5
5
5
5
6
6
6
6
6
6
6
6
6
7
7
7
7
7
7
9
9
9
9
9
9
9
Correct
33.3
40.7
48.1
48.1
59.3
70.4
70.4
77.8
77.8
77.8
70.4
70.4
70.4
74.1
70.4
70.4
74.1
74.1
74.1
74.1
74.1
74.1
74.1
74.1
70.4
70.4
70.4
70.4
70.4
70.4
70.4
70.4
70.4
66.7
66.7
66.7
66.7
70.4
70.4
63.0
63.0
63.0
63.0
63.0
63.0
66.7
Percentages
Sensi- Speci- False
tivity ficity
POS
100.0
100.0
100.0
100.0
100.0
100.0
100.0
88.9
88.9
88.9
66.7
66.7
66.7
66.7
55.6
55.6
55.6
55.6
55.6
55.6
44.4
44.4
44.4
44.4
33.3
33.3
33.3
33.3
33.3
33.3
33.3
33.3
33.3
22.2
22.2
22.2
22.2
22.2
22.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
11.1
22.2
22.2
38.9
55.6
55.6
72.2
72.2
72.2
72.2
72.2
72.2
77.8
77.8
77.8
83.3
83.3
83.3
83.3
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
88.9
94.4
94.4
94.4
94.4
94.4
94.4
94.4
94.4
100.0
66.7
64.0
60.9
60.9
55.0
47.1
47.1
38.5
38.5
38.5
45.5
45.5
45.5
40.0
44.4
44.4
37.5
37.5
37.5
37.5
33.3
33.3
33.3
33.3
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
40.0
50.0
50.0
50.0
50.0
33.3
33.3
100.0
100.0
100.0
100.0
100.0
100.0
.
False
NEG
.
0.0
0.0
0.0
0.0
0.0
0.0
7.1
7.1
7.1
18.8
18.8
18.8
17.6
22.2
22.2
21.1
21.1
21.1
21.1
23.8
23.8
23.8
23.8
27.3
27.3
27.3
27.3
27.3
27.3
27.3
27.3
27.3
30.4
30.4
30.4
30.4
29.2
29.2
34.6
34.6
34.6
34.6
34.6
34.6
33.3
Example 58.2: Logistic Modeling with Categorical Predictors F 4635
Each row of the “Classification Table” corresponds to a cutpoint applied to the predicted probabilities, which
is given in the Prob Level column. The 2 2 frequency tables of observed and predicted responses are given
by the next four columns. For example, with a cutpoint of 0.5, 4 events and 16 nonevents were classified
correctly. On the other hand, 2 nonevents were incorrectly classified as events and 5 events were incorrectly
classified as nonevents. For this cutpoint, the correct classification rate is 20/27 (=74.1%), which is given
in the sixth column. Accuracy of the classification is summarized by the sensitivity, specificity, and false
positive and negative rates, which are displayed in the last four columns. You can control the number of
cutpoints used, and their values, by using the PPROB= option.
Example 58.2: Logistic Modeling with Categorical Predictors
Consider a study of the analgesic effects of treatments on elderly patients with neuralgia. Two test treatments
and a placebo are compared. The response variable is whether the patient reported pain or not. Researchers
recorded the age and gender of 60 patients and the duration of complaint before the treatment began. The
following DATA step creates the data set Neuralgia:
data Neuralgia;
input Treatment
datalines;
P F 68
1 No
P M 66 26 Yes
A F 71 12 No
A M 71 17 Yes
B F 66 12 No
A F 64 17 No
P M 70
1 Yes
A F 64 30 No
B F 78
1 No
B M 75 30 Yes
A M 70 12 No
B M 70
1 No
P M 78 12 Yes
P M 66
4 Yes
A M 78 15 Yes
P F 72 27 No
B F 65
7 No
P M 67 17 Yes
P F 67
1 Yes
A F 74
1 No
;
$ Sex $ Age Duration Pain $ @@;
B
B
B
A
A
P
B
A
P
P
A
B
B
P
B
P
P
B
A
B
M
F
F
F
M
M
M
M
M
M
F
M
M
F
M
F
F
M
M
M
74
67
72
63
62
74
66
70
83
77
69
67
77
65
75
70
68
70
67
80
16
28
50
27
42
4
19
28
1
29
12
23
1
29
21
13
27
22
10
21
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
No
No
Yes
P
B
B
A
P
A
B
A
B
P
B
A
B
P
A
A
P
A
P
A
F
F
F
F
F
F
M
M
F
F
F
M
F
M
F
M
M
M
F
F
67
77
76
69
64
72
59
69
69
79
65
76
69
60
67
75
68
65
72
69
30
16
9
18
1
25
29
1
42
20
14
25
24
26
11
6
11
15
11
3
No
No
Yes
Yes
Yes
No
No
No
No
Yes
No
Yes
No
Yes
No
Yes
Yes
No
Yes
No
The data set Neuralgia contains five variables: Treatment, Sex, Age, Duration, and Pain. The last variable,
Pain, is the response variable. A specification of Pain=Yes indicates there was pain, and Pain=No indicates
no pain. The variable Treatment is a categorical variable with three levels: A and B represent the two test
treatments, and P represents the placebo treatment. The gender of the patients is given by the categorical
variable Sex. The variable Age is the age of the patients, in years, when treatment began. The duration of
complaint, in months, before the treatment began is given by the variable Duration.
The following statements use the LOGISTIC procedure to fit a two-way logit with interaction model for the
effect of Treatment and Sex, with Age and Duration as covariates. The categorical variables Treatment and
Sex are declared in the CLASS statement.
4636 F Chapter 58: The LOGISTIC Procedure
proc logistic data=Neuralgia;
class Treatment Sex;
model Pain= Treatment Sex Treatment*Sex Age Duration / expb;
run;
In this analysis, PROC LOGISTIC models the probability of no pain (Pain=No). By default, effect coding is
used to represent the CLASS variables. Two design variables are created for Treatment and one for Sex, as
shown in Output 58.2.1.
Output 58.2.1 Effect Coding of CLASS Variables
The LOGISTIC Procedure
Class Level Information
Class
Value
Design
Variables
Treatment
A
B
P
1
0
-1
Sex
F
M
1
-1
0
1
-1
PROC LOGISTIC displays a table of the Type 3 analysis of effects based on the Wald test (Output 58.2.2).
Note that the Treatment*Sex interaction and the duration of complaint are not statistically significant (p =
0.9318 and p = 0.8752, respectively). This indicates that there is no evidence that the treatments affect pain
differently in men and women, and no evidence that the pain outcome is related to the duration of pain.
Output 58.2.2 Wald Tests of Individual Effects
Type 3 Analysis of Effects
Effect
Treatment
Sex
Treatment*Sex
Age
Duration
DF
Wald
Chi-Square
Pr > ChiSq
2
1
2
1
1
11.9886
5.3104
0.1412
7.2744
0.0247
0.0025
0.0212
0.9318
0.0070
0.8752
Parameter estimates are displayed in Output 58.2.3. The Exp(Est) column contains the exponentiated
parameter estimates requested with the EXPB option. These values can, but do not necessarily, represent odds
ratios for the corresponding variables. For continuous explanatory variables, the Exp(Est) value corresponds
to the odds ratio for a unit increase of the corresponding variable. For CLASS variables that use effect coding,
the Exp(Est) values have no direct interpretation as a comparison of levels. However, when the reference
coding is used, the Exp(Est) values represent the odds ratio between the corresponding level and the reference
Example 58.2: Logistic Modeling with Categorical Predictors F 4637
level. Following the parameter estimates table, PROC LOGISTIC displays the odds ratio estimates for those
variables that are not involved in any interaction terms. If the variable is a CLASS variable, the odds ratio
estimate comparing each level with the reference level is computed regardless of the coding scheme. In this
analysis, since the model contains the Treatment*Sex interaction term, the odds ratios for Treatment and
Sex were not computed. The odds ratio estimates for Age and Duration are precisely the values given in the
Exp(Est) column in the parameter estimates table.
Output 58.2.3 Parameter Estimates with Effect Coding
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Treatment
Treatment
Sex
Treatment*Sex
Treatment*Sex
Age
Duration
A
B
F
A F
B F
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Exp(Est)
1
1
1
1
1
1
1
1
19.2236
0.8483
1.4949
0.9173
-0.2010
0.0487
-0.2688
0.00523
7.1315
0.5502
0.6622
0.3981
0.5568
0.5563
0.0996
0.0333
7.2661
2.3773
5.0956
5.3104
0.1304
0.0077
7.2744
0.0247
0.0070
0.1231
0.0240
0.0212
0.7180
0.9302
0.0070
0.8752
2.232E8
2.336
4.459
2.503
0.818
1.050
0.764
1.005
Odds Ratio Estimates
Effect
Age
Duration
Point
Estimate
0.764
1.005
95% Wald
Confidence Limits
0.629
0.942
0.929
1.073
The following PROC LOGISTIC statements illustrate the use of forward selection on the data set Neuralgia
to identify the effects that differentiate the two Pain responses. The option SELECTION=FORWARD is
specified to carry out the forward selection. The term Treatment|[email protected] illustrates another way to specify
main effects and two-way interactions. (Note that, in this case, the “@2” is unnecessary because no
interactions besides the two-way interaction are possible).
proc logistic data=Neuralgia;
class Treatment Sex;
model Pain=Treatment|[email protected] Age Duration
/selection=forward expb;
run;
Results of the forward selection process are summarized in Output 58.2.4. The variable Treatment is selected
first, followed by Age and then Sex. The results are consistent with the previous analysis (Output 58.2.2) in
which the Treatment*Sex interaction and Duration are not statistically significant.
4638 F Chapter 58: The LOGISTIC Procedure
Output 58.2.4 Effects Selected into the Model
The LOGISTIC Procedure
Summary of Forward Selection
Step
1
2
3
Effect
Entered
DF
Number
In
Score
Chi-Square
Pr > ChiSq
2
1
1
1
2
3
13.7143
10.6038
5.9959
0.0011
0.0011
0.0143
Treatment
Age
Sex
Output 58.2.5 shows the Type 3 analysis of effects, the parameter estimates, and the odds ratio estimates
for the selected model. All three variables, Treatment, Age, and Sex, are statistically significant at the 0.05
level (p=0.0018, p=0.0213, and p=0.0057, respectively). Since the selected model does not contain the
Treatment*Sex interaction, odds ratios for Treatment and Sex are computed. The estimated odds ratio is
24.022 for treatment A versus placebo, 41.528 for Treatment B versus placebo, and 6.194 for female patients
versus male patients. Note that these odds ratio estimates are not the same as the corresponding values in the
Exp(Est) column in the parameter estimates table because effect coding was used. From Output 58.2.5, it is
evident that both Treatment A and Treatment B are better than the placebo in reducing pain; females tend to
have better improvement than males; and younger patients are faring better than older patients.
Output 58.2.5 Type 3 Effects and Parameter Estimates with Effect Coding
Type 3 Analysis of Effects
Effect
Treatment
Sex
Age
DF
Wald
Chi-Square
Pr > ChiSq
2
1
1
12.6928
5.3013
7.6314
0.0018
0.0213
0.0057
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Treatment A
Treatment B
Sex
F
Age
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Exp(Est)
1
1
1
1
1
19.0804
0.8772
1.4246
0.9118
-0.2650
6.7882
0.5274
0.6036
0.3960
0.0959
7.9007
2.7662
5.5711
5.3013
7.6314
0.0049
0.0963
0.0183
0.0213
0.0057
1.9343E8
2.404
4.156
2.489
0.767
Example 58.2: Logistic Modeling with Categorical Predictors F 4639
Output 58.2.5 continued
Odds Ratio Estimates
Effect
Treatment A vs P
Treatment B vs P
Sex
F vs M
Age
Point
Estimate
24.022
41.528
6.194
0.767
95% Wald
Confidence Limits
3.295
4.500
1.312
0.636
175.121
383.262
29.248
0.926
Finally, the following statements refit the previously selected model, except that reference coding is used for
the CLASS variables instead of effect coding:
ods graphics on;
proc logistic data=Neuralgia plots(only)=(oddsratio(range=clip));
class Treatment Sex /param=ref;
model Pain= Treatment Sex Age / noor;
oddsratio Treatment;
oddsratio Sex;
oddsratio Age;
contrast 'Pairwise A vs P' Treatment 1 0 / estimate=exp;
contrast 'Pairwise B vs P' Treatment 0 1 / estimate=exp;
contrast 'Pairwise A vs B' Treatment 1 -1 / estimate=exp;
contrast 'Female vs Male' Sex 1 / estimate=exp;
effectplot / at(Sex=all) noobs;
effectplot slicefit(sliceby=Sex plotby=Treatment) / noobs;
run;
ods graphics off;
The ODDSRATIO statements compute the odds ratios for the covariates, and the NOOR option suppresses
the default odds ratio table. Four CONTRAST statements are specified; they provide another method of
producing the odds ratios. The three contrasts labeled ‘Pairwise’ specify a contrast vector, L, for each of
the pairwise comparisons between the three levels of Treatment. The contrast labeled ‘Female vs Male’
compares female to male patients. The option ESTIMATE=EXP is specified in all CONTRAST statements
to exponentiate the estimates of L0 ˇ. With the given specification of contrast coefficients, the first of the
‘Pairwise’ CONTRAST statements corresponds to the odds ratio of A versus P, the second corresponds to B
versus P, and the third corresponds to A versus B. You can also specify the ‘Pairwise’ contrasts in a single
contrast statement with three rows. The ‘Female vs Male’ CONTRAST statement corresponds to the odds
ratio that compares female to male patients.
The PLOTS(ONLY)= option displays only the requested odds ratio plot when ODS Graphics is enabled.
The EFFECTPLOT statements do not honor the ONLY option, and display the fitted model. The first
EFFECTPLOT statement by default produces a plot of the predicted values against the continuous Age
variable, grouped by the Treatment levels. The AT option produces one plot for males and another for females;
the NOOBS option suppresses the display of the observations. In the second EFFECTPLOT statement, a
SLICEFIT plot is specified to display the Age variable on the X axis, the fits are grouped by the Sex levels,
and the PLOTBY= option produces a panel of plots that displays each level of the Treatment variable.
The reference coding is shown in Output 58.2.6. The Type 3 analysis of effects and the parameter estimates for
the reference coding are displayed in Output 58.2.7. Although the parameter estimates are different because
4640 F Chapter 58: The LOGISTIC Procedure
of the different parameterizations, the “Type 3 Analysis of Effects” table remains the same as in Output 58.2.5.
With effect coding, the treatment A parameter estimate (0.8772) estimates the effect of treatment A compared
to the average effect of treatments A, B, and placebo. The treatment A estimate (3.1790) under the reference
coding estimates the difference in effect of treatment A and the placebo treatment.
Output 58.2.6 Reference Coding of CLASS Variables
The LOGISTIC Procedure
Class Level Information
Design
Variables
Class
Value
Treatment
A
B
P
1
0
0
Sex
F
M
1
0
0
1
0
Output 58.2.7 Type 3 Effects and Parameter Estimates with Reference Coding
Type 3 Analysis of Effects
Effect
Treatment
Sex
Age
DF
Wald
Chi-Square
Pr > ChiSq
2
1
1
12.6928
5.3013
7.6314
0.0018
0.0213
0.0057
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Treatment A
Treatment B
Sex
F
Age
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
15.8669
3.1790
3.7264
1.8235
-0.2650
6.4056
1.0135
1.1339
0.7920
0.0959
6.1357
9.8375
10.8006
5.3013
7.6314
0.0132
0.0017
0.0010
0.0213
0.0057
The ODDSRATIO statement results are shown in Output 58.2.8, and the resulting plot is displayed in
Output 58.2.9. Note in Output 58.2.9 that the odds ratio confidence limits are truncated due to specifying the
RANGE=CLIP option; this enables you to see which intervals contain “1” more clearly. The odds ratios are
identical to those shown in the “Odds Ratio Estimates” table in Output 58.2.5 with the addition of the odds
ratio for “Treatment A vs B”. Both treatments A and B are highly effective over placebo in reducing pain, as
can be seen from the odds ratios comparing treatment A against P and treatment B against P (the second
and third rows in the table). However, the 95% confidence interval for the odds ratio comparing treatment A
Example 58.2: Logistic Modeling with Categorical Predictors F 4641
to B is (0.0932, 3.5889), indicating that the pain reduction effects of these two test treatments are not very
different. Again, the ’Sex F vs M’ odds ratio shows that female patients fared better in obtaining relief from
pain than male patients. The odds ratio for Age shows that a patient one year older is 0.77 times as likely to
show no pain; that is, younger patients have more improvement than older patients.
Output 58.2.8 Results from the ODDSRATIO Statements
Odds Ratio Estimates and Wald Confidence Intervals
Odds Ratio
Treatment A vs B
Treatment A vs P
Treatment B vs P
Sex F vs M
Age
Estimate
0.578
24.022
41.528
6.194
0.767
95% Confidence Limits
0.093
3.295
4.500
1.312
0.636
3.589
175.121
383.262
29.248
0.926
Output 58.2.9 Plot of the ODDSRATIO Statement Results
4642 F Chapter 58: The LOGISTIC Procedure
Output 58.2.10 contains two tables: the “Contrast Test Results” table and the “Contrast Estimation and
Testing Results by Row” table. The former contains the overall Wald test for each CONTRAST statement.
The latter table contains estimates and tests of individual contrast rows. The estimates for the first two rows
of the ’Pairwise’ CONTRAST statements are the same as those given in the two preceding odds ratio tables
(Output 58.2.7 and Output 58.2.8). The third row estimates the odds ratio comparing A to B, agreeing with
Output 58.2.8, and the last row computes the odds ratio comparing pain relief for females to that for males.
Output 58.2.10 Results of CONTRAST Statements
Contrast Test Results
Contrast
Pairwise A vs P
Pairwise B vs P
Pairwise A vs B
Female vs Male
DF
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
9.8375
10.8006
0.3455
5.3013
0.0017
0.0010
0.5567
0.0213
Contrast Estimation and Testing Results by Row
Contrast
Type
Pairwise A vs P
Pairwise B vs P
Pairwise A vs B
Female vs Male
EXP
EXP
EXP
EXP
Row
Estimate
Standard
Error
Alpha
1
1
1
1
24.0218
41.5284
0.5784
6.1937
24.3473
47.0877
0.5387
4.9053
0.05
0.05
0.05
0.05
Confidence Limits
3.2951
4.4998
0.0932
1.3116
175.1
383.3
3.5889
29.2476
Contrast Estimation and Testing Results by Row
Contrast
Type
Pairwise A vs P
Pairwise B vs P
Pairwise A vs B
Female vs Male
EXP
EXP
EXP
EXP
Row
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
9.8375
10.8006
0.3455
5.3013
0.0017
0.0010
0.5567
0.0213
ANCOVA-style plots of the model-predicted probabilities against the Age variable for each combination of
Treatment and Sex are displayed in Output 58.2.11 and Output 58.2.12. These plots confirm that females
always have a higher probability of pain reduction in each treatment group, the placebo treatment has a lower
probability of success than the other treatments, and younger patients respond to treatment better than older
patients.
Example 58.2: Logistic Modeling with Categorical Predictors F 4643
Output 58.2.11 Model-Predicted Probabilities by Sex
4644 F Chapter 58: The LOGISTIC Procedure
Output 58.2.12 Model-Predicted Probabilities by Treatment
Example 58.3: Ordinal Logistic Regression
Consider a study of the effects on taste of various cheese additives. Researchers tested four cheese additives
and obtained 52 response ratings for each additive. Each response was measured on a scale of nine categories
ranging from strong dislike (1) to excellent taste (9). The data, given in McCullagh and Nelder (1989, p. 175)
in the form of a two-way frequency table of additive by rating, are saved in the data set Cheese by using the
following program. The variable y contains the response rating. The variable Additive specifies the cheese
additive (1, 2, 3, or 4). The variable freq gives the frequency with which each additive received each rating.
data Cheese;
do Additive = 1 to 4;
do y = 1 to 9;
input freq @@;
output;
end;
end;
label y='Taste Rating';
datalines;
Example 58.3: Ordinal Logistic Regression F 4645
0
6
1
0
;
0 1 7 8
9 12 11 7
1 6 8 23
0 0 1 3
8 19 8 1
6 1 0 0
7 5 1 0
7 14 16 11
The response variable y is ordinally scaled. A cumulative logit model is used to investigate the effects of
the cheese additives on taste. The following statements invoke PROC LOGISTIC to fit this model with
y as the response variable and three indicator variables as explanatory variables, with the fourth additive
as the reference level. With this parameterization, each Additive parameter compares an additive to the
fourth additive. The COVB option displays the estimated covariance matrix, and the NOODDSRATIO
option suppresses the default odds ratio table. The ODDSRATIO statement computes odds ratios for all
combinations of the Additive levels. The PLOTS option produces a graphical display of the odds ratios, and
the EFFECTPLOT statement displays the predicted probabilities.
ods graphics on;
proc logistic data=Cheese plots(only)=oddsratio(range=clip);
freq freq;
class Additive (param=ref ref='4');
model y=Additive / covb nooddsratio;
oddsratio Additive;
effectplot / polybar;
title 'Multiple Response Cheese Tasting Experiment';
run;
ods graphics off;
The “Response Profile” table in Output 58.3.1 shows that the strong dislike (y=1) end of the rating scale is
associated with lower Ordered Values in the “Response Profile” table; hence the probability of disliking the
additives is modeled.
The score chi-square for testing the proportional odds assumption is 17.287, which is not significant with
respect to a chi-square distribution with 21 degrees of freedom .p D 0:694/. This indicates that the
proportional odds assumption is reasonable. The positive value (1.6128) for the parameter estimate for
Additive1 indicates a tendency toward the lower-numbered categories of the first cheese additive relative
to the fourth. In other words, the fourth additive tastes better than the first additive. The second and third
additives are both less favorable than the fourth additive. The relative magnitudes of these slope estimates
imply the preference ordering: fourth, first, third, second.
Output 58.3.1 Proportional Odds Model Regression Analysis
Multiple Response Cheese Tasting Experiment
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Frequency Variable
Model
Optimization Technique
WORK.CHEESE
y
9
freq
cumulative logit
Fisher's scoring
Taste Rating
4646 F Chapter 58: The LOGISTIC Procedure
Output 58.3.1 continued
Number
Number
Sum of
Sum of
of Observations Read
of Observations Used
Frequencies Read
Frequencies Used
36
28
208
208
Response Profile
Ordered
Value
y
Total
Frequency
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
9
7
10
19
27
41
28
39
25
12
Probabilities modeled are cumulated over the lower Ordered Values.
NOTE: 8 observations having nonpositive frequencies or weights were excluded
since they do not contribute to the analysis.
Class Level Information
Class
Value
Additive
1
2
3
4
Design Variables
1
0
0
0
0
1
0
0
0
0
1
0
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Score Test for the Proportional Odds Assumption
Chi-Square
DF
Pr > ChiSq
17.2866
21
0.6936
Example 58.3: Ordinal Logistic Regression F 4647
Output 58.3.1 continued
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
875.802
902.502
859.802
733.348
770.061
711.348
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
148.4539
111.2670
115.1504
3
3
3
<.0001
<.0001
<.0001
Likelihood Ratio
Score
Wald
Type 3 Analysis of Effects
Effect
Additive
DF
Wald
Chi-Square
Pr > ChiSq
3
115.1504
<.0001
Analysis of Maximum Likelihood Estimates
Parameter
Intercept
Intercept
Intercept
Intercept
Intercept
Intercept
Intercept
Intercept
Additive
Additive
Additive
1
2
3
4
5
6
7
8
1
2
3
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
1
1
1
1
1
1
-7.0801
-6.0249
-4.9254
-3.8568
-2.5205
-1.5685
-0.0669
1.4930
1.6128
4.9645
3.3227
0.5624
0.4755
0.4272
0.3902
0.3431
0.3086
0.2658
0.3310
0.3778
0.4741
0.4251
158.4851
160.5500
132.9484
97.7087
53.9704
25.8374
0.0633
20.3439
18.2265
109.6427
61.0931
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.8013
<.0001
<.0001
<.0001
<.0001
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
67.6
9.8
22.6
18635
Somers' D
Gamma
Tau-a
c
0.578
0.746
0.500
0.789
The odds ratio results in Output 58.3.2 show the preferences more clearly. For example, the “Additive 1 vs
4” odds ratio says that the first additive has 5.017 times the odds of receiving a lower score than the fourth
4648 F Chapter 58: The LOGISTIC Procedure
additive; that is, the first additive is 5.017 times more likely than the fourth additive to receive a lower score.
Output 58.3.3 displays the odds ratios graphically; the range of the confidence limits is truncated by the
RANGE=CLIP option, so you can see that “1” is not contained in any of the intervals.
Output 58.3.2 Odds Ratios of All Pairs of Additive Levels
Odds Ratio Estimates and Wald Confidence Intervals
Odds Ratio
Additive
Additive
Additive
Additive
Additive
Additive
1
1
1
2
2
3
Estimate
vs
vs
vs
vs
vs
vs
2
3
4
3
4
4
0.035
0.181
5.017
5.165
143.241
27.734
95% Confidence Limits
0.015
0.087
2.393
2.482
56.558
12.055
0.080
0.376
10.520
10.746
362.777
63.805
Output 58.3.3 Plot of Odds Ratios for Additive
The estimated covariance matrix of the parameters is displayed in Output 58.3.4.
Example 58.3: Ordinal Logistic Regression F 4649
Output 58.3.4 Estimated Covariance Matrix
Estimated Covariance Matrix
Parameter
Intercept_1
Intercept_2
Intercept_3
Intercept_4
Intercept_5
Intercept_6
Intercept_7
Intercept_8
Additive1
Additive2
Additive3
Intercept_
1
Intercept_
2
Intercept_
3
Intercept_
4
Intercept_
5
0.316291
0.219581
0.176278
0.147694
0.114024
0.091085
0.057814
0.041304
-0.09419
-0.18686
-0.13565
0.219581
0.226095
0.177806
0.147933
0.11403
0.091081
0.057813
0.041304
-0.09421
-0.18161
-0.13569
0.176278
0.177806
0.182473
0.148844
0.114092
0.091074
0.057807
0.0413
-0.09427
-0.1687
-0.1352
0.147694
0.147933
0.148844
0.152235
0.114512
0.091109
0.05778
0.041277
-0.09428
-0.14717
-0.13118
0.114024
0.11403
0.114092
0.114512
0.117713
0.091821
0.057721
0.041162
-0.09246
-0.11415
-0.11207
Estimated Covariance Matrix
Parameter
Intercept_1
Intercept_2
Intercept_3
Intercept_4
Intercept_5
Intercept_6
Intercept_7
Intercept_8
Additive1
Additive2
Additive3
Intercept_
6
Intercept_
7
Intercept_
8
Additive1
Additive2
Additive3
0.091085
0.091081
0.091074
0.091109
0.091821
0.09522
0.058312
0.041324
-0.08521
-0.09113
-0.09122
0.057814
0.057813
0.057807
0.05778
0.057721
0.058312
0.07064
0.04878
-0.06041
-0.05781
-0.05802
0.041304
0.041304
0.0413
0.041277
0.041162
0.041324
0.04878
0.109562
-0.04436
-0.0413
-0.04143
-0.09419
-0.09421
-0.09427
-0.09428
-0.09246
-0.08521
-0.06041
-0.04436
0.142715
0.094072
0.092128
-0.18686
-0.18161
-0.1687
-0.14717
-0.11415
-0.09113
-0.05781
-0.0413
0.094072
0.22479
0.132877
-0.13565
-0.13569
-0.1352
-0.13118
-0.11207
-0.09122
-0.05802
-0.04143
0.092128
0.132877
0.180709
Output 58.3.5 displays the probability of each taste rating y within each additive. You can see that Additive=1
mostly receives ratings of 5 to 7, Additive=2 mostly receives ratings of 2 to 5, Additive=3 mostly receives
ratings of 4 to 6, and Additive=4 mostly receives ratings of 7 to 9, which also confirms the previously
discussed preference orderings.
4650 F Chapter 58: The LOGISTIC Procedure
Output 58.3.5 Model-Predicted Probabilities
Example 58.4: Nominal Response Data: Generalized Logits Model
Over the course of one school year, third graders from three different schools are exposed to three different
styles of mathematics instruction: a self-paced computer-learning style, a team approach, and a traditional
class approach. The students are asked which style they prefer and their responses, classified by the type
of program they are in (a regular school day versus a regular day supplemented with an afternoon school
program), are displayed in Table 58.15. The data set is from Stokes, Davis, and Koch (2012), and is also
analyzed in the section “Generalized Logits Model” on page 1936 in Chapter 32, “The CATMOD Procedure.”
Example 58.4: Nominal Response Data: Generalized Logits Model F 4651
Table 58.15
School
Program
1
1
2
2
3
3
Regular
Afternoon
Regular
Afternoon
Regular
Afternoon
School Program Data
Learning Style Preference
Self
Team
Class
10
5
21
16
15
12
17
12
17
12
15
12
26
50
26
36
16
20
The levels of the response variable (self, team, and class) have no essential ordering, so a logistic regression
is performed on the generalized logits. The model to be fit is
hij
log
D ˛j C x0hi ˇj
hi r
where hij is the probability that a student in school h and program i prefers teaching style j, j ¤ r, and style
r is the baseline style (in this case, class). There are separate sets of intercept parameters ˛j and regression
parameters ˇj for each logit, and the vector xhi is the set of explanatory variables for the hith population.
Thus, two logits are modeled for each school and program combination: the logit comparing self to class and
the logit comparing team to class.
The following statements create the data set school and request the analysis. The LINK=GLOGIT option
forms the generalized logits. The response variable option ORDER=DATA means that the response variable
levels are ordered as they exist in the data set: self, team, and class; thus, the logits are formed by comparing
self to class and by comparing team to class. The ODDSRATIO statement produces odds ratios in the
presence of interactions, and a graphical display of the requested odds ratios is produced when ODS Graphics
is enabled.
data school;
length Program $ 9;
input School Program $ Style $ Count @@;
datalines;
1 regular
self 10 1 regular
team 17 1
1 afternoon self 5 1 afternoon team 12 1
2 regular
self 21 2 regular
team 17 2
2 afternoon self 16 2 afternoon team 12 2
3 regular
self 15 3 regular
team 15 3
3 afternoon self 12 3 afternoon team 12 3
;
regular
afternoon
regular
afternoon
regular
afternoon
class
class
class
class
class
class
26
50
26
36
16
20
ods graphics on;
proc logistic data=school;
freq Count;
class School Program(ref=first);
model Style(order=data)=School Program School*Program / link=glogit;
oddsratio program;
run;
ods graphics off;
4652 F Chapter 58: The LOGISTIC Procedure
Summary information about the model, the response variable, and the classification variables are displayed in
Output 58.4.1.
Output 58.4.1 Analysis of Saturated Model
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Frequency Variable
Model
Optimization Technique
Number
Number
Sum of
Sum of
WORK.SCHOOL
Style
3
Count
generalized logit
Newton-Raphson
of Observations Read
of Observations Used
Frequencies Read
Frequencies Used
18
18
338
338
Response Profile
Ordered
Value
Style
Total
Frequency
1
2
3
self
team
class
79
85
174
Logits modeled use Style='class' as the reference category.
Class Level Information
Class
Value
Design
Variables
School
1
2
3
1
0
-1
Program
afternoon
regular
-1
1
0
1
-1
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
The “Testing Global Null Hypothesis: BETA=0” table in Output 58.4.2 shows that the parameters are
significantly different from zero.
Example 58.4: Nominal Response Data: Generalized Logits Model F 4653
Output 58.4.2 Analysis of Saturated Model
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
699.404
707.050
695.404
689.156
735.033
665.156
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
30.2480
28.3738
25.6828
10
10
10
0.0008
0.0016
0.0042
Likelihood Ratio
Score
Wald
However, the “Type 3 Analysis of Effects” table in Output 58.4.3 shows that the interaction effect is clearly
nonsignificant.
Output 58.4.3 Analysis of Saturated Model
Type 3 Analysis of Effects
Effect
School
Program
School*Program
DF
Wald
Chi-Square
Pr > ChiSq
4
2
4
14.5522
10.4815
1.7439
0.0057
0.0053
0.7827
Analysis of Maximum Likelihood Estimates
Parameter
Style
Intercept
Intercept
School
School
School
School
Program
Program
School*Program
School*Program
School*Program
School*Program
self
team
self
team
self
team
self
team
self
team
self
team
1
1
2
2
regular
regular
1
1
2
2
regular
regular
regular
regular
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
1
1
1
1
1
1
1
-0.8097
-0.6585
-0.8194
-0.2675
0.2974
-0.1033
0.3985
0.3537
0.2751
0.1474
-0.0998
-0.0168
0.1488
0.1366
0.2281
0.1881
0.1919
0.1898
0.1488
0.1366
0.2281
0.1881
0.1919
0.1898
29.5989
23.2449
12.9066
2.0233
2.4007
0.2961
7.1684
6.7071
1.4547
0.6143
0.2702
0.0079
<.0001
<.0001
0.0003
0.1549
0.1213
0.5863
0.0074
0.0096
0.2278
0.4332
0.6032
0.9293
4654 F Chapter 58: The LOGISTIC Procedure
The table produced by the ODDSRATIO statement is displayed in Output 58.4.4. The differences between
the program preferences are small across all the styles (logits) compared to their variability as displayed by
the confidence limits in Output 58.4.5, confirming that the interaction effect is nonsignificant.
Output 58.4.4 Odds Ratios for Style
Odds Ratio Estimates and Wald Confidence Intervals
Odds Ratio
Style
Style
Style
Style
Style
Style
self:
team:
self:
team:
self:
team:
Estimate
Program
Program
Program
Program
Program
Program
regular
regular
regular
regular
regular
regular
vs
vs
vs
vs
vs
vs
afternoon
afternoon
afternoon
afternoon
afternoon
afternoon
at
at
at
at
at
at
School=1
School=1
School=2
School=2
School=3
School=3
3.846
2.724
1.817
1.962
1.562
1.562
95% Confidence Limits
1.190
1.132
0.798
0.802
0.572
0.572
12.435
6.554
4.139
4.799
4.265
4.265
Output 58.4.5 Plot of Odds Ratios for Style
Since the interaction effect is clearly nonsignificant, a main-effects model is fit with the following statements.
The EFFECTPLOT statement creates a plot of the predicted values versus the levels of the School variable
Example 58.4: Nominal Response Data: Generalized Logits Model F 4655
at each level of the Program variables. The CLM option adds confidence bars, and the NOOBS option
suppresses the display of the observations.
ods graphics on;
proc logistic data=school;
freq Count;
class School Program(ref=first);
model Style(order=data)=School Program / link=glogit;
effectplot interaction(plotby=Program) / clm noobs;
run;
ods graphics off;
All of the global fit tests in Output 58.4.6 suggest the model is significant, and the Type 3 tests show that the
school and program effects are also significant.
Output 58.4.6 Analysis of Main-Effects Model
The LOGISTIC Procedure
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
699.404
707.050
695.404
682.934
713.518
666.934
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
28.4704
27.1190
25.5881
6
6
6
<.0001
0.0001
0.0003
Likelihood Ratio
Score
Wald
Type 3 Analysis of Effects
Effect
DF
Wald
Chi-Square
Pr > ChiSq
School
Program
4
2
14.8424
10.9160
0.0050
0.0043
The parameter estimates, tests for individual parameters, and odds ratios are displayed in Output 58.4.7. The
Program variable has nearly the same effect on both logits, while School=1 has the largest effect of the
schools.
4656 F Chapter 58: The LOGISTIC Procedure
Output 58.4.7 Estimates
Analysis of Maximum Likelihood Estimates
Parameter
Style
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
Intercept
School
School
School
School
Program
Program
self
team
self
team
self
team
self
team
1
1
1
1
1
1
1
1
-0.7978
-0.6589
-0.7992
-0.2786
0.2836
-0.0985
0.3737
0.3713
0.1465
0.1367
0.2198
0.1867
0.1899
0.1892
0.1410
0.1353
29.6502
23.2300
13.2241
2.2269
2.2316
0.2708
7.0272
7.5332
<.0001
<.0001
0.0003
0.1356
0.1352
0.6028
0.0080
0.0061
1
1
2
2
regular
regular
Odds Ratio Estimates
Effect
School
School
School
School
Program
Program
Style
1 vs 3
1 vs 3
2 vs 3
2 vs 3
regular vs afternoon
regular vs afternoon
self
team
self
team
self
team
Point
Estimate
0.269
0.519
0.793
0.622
2.112
2.101
95% Wald
Confidence Limits
0.127
0.267
0.413
0.317
1.215
1.237
0.570
1.010
1.522
1.219
3.670
3.571
The interaction plots in Output 58.4.8 show that School=1 and Program=afternoon have a preference
for the traditional classroom style. Of course, since these are not simultaneous confidence intervals, the
nonoverlapping 95% confidence limits do not take the place of an actual test.
Example 58.5: Stratified Sampling F 4657
Output 58.4.8 Model-Predicted Probabilities
Example 58.5: Stratified Sampling
Consider the hypothetical example in Fleiss (1981, pp. 6–7), in which a test is applied to a sample of 1,000
people known to have a disease and to another sample of 1,000 people known not to have the same disease.
In the diseased sample, 950 test positive; in the nondiseased sample, only 10 test positive. If the true disease
rate in the population is 1 in 100, specifying PEVENT=0.01 results in the correct false positive and negative
rates for the stratified sampling scheme. Omitting the PEVENT= option is equivalent to using the overall
sample disease rate (1000/2000 = 0.5) as the value of the PEVENT= option, which would ignore the stratified
sampling.
The statements to create the data set and perform the analysis are as follows:
4658 F Chapter 58: The LOGISTIC Procedure
data Screen;
do Disease='Present','Absent';
do Test=1,0;
input Count @@;
output;
end;
end;
datalines;
950 50
10 990
;
proc logistic data=Screen;
freq Count;
model Disease(event='Present')=Test
/ pevent=.5 .01 ctable pprob=.5;
run;
The response variable option EVENT= indicates that Disease=’Present’ is the event. The CTABLE option is
specified to produce a classification table. Specifying PPROB=0.5 indicates a cutoff probability of 0.5. A
list of two probabilities, 0.5 and 0.01, is specified for the PEVENT= option; 0.5 corresponds to the overall
sample disease rate, and 0.01 corresponds to a true disease rate of 1 in 100.
The classification table is shown in Output 58.5.1.
Output 58.5.1 False Positive and False Negative Rates
The LOGISTIC Procedure
Classification Table
Correct
NonEvent Event
Incorrect
NonEvent Event
Percentages
Sensi- Speci- False
tivity ficity
POS
Prob
Event
Prob
Level
0.500
0.500
950
990
10
50
97.0
95.0
99.0
1.0
4.8
0.010
0.500
950
990
10
50
99.0
95.0
99.0
51.0
0.1
Correct
False
NEG
In the classification table, the column “Prob Level” represents the cutoff values (the settings of the PPROB=
option) for predicting whether an observation is an event. The “Correct” columns list the numbers of subjects
that are correctly predicted as events and nonevents, respectively, and the “Incorrect” columns list the number
of nonevents incorrectly predicted as events and the number of events incorrectly predicted as nonevents,
respectively. For PEVENT=0.5, the false positive rate is 1% and the false negative rate is 4.8%. These results
ignore the fact that the samples were stratified and incorrectly assume that the overall sample proportion of
disease (which is 0.5) estimates the true disease rate. For a true disease rate of 0.01, the false positive rate and
the false negative rate are 51% and 0.1%, respectively, as shown in the second line of the classification table.
Example 58.6: Logistic Regression Diagnostics F 4659
Example 58.6: Logistic Regression Diagnostics
In a controlled experiment to study the effect of the rate and volume of air intake on a transient reflex
vasoconstriction in the skin of the digits, 39 tests under various combinations of rate and volume of air
intake were obtained (Finney 1947). The endpoint of each test is whether or not vasoconstriction occurred.
Pregibon (1981) uses this set of data to illustrate the diagnostic measures he proposes for detecting influential
observations and to quantify their effects on various aspects of the maximum likelihood fit.
The vasoconstriction data are saved in the data set vaso:
data vaso;
length Response $12;
input Volume Rate Response @@;
LogVolume=log(Volume);
LogRate=log(Rate);
datalines;
3.70 0.825 constrict
3.50
1.25 2.50
constrict
0.75
0.80 3.20
constrict
0.70
0.60 0.75
no_constrict
1.10
0.90 0.75
no_constrict
0.90
0.80 0.57
no_constrict
0.55
0.60 3.00
no_constrict
1.40
0.75 3.75
constrict
2.30
3.20 1.60
constrict
0.85
1.70 1.06
no_constrict
1.80
0.40 2.00
no_constrict
0.95
1.35 1.35
no_constrict
1.50
1.60 1.78
constrict
0.60
1.80 1.50
constrict
0.95
1.90 0.95
constrict
1.60
2.70 0.75
constrict
2.35
1.10 1.83
no_constrict
1.10
1.20 2.00
constrict
0.80
0.95 1.90
no_constrict
0.75
1.30 1.625 constrict
;
1.09
1.50
3.50
1.70
0.45
2.75
2.33
1.64
1.415
1.80
1.36
1.36
1.50
1.90
0.40
0.03
2.20
3.33
1.90
constrict
constrict
constrict
no_constrict
no_constrict
no_constrict
constrict
constrict
constrict
constrict
no_constrict
no_constrict
no_constrict
no_constrict
no_constrict
no_constrict
constrict
constrict
no_constrict
In the data set vaso, the variable Response represents the outcome of a test. The variable LogVolume
represents the log of the volume of air intake, and the variable LogRate represents the log of the rate of air
intake.
The following statements invoke PROC LOGISTIC to fit a logistic regression model to the vasoconstriction
data, where Response is the response variable, and LogRate and LogVolume are the explanatory variables.
Regression diagnostics are displayed when ODS Graphics is enabled, and the INFLUENCE option is specified
to display a table of the regression diagnostics.
ods graphics on;
title 'Occurrence of Vasoconstriction';
proc logistic data=vaso;
model Response=LogRate LogVolume/influence;
run;
ods graphics off;
4660 F Chapter 58: The LOGISTIC Procedure
Results of the model fit are shown in Output 58.6.1. Both LogRate and LogVolume are statistically significant
to the occurrence of vasoconstriction (p = 0.0131 and p = 0.0055, respectively). Their positive parameter
estimates indicate that a higher inspiration rate or a larger volume of air intake is likely to increase the
probability of vasoconstriction.
Output 58.6.1 Logistic Regression Analysis for Vasoconstriction Data
Occurrence of Vasoconstriction
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.VASO
Response
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
39
39
Response Profile
Ordered
Value
Response
1
2
Total
Frequency
constrict
no_constrict
20
19
Probability modeled is Response='constrict'.
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
56.040
57.703
54.040
35.227
40.218
29.227
Example 58.6: Logistic Regression Diagnostics F 4661
Output 58.6.1 continued
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
24.8125
16.6324
7.8876
2
2
2
<.0001
0.0002
0.0194
Likelihood Ratio
Score
Wald
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
LogRate
LogVolume
1
1
1
-2.8754
4.5617
5.1793
1.3208
1.8380
1.8648
4.7395
6.1597
7.7136
0.0295
0.0131
0.0055
Odds Ratio Estimates
Effect
LogRate
LogVolume
Point
Estimate
95% Wald
Confidence Limits
95.744
177.562
2.610
4.592
>999.999
>999.999
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
93.7
6.3
0.0
380
Somers' D
Gamma
Tau-a
c
0.874
0.874
0.448
0.937
The INFLUENCE option displays the values of the explanatory variables (LogRate and LogVolume) for
each observation, a column for each diagnostic produced, and the case number that represents the sequence
number of the observation (Output 58.6.2).
4662 F Chapter 58: The LOGISTIC Procedure
Output 58.6.2 Regression Diagnostics from the INFLUENCE Option
Regression Diagnostics
Covariates
Case
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
LogRate
Log
Volume
Pearson
Residual
Deviance
Residual
Hat
Matrix
Diagonal
Intercept
DfBeta
LogRate
DfBeta
-0.1924
0.0862
0.9163
0.4055
1.1632
1.2528
-0.2877
0.5306
-0.2877
-0.7985
-0.5621
1.0116
1.0986
0.8459
1.3218
0.4947
0.4700
0.3471
0.0583
0.5878
0.6931
0.3075
0.3001
0.3075
0.5766
0.4055
0.4055
0.6419
-0.0513
-0.9163
-0.2877
-3.5066
0.6043
0.7885
0.6931
1.2030
0.6419
0.6419
0.4855
1.3083
1.2528
0.2231
-0.2877
-0.2231
-0.3567
-0.5108
0.0953
-0.1054
-0.1054
-0.2231
-0.5978
-0.5108
0.3365
-0.2877
0.8329
1.1632
-0.1625
0.5306
0.5878
-0.9163
-0.0513
0.3001
0.4055
0.4700
-0.5108
0.5878
-0.0513
0.6419
0.4700
0.9933
0.8544
0.0953
0.0953
0.1823
-0.2231
-0.0513
-0.2877
0.2624
0.2205
0.1349
0.2923
3.5181
0.5287
0.6090
-0.0328
-1.0196
-0.0938
-0.0293
-0.0370
-0.5073
-0.7751
0.2559
0.4352
0.1576
0.0709
2.9062
-1.0718
0.2405
-0.1076
-0.4193
-1.0242
-1.3684
0.3347
-0.1595
0.3645
-0.8989
0.8981
-0.0992
0.6198
-0.00073
-1.2062
0.5447
0.5404
0.4828
-0.8989
-0.4874
0.7053
0.3082
0.1899
0.4049
2.2775
0.7021
0.7943
-0.0464
-1.1939
-0.1323
-0.0414
-0.0523
-0.6768
-0.9700
0.3562
0.5890
0.2215
0.1001
2.1192
-1.2368
0.3353
-0.1517
-0.5691
-1.1978
-1.4527
0.4608
-0.2241
0.4995
-1.0883
1.0876
-0.1400
0.8064
-0.00103
-1.3402
0.7209
0.7159
0.6473
-1.0883
-0.6529
0.8987
0.0927
0.0429
0.0612
0.0867
0.1158
0.1524
0.00761
0.0559
0.0342
0.00721
0.00969
0.1481
0.1628
0.0551
0.1336
0.0402
0.0172
0.0954
0.1315
0.0525
0.0373
0.1015
0.0761
0.0717
0.0587
0.0548
0.0661
0.0647
0.1682
0.0507
0.2459
0.000022
0.0510
0.0601
0.0552
0.1177
0.0647
0.1000
0.0531
-0.0165
-0.0134
-0.0492
1.0734
-0.0832
-0.0922
-0.00280
-0.1444
-0.0178
-0.00245
-0.00361
-0.1173
-0.0931
-0.0414
-0.0940
-0.0198
-0.00630
0.9595
-0.2591
-0.0331
-0.0180
-0.1449
-0.1961
-0.1281
-0.0403
-0.0366
-0.0327
-0.1423
0.2367
-0.0224
0.1165
-3.22E-6
-0.0882
-0.0425
-0.0340
-0.0867
-0.1423
-0.1395
0.0326
0.0193
0.0151
0.0660
-0.9302
0.1411
0.1710
0.00274
0.0613
0.0173
0.00246
0.00358
0.0647
-0.00946
0.0538
0.1408
0.0234
0.00701
-0.8279
0.2024
0.0421
0.0158
0.1237
0.1275
0.0410
0.0570
0.0329
0.0496
0.0617
-0.1950
0.0227
-0.0996
3.405E-6
-0.0137
0.0877
0.0755
0.1381
0.0617
0.1032
0.0190
Example 58.6: Logistic Regression Diagnostics F 4663
Output 58.6.2 continued
Regression Diagnostics
Case
Number
Log
Volume
DfBeta
Confidence
Interval
Displacement
C
Confidence
Interval
Displacement
CBar
Delta
Deviance
Delta
Chi-Square
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
0.0556
0.0261
0.0589
-1.0180
0.0583
0.0381
0.00265
0.0570
0.0153
0.00211
0.00319
0.1651
0.1775
0.0527
0.0643
0.0307
0.00914
-0.8477
-0.00488
0.0518
0.0208
0.1179
0.0357
-0.1004
0.0708
0.0373
0.0788
0.1025
0.0286
0.0159
0.1322
2.48E-6
-0.00216
0.0671
0.0711
0.0631
0.1025
0.1397
0.0489
0.00548
0.000853
0.00593
1.2873
0.0414
0.0787
8.321E-6
0.0652
0.000322
6.256E-6
0.000014
0.0525
0.1395
0.00404
0.0337
0.00108
0.000089
0.9845
0.2003
0.00338
0.000465
0.0221
0.0935
0.1558
0.00741
0.00156
0.0101
0.0597
0.1961
0.000554
0.1661
1.18E-11
0.0824
0.0202
0.0180
0.0352
0.0597
0.0293
0.0295
0.00497
0.000816
0.00557
1.1756
0.0366
0.0667
8.258E-6
0.0616
0.000311
6.211E-6
0.000013
0.0447
0.1168
0.00382
0.0292
0.00104
0.000088
0.8906
0.1740
0.00320
0.000448
0.0199
0.0864
0.1447
0.00698
0.00147
0.00941
0.0559
0.1631
0.000526
0.1253
1.18E-11
0.0782
0.0190
0.0170
0.0311
0.0559
0.0264
0.0279
0.1000
0.0369
0.1695
6.3626
0.5296
0.6976
0.00216
1.4870
0.0178
0.00172
0.00274
0.5028
1.0577
0.1307
0.3761
0.0501
0.0101
5.3817
1.7037
0.1156
0.0235
0.3437
1.5212
2.2550
0.2193
0.0517
0.2589
1.2404
1.3460
0.0201
0.7755
1.065E-6
1.8744
0.5387
0.5295
0.4501
1.2404
0.4526
0.8355
0.0536
0.0190
0.0910
13.5523
0.3161
0.4376
0.00109
1.1011
0.00911
0.000862
0.00138
0.3021
0.7175
0.0693
0.2186
0.0259
0.00511
9.3363
1.3227
0.0610
0.0120
0.1956
1.1355
2.0171
0.1190
0.0269
0.1423
0.8639
0.9697
0.0104
0.5095
5.324E-7
1.5331
0.3157
0.3091
0.2641
0.8639
0.2639
0.5254
Since ODS Graphics is enabled, influence plots are displayed in Outputs 58.6.3 through 58.6.5. For general
information about ODS Graphics, see Chapter 21, “Statistical Graphics Using ODS.” For specific information
about the graphics available in the LOGISTIC procedure, see the section “ODS Graphics” on page 4616.
The vertical axis of an index plot represents the value of the diagnostic, and the horizontal axis represents the
sequence (case number) of the observation. The index plots are useful for identification of extreme values.
The index plots of the Pearson residuals and the deviance residuals (Output 58.6.3) indicate that case 4 and
4664 F Chapter 58: The LOGISTIC Procedure
case 18 are poorly accounted for by the model. The index plot of the diagonal elements of the hat matrix
(Output 58.6.3) suggests that case 31 is an extreme point in the design space. The index plots of DFBETAS
(Output 58.6.5) indicate that case 4 and case 18 are causing instability in all three parameter estimates. The
other four index plots in Outputs 58.6.3 and 58.6.4 also point to these two cases as having a large impact on
the coefficients and goodness of fit.
Output 58.6.3 Residuals, Hat Matrix, and CI Displacement C
Example 58.6: Logistic Regression Diagnostics F 4665
Output 58.6.4 CI Displacement CBar, Change in Deviance and Pearson Chi-Square
Output 58.6.5 DFBETAS Plots
4666 F Chapter 58: The LOGISTIC Procedure
Other versions of diagnostic plots can be requested by specifying the appropriate options in the PLOTS=
option. For example, the following statements produce three other sets of influence diagnostic plots: the
PHAT option plots several diagnostics against the predicted probabilities (Output 58.6.6), the LEVERAGE
option plots several diagnostics against the leverage (Output 58.6.7), and the DPC option plots the deletion
diagnostics against the predicted probabilities and colors the observations according to the confidence interval
displacement diagnostic (Output 58.6.8). The LABEL option displays the observation numbers on the plots.
In all plots, you are looking for the outlying observations, and again cases 4 and 18 are noted.
ods graphics on;
proc logistic data=vaso plots(only label)=(phat leverage dpc);
model Response=LogRate LogVolume;
run;
ods graphics off;
Output 58.6.6 Diagnostics versus Predicted Probability
Example 58.6: Logistic Regression Diagnostics F 4667
Output 58.6.7 Diagnostics versus Leverage
Output 58.6.8 Three Diagnostics
4668 F Chapter 58: The LOGISTIC Procedure
Example 58.7: ROC Curve, Customized Odds Ratios, Goodness-of-Fit
Statistics, R-Square, and Confidence Limits
This example plots an ROC curve, estimates a customized odds ratio, produces the traditional goodness-of-fit
analysis, displays the generalized R Square measures for the fitted model, calculates the normal confidence
intervals for the regression parameters, and produces a display of the probability function and prediction
curves for the fitted model. The data consist of three variables: n (number of subjects in the sample), disease
(number of diseased subjects in the sample), and age (age for the sample). A linear logistic regression model
is used to study the effect of age on the probability of contracting the disease. The statements to produce the
data set and perform the analysis are as follows:
data Data1;
input disease n age;
datalines;
0 14 25
0 20 35
0 19 45
7 18 55
6 12 65
17 17 75
;
ods graphics on;
proc logistic data=Data1 plots(only)=roc(id=obs);
model disease/n=age / scale=none
clparm=wald
clodds=pl
rsquare;
units age=10;
effectplot;
run;
ods graphics off;
The option SCALE=NONE is specified to produce the deviance and Pearson goodness-of-fit analysis without
adjusting for overdispersion. The RSQUARE option is specified to produce generalized R Square measures
of the fitted model. The CLPARM=WALD option is specified to produce the Wald confidence intervals for
the regression parameters. The UNITS statement is specified to produce customized odds ratio estimates for a
change of 10 years in the age variable, and the CLODDS=PL option is specified to produce profile-likelihood
confidence limits for the odds ratio. The PLOTS= option with ODS Graphics enabled produces a graphical
display of the ROC curve, and the EFFECTPLOT statement displays the model fit.
The results in Output 58.7.1 show that the deviance and Pearson statistics indicate no lack of fit in the model.
Example 58.7: ROC Curve...and Confidence Limits F 4669
Output 58.7.1 Deviance and Pearson Goodness-of-Fit Analysis
The LOGISTIC Procedure
Deviance and Pearson Goodness-of-Fit Statistics
Criterion
Deviance
Pearson
Value
DF
Value/DF
Pr > ChiSq
7.7756
6.6020
4
4
1.9439
1.6505
0.1002
0.1585
Number of events/trials observations: 6
Output 58.7.2 shows that the R-square for the model is 0.74. The odds of an event increases by a factor of
7.9 for each 10-year increase in age.
Output 58.7.2 R-Square, Confidence Intervals, and Customized Odds Ratio
Model Fit Statistics
Criterion
Intercept
Only
AIC
SC
-2 Log L
R-Square
Intercept and Covariates
Log
Full Log
Likelihood
Likelihood
124.173
126.778
122.173
0.5215
52.468
57.678
48.468
18.075
23.285
14.075
Max-rescaled R-Square
0.8925
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
73.7048
55.3274
23.3475
1
1
1
<.0001
<.0001
<.0001
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
age
1
1
-12.5016
0.2066
2.5555
0.0428
23.9317
23.3475
<.0001
<.0001
4670 F Chapter 58: The LOGISTIC Procedure
Output 58.7.2 continued
Association of Predicted Probabilities and Observed Responses
Percent Concordant
Percent Discordant
Percent Tied
Pairs
92.6
2.0
5.4
2100
Somers' D
Gamma
Tau-a
c
0.906
0.958
0.384
0.953
Parameter Estimates and Wald
Confidence Intervals
Parameter
Estimate
95% Confidence Limits
Intercept
age
-12.5016
0.2066
-17.5104
0.1228
-7.4929
0.2904
Odds Ratio Estimates and Profile-Likelihood Confidence Intervals
Effect
age
Unit
Estimate
10.0000
7.892
95% Confidence Limits
3.881
21.406
Since ODS Graphics is enabled, a graphical display of the ROC curve is produced as shown in Output 58.7.3.
Example 58.7: ROC Curve...and Confidence Limits F 4671
Output 58.7.3 Receiver Operating Characteristic Curve
Note that the area under the ROC curve is estimated by the statistic c in the “Association of Predicted
Probabilities and Observed Responses” table. In this example, the area under the ROC curve is 0.953.
Since there is only one continuous covariate and since ODS Graphics is enabled, the EFFECTPLOT statement
produces a graphical display of the predicted probability curve with bounding 95% confidence limits as
shown in Output 58.7.4.
4672 F Chapter 58: The LOGISTIC Procedure
Output 58.7.4 Predicted Probability and 95% Prediction Limits
Example 58.8: Comparing Receiver Operating Characteristic Curves
DeLong, DeLong, and Clarke-Pearson (1988) report on 49 patients with ovarian cancer who also suffer from
an intestinal obstruction. Three (correlated) screening tests are measured to determine whether a patient will
benefit from surgery. The three tests are the K-G score and two measures of nutritional status: total protein
and albumin. The data are as follows:
data roc;
input alb tp totscore popind @@;
totscore = 10 - totscore;
datalines;
3.0 5.8 10 0
3.2 6.3 5 1
3.9 6.8
3.2 5.8 3 1
0.9 4.0 5 0
2.5 5.7
3.8 5.7 5 1
3.7 6.7 6 1
3.2 5.4
4.1 6.6 5 1
3.6 5.7 5 1
4.3 7.0
2.3 4.4 6 1
4.2 7.6 4 0
4.0 6.6
3.8 6.8 7 1
3.0 4.7 8 0
4.5 7.4
3.1 6.6 6 1
4.1 8.2 6 1
4.3 7.0
3.2 5.1 5 1
2.6 4.7 6 1
3.3 6.8
3
8
4
4
6
5
5
6
1
0
1
1
0
1
1
0
2.8
1.6
3.8
3.6
3.5
3.7
4.3
1.7
4.8
5.6
6.6
6.7
5.8
7.4
6.5
4.0
6
5
6
4
6
5
4
7
0
1
1
0
1
1
1
0
Example 58.8: Comparing Receiver Operating Characteristic Curves F 4673
3.7 6.1
2.9 5.7
3.3 5.7
;
5 1
9 0
6 1
3.3 6.3
2.1 4.8
3.7 6.9
7 1
7 1
5 1
4.2 7.7
2.8 6.2
3.6 6.6
6 1
8 0
5 1
3.5 6.2
4.0 7.0
5 1
7 1
In the following statements, the NOFIT option is specified in the MODEL statement to prevent PROC
LOGISTIC from fitting the model with three covariates. Each ROC statement lists one of the covariates, and
PROC LOGISTIC then fits the model with that single covariate. Note that the original data set contains six
more records with missing values for one of the tests, but PROC LOGISTIC ignores all records with missing
values; hence there is a common sample size for each of the three models. The ROCCONTRAST statement
implements the nonparametric approach of DeLong, DeLong, and Clarke-Pearson (1988) to compare the
three ROC curves, the REFERENCE option specifies that the K-G Score curve is used as the reference curve
in the contrast, the E option displays the contrast coefficients, and the ESTIMATE option computes and
tests each comparison. With ODS Graphics enabled, the plots=roc(id=prob) specification in the PROC
LOGISTIC statement displays several plots, and the plots of individual ROC curves have certain points
labeled with their predicted probabilities.
ods graphics on;
proc logistic data=roc plots=roc(id=prob);
model popind(event='0') = alb tp totscore / nofit;
roc 'Albumin' alb;
roc 'K-G Score' totscore;
roc 'Total Protein' tp;
roccontrast reference('K-G Score') / estimate e;
run;
ods graphics off;
The initial model information is displayed in Output 58.8.1.
Output 58.8.1 Initial LOGISTIC Output
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Model
Optimization Technique
WORK.ROC
popind
2
binary logit
Fisher's scoring
Number of Observations Read
Number of Observations Used
43
43
Response Profile
Ordered
Value
popind
Total
Frequency
1
2
0
1
12
31
Probability modeled is popind=0.
4674 F Chapter 58: The LOGISTIC Procedure
Output 58.8.1 continued
Score Test for Global Null Hypothesis
Chi-Square
DF
Pr > ChiSq
10.7939
3
0.0129
For each ROC model, the model fitting details in Outputs 58.8.2, 58.8.4, and 58.8.6 can be suppressed with
the ROCOPTIONS(NODETAILS) option; however, the convergence status is always displayed.
The ROC curves for the three models are displayed in Outputs 58.8.3, 58.8.5, and 58.8.7. Note that the labels
on the ROC curve are produced by specifying the ID=PROB option, and are the predicted probabilities for
the cutpoints.
Output 58.8.2 Fit Tables for Popind=Alb
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
52.918
54.679
50.918
49.384
52.907
45.384
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
5.5339
5.6893
4.6869
1
1
1
0.0187
0.0171
0.0304
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
alb
1
1
2.4646
-1.0520
1.5913
0.4859
2.3988
4.6869
0.1214
0.0304
Example 58.8: Comparing Receiver Operating Characteristic Curves F 4675
Output 58.8.2 continued
Odds Ratio Estimates
Effect
alb
Point
Estimate
0.349
95% Wald
Confidence Limits
0.135
0.905
Output 58.8.3 ROC Curve for Popind=Alb
Output 58.8.4 Fit Tables for Popind=Totscore
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
4676 F Chapter 58: The LOGISTIC Procedure
Output 58.8.4 continued
Model Fit Statistics
Criterion
Intercept
Only
Intercept
and
Covariates
52.918
54.679
50.918
46.262
49.784
42.262
AIC
SC
-2 Log L
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
8.6567
8.3613
6.3845
1
1
1
0.0033
0.0038
0.0115
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
totscore
1
1
2.1542
-0.7696
1.2477
0.3046
2.9808
6.3845
0.0843
0.0115
Odds Ratio Estimates
Effect
totscore
Point
Estimate
0.463
95% Wald
Confidence Limits
0.255
0.841
Example 58.8: Comparing Receiver Operating Characteristic Curves F 4677
Output 58.8.5 ROC Curve for Popind=Totscore
Output 58.8.6 Fit Tables for Popind=Tp
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
52.918
54.679
50.918
51.794
55.316
47.794
4678 F Chapter 58: The LOGISTIC Procedure
Output 58.8.6 continued
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
3.1244
3.1123
2.9059
1
1
1
0.0771
0.0777
0.0883
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
tp
1
1
2.8295
-0.6279
2.2065
0.3683
1.6445
2.9059
0.1997
0.0883
Odds Ratio Estimates
Effect
tp
Point
Estimate
0.534
95% Wald
Confidence Limits
0.259
1.099
Example 58.8: Comparing Receiver Operating Characteristic Curves F 4679
Output 58.8.7 ROC Curve for Popind=Tp
All ROC curves being compared are also overlaid on the same plot, as shown in Output 58.8.8.
4680 F Chapter 58: The LOGISTIC Procedure
Output 58.8.8 Overlay of All Models Being Compared
Output 58.8.9 displays the association statistics, and displays the area under the ROC curve along with its
standard error and a confidence interval for each model in the comparison. The confidence interval for Total
Protein contains 0.50; hence it is not significantly different from random guessing, which is represented by
the diagonal line in the preceding ROC plots.
Output 58.8.9 ROC Association Table
ROC Association Statistics
ROC Model
Albumin
K-G Score
Total Protein
-------------- Mann-Whitney ------------Standard
95% Wald
Area
Error
Confidence Limits
0.7366
0.7258
0.6478
0.0927
0.1028
0.1000
0.5549
0.5243
0.4518
0.9182
0.9273
0.8439
Somers' D
(Gini)
Gamma
Tau-a
0.4731
0.4516
0.2957
0.4809
0.5217
0.3107
0.1949
0.1860
0.1218
Example 58.9: Goodness-of-Fit Tests and Subpopulations F 4681
Output 58.8.10 shows that the contrast used ’K-G Score’ as the reference level. This table is produced by
specifying the E option in the ROCCONTRAST statement.
Output 58.8.10 ROC Contrast Coefficients
ROC Contrast Coefficients
ROC Model
Row1
Row2
1
-1
0
0
-1
1
Albumin
K-G Score
Total Protein
Output 58.8.11 shows that the 2-degrees-of-freedom test that the ’K-G Score’ is different from at least one
other test is not significant at the 0.05 level.
Output 58.8.11 ROC Test Results (2 Degrees of Freedom)
ROC Contrast Test Results
Contrast
Reference = K-G Score
DF
Chi-Square
Pr > ChiSq
2
2.5340
0.2817
Output 58.8.12 is produced by specifying the ESTIMATE option in the ROCCONTRAST statement. Each
row shows that the curves are not significantly different.
Output 58.8.12 ROC Contrast Row Estimates (1-Degree-of-Freedom Tests)
ROC Contrast Estimation and Testing Results by Row
Contrast
Albumin - K-G Score
Total Protein - K-G Score
Estimate
0.0108
-0.0780
Standard
95% Wald
Error Confidence Limits Chi-Square
0.0953
0.1046
-0.1761
-0.2830
0.1976
0.1271
Pr >
ChiSq
0.0127 0.9102
0.5554 0.4561
Example 58.9: Goodness-of-Fit Tests and Subpopulations
A study is done to investigate the effects of two binary factors, A and B, on a binary response, Y. Subjects are
randomly selected from subpopulations defined by the four possible combinations of levels of A and B. The
number of subjects responding with each level of Y is recorded, and the following DATA step creates the data
set One:
4682 F Chapter 58: The LOGISTIC Procedure
data One;
do A=0,1;
do B=0,1;
do Y=1,2;
input F @@;
output;
end;
end;
end;
datalines;
23 63 31 70 67 100 70 104
;
The following statements fit a full model to examine the main effects of A and B as well as the interaction
effect of A and B:
proc logistic data=One;
freq F;
model Y=A B A*B;
run;
Results of the model fit are shown in Output 58.9.1. Notice that neither the A*B interaction nor the B main
effect is significant.
Output 58.9.1 Full Model Fit
The LOGISTIC Procedure
Model Information
Data Set
Response Variable
Number of Response Levels
Frequency Variable
Model
Optimization Technique
Number
Number
Sum of
Sum of
WORK.ONE
Y
2
F
binary logit
Fisher's scoring
of Observations Read
of Observations Used
Frequencies Read
Frequencies Used
8
8
528
528
Response Profile
Ordered
Value
Y
Total
Frequency
1
2
1
2
191
337
Probability modeled is Y=1.
Example 58.9: Goodness-of-Fit Tests and Subpopulations F 4683
Output 58.9.1 continued
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Intercept
Only
Intercept
and
Covariates
693.061
697.330
691.061
691.914
708.990
683.914
Testing Global Null Hypothesis: BETA=0
Test
Chi-Square
DF
Pr > ChiSq
7.1478
6.9921
6.9118
3
3
3
0.0673
0.0721
0.0748
Likelihood Ratio
Score
Wald
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
A
B
A*B
1
1
1
1
-1.0074
0.6069
0.1929
-0.1883
0.2436
0.2903
0.3254
0.3933
17.1015
4.3714
0.3515
0.2293
<.0001
0.0365
0.5533
0.6321
Pearson and deviance goodness-of-fit tests cannot be obtained for this model since a full model containing
four parameters is fit, leaving no residual degrees of freedom. For a binary response model, the goodness-offit tests have m q degrees of freedom, where m is the number of subpopulations and q is the number of
model parameters. In the preceding model, m D q D 4, resulting in zero degrees of freedom for the tests.
The following statements fit a reduced model containing only the A effect, so two degrees of freedom become
available for testing goodness of fit. Specifying the SCALE=NONE option requests the Pearson and deviance
statistics. With single-trial syntax, the AGGREGATE= option is needed to define the subpopulations in the
study. Specifying AGGREGATE=(A B) creates subpopulations of the four combinations of levels of A and B.
Although the B effect is being dropped from the model, it is still needed to define the original subpopulations
in the study. If AGGREGATE=(A) were specified, only two subpopulations would be created from the levels
of A, resulting in m D q D 2 and zero degrees of freedom for the tests.
proc logistic data=One;
freq F;
model Y=A / scale=none aggregate=(A B);
run;
4684 F Chapter 58: The LOGISTIC Procedure
The goodness-of-fit tests in Output 58.9.2 show that dropping the B main effect and the A*B interaction
simultaneously does not result in significant lack of fit of the model. The tests’ large p-values indicate
insufficient evidence for rejecting the null hypothesis that the model fits.
Output 58.9.2 Reduced Model Fit
The LOGISTIC Procedure
Deviance and Pearson Goodness-of-Fit Statistics
Criterion
Deviance
Pearson
Value
DF
Value/DF
Pr > ChiSq
0.3541
0.3531
2
2
0.1770
0.1765
0.8377
0.8382
Number of unique profiles: 4
Example 58.10: Overdispersion
In a seed germination test, seeds of two cultivars were planted in pots of two soil conditions. The following
statements create the data set seeds, which contains the observed proportion of seeds that germinated for
various combinations of cultivar and soil condition. The variable n represents the number of seeds planted in
a pot, and the variable r represents the number germinated. The indicator variables cult and soil represent the
cultivar and soil condition, respectively.
data seeds;
input pot n r cult soil;
datalines;
1 16
8
0
0
2 51
26
0
0
3 45
23
0
0
4 39
10
0
0
5 36
9
0
0
6 81
23
1
0
7 30
10
1
0
8 39
17
1
0
9 28
8
1
0
10 62
23
1
0
11 51
32
0
1
12 72
55
0
1
13 41
22
0
1
14 12
3
0
1
15 13
10
0
1
16 79
46
1
1
17 30
15
1
1
18 51
32
1
1
19 74
53
1
1
20 56
12
1
1
;
Example 58.10: Overdispersion F 4685
PROC LOGISTIC is used as follows to fit a logit model to the data, with cult, soil, and cult soil interaction
as explanatory variables. The option SCALE=NONE is specified to display goodness-of-fit statistics.
proc logistic data=seeds;
model r/n=cult soil cult*soil/scale=none;
title 'Full Model With SCALE=NONE';
run;
Results of fitting the full factorial model are shown in Output 58.10.1. Both Pearson 2 and deviance are
highly significant (p < 0:0001), suggesting that the model does not fit well.
Output 58.10.1 Results of the Model Fit for the Two-Way Layout
Full Model With SCALE=NONE
The LOGISTIC Procedure
Deviance and Pearson Goodness-of-Fit Statistics
Criterion
Deviance
Pearson
Value
DF
Value/DF
Pr > ChiSq
68.3465
66.7617
16
16
4.2717
4.1726
<.0001
<.0001
Number of events/trials observations: 20
Model Fit Statistics
Criterion
Intercept
Only
AIC
SC
-2 Log L
Intercept and Covariates
Log
Full Log
Likelihood
Likelihood
1256.852
1261.661
1254.852
1213.003
1232.240
1205.003
156.533
175.769
148.533
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
49.8488
49.1682
47.7623
3
3
3
<.0001
<.0001
<.0001
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
cult
soil
cult*soil
1
1
1
1
-0.3788
-0.2956
0.9781
-0.1239
0.1489
0.2020
0.2128
0.2790
6.4730
2.1412
21.1234
0.1973
0.0110
0.1434
<.0001
0.6569
4686 F Chapter 58: The LOGISTIC Procedure
If the link function and the model specification are correct and if there are no outliers, then the lack
of fit might be due to overdispersion. Without adjusting for the overdispersion, the standard errors are
likely to be underestimated, causing the Wald tests to be too sensitive. In PROC LOGISTIC, there are
three SCALE= options to accommodate overdispersion. With unequal sample sizes for the observations,
SCALE=WILLIAMS is preferred. The Williams model estimates a scale parameter by equating the value
of Pearson 2 for the full model to its approximate expected value. The full model considered in the following
statements is the model with cultivar, soil condition, and their interaction. Using a full model reduces the risk
of contaminating with lack of fit due to incorrect model specification.
proc logistic data=seeds;
model r/n=cult soil cult*soil / scale=williams;
title 'Full Model With SCALE=WILLIAMS';
run;
Results of using Williams’ method are shown in Output 58.10.2. The estimate of is 0.075941 and is given
in the formula for the Weight Variable at the beginning of the displayed output.
Output 58.10.2 Williams’ Model for Overdispersion
Full Model With SCALE=WILLIAMS
The LOGISTIC Procedure
Model Information
Data Set
Response Variable (Events)
Response Variable (Trials)
Weight Variable
Model
Optimization Technique
Number
Number
Sum of
Sum of
Sum of
Sum of
WORK.SEEDS
r
n
1 / ( 1 + 0.075941 * (n - 1) )
binary logit
Fisher's scoring
of Observations Read
of Observations Used
Frequencies Read
Frequencies Used
Weights Read
Weights Used
20
20
906
906
198.3216
198.3216
Response Profile
Ordered
Value
1
2
Binary
Outcome
Event
Nonevent
Total
Frequency
Total
Weight
437
469
92.95346
105.36819
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Example 58.10: Overdispersion F 4687
Output 58.10.2 continued
Deviance and Pearson Goodness-of-Fit Statistics
Criterion
Deviance
Pearson
Value
DF
Value/DF
Pr > ChiSq
16.4402
16.0000
16
16
1.0275
1.0000
0.4227
0.4530
Number of events/trials observations: 20
NOTE: Since the Williams method was used to accommodate overdispersion, the
Pearson chi-squared statistic and the deviance can no longer be used to
assess the goodness of fit of the model.
Model Fit Statistics
Criterion
Intercept
Only
AIC
SC
-2 Log L
Intercept and Covariates
Log
Full Log
Likelihood
Likelihood
276.155
280.964
274.155
273.586
292.822
265.586
44.579
63.815
36.579
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
8.5687
8.4856
8.3069
3
3
3
0.0356
0.0370
0.0401
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
cult
soil
cult*soil
1
1
1
1
-0.3926
-0.2618
0.8309
-0.0532
0.2932
0.4160
0.4223
0.5835
1.7932
0.3963
3.8704
0.0083
0.1805
0.5290
0.0491
0.9274
Since neither cult nor cult soil is statistically significant (p = 0.5290 and p = 0.9274, respectively), a
reduced model that contains only the soil condition factor is fitted, with the observations weighted by
1=.1 C 0:075941.N 1//. This can be done conveniently in PROC LOGISTIC by including the scale
estimate in the SCALE=WILLIAMS option as follows:
proc logistic data=seeds;
model r/n=soil / scale=williams(0.075941);
title 'Reduced Model With SCALE=WILLIAMS(0.075941)';
run;
4688 F Chapter 58: The LOGISTIC Procedure
Results of the reduced model fit are shown in Output 58.10.3. Soil condition remains a significant factor (p =
0.0064) for the seed germination.
Output 58.10.3 Reduced Model with Overdispersion Controlled
Reduced Model With SCALE=WILLIAMS(0.075941)
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
soil
1
1
-0.5249
0.7910
0.2076
0.2902
6.3949
7.4284
0.0114
0.0064
Example 58.11: Conditional Logistic Regression for Matched Pairs Data
In matched pairs, or case-control, studies, conditional logistic regression is used to investigate the relationship
between an outcome of being an event (case) or a nonevent (control) and a set of prognostic factors.
The following data are a subset of the data from the Los Angeles Study of the Endometrial Cancer Data
in Breslow and Day (1980). There are 63 matched pairs, each consisting of a case of endometrial cancer
(Outcome=1) and a control (Outcome=0). The case and corresponding control have the same ID. Two
prognostic factors are included: Gall (an indicator variable for gall bladder disease) and Hyper (an indicator
variable for hypertension). The goal of the case-control analysis is to determine the relative risk for gall
bladder disease, controlling for the effect of hypertension.
data Data1;
do ID=1 to 63;
do Outcome = 1 to 0
input Gall Hyper
output;
end;
end;
datalines;
0 0 0 0
0 0 0 0
0
0 1 0 0
1 0 0 0
1
1 0 0 0
0 0 0 1
1
0 1 0 0
0 0 1 1
0
0 0 1 1
0 1 0 1
0
0 0 0 1
1 0 0 1
0
0 1 0 0
0 1 0 0
0
0 0 0 1
0 1 0 0
0
0 0 0 0
0 1 1 0
0
0 0 0 0
1 1 0 0
0
0 0 0 0
0 1 0 1
0
0 0 0 0
1 1 1 0
0
1 0 1 0
0 1 0 0
1
;
by -1;
@@;
1
1
0
0
1
0
1
1
0
1
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
1
1
1
0
1
0
1
1
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
1
0
0
0
1
0
1
0
1
1
0
0
0
1
0
1
1
1
0
1
0
1
0
0
0
0
0
0
1
0
0
0
0
0
1
0
1
0
0
0
1
0
0
1
0
0
Example 58.11: Conditional Logistic Regression for Matched Pairs Data F 4689
There are several ways to approach this problem with PROC LOGISTIC:
• Specify the STRATA statement to perform a conditional logistic regression.
• Specify EXACT and STRATA statements to perform an exact logistic regression on the original data
set, if you believe the data set is too small or too sparse for the usual asymptotics to hold.
• Transform each matched pair into a single observation, and then specify a PROC LOGISTIC statement
on this transformed data without a STRATA statement; this also performs a conditional logistic
regression and produces essentially the same results.
• Specify an EXACT statement on the transformed data.
SAS statements and selected results for these four approaches are given in the remainder of this example.
Conditional Analysis Using the STRATA Statement
In the following statements, PROC LOGISTIC is invoked with the ID variable declared in the STRATA
statement to obtain the conditional logistic model estimates for a model containing Gall as the only predictor
variable:
proc logistic data=Data1;
strata ID;
model outcome(event='1')=Gall;
run;
Results from the conditional logistic analysis are shown in Output 58.11.1. Note that there is no intercept
term in the “Analysis of Maximum Likelihood Estimates” tables.
The odds ratio estimate for Gall is 2.60, which is marginally significant (p = 0.0694) and which is an estimate
of the relative risk for gall bladder disease. A 95% confidence interval for this relative risk is (0.927, 7.293).
Output 58.11.1 Conditional Logistic Regression (Gall as Risk Factor)
The LOGISTIC Procedure
Conditional Analysis
Model Information
Data Set
Response Variable
Number of Response Levels
Number of Strata
Model
Optimization Technique
WORK.DATA1
Outcome
2
63
binary logit
Newton-Raphson ridge
Number of Observations Read
Number of Observations Used
126
126
4690 F Chapter 58: The LOGISTIC Procedure
Output 58.11.1 continued
Response Profile
Ordered
Value
Outcome
Total
Frequency
1
2
0
1
63
63
Probability modeled is Outcome=1.
Strata Summary
Outcome
------0
1
Response
Pattern
1
1
Number of
Strata
Frequency
63
126
1
Newton-Raphson Ridge Optimization
Without Parameter Scaling
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Criterion
AIC
SC
-2 Log L
Without
Covariates
With
Covariates
87.337
87.337
87.337
85.654
88.490
83.654
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
3.6830
3.5556
3.2970
1
1
1
0.0550
0.0593
0.0694
Analysis of Conditional Maximum Likelihood Estimates
Parameter
Gall
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
0.9555
0.5262
3.2970
0.0694
Example 58.11: Conditional Logistic Regression for Matched Pairs Data F 4691
Output 58.11.1 continued
Odds Ratio Estimates
Point
Estimate
Effect
Gall
95% Wald
Confidence Limits
2.600
0.927
7.293
Exact Analysis Using the STRATA Statement
When you believe there are not enough data or that the data are too sparse, you can perform a stratified exact
logistic regression. The following statements perform stratified exact logistic regressions on the original data
set by specifying both the STRATA and EXACT statements:
proc logistic data=Data1 exactonly;
strata ID;
model outcome(event='1')=Gall;
exact Gall / estimate=both;
run;
Output 58.11.2 Exact Logistic Regression (Gall as Risk Factor)
The LOGISTIC Procedure
Exact Conditional Analysis
Exact Conditional Tests
Effect
Test
Gall
Score
Probability
--- p-Value --Exact
Mid
Statistic
3.5556
0.0327
0.0963
0.0963
0.0799
0.0799
Exact Parameter Estimates
Parameter
Gall
Estimate
Standard
Error
0.9555
0.5262
95% Confidence
Limits
-0.1394
Two-sided
p-Value
2.2316
Exact Odds Ratios
Parameter
Gall
Estimate
2.600
95% Confidence
Limits
0.870
9.315
Two-sided
p-Value
0.0963
0.0963
4692 F Chapter 58: The LOGISTIC Procedure
Note that the score statistic in the “Conditional Exact Tests” table in Output 58.11.2 is identical to the score
statistic in Output 58.11.1 from the conditional analysis. The exact odds ratio confidence interval is much
wider than its conditional analysis counterpart, but the parameter estimates are similar. The exact analysis
confirms the marginal significance of Gall as a predictor variable.
Conditional Analysis Using Transformed Data
When each matched set consists of one event and one nonevent, the conditional likelihood is given by
Y
.1 C exp. ˇ 0 .xi1 xi 0 /// 1
i
where xi1 and xi 0 are vectors representing the prognostic factors for the event and nonevent, respectively, of
the ith matched set. This likelihood is identical to the likelihood of fitting a logistic regression model to a set
of data with constant response, where the model contains no intercept term and has explanatory variables
given by di D xi1 xi 0 (Breslow 1982).
To apply this method, the following DATA step transforms each matched pair into a single observation, where
the variables Gall and Hyper contain the differences between the corresponding values for the case and the
control (case–control). The variable Outcome, which will be used as the response variable in the logistic
regression model, is given a constant value of 0 (which is the Outcome value for the control, although any
constant, numeric or character, will suffice).
data Data2;
set Data1;
drop id1 gall1 hyper1;
retain id1 gall1 hyper1 0;
if (ID = id1) then do;
Gall=gall1-Gall; Hyper=hyper1-Hyper;
output;
end;
else do;
id1=ID; gall1=Gall; hyper1=Hyper;
end;
run;
Note that there are 63 observations in the data set, one for each matched pair. Since the number of observations
n is halved, statistics that depend on n such as R Square (see the “Generalized Coefficient of Determination”
on page 4568 section) will be incorrect. The variable Outcome has a constant value of 0.
In the following statements, PROC LOGISTIC is invoked with the NOINT option to obtain the conditional
logistic model estimates. Because the option CLODDS=PL is specified, PROC LOGISTIC computes a 95%
profile-likelihood confidence interval for the odds ratio for each predictor variable; note that profile-likelihood
confidence intervals are not currently available when a STRATA statement is specified.
proc logistic data=Data2;
model outcome=Gall / noint clodds=PL;
run;
The results are not displayed here.
Example 58.12: Firth’s Penalized Likelihood Compared with Other Approaches F 4693
Exact Analysis Using Transformed Data
Sometimes the original data set in a matched-pairs study is too large for the exact methods to handle. In such
cases it might be possible to use the transformed data set. The following statements perform exact logistic
regressions on the transformed data set. The results are not displayed here.
proc logistic data=Data2 exactonly;
model outcome=Gall / noint;
exact Gall / estimate=both;
run;
Example 58.12: Firth’s Penalized Likelihood Compared with Other
Approaches
Firth’s penalized likelihood approach is a method of addressing issues of separability, small sample sizes,
and bias of the parameter estimates. This example performs some comparisons between results from using
the FIRTH option to results from the usual unconditional, conditional, and exact logistic regression analyses.
When the sample size is large enough, the unconditional estimates and the Firth penalized-likelihood
estimates should be nearly the same. These examples show that Firth’s penalized likelihood approach
compares favorably with unconditional, conditional, and exact logistic regression; however, this is not an
exhaustive analysis of Firth’s method. For more detailed analyses with separable data sets, see Heinze
(2006, 1999) and Heinze and Schemper (2002).
Comparison on 2x2 Tables with One Zero Cell
A 22 table with one cell having zero frequency, where the rows of the table are the levels of a covariate
while the columns are the levels of the response variable, is an example of a quasi-completely separated
data set. The parameter estimate for the covariate under unconditional logistic regression will move off to
infinity, although PROC LOGISTIC will stop the iterations at an earlier point in the process. An exact logistic
regression is sometimes performed to determine the importance of the covariate in describing the variation in
the data, but the median-unbiased parameter estimate, while finite, might not be near the true value, and one
confidence limit (for this example, the upper) is always infinite.
The following DATA step produces 1000 different 22 tables, all following an underlying probability
structure, with one cell having a near zero probability of being observed:
%let beta0=-15;
%let beta1=16;
data one;
keep sample X y pry;
do sample=1 to 1000;
do i=1 to 100;
X=rantbl(987987,.4,.6)-1;
xb= &beta0 + X*&beta1;
exb=exp(xb);
pry= exb/(1+exb);
cut= ranuni(393993);
if (pry < cut) then y=1; else y=0;
output;
end;
end;
run;
4694 F Chapter 58: The LOGISTIC Procedure
The following statements perform the bias-corrected and exact logistic regression on each of the 1000
different data sets, output the odds ratio tables by using the ODS OUTPUT statement, and compute various
statistics across the data sets by using the MEANS procedure:
ods exclude all;
proc logistic data=one;
by sample;
class X(param=ref);
model y(event='1')=X / firth clodds=pl;
ods output cloddspl=firth;
run;
proc logistic data=one exactonly;
by sample;
class X(param=ref);
model y(event='1')=X;
exact X / estimate=odds;
ods output exactoddsratio=exact;
run;
ods select all;
proc means data=firth;
var LowerCL OddsRatioEst UpperCL;
run;
proc means data=exact;
var LowerCL Estimate UpperCL;
run;
The results of the PROC MEANS statements are summarized in Table 58.16. You can see that the odds ratios
are all quite large; the confidence limits on every table suggest that the covariate X is a significant factor in
explaining the variability in the data.
Table 58.16
Method
Firth
Exact
Odds Ratio Results
Mean Estimate
Standard Error
Minimum Lower CL
Maximum Upper CL
231.59
152.02
83.57
52.30
10.40
11.16
111317
1
Comparison on Case-Control Data
Case-control models contain an intercept term for every case-control pair in the data set. This means that
there are a large number of parameters compared to the number of observations. Breslow and Day (1980)
note that the estimates from unconditional logistic regression are biased with the corresponding odds ratios
off by a power of 2 from the true value; conditional logistic regression was developed to remedy this.
The following DATA step produces 1000 case-control data sets, with pair indicating the strata:
Example 58.12: Firth’s Penalized Likelihood Compared with Other Approaches F 4695
%let beta0=1;
%let beta1=2;
data one;
do sample=1 to 1000;
do pair=1 to 20;
ran=ranuni(939393);
a=3*ranuni(9384984)-1;
pdf0= pdf('NORMAL',a,.4,1);
pdf1= pdf('NORMAL',a,1,1);
pry0= pdf0/(pdf0+pdf1);
pry1= 1-pry0;
xb= log(pry0/pry1);
x= (xb-&beta0*pair/100) / &beta1;
y=0;
output;
x= (-xb-&beta0*pair/100) / &beta1;
y=1;
output;
end;
end;
run;
Unconditional, conditional, exact, and Firth-adjusted analyses are performed on the data sets, and the mean,
minimum, and maximum odds ratios and the mean upper and lower limits for the odds ratios are displayed in
Table 58.17. C AUTION : Due to the exact analyses, this program takes a long time and a lot of resources to
run. You might want to reduce the number of samples generated.
ods exclude all;
proc logistic data=one;
by sample;
class pair / param=ref;
model y=x pair / clodds=pl;
ods output cloddspl=oru;
run;
data oru;
set oru;
if Effect='x';
rename lowercl=lclu uppercl=uclu oddsratioest=orestu;
run;
proc logistic data=one;
by sample;
strata pair;
model y=x / clodds=wald;
ods output cloddswald=orc;
run;
data orc;
set orc;
if Effect='x';
rename lowercl=lclc uppercl=uclc oddsratioest=orestc;
run;
4696 F Chapter 58: The LOGISTIC Procedure
proc logistic data=one exactonly;
by sample;
strata pair;
model y=x;
exact x / estimate=both;
ods output ExactOddsRatio=ore;
run;
proc logistic data=one;
by sample;
class pair / param=ref;
model y=x pair / firth clodds=pl;
ods output cloddspl=orf;
run;
data orf;
set orf;
if Effect='x';
rename lowercl=lclf uppercl=uclf oddsratioest=orestf;
run;
data all;
merge oru orc ore orf;
run;
ods select all;
proc means data=all;
run;
You can see from Table 58.17 that the conditional, exact, and Firth-adjusted results are all comparable, while
the unconditional results are several orders of magnitude different.
Table 58.17 Odds Ratio Estimates
Method
Unconditional
Conditional
Exact
Firth
N
Minimum
Mean
Maximum
1000
1000
1000
1000
0.00045
0.021
0.021
0.018
112.09
4.20
4.20
4.89
38038
195
195
71
Further examination of the data set all shows that the differences between the square root of the unconditional
odds ratio estimates and the conditional estimates have mean –0.00019 and standard deviation 0.0008,
verifying that the unconditional odds ratio is about the square of the conditional odds ratio. The conditional
and exact conditional odds ratios are also nearly equal, with their differences having mean 3E–7 and standard
deviation 6E–6. The differences between the Firth and the conditional odds ratios can be large (mean 0.69,
Firth Conditional
standard deviation 5.40), but their relative differences,
, have mean 0.20 with standard
Conditional
deviation 0.19, so the largest differences occur with the larger estimates.
Example 58.13: Complementary Log-Log Model for Infection Rates
Antibodies produced in response to an infectious disease like malaria remain in the body after the individual
has recovered from the disease. A serological test detects the presence or absence of such antibodies. An
Example 58.13: Complementary Log-Log Model for Infection Rates F 4697
individual with such antibodies is called seropositive. In geographic areas where the disease is endemic, the
inhabitants are at fairly constant risk of infection. The probability of an individual never having been infected
in Y years is exp. Y /, where is the mean number of infections per year (see the appendix of Draper,
Voller, and Carpenter 1972). Rather than estimating the unknown , epidemiologists want to estimate the
probability of a person living in the area being infected in one year. This infection rate is given by
D1
e
The following statements create the data set sero, which contains the results of a serological survey of malarial
infection. Individuals of nine age groups (Group) were tested. The variable A represents the midpoint of the
age range for each age group. The variable N represents the number of individuals tested in each age group,
and the variable R represents the number of individuals that are seropositive.
data sero;
input Group A N R;
X=log(A);
label X='Log of Midpoint of Age Range';
datalines;
1 1.5 123 8
2 4.0 132 6
3 7.5 182 18
4 12.5 140 14
5 17.5 138 20
6 25.0 161 39
7 35.0 133 19
8 47.0
92 25
9 60.0
74 44
;
For the ith group with the age midpoint Ai , the probability of being seropositive is pi D 1
follows that
log. log.1
exp. Ai /. It
pi // D log./ C log.Ai /
By fitting a binomial model with a complementary log-log link function and by using X=log(A) as an offset
term, you can estimate ˛ D log./ as an intercept parameter. The following statements invoke PROC
LOGISTIC to compute the maximum likelihood estimate of ˛. The LINK=CLOGLOG option is specified to
request the complementary log-log link function. Also specified is the CLPARM=PL option, which requests
the profile-likelihood confidence limits for ˛.
proc logistic data=sero;
model R/N= / offset=X
link=cloglog
clparm=pl
scale=none;
title 'Constant Risk of Infection';
run;
Results of fitting this constant risk model are shown in Output 58.13.1.
4698 F Chapter 58: The LOGISTIC Procedure
Output 58.13.1 Modeling Constant Risk of Infection
Constant Risk of Infection
The LOGISTIC Procedure
Model Information
Data Set
Response Variable (Events)
Response Variable (Trials)
Offset Variable
Model
Optimization Technique
WORK.SERO
R
N
X
binary cloglog
Fisher's scoring
Number
Number
Sum of
Sum of
of Observations Read
of Observations Used
Frequencies Read
Frequencies Used
Log of Midpoint of Age Range
9
9
1175
1175
Response Profile
Ordered
Value
1
2
Binary
Outcome
Total
Frequency
Event
Nonevent
193
982
Intercept-Only Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
-2 Log L = 967.1158
Deviance and Pearson Goodness-of-Fit Statistics
Criterion
Deviance
Pearson
Value
DF
Value/DF
Pr > ChiSq
41.5032
50.6883
8
8
5.1879
6.3360
<.0001
<.0001
Number of events/trials observations: 9
Analysis of Maximum Likelihood Estimates
Parameter
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept
X
1
0
-4.6605
1.0000
0.0725
0
4133.5626
.
<.0001
.
Example 58.13: Complementary Log-Log Model for Infection Rates F 4699
Output 58.13.1 continued
Parameter Estimates and Profile-Likelihood
Confidence Intervals
Parameter
Estimate
Intercept
-4.6605
95% Confidence Limits
-4.8057
-4.5219
Output 58.13.1 shows that the maximum likelihood estimate of ˛ D log./ and its estimated standard error
are b
˛ D 4:6605 and b
b
D 0:0725, respectively. The infection rate is estimated as
˛
b
D1
e b
D1
e
eb̌0
D1
e
e
4:6605
D 0:00942
The 95% confidence interval for , obtained by back-transforming the 95% confidence interval for ˛, is
(0.0082, 0.0108); that is, there is a 95% chance that, in repeated sampling, the interval of 8 to 11 infections
per thousand individuals contains the true infection rate.
The goodness-of-fit statistics for the constant risk model are statistically significant (p < 0:0001), indicating
that the assumption of constant risk of infection is not correct. You can fit a more extensive model by
allowing a separate risk of infection for each age group. Suppose i is the mean number of infections per
year for the ith age group. The probability of seropositive for the ith group with the age midpoint Ai is
pi D 1 exp. i Ai /, so that
log. log.1
pi // D log.i / C log.Ai /
In the following statements, a complementary log-log model is fit containing Group as an explanatory
classification variable with the GLM coding (so that a dummy variable is created for each age group), no
intercept term, and X=log(A) as an offset term. The ODS OUTPUT statement saves the estimates and
their 95% profile-likelihood confidence limits to the ClparmPL data set. Note that log.i / is the regression
parameter associated with GroupD i .
proc logistic data=sero;
ods output ClparmPL=ClparmPL;
class Group / param=glm;
model R/N=Group / noint
offset=X
link=cloglog
clparm=pl;
title 'Infectious Rates and 95% Confidence Intervals';
run;
Results of fitting the model with a separate risk of infection are shown in Output 58.13.2.
4700 F Chapter 58: The LOGISTIC Procedure
Output 58.13.2 Modeling Separate Risk of Infection
Infectious Rates and 95% Confidence Intervals
The LOGISTIC Procedure
Analysis of Maximum Likelihood Estimates
Parameter
Group
Group
Group
Group
Group
Group
Group
Group
Group
X
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
1
1
1
1
0
-3.1048
-4.4542
-4.2769
-4.7761
-4.7165
-4.5012
-5.4252
-4.9987
-4.1965
1.0000
0.3536
0.4083
0.2358
0.2674
0.2238
0.1606
0.2296
0.2008
0.1559
0
77.0877
119.0164
328.9593
319.0600
443.9920
785.1350
558.1114
619.4666
724.3157
.
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
.
1
2
3
4
5
6
7
8
9
Parameter Estimates and Profile-Likelihood
Confidence Intervals
Parameter
Group
Group
Group
Group
Group
Group
Group
Group
Group
Estimate
1
2
3
4
5
6
7
8
9
-3.1048
-4.4542
-4.2769
-4.7761
-4.7165
-4.5012
-5.4252
-4.9987
-4.1965
95% Confidence Limits
-3.8880
-5.3769
-4.7775
-5.3501
-5.1896
-4.8333
-5.9116
-5.4195
-4.5164
-2.4833
-3.7478
-3.8477
-4.2940
-4.3075
-4.2019
-5.0063
-4.6289
-3.9037
For the first age group (Group=1), the point estimate of log.1 / is –3.1048, which transforms into an infection
rate of 1 exp. exp. 3:1048// D 0:0438. A 95% confidence interval for this infection rate is obtained by
transforming the 95% confidence interval for log.1 /. For the first age group, the lower and upper confidence
limits are 1 exp. exp. 3:8880/ D 0:0203 and 1 exp. exp. 2:4833// D 0:0801, respectively; that is,
there is a 95% chance that, in repeated sampling, the interval of 20 to 80 infections per thousand individuals
contains the true infection rate. The following statements perform this transformation on the estimates and
confidence limits saved in the ClparmPL data set; the resulting estimated infection rates in one year’s time
for each age group are displayed in Table 58.18. Note that the infection rate for the first age group is high
compared to that of the other age groups.
data ClparmPL;
set ClparmPL;
Estimate=round( 1000*( 1-exp(-exp(Estimate)) ) );
LowerCL =round( 1000*( 1-exp(-exp(LowerCL )) ) );
UpperCL =round( 1000*( 1-exp(-exp(UpperCL )) ) );
run;
Example 58.14: Complementary Log-Log Model for Interval-Censored Survival Times F 4701
Table 58.18
Age
Group
Infection Rate in One Year
Number Infected per 1,000 People
Point
95% Confidence Limits
Estimate Lower
Upper
1
2
3
4
5
6
7
8
9
44
12
14
8
9
11
4
7
15
20
5
8
5
6
8
3
4
11
80
23
21
14
13
15
7
10
20
Example 58.14: Complementary Log-Log Model for Interval-Censored
Survival Times
Often survival times are not observed more precisely than the interval (for instance, a day) within which
the event occurred. Survival data of this form are known as grouped or interval-censored data. A discrete
analog of the continuous proportional hazards model (Prentice and Gloeckler 1978; Allison 1982) is used to
investigate the relationship between these survival times and a set of explanatory variables.
Suppose Ti is the discrete survival time variable of the ith subject with covariates xi . The discrete-time
hazard rate i t is defined as
i t D Pr.Ti D t j Ti t; xi /;
t D 1; 2; : : :
Using elementary properties of conditional probabilities, it can be shown that
Pr.Ti D t / D i t
tY
1
.1
ij / and
Pr.Ti > t / D
j D1
t
Y
.1
ij /
j D1
Suppose ti is the observed survival time of the ith subject. Suppose ıi D 1 if Ti D ti is an event time and 0
otherwise. The likelihood for the grouped survival data is given by
L D
Y
ŒPr.Ti D ti /ıi ŒPr.Ti > ti /1
ıi
i
ti
Y i t ıi Y
i
D
.1
1 i ti
i
D
Y
ij /
j D1
ti
Y
i j D1
ij
1 ij
yij
.1
ij /
where yij D 1 if the ith subject experienced an event at time Ti D j and 0 otherwise.
4702 F Chapter 58: The LOGISTIC Procedure
Note that the likelihood L for the grouped survival data is the same as the likelihood of a binary response
model with event probabilities ij . If the data are generated by a continuous-time proportional hazards
model, Prentice and Gloeckler (1978) have shown that
ij D 1
exp. exp.˛j C ˇ 0 xi //
which can be rewritten as
log. log.1
ij // D ˛j C ˇ 0 xi
where the coefficient vector ˇ is identical to that of the continuous-time proportional hazards model, and
˛j is a constant related to the conditional survival probability in the interval defined by Ti D j at xi D 0.
The grouped data survival model is therefore equivalent to the binary response model with complementary
log-log link function. To fit the grouped survival model by using PROC LOGISTIC, you must treat each
discrete time unit for each subject as a separate observation. For each of these observations, the response is
dichotomous, corresponding to whether or not the subject died in the time unit.
Consider a study of the effect of insecticide on flour beetles. Four different concentrations of an insecticide
were sprayed on separate groups of flour beetles. The following DATA step saves the number of male and
female flour beetles dying in successive intervals in the data set Beetles:
data Beetles(keep=time sex conc freq);
input time m20 f20 m32 f32 m50 f50 m80 f80;
conc=.20; freq= m20; sex=1; output;
freq= f20; sex=2; output;
conc=.32; freq= m32; sex=1; output;
freq= f32; sex=2; output;
conc=.50; freq= m50; sex=1; output;
freq= f50; sex=2; output;
conc=.80; freq= m80; sex=1; output;
freq= f80; sex=2; output;
datalines;
1
3
0 7 1 5 0 4 2
2 11
2 10 5 8 4 10 7
3 10
4 11 11 11 6 8 15
4
7
8 16 10 15 6 14 9
5
4
9 3 5 4 3 8 3
6
3
3 2 1 2 1 2 4
7
2
0 1 0 1 1 1 1
8
1
0 0 1 1 4 0 1
9
0
0 1 1 0 0 0 0
10
0
0 0 0 0 0 1 1
11
0
0 0 0 1 1 0 0
12
1
0 0 0 0 1 0 0
13
1
0 0 0 0 1 0 0
14 101 126 19 47 7 17 2 4
;
The data set Beetles contains four variables: time, sex, conc, and freq. The variable time represents the
interval death time; for example, time=2 is the interval between day 1 and day 2. Insects surviving the
duration (13 days) of the experiment are given a time value of 14. The variable sex represents the sex of
the insects (1=male, 2=female), conc represents the concentration of the insecticide (mg/cm2 ), and freq
represents the frequency of the observations.
Example 58.14: Complementary Log-Log Model for Interval-Censored Survival Times F 4703
To use PROC LOGISTIC with the grouped survival data, you must expand the data so that each beetle has a
separate record for each day of survival. A beetle that died in the third day (time=3) would contribute three
observations to the analysis, one for each day it was alive at the beginning of the day. A beetle that survives
the 13-day duration of the experiment (time=14) would contribute 13 observations.
The following DATA step creates a new data set named Days containing the beetle-day observations from the
data set Beetles. In addition to the variables sex, conc, and freq, the data set contains an outcome variable y
and a classification variable day. The variable y has a value of 1 if the observation corresponds to the day that
the beetle died, and it has a value of 0 otherwise. An observation for the first day will have a value of 1 for
day; an observation for the second day will have a value of 2 for day, and so on. For instance, Output 58.14.1
shows an observation in the Beetles data set with time=3, and Output 58.14.2 shows the corresponding
beetle-day observations in the data set Days.
data Days;
set Beetles;
do day=1 to time;
if (day < 14) then do;
y= (day=time);
output;
end;
end;
run;
Output 58.14.1 An Observation with Time=3 in Beetles Data Set
Obs
17
time
3
conc
0.2
freq
sex
10
1
Output 58.14.2 Corresponding Beetle-Day Observations in Days
Obs
25
26
27
time
3
3
3
conc
0.2
0.2
0.2
freq
sex
day
y
10
10
10
1
1
1
1
2
3
0
0
1
The following statements invoke PROC LOGISTIC to fit a complementary log-log model for binary data with
the response variable Y and the explanatory variables day, sex, and Variableconc. Specifying the EVENT=
option ensures that the event (y=1) probability is modeled. The GLM coding in the CLASS statement creates
an indicator column in the design matrix for each level of day. The coefficients of the indicator effects for
day can be used to estimate the baseline survival function. The NOINT option is specified to prevent any
redundancy in estimating the coefficients of day. The Newton-Raphson algorithm is used for the maximum
likelihood estimation of the parameters.
4704 F Chapter 58: The LOGISTIC Procedure
proc logistic data=Days outest=est1;
class day / param=glm;
model y(event='1')= day sex conc
/ noint link=cloglog technique=newton;
freq freq;
run;
Results of the model fit are given in Output 58.14.3. Both sex and conc are statistically significant for the
survival of beetles sprayed by the insecticide. Female beetles are more resilient to the chemical than male
beetles, and increased concentration of the insecticide increases its effectiveness.
Output 58.14.3 Parameter Estimates for the Grouped Proportional Hazards Model
Analysis of Maximum Likelihood Estimates
Parameter
day
day
day
day
day
day
day
day
day
day
day
day
day
sex
conc
1
2
3
4
5
6
7
8
9
10
11
12
13
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
-3.9314
-2.8751
-2.3985
-1.9953
-2.4920
-3.1060
-3.9704
-3.7917
-5.1540
-5.1350
-5.1131
-5.1029
-5.0951
-0.5651
3.0918
0.2934
0.2412
0.2299
0.2239
0.2515
0.3037
0.4230
0.4007
0.7316
0.7315
0.7313
0.7313
0.7313
0.1141
0.2288
179.5602
142.0596
108.8833
79.3960
98.1470
104.5799
88.1107
89.5233
49.6329
49.2805
48.8834
48.6920
48.5467
24.5477
182.5665
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
The coefficients of parameters for the day variable are the maximum likelihood estimates of ˛1 ; : : : ; ˛13 ,
respectively. The baseline survivor function S0 .t / is estimated by
Y
b
SO0 .t/ D Pr.T
> t/ D
exp. exp.b
˛ j //
j t
and the survivor function for a given covariate pattern (sex=x1 and conc=x2 ) is estimated by
O
S.t/
D ŒSO0 .t /exp.
0:5651x1 C3:0918x2 /
The following statements compute the survival curves for male and female flour beetles exposed to the
insecticide in concentrations of 0.20 mg/cm2 and 0.80 mg/cm2 :
Example 58.14: Complementary Log-Log Model for Interval-Censored Survival Times F 4705
data one (keep=day survival element s_m20 s_f20 s_m80 s_f80);
array dd day1-day13;
array sc[4] m20 f20 m80 f80;
array s_sc[4] s_m20 s_f20 s_m80 s_f80 (1 1 1 1);
set est1;
m20= exp(sex + .20 * conc);
f20= exp(2 * sex + .20 * conc);
m80= exp(sex + .80 * conc);
f80= exp(2 * sex + .80 * conc);
survival=1;
day=0;
output;
do over dd;
element= exp(-exp(dd));
survival= survival * element;
do i=1 to 4;
s_sc[i] = survival ** sc[i];
end;
day + 1;
output;
end;
run;
Instead of plotting the curves as step functions, the following statements use the PBSPLINE statement in the
SGPLOT procedure to smooth the curves with a penalized B-spline. See Chapter 101, “The TRANSREG
Procedure,” for details about the implementation of the penalized B-spline method. The SAS autocall macro
%MODSTYLE is specified to change the marker symbols for the plot. For more information about the
%MODSTYLE macro, see the section “Style Template Modification Macro” on page 673 in Chapter 21,
“Statistical Graphics Using ODS.” The smoothed survival curves are displayed in Output 58.14.4.
%modstyle(name=LogiStyle,parent=htmlblue,markers=circlefilled);
ods listing style=LogiStyle;
proc sgplot data=one;
title 'Flour Beetles Sprayed with Insecticide';
xaxis grid integer;
yaxis grid label='Survival Function';
pbspline y=s_m20 x=day /
legendlabel = "Male at 0.20 conc." name="pred1";
pbspline y=s_m80 x=day /
legendlabel = "Male at 0.80 conc." name="pred2";
pbspline y=s_f20 x=day /
legendlabel = "Female at 0.20 conc." name="pred3";
pbspline y=s_f80 x=day /
legendlabel = "Female at 0.80 conc." name="pred4";
discretelegend "pred1" "pred2" "pred3" "pred4" / across=2;
run;
4706 F Chapter 58: The LOGISTIC Procedure
Output 58.14.4 Predicted Survival at Insecticide Concentrations of 0.20 and 0.80 mg/cm2
The probability of survival is displayed on the vertical axis. Notice that most of the insecticide effect occurs
by day 6 for both the high and low concentrations.
Example 58.15: Scoring Data Sets
This example first illustrates the syntax used for scoring data sets, then uses a previously scored data set
to score a new data set. A generalized logit model is fit to the remote-sensing data set used in the section
“Example 35.4: Linear Discriminant Analysis of Remote-Sensing Data on Crops” on page 2296 in Chapter 35,
“The DISCRIM Procedure,” to illustrate discrimination and classification methods. In the following DATA
step, the response variable is Crop and the prognostic factors are x1 through x4:
Example 58.15: Scoring Data Sets F 4707
data Crops;
length Crop $ 10;
infile datalines truncover;
input Crop $ @@;
do i=1 to 3;
input x1-x4 @@;
if (x1 ^= .) then output;
end;
input;
datalines;
Corn
16 27 31 33 15 23 30
Corn
18 20 25 23 15 15 31
Corn
12 15 16 73
Soybeans
20 23 23 25 24 24 25
Soybeans
27 45 24 12 12 13 15
Cotton
31 32 33 34 29 24 26
Cotton
26 25 23 24 53 48 75
Sugarbeets 22 23 25 42 25 25 24
Sugarbeets 54 23 21 54 25 43 32
Clover
12 45 32 54 24 58 25
Clover
51 31 31 16 96 48 54
Clover
56 13 13 71 32 13 27
Clover
53 08 06 54 32 32 62
;
30
32
16 27 27 26
15 32 32 15
32
42
28
26
26
15
34
62
32
16
21
22
34
34
34
26
87
31
36
25
32
32
35
25
54
54
31
26
23
31
28
25
16
2
61
11
54
24
43
45
78
52
54
21
11
32
In the following statements, you specify a SCORE statement to use the fitted model to score the Crops
data. The data together with the predicted values are saved in the data set Score1. The output from the
EFFECTPLOT statement is discussed at the end of this section.
ods graphics on;
proc logistic data=Crops;
model Crop=x1-x4 / link=glogit;
score out=Score1;
effectplot slicefit(x=x3);
run;
ods graphics off;
In the following statements, the model is fit again, and the data and the predicted values are saved into the
data set Score2. The OUTMODEL= option saves the fitted model information in the permanent SAS data
set sasuser.CropModel, and the STORE statement saves the fitted model information into the SAS data set
CropModel2. Both the OUTMODEL= option and the STORE statement are specified to illustrate their use;
you would usually specify only one of these model-storing methods.
proc logistic data=Crops outmodel=sasuser.CropModel;
model Crop=x1-x4 / link=glogit;
score data=Crops out=Score2;
store CropModel2;
run;
To score data without refitting the model, specify the INMODEL= option to identify a previously saved SAS
data set of model information. In the following statements, the model is read from the sasuser.CropModel
data set, and the data and the predicted values are saved in the data set Score3. Note that the data set being
scored does not have to include the response variable.
4708 F Chapter 58: The LOGISTIC Procedure
proc logistic inmodel=sasuser.CropModel;
score data=Crops out=Score3;
run;
Another method available to score the data without refitting the model is to invoke the PLM procedure. In
the following statements, the stored model is named in the SOURCE= option. The PREDICTED= option
computes the linear predictors, and the ILINK option transforms the linear predictors to the probability scale.
The SCORE statement scores the Crops data set, and the predicted probabilities are saved in the data set
ScorePLM. See Chapter 73, “The PLM Procedure,” for more information.
proc plm source=CropModel2;
score data=Crops out=ScorePLM predicted=p / ilink;
run;
For each observation in the Crops data set, the ScorePLM data set contains 5 observations—one for each
level of the response variable. The following statements transform this data set into a form that is similar to
the other scored data sets in this example:
proc transpose data=ScorePLM out=Score4 prefix=P_ let;
id _LEVEL_;
var p;
by x1-x4 notsorted;
run;
data Score4(drop=_NAME_ _LABEL_);
merge Score4 Crops(keep=Crop x1-x4);
F_Crop=Crop;
run;
proc summary data=ScorePLM nway;
by x1-x4 notsorted;
var p;
output out=into maxid(p(_LEVEL_))=I_Crop;
run;
data Score4;
merge Score4 into(keep=I_Crop);
run;
To set prior probabilities on the responses, specify the PRIOR= option to identify a SAS data set containing
the response levels and their priors. In the following statements, the Prior data set contains the values of the
response variable (because this example uses single-trial MODEL statement syntax) and a _PRIOR_ variable
containing values proportional to the default priors. The data and the predicted values are saved in the data
set Score5.
Example 58.15: Scoring Data Sets F 4709
data Prior;
length Crop $10.;
input Crop _PRIOR_;
datalines;
Clover
11
Corn
7
Cotton
6
Soybeans
6
Sugarbeets 6
;
proc logistic inmodel=sasuser.CropModel;
score data=Crops prior=prior out=Score5 fitstat;
run;
The “Fit Statistics for SCORE Data” table displayed in Output 58.15.1 shows that 47.22% of the observations
are misclassified.
Output 58.15.1 Fit Statistics for Data Set Prior
Fit Statistics for SCORE Data
Data Set
WORK.CROPS
Data Set
WORK.CROPS
Total
Frequency
Log
Likelihood
Error
Rate
AIC
AICC
BIC
36
-32.2247
0.4722
104.4493
160.4493
136.1197
SC
R-Square
Max-Rescaled
R-Square
AUC
Brier
Score
136.1197
0.744081
0.777285
.
0.492712
The data sets Score1, Score2, Score3, Score4, and Score5 are identical. The following statements display
the scoring results in Output 58.15.2:
proc freq data=Score1;
table F_Crop*I_Crop / nocol nocum nopercent;
run;
4710 F Chapter 58: The LOGISTIC Procedure
Output 58.15.2 Classification of Data Used for Scoring
Table of F_Crop by I_Crop
F_Crop(From: Crop)
I_Crop(Into: Crop)
Frequency
Row Pct
|
|Clover |Corn
|Cotton |Soybeans|Sugarbee|
|
|
|
|
|ts
|
-----------+--------+--------+--------+--------+--------+
Clover
|
6 |
0 |
2 |
2 |
1 |
| 54.55 |
0.00 | 18.18 | 18.18 |
9.09 |
-----------+--------+--------+--------+--------+--------+
Corn
|
0 |
7 |
0 |
0 |
0 |
|
0.00 | 100.00 |
0.00 |
0.00 |
0.00 |
-----------+--------+--------+--------+--------+--------+
Cotton
|
4 |
0 |
1 |
1 |
0 |
| 66.67 |
0.00 | 16.67 | 16.67 |
0.00 |
-----------+--------+--------+--------+--------+--------+
Soybeans
|
1 |
1 |
1 |
3 |
0 |
| 16.67 | 16.67 | 16.67 | 50.00 |
0.00 |
-----------+--------+--------+--------+--------+--------+
Sugarbeets |
2 |
0 |
0 |
2 |
2 |
| 33.33 |
0.00 |
0.00 | 33.33 | 33.33 |
-----------+--------+--------+--------+--------+--------+
Total
13
8
4
8
3
Total
11
7
6
6
6
36
The following statements use the previously fitted and saved model in the sasuser.CropModel data set to
score the observations in a new data set, Test. The results of scoring the test data are saved in the ScoredTest
data set and displayed in Output 58.15.3.
data Test;
input Crop
datalines;
Corn
16
Soybeans
21
Cotton
29
Sugarbeets 54
Clover
32
;
$ 1-10 x1-x4;
27
25
24
23
32
31
23
26
21
62
33
24
28
54
16
proc logistic noprint inmodel=sasuser.CropModel;
score data=Test out=ScoredTest;
run;
proc print data=ScoredTest label noobs;
var F_Crop I_Crop P_Clover P_Corn P_Cotton P_Soybeans P_Sugarbeets;
run;
Example 58.15: Scoring Data Sets F 4711
Output 58.15.3 Classification of Test Data
From: Crop
Into:
Crop
Corn
Soybeans
Cotton
Sugarbeets
Clover
Corn
Soybeans
Clover
Clover
Cotton
Predicted
Probability:
Crop=Clover
Predicted
Probability:
Crop=Corn
0.00342
0.04801
0.43180
0.66681
0.41301
0.90067
0.03157
0.00015
0.00000
0.13386
Predicted
Probability:
Crop=Cotton
Predicted
Probability:
Crop=Soybeans
Predicted
Probability:
Crop=Sugarbeets
0.00500
0.02865
0.21267
0.17364
0.43649
0.08675
0.82933
0.07623
0.00000
0.00033
0.00416
0.06243
0.27914
0.15955
0.01631
The EFFECTPLOT statement that is specified in the first PROC LOGISTIC invocation produces a plot
of the model-predicted probabilities versus X3 while holding the other three covariates at their means
(Output 58.15.4). This plot shows how the value of X3 affects the probabilities of the various crops when
the other prognostic factors are fixed at their means. If you are interested in the effect of X3 when the other
covariates are fixed at a certain level—say, 10—specify the following EFFECTPLOT statement.
effectplot slicefit(x=x3) / at(x1=10 x2=10 x4=10)
4712 F Chapter 58: The LOGISTIC Procedure
Output 58.15.4 Model-Predicted Probabilities
Example 58.16: Using the LSMEANS Statement
Recall the main-effects model fit to the Neuralgia data set in Example 58.2. The Treatment*Sex interaction,
which was previously shown to be nonsignificant, is added back into the model for this discussion.
In the following statements, the ODDSRATIO statement is specified to produce odds ratios of pairwise
differences of the Treatment parameters in the presence of the Sex interaction. The LSMEANS statement is
specified with several options: the E option displays the coefficients that are used to compute the LS-means
for each Treatment level, the DIFF option takes all pairwise differences of the LS-means for the levels of
the Treatment variable, the ODDSRATIO option computes odds ratios of these differences, the CL option
produces confidence intervals for the differences and odds ratios, and the ADJUST=BON option performs a
very conservative adjustment of the p-values and confidence intervals.
proc logistic data=Neuralgia;
class Treatment Sex / param=glm;
model Pain= Treatment|Sex Age;
oddsratio Treatment;
lsmeans Treatment / e diff oddsratio cl adjust=bon;
run;
Example 58.16: Using the LSMEANS Statement F 4713
The results from the ODDSRATIO statement are displayed in Output 58.16.1. All pairwise differences of
levels of the Treatment effect are compared. However, because of the interaction between the Treatment and
Sex variables, each difference is computed at each of the two levels of the Sex variable. These results show
that the difference between Treatment levels A and B is insignificant for both genders.
To compute these odds ratios, you must first construct a linear combination of the parameters, l 0 ˇ, for each
level that is compared with all other levels fixed at some value. For example, to compare Treatment=A with
B for Sex=F, you fix the Age variable at its mean, 70.05, and construct the following l vectors:
Intercept
lA0
lB0
lA0
lB0
1
1
0
Treatment
A B P
Sex
F M
1
0
1
1
1
0
0
1
–1
0
0
0
0
0
0
AF
1
0
1
Treatment*Sex
AM BF BM PF
0
0
0
0
1
–1
0
0
0
0
0
0
PM
Age
0
0
0
70.05
70.05
0
Then the odds ratio for Treatment A versus B at Sex=F is computed as exp..lA0 lB0 /ˇ/. Different l vectors
must be similarly constructed when Sex=M because the resulting odds ratio will be different due to the
interaction.
Output 58.16.1 Odds Ratios from the ODDSRATIO Statement
Odds Ratio Estimates and Wald Confidence Intervals
Odds Ratio
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Estimate
A
A
B
A
A
B
vs
vs
vs
vs
vs
vs
B
P
P
B
P
P
at
at
at
at
at
at
Sex=F
Sex=F
Sex=F
Sex=M
Sex=M
Sex=M
0.398
16.892
42.492
0.663
34.766
52.458
95% Confidence Limits
0.016
1.269
2.276
0.078
1.807
2.258
9.722
224.838
793.254
5.623
668.724
>999.999
The results from the LSMEANS statement are displayed in Output 58.16.2 through Output 58.16.4.
The LS-means are computed by constructing each of the l coefficient vectors shown in Output 58.16.2, and
then computing l 0 ˇ. The LS-means are not estimates of the event probabilities; they are estimates of the
linear predictors on the logit scale. In order to obtain event probabilities, you need to apply the inverse-link
transformation by specifying the ILINK option in the LSMEANS statement. Notice in Output 58.16.2 that
the Sex rows do not indicate either Sex=F or Sex=M. Instead, the LS-means are computed at an average of
these two levels, so only one result needs to be reported. For more information about the construction of
LS-means, see the section “Construction of Least Squares Means” on page 3533 in Chapter 44, “The GLM
Procedure.”
4714 F Chapter 58: The LOGISTIC Procedure
Output 58.16.2 Treatment LS-Means Coefficients
Coefficients for Treatment Least Squares Means
Parameter
Treatment
Intercept: Pain=No
Treatment A
Treatment B
Treatment P
Sex F
Sex M
Treatment A * Sex F
Treatment A * Sex M
Treatment B * Sex F
Treatment B * Sex M
Treatment P * Sex F
Treatment P * Sex M
Age
Sex
A
B
P
Row1
Row2
Row3
1
1
1
1
1
F
M
F
M
F
M
F
M
A
A
B
B
P
P
0.5
0.5
0.5
0.5
0.5
0.5
1
0.5
0.5
0.5
0.5
70.05
70.05
0.5
0.5
70.05
The Treatment LS-means shown in Output 58.16.3 are all significantly nonzero at the 0.05 level. These
LS-means are predicted population margins of the logits; that is, they estimate the marginal means over a
balanced population, and they are effectively the within-Treatment means appropriately adjusted for the other
effects in the model. The LS-means are not event probabilities; in order to obtain event probabilities, you
need to apply the inverse-link transformation by specifying the ILINK option in the LSMEANS statement.
For more information about LS-means, see the section “LSMEANS Statement” on page 460 in Chapter 19,
“Shared Concepts and Topics.”
Output 58.16.3 Treatment LS-Means
Treatment Least Squares Means
Treatment
A
B
P
Estimate
Standard
Error
z Value
Pr > |z|
Alpha
Lower
Upper
1.3195
1.9864
-1.8682
0.6664
0.7874
0.7620
1.98
2.52
-2.45
0.0477
0.0116
0.0142
0.05
0.05
0.05
0.01331
0.4431
-3.3618
2.6257
3.5297
-0.3747
Pairwise differences between the Treatment LS-means, requested with the DIFF option, are displayed in
Output 58.16.4. The LS-mean for the level that is displayed in the _Treatment column is subtracted from
the LS-mean for the level in the Treatment column, so the first row displays the LS-mean for Treatment
level A minus the LS-mean for Treatment level B. The Pr > |z| column indicates that the A and B levels
are not significantly different; however, both of these levels are different from level P. If the inverse-link
transformation is specified with the ILINK option, then these differences do not transform back to differences
in probabilities.
There are two odds ratios for Treatment level A versus B in Output 58.16.1; these are constructed at each
level of the interacting covariate Sex. In contrast, there is only one LS-means odds ratio for Treatment
level A versus B in Output 58.16.4. This odds ratio is computed at an average of the interacting effects by
Example 58.16: Using the LSMEANS Statement F 4715
creating the l vectors shown in Output 58.16.2 (the Row1 column corresponds to lA and the Row2 column
corresponds to lB ) and computing exp.lA0 ˇ lB0 ˇ/.
Since multiple tests are performed, you can protect yourself from falsely significant results by adjusting
your p-values for multiplicity. The ADJUST=BON option performs the very conservative Bonferroni
adjustment, and adds the columns labeled with ‘Adj’ to Output 58.16.4. Comparing the Pr > |z| column to the
Adj P column, you can see that the p-values are adjusted upwards; in this case, there is no change in your
conclusions. The confidence intervals are also adjusted for multiplicity—all adjusted intervals are wider than
the unadjusted intervals, but again your conclusions in this example are unchanged.
Output 58.16.4 Differences and Odds Ratios for the Treatment LS-Means
Differences of Treatment Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Treatment
_Treatment
A
A
B
B
P
P
Estimate
Standard
Error
z Value
Pr > |z|
Adj P
Alpha
-0.6669
3.1877
3.8547
1.0026
1.0376
1.2126
-0.67
3.07
3.18
0.5059
0.0021
0.0015
1.0000
0.0064
0.0044
0.05
0.05
0.05
Differences of Treatment Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Treatment
_Treatment
A
A
B
B
P
P
Lower
Upper
Adj
Lower
Adj
Upper
Odds
Ratio
-2.6321
1.1541
1.4780
1.2982
5.2214
6.2313
-3.0672
0.7037
0.9517
1.7334
5.6717
6.7576
0.513
24.234
47.213
Differences of Treatment Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Treatment
_Treatment
A
A
B
B
P
P
Lower
Confidence
Limit for
Odds Ratio
Upper
Confidence
Limit for
Odds Ratio
Adj Lower
Odds Ratio
Adj Upper
Odds Ratio
0.072
3.171
4.384
3.663
185.195
508.441
0.047
2.021
2.590
5.660
290.542
860.612
If you want to jointly test whether the active treatments are different from the placebo, you can specify a
custom hypothesis test with the LSMESTIMATE statement. In the following statements, the LS-means for
the two treatments are contrasted against the LS-mean of the placebo, and the JOINT option performs a joint
test that the two treatments are not different from placebo.
4716 F Chapter 58: The LOGISTIC Procedure
proc logistic data=Neuralgia;
class Treatment Sex / param=glm;
model Pain= Treatment|Sex Age;
lsmestimate treatment 1 0 -1, 0 1 -1 / joint;
run;
Output 58.16.5 displays the results from the LSMESTIMATE statement. The “Least Squares Means Estimate”
table displays the differences of the two active treatments against the placebo, and the results are identical
to the second and third rows of Output 58.16.3. The “Chi-Square Test for Least Squares Means Estimates”
table displays the joint test. In all of these tests, you reject the null hypothesis that the treatment has the same
effect as the placebo.
Output 58.16.5 Custom LS-Mean Tests
Least Squares Means Estimates
Effect
Label
Estimate
Standard
Error
z Value
Pr > |z|
Treatment
Treatment
Row 1
Row 2
3.1877
3.8547
1.0376
1.2126
3.07
3.18
0.0021
0.0015
Chi-Square Test for Least
Squares Means Estimates
Effect
Treatment
Num
DF
Chi-Square
Pr > ChiSq
2
12.13
0.0023
If you want to work with LS-means but you prefer to compute the Treatment odds ratios within the Sex
levels in the same fashion as the ODDSRATIO statement does, you can specify the SLICE statement. In
the following statements, you specify the same options in the SLICE statement as you do in the LSMEANS
statement, except that you also specify the SLICEBY= option to perform an LS-means analysis partitioned
into sets that are defined by the Sex variable:
proc logistic data=Neuralgia;
class Treatment Sex / param=glm;
model Pain= Treatment|Sex Age;
slice Treatment*Sex / sliceby=Sex diff oddsratio cl adjust=bon;
run;
The results for Sex=F are displayed in Output 58.16.6 and Output 58.16.7. The joint test in Output 58.16.6
tests the equality of the LS-means of the levels of Treatment for Sex=F, and rejects equality at level 0.05. In
Output 58.16.7, the odds ratios and confidence intervals match those reported for Sex=F in Output 58.16.1,
and multiplicity adjustments are performed.
Example 58.16: Using the LSMEANS Statement F 4717
Output 58.16.6 Joint Test of Treatment Equality for Females
Chi-Square Test for Treatment*Sex
Least Squares Means Slice
Slice
Num
DF
Chi-Square
Pr > ChiSq
Sex F
2
8.22
0.0164
Output 58.16.7 Differences of the Treatment LS-Means for Females
Simple Differences of Treatment*Sex Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Slice
Treatment
_Treatment
Sex F
Sex F
Sex F
A
A
B
B
P
P
Estimate
Standard
Error
z Value
Pr > |z|
Adj P
-0.9224
2.8269
3.7493
1.6311
1.3207
1.4933
-0.57
2.14
2.51
0.5717
0.0323
0.0120
1.0000
0.0970
0.0361
Simple Differences of Treatment*Sex Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Slice
Treatment
_Treatment
Sex F
Sex F
Sex F
A
A
B
B
P
P
Alpha
Lower
Upper
Adj
Lower
Adj
Upper
0.05
0.05
0.05
-4.1193
0.2384
0.8225
2.2744
5.4154
6.6761
-4.8272
-0.3348
0.1744
2.9824
5.9886
7.3243
Simple Differences of Treatment*Sex Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Slice
Treatment
_Treatment
Sex F
Sex F
Sex F
A
A
B
B
P
P
Odds
Ratio
Lower
Confidence
Limit for
Odds Ratio
Upper
Confidence
Limit for
Odds Ratio
0.398
16.892
42.492
0.016
1.269
2.276
9.722
224.838
793.254
Simple Differences of Treatment*Sex
Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Slice
Treatment
_Treatment
Sex F
Sex F
Sex F
A
A
B
B
P
P
Adj Lower
Odds Ratio
Adj Upper
Odds Ratio
0.008
0.715
1.190
19.734
398.848
>999.999
Similarly, the results for Sex=M are shown in Output 58.16.8 and Output 58.16.9.
4718 F Chapter 58: The LOGISTIC Procedure
Output 58.16.8 Joint Test of Treatment Equality for Males
Chi-Square Test for Treatment*Sex
Least Squares Means Slice
Slice
Num
DF
Chi-Square
Pr > ChiSq
Sex M
2
6.64
0.0361
Output 58.16.9 Differences of the Treatment LS-Means for Males
Simple Differences of Treatment*Sex Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Slice
Treatment
_Treatment
Sex M
Sex M
Sex M
A
A
B
B
P
P
Estimate
Standard
Error
z Value
Pr > |z|
Adj P
-0.4114
3.5486
3.9600
1.0910
1.5086
1.6049
-0.38
2.35
2.47
0.7061
0.0187
0.0136
1.0000
0.0560
0.0408
Simple Differences of Treatment*Sex Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Slice
Treatment
_Treatment
Sex M
Sex M
Sex M
A
A
B
B
P
P
Alpha
Lower
Upper
Adj
Lower
Adj
Upper
0.05
0.05
0.05
-2.5496
0.5919
0.8145
1.7268
6.5054
7.1055
-3.0231
-0.06286
0.1180
2.2003
7.1601
7.8021
Simple Differences of Treatment*Sex Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Slice
Treatment
_Treatment
Sex M
Sex M
Sex M
A
A
B
B
P
P
Odds
Ratio
Lower
Confidence
Limit for
Odds Ratio
Upper
Confidence
Limit for
Odds Ratio
0.663
34.766
52.458
0.078
1.807
2.258
5.623
668.724
>999.999
Simple Differences of Treatment*Sex
Least Squares Means
Adjustment for Multiple Comparisons: Bonferroni
Slice
Treatment
_Treatment
Sex M
Sex M
Sex M
A
A
B
B
P
P
Adj Lower
Odds Ratio
Adj Upper
Odds Ratio
0.049
0.939
1.125
9.028
>999.999
>999.999
Example 58.17: Partial Proportional Odds Model F 4719
Example 58.17: Partial Proportional Odds Model
Cameron and Trivedi (1998, p. 68) studied the number of doctor visits from the Australian Health Survey
1977–78. The data set contains a dependent variable, dvisits, which contains the number of doctor visits
in the past two weeks (0, 1, or 2, where 2 represents two or more visits) and the following explanatory
variables: sex, which indicates whether the patient is female; age, which contains the patient’s age in years
divided by 100; income, which contains the patient’s annual income (in units of $10,000); levyplus, which
indicates whether the patient has private health insurance; freepoor, which indicates that the patient has
free government health insurance due to low income; freerepa, which indicates that the patient has free
government health insurance for other reasons; illness, which contains the number of illnesses in the past two
weeks; actdays, which contains the number of days the illness caused reduced activity; hscore, which is a
questionnaire score; chcond1, which indicates a chronic condition that does not limit activity; and chcond2,
which indicates a chronic condition that limits activity.
data docvisit;
input sex age agesq income levyplus freepoor freerepa
illness actdays hscore chcond1 chcond2 dvisits;
if ( dvisits > 2) then dvisits = 2;
datalines;
1 0.19 0.0361 0.55 1 0 0 1 4 1 0 0 1
1 0.19 0.0361 0.45 1 0 0 1 2 1 0 0 1
0 0.19 0.0361 0.90 0 0 0 3 0 0 0 0 1
... more lines ...
1 0.37 0.1369 0.25
1 0.52 0.2704 0.65
0 0.72 0.5184 0.25
;
0
0
0
0
0
0
1
0
1
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
Because the response variable dvisits has three levels, the proportional odds model constructs two response
functions. There is an intercept parameter for each of the two response functions, ˛1 < ˛2 , and common
slope parameters ˇ D .ˇ1 ; : : : ; ˇ12 / across the functions. The model can be written as
logit.Pr.Y i j x// D ˛i C ˇ 0 x;
i D 1; 2
The following statements fit a proportional odds model to this data:
proc logistic data=docvisit;
model dvisits = sex age agesq income levyplus
freepoor freerepa illness actdays hscore
chcond1 chcond2;
run;
Selected results are displayed in Output 58.17.1.
4720 F Chapter 58: The LOGISTIC Procedure
Output 58.17.1 Test of Proportional Odds Assumption
Score Test for the Proportional Odds Assumption
Chi-Square
DF
Pr > ChiSq
27.4256
12
0.0067
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
734.2971
811.8964
690.7156
12
12
12
<.0001
<.0001
<.0001
The test of the proportional odds assumption in Output 58.17.1 rejects the null hypothesis that all the slopes
are equal across the two response functions. This test is very anticonservative; that is, it tends to reject the
null hypothesis even when the proportional odds assumption is reasonable.
The proportional odds assumption for ordinal response models can be relaxed by specifying the
UNEQUALSLOPES option in the MODEL statement. A general model has different slope parameters
ˇi D .ˇ1;i ; : : : ; ˇ12;i / for every logit i:
logit.Pr.Y i j x// D ˛i C ˇi0 x;
i D 1; 2
The general model is fit with the following statements. The TEST statements test the proportional odds
assumption for each of the covariates in the model.
proc logistic data=docvisit;
model dvisits = sex age agesq income levyplus
freepoor freerepa illness actdays hscore
chcond1 chcond2 / unequalslopes;
sex:
test sex_0
=sex_1;
age:
test age_0
=age_1;
agesq:
test agesq_0
=agesq_1;
income:
test income_0 =income_1;
levyplus: test levyplus_0=levyplus_1;
freepoor: test freepoor_0=freepoor_1;
freerepa: test freerepa_0=freerepa_1;
illness: test illness_0 =illness_1;
actdays: test actdays_0 =actdays_1;
hscore:
test hscore_0 =hscore_1;
chcond1: test chcond1_0 =chcond1_1;
chcond2: test chcond2_0 =chcond2_1;
run;
Selected results from fitting the general model to the data are displayed in Output 58.17.2.
Example 58.17: Partial Proportional Odds Model F 4721
Output 58.17.2 Results for the General Model
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
761.4797
957.6793
688.2306
24
24
24
<.0001
<.0001
<.0001
Analysis of Maximum Likelihood Estimates
Parameter
dvisits
Intercept
Intercept
sex
sex
age
age
agesq
agesq
income
income
levyplus
levyplus
freepoor
freepoor
freerepa
freerepa
illness
illness
actdays
actdays
hscore
hscore
chcond1
chcond1
chcond2
chcond2
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
0
1
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2.3238
4.2862
-0.2637
-0.1232
1.7489
-2.0974
-2.4718
2.6883
-0.00857
0.6464
-0.2658
-0.2869
0.6773
0.9020
-0.4044
-0.0958
-0.2645
-0.3083
-0.1521
-0.1863
-0.0620
-0.0568
-0.1140
-0.2478
-0.2660
-0.3146
0.2754
0.4890
0.0818
0.1451
1.5115
2.6003
1.6636
2.8398
0.1266
0.2375
0.0997
0.1820
0.2601
0.4911
0.1382
0.2361
0.0287
0.0499
0.0116
0.0134
0.0172
0.0252
0.0909
0.1743
0.1255
0.2116
71.2018
76.8368
10.3909
0.7210
1.3389
0.6506
2.2076
0.8961
0.0046
7.4075
7.0999
2.4848
6.7811
3.3730
8.5637
0.1648
84.6792
38.1652
172.2764
193.7700
12.9996
5.0940
1.5721
2.0201
4.4918
2.2106
<.0001
<.0001
0.0013
0.3958
0.2472
0.4199
0.1373
0.3438
0.9460
0.0065
0.0077
0.1150
0.0092
0.0663
0.0034
0.6848
<.0001
<.0001
<.0001
<.0001
0.0003
0.0240
0.2099
0.1552
0.0341
0.1371
4722 F Chapter 58: The LOGISTIC Procedure
Output 58.17.2 continued
Linear Hypotheses Testing Results
Label
sex
age
agesq
income
levyplus
freepoor
freerepa
illness
actdays
hscore
chcond1
chcond2
Wald
Chi-Square
DF
Pr > ChiSq
1.0981
2.5658
3.8309
8.8006
0.0162
0.2569
2.0099
0.8630
6.9407
0.0476
0.6906
0.0615
1
1
1
1
1
1
1
1
1
1
1
1
0.2947
0.1092
0.0503
0.0030
0.8989
0.6122
0.1563
0.3529
0.0084
0.8273
0.4060
0.8042
The preceding general model fits 12 2 D 24 slope parameters, and the model seems to overfit the data. You
can obtain a more parsimonious model by specifying a subset of the parameters to have nonproportional odds.
The following statements allow the parameters for the variables in the “Linear Hypotheses Testing Results”
table that have p-values less than 0.1 (actdays, agesq, and income) to vary across the response functions:
proc logistic data=docvisit;
model dvisits= sex age agesq income levyplus freepoor
freerepa illness actdays hscore chcond1 chcond2
/ unequalslopes=(actdays agesq income);
run;
Selected results from fitting this partial proportional odds model are displayed in Output 58.17.3. For more
information about model selection for partial proportional odds models, see Derr (2013).
Output 58.17.3 Results for Partial Proportional Odds Model
Testing Global Null Hypothesis: BETA=0
Test
Likelihood Ratio
Score
Wald
Chi-Square
DF
Pr > ChiSq
752.5512
947.3269
683.4719
15
15
15
<.0001
<.0001
<.0001
Example 58.17: Partial Proportional Odds Model F 4723
Output 58.17.3 continued
Analysis of Maximum Likelihood Estimates
Parameter
dvisits
Intercept
Intercept
sex
age
agesq
agesq
income
income
levyplus
freepoor
freerepa
illness
actdays
actdays
hscore
chcond1
chcond2
0
1
0
1
0
1
0
1
DF
Estimate
Standard
Error
Wald
Chi-Square
Pr > ChiSq
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2.3882
3.7597
-0.2485
1.3000
-2.0110
-0.8789
0.0209
0.4283
-0.2703
0.6936
-0.3648
-0.2707
-0.1522
-0.1868
-0.0609
-0.1200
-0.2628
0.2716
0.3138
0.0807
1.4864
1.6345
1.6512
0.1261
0.2221
0.0989
0.2589
0.1358
0.0281
0.0115
0.0129
0.0166
0.0901
0.1227
77.2988
143.5386
9.4789
0.7649
1.5139
0.2833
0.0275
3.7190
7.4735
7.1785
7.2155
92.7123
173.5696
209.7134
13.5137
1.7756
4.5849
<.0001
<.0001
0.0021
0.3818
0.2186
0.5945
0.8683
0.0538
0.0063
0.0074
0.0072
<.0001
<.0001
<.0001
0.0002
0.1827
0.0323
The partial proportional odds model can be written in the same form as the general model by letting
x D .x1 ; : : : ; xq ; xqC1 ; : : : ; x12 / and ˇi D .ˇ1 ; : : : ; ˇq ; ˇqC1;i ; : : : ; ˇ12;i /, so the first q parameters have
proportional odds and the remaining parameters do not. The last 12–q parameters can be rewritten to have a
common slope: ˇqCj C qCj;i ; j D 1; : : : ; 12 q, where the new parameters i contain the increments
from the common slopes. The model in this form makes it obvious that the proportional odds model is a
submodel of the partial proportional odds models, and both of these are submodels of the general model.
This means that you can use likelihood ratio tests to compare models.
You can use the following statements to compute the likelihood ratio tests from the Likelihood Ratio row of
the “Testing Global Null hypothesis: BETA=0” tables in the preceding outputs:
data a;
label p='Pr>ChiSq';
format p 8.6;
input Test $10. ChiSq1 DF1 ChiSq2 DF2;
ChiSq= ChiSq1-ChiSq2;
DF= DF1-DF2;
p=1-probchi(ChiSq,DF);
keep Test Chisq DF p;
datalines;
Gen vs PO
761.4797 24
734.2971 12
PPO vs PO
752.5512 15
734.2971 12
Gen vs PPO
761.4797 24
752.5512 15
;
proc print data=a label noobs;
var Test ChiSq DF p;
run;
4724 F Chapter 58: The LOGISTIC Procedure
Output 58.17.4 Likelihood Ratio Tests
Test
ChiSq
DF
Pr>ChiSq
Gen vs PO
PPO vs PO
Gen vs PPO
27.1826
18.2541
8.9285
12
3
9
0.007273
0.000390
0.443900
Therefore, you reject the proportional odds model in favor of both the general model and the partial
proportional odds model, and the partial proportional odds model fits as well as the general model. The
likelihood ratio test of the general model versus the proportional odds model is very similar to the score test
of the proportional odds assumption in Output 58.17.1 because of the large sample size (Stokes, Davis, and
Koch 2000, p. 249).
N OTE : The proportional odds model has increasing intercepts, which ensures the increasing nature of
the cumulative response functions. However, none of the parameters in the partial proportional odds or
general models are constrained. Because of this, sometimes during the optimization process a predicted
individual probability can be negative; the optimization continues because it might recover from this situation.
Sometimes your final model will predict negative individual probabilities for some of the observations; in this
case a message is displayed, and you should check your data for outliers and possibly redefine your model.
Other times the model fits your data well, but if you try to score new data you can get negative individual
probabilities. This means the model is not appropriate for the data you are trying to score, a message is
displayed, and the estimates are set to missing.
References
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4728 F Chapter 58: The LOGISTIC Procedure
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Subject Index
Akaike’s information criterion
LOGISTIC procedure, 4567
backward elimination
LOGISTIC procedure, 4541, 4566
Bayes’ theorem
LOGISTIC procedure, 4539, 4578
best subset selection
LOGISTIC procedure, 4533, 4541, 4567
branch-and-bound algorithm
LOGISTIC procedure, 4567
classification table
LOGISTIC procedure, 4539, 4577, 4578, 4658
complete separation
LOGISTIC procedure, 4565
conditional logistic regression
LOGISTIC procedure, 4594
LOGISTIC procedure, 4555
confidence intervals
profile likelihood (LOGISTIC), 4539, 4571
Wald (LOGISTIC), 4543, 4572
confidence limits
LOGISTIC procedure, 4576
convergence criterion
profile likelihood (LOGISTIC), 4539
correct classification rate
LOGISTIC procedure, 4578
descriptive statistics
LOGISTIC procedure, 4504
deviance
LOGISTIC procedure, 4533, 4541, 4580
deviance residuals
LOGISTIC procedure, 4587
DFBETAS statistics
LOGISTIC procedure, 4588
dispersion parameter
LOGISTIC procedure, 4580
estimability checking
LOGISTIC procedure, 4515
exact conditional logistic regression, see exact logistic
regression
exact logistic regression
LOGISTIC procedure, 4597
LOGISTIC procedure, 4520
false negative, false positive rate
LOGISTIC procedure, 4539, 4578, 4658
Firth’s penalized likelihood
LOGISTIC procedure, 4564
Fisher scoring algorithm
LOGISTIC procedure, 4542, 4543, 4563
forward selection
LOGISTIC procedure, 4541, 4566
frequency variable
LOGISTIC procedure, 4525
gradient
LOGISTIC procedure, 4569
hat matrix
LOGISTIC procedure, 4587
Hessian matrix
LOGISTIC procedure, 4542, 4569
hierarchy
LOGISTIC procedure, 4535
Hosmer-Lemeshow test
LOGISTIC procedure, 4537, 4581
test statistic (LOGISTIC), 4582
infinite parameter estimates
LOGISTIC procedure, 4538, 4565
initial values
LOGISTIC procedure, 4603
leverage
LOGISTIC procedure, 4587
likelihood residuals
LOGISTIC procedure, 4587
link function
LOGISTIC procedure, 4485, 4537, 4561, 4570
log likelihood
output data sets (LOGISTIC), 4500
log odds
LOGISTIC procedure, 4573
LOGISTIC procedure
Akaike’s information criterion, 4567
Bayes’ theorem, 4539
best subset selection, 4533
branch-and-bound algorithm, 4567
classification table, 4539, 4577, 4578, 4658
conditional logistic regression, 4594
confidence intervals, 4539, 4543, 4571, 4572
confidence limits, 4576
convergence criterion, 4532
customized odds ratio, 4557
descriptive statistics, 4504
deviance, 4533, 4541, 4580
DFBETAS diagnostic, 4588
dispersion parameter, 4580
displayed output, 4609
estimability checking, 4515
exact logistic regression, 4597
existence of MLEs, 4565
Firth’s penalized likelihood, 4564
Fisher scoring algorithm, 4542, 4543, 4563
frequency variable, 4525
goodness of fit, 4533, 4541
gradient, 4569
hat matrix, 4587
Hessian matrix, 4542, 4569
hierarchy, 4535
Hosmer-Lemeshow test, 4537, 4581, 4582
infinite parameter estimates, 4538
initial values, 4603
introductory example, 4488
leverage, 4587
link function, 4485, 4537, 4561, 4570
log odds, 4573
maximum likelihood algorithms, 4563
missing values, 4559
model fitting criteria, 4567
model hierarchy, 4486, 4535
model selection, 4531, 4541, 4566
multiple classifications, 4540
Newton-Raphson algorithm, 4542, 4543, 4563,
4564
odds ratio confidence limits, 4533, 4540
odds ratio estimation, 4572
odds ratios with interactions, 4544
ODS graph names, 4616
ODS table names, 4614
optimization, 4544
output data sets, 4500, 4601, 4603, 4604, 4606
overdispersion, 4540, 4579, 4580
parallel lines assumption, 4543, 4719
partial proportional odds model, 4543, 4719
Pearson’s chi-square, 4533, 4541, 4580
predicted probabilities, 4576
prior event probability, 4539, 4579, 4658
profile-likelihood convergence criterion, 4539
rank correlation, 4575
regression diagnostics, 4586
residuals, 4587
response level ordering, 4498, 4529, 4559
ROC curve, 4539, 4551, 4582, 4606
ROC curve, comparing, 4551, 4583
Schwarz criterion, 4567
score statistics, 4569
scoring data sets, 4552, 4589
selection methods, 4531, 4541, 4566
singular contrast matrix, 4515
subpopulation, 4533, 4541, 4580
testing linear hypotheses, 4557, 4585
Williams’ method, 4580
logistic regression, see also LOGISTIC procedure
LOGISTIC procedure
conditional logistic regression, 4555
convergence criterion, 4523
exact logistic regression, 4520
stratified exact logistic regression, 4555
maximum likelihood
algorithms (LOGISTIC), 4563
estimates (LOGISTIC), 4565
missing values
LOGISTIC procedure, 4559
model
fitting criteria (LOGISTIC), 4567
hierarchy (LOGISTIC), 4486, 4535
model selection
LOGISTIC procedure, 4531, 4541, 4566
multiple classifications
cutpoints (LOGISTIC), 4540
Newton-Raphson algorithm
LOGISTIC procedure, 4542, 4543, 4563, 4564
odds ratio
confidence limits (LOGISTIC), 4533, 4540
customized (LOGISTIC), 4557
estimation (LOGISTIC), 4572
with interactions (LOGISTIC), 4544
ODS graph names
LOGISTIC procedure, 4616
optimization
LOGISTIC procedure, 4544
options summary
EFFECT statement, 4517
ESTIMATE statement, 4519
output data sets
LOGISTIC procedure, 4601, 4603, 4604, 4606
overdispersion
LOGISTIC procedure, 4540, 4579, 4580
overlap of data points
LOGISTIC procedure, 4566
parallel lines assumption
LOGISTIC procedure, 4543, 4719
partial proportional odds model
LOGISTIC procedure, 4543, 4719
Pearson residuals
LOGISTIC procedure, 4587
Pearson’s chi-square
LOGISTIC procedure, 4533, 4541, 4580
predicted probabilities
LOGISTIC procedure, 4576
prior event probability
LOGISTIC procedure, 4539, 4578, 4579, 4658
quasi-complete separation
LOGISTIC procedure, 4565
R-square statistic
LOGISTIC procedure, 4540, 4568
rank correlation
LOGISTIC procedure, 4575
receiver operating characteristic, see ROC curve
regression diagnostics
LOGISTIC procedure, 4586
residuals
LOGISTIC procedure, 4587
response level ordering
LOGISTIC procedure, 4498, 4529, 4559
reverse response level ordering
LOGISTIC procedure, 4559
ROC curve
comparing (LOGISTIC), 4551, 4583
LOGISTIC procedure, 4539, 4551, 4582, 4606
Schwarz criterion
LOGISTIC procedure, 4567
score statistics
LOGISTIC procedure, 4569
selection methods, see model selection
singularity criterion
contrast matrix (LOGISTIC), 4515
standardized deviance residuals
LOGISTIC procedure, 4587
standardized Pearson residuals
LOGISTIC procedure, 4587
stepwise selection
LOGISTIC procedure, 4541, 4567, 4618
stratified exact logistic regression
LOGISTIC procedure, 4555
subpopulation
LOGISTIC procedure, 4541
survivor function
estimates (LOGISTIC), 4704
testing linear hypotheses
LOGISTIC procedure, 4557, 4585
Williams’ method
overdispersion (LOGISTIC), 4580
Syntax Index
ABSFCONV option
MODEL statement (LOGISTIC), 4523, 4532
ADJACENTPAIRS option
ROCCONTRAST statement (LOGISTIC), 4552
AGGREGATE= option
MODEL statement (LOGISTIC), 4533
ALPHA= option
CONTRAST statement (LOGISTIC), 4514
EXACT statement (LOGISTIC), 4521
MODEL statement (LOGISTIC), 4533
OUTPUT statement (LOGISTIC), 4547
PROC LOGISTIC statement, 4497
SCORE statement (LOGISTIC), 4553
AT option
ODDSRATIO statement (LOGISTIC), 4545
BEST= option
MODEL statement (LOGISTIC), 4533
BINWIDTH= option
MODEL statement (LOGISTIC), 4533
BY statement
LOGISTIC procedure, 4509
C= option
OUTPUT statement (LOGISTIC), 4547
CBAR= option
OUTPUT statement (LOGISTIC), 4547
CHECKDEPENDENCY= option
STRATA statement (LOGISTIC), 4556
CL option
MODEL statement (LOGISTIC), 4543
CL= option
ODDSRATIO statement (LOGISTIC), 4545
CLASS statement
LOGISTIC procedure, 4510
CLM option
SCORE statement (LOGISTIC), 4553
CLODDS= option
MODEL statement (LOGISTIC), 4533
CLPARM= option
MODEL statement (LOGISTIC), 4534
CLTYPE= option
EXACT statement (LOGISTIC), 4521
CODE statement
LOGISTIC procedure, 4513
CONTRAST statement
LOGISTIC procedure, 4513
CORRB option
MODEL statement (LOGISTIC), 4534
COV option
ROCCONTRAST statement (LOGISTIC), 4552
COVB option
MODEL statement (LOGISTIC), 4534
COVOUT option
PROC LOGISTIC statement, 4498
CPREFIX= option
CLASS statement (LOGISTIC), 4510
CTABLE option
MODEL statement (LOGISTIC), 4534
CUMULATIVE option
SCORE statement (LOGISTIC), 4553
DATA= option
PROC LOGISTIC statement, 4498
SCORE statement (LOGISTIC), 4553
DEFAULT= option
UNITS statement (LOGISTIC), 4558
DESCENDING option
CLASS statement (LOGISTIC), 4510
MODEL statement, 4529
PROC LOGISTIC statement, 4498
DETAILS option
MODEL statement (LOGISTIC), 4534
DFBETAS= option
OUTPUT statement (LOGISTIC), 4547
DIFCHISQ= option
OUTPUT statement (LOGISTIC), 4548
DIFDEV= option
OUTPUT statement (LOGISTIC), 4548
DIFF= option
ODDSRATIO statement (LOGISTIC), 4545
E option
CONTRAST statement (LOGISTIC), 4514
ROCCONTRAST statement (LOGISTIC), 4552
EFFECT statement
LOGISTIC procedure, 4516
EFFECTPLOT statement
LOGISTIC procedure, 4518
ESTIMATE option
EXACT statement (LOGISTIC), 4521
ROCCONTRAST statement (LOGISTIC), 4552
ESTIMATE statement
LOGISTIC procedure, 4519
ESTIMATE= option
CONTRAST statement (LOGISTIC), 4514
EVENT= option
MODEL statement, 4529
EXACT statement
LOGISTIC procedure, 4520
EXACTONLY option
PROC LOGISTIC statement, 4498
EXACTOPTIONS option
PROC LOGISTIC statement, 4498
EXACTOPTIONS statement
LOGISTIC procedure, 4522
EXPEST option
MODEL statement (LOGISTIC), 4534
FAST option
MODEL statement (LOGISTIC), 4534
FCONV= option
MODEL statement (LOGISTIC), 4523, 4535
FIRTH option
MODEL statement (LOGISTIC), 4535
FITSTAT option
SCORE statement (LOGISTIC), 4554
FREQ statement
LOGISTIC procedure, 4525
GCONV= option
MODEL statement (LOGISTIC), 4535
H= option
OUTPUT statement (LOGISTIC), 4548
HIERARCHY= option
MODEL statement (LOGISTIC), 4535
ID statement
LOGISTIC procedure, 4526
INCLUDE= option
MODEL statement (LOGISTIC), 4536
INEST= option
PROC LOGISTIC statement, 4499
INFLUENCE option
MODEL statement (LOGISTIC), 4536
INFO option
STRATA statement (LOGISTIC), 4556
INMODEL= option
PROC LOGISTIC statement, 4499
IPLOTS option
MODEL statement (LOGISTIC), 4536
ITPRINT option
MODEL statement (LOGISTIC), 4537
JOINT option
EXACT statement (LOGISTIC), 4521
JOINTONLY option
EXACT statement (LOGISTIC), 4521
LACKFIT option
MODEL statement (LOGISTIC), 4537
LINK= option
MODEL statement (LOGISTIC), 4537
ROC statement (LOGISTIC), 4551
LOGISTIC procedure, 4496
ID statement, 4526
NLOPTIONS statement, 4544
syntax, 4496
LOGISTIC procedure, BY statement, 4509
LOGISTIC procedure, CONTRAST statement, 4513
ALPHA= option, 4514
E option, 4514
ESTIMATE= option, 4514
SINGULAR= option, 4515
LOGISTIC procedure, FREQ statement, 4525
LOGISTIC procedure, MODEL statement, 4529
ABSFCONV option, 4532
AGGREGATE= option, 4533
ALPHA= option, 4533
BEST= option, 4533
BINWIDTH= option, 4533
CL option, 4543
CLODDS= option, 4533
CLPARM= option, 4534
CORRB option, 4534
COVB option, 4534
CTABLE option, 4534
DESCENDING option, 4529
DETAILS option, 4534
EVENT= option, 4529
EXPEST option, 4534
FAST option, 4534
FCONV= option, 4535
FIRTH option, 4535
GCONV= option, 4535
HIERARCHY= option, 4535
INCLUDE= option, 4536
INFLUENCE option, 4536
IPLOTS option, 4536
ITPRINT option, 4537
LACKFIT option, 4537
LINK= option, 4537
MAXFUNCTION= option, 4537
MAXITER= option, 4538
MAXSTEP= option, 4538
NOCHECK option, 4538
NODESIGNPRINT= option, 4538
NODUMMYPRINT= option, 4538
NOFIT option, 4538
NOINT option, 4538
NOLOGSCALE option, 4538
NOODDSRATIO option, 4538
NOOR option, 4538
OFFSET= option, 4539
ORDER= option, 4530
OUTROC= option, 4539
PARMLABEL option, 4539
PCORR option, 4539
PEVENT= option, 4539
PLCL option, 4539
PLCONV= option, 4539
PLRL option, 4539
PPROB= option, 4539
REFERENCE= option, 4530
RIDGING= option, 4540
RISKLIMITS option, 4540
ROCEPS= option, 4540
RSQUARE option, 4540
SCALE= option, 4540
SELECTION= option, 4541
SEQUENTIAL option, 4541
SINGULAR= option, 4542
SLENTRY= option, 4542
SLSTAY= option, 4542
START= option, 4542
STB option, 4542
STOP= option, 4542
STOPRES option, 4543
TECHNIQUE= option, 4543
UNEQUALSLOPES option, 4543
WALDCL option, 4543
WALDRL option, 4540
XCONV= option, 4544
LOGISTIC procedure, ODDSRATIO statement, 4544
AT option, 4545
CL= option, 4545
DIFF= option, 4545
PLCONV= option, 4545
PLMAXITER= option, 4545
PLSINGULAR= option, 4546
LOGISTIC procedure, OUTPUT statement, 4546
ALPHA= option, 4547
C= option, 4547
CBAR= option, 4547
DFBETAS= option, 4547
DIFCHISQ= option, 4548
DIFDEV= option, 4548
H= option, 4548
LOWER= option, 4548
OUT= option, 4548
PREDICTED= option, 4548
PREDPROBS= option, 4548
RESCHI= option, 4549
RESDEV= option, 4549
RESLIK= option, 4549
STDRESCHI= option, 4549
STDRESDEV= option, 4549
STDXBETA = option, 4549
UPPER= option, 4549
XBETA= option, 4549
LOGISTIC procedure, PROC LOGISTIC statement,
4497
ALPHA= option, 4497
COVOUT option, 4498
DATA= option, 4498
DESCENDING option, 4498
EXACTOPTIONS option, 4498
INEST= option, 4499
INMODEL= option, 4499
MULTIPASS option, 4499
NAMELEN= option, 4499
NOCOV option, 4499
NOPRINT option, 4499
ORDER= option, 4499
OUTDESIGN= option, 4499
OUTDESIGNONLY option, 4500
OUTEST= option, 4500
OUTMODEL= option, 4500
PLOTS option, 4500
ROCOPTIONS option, 4503
SIMPLE option, 4504
TRUNCATE option, 4504
LOGISTIC procedure, ROC statement, 4551
LINK= option, 4551
NOOFFSET option, 4551
LOGISTIC procedure, ROCCONTRAST statement,
4551
ADJACENTPAIRS option, 4552
COV option, 4552
E option, 4552
ESTIMATE option, 4552
REFERENCE option, 4551
LOGISTIC procedure, SCORE statement, 4552
ALPHA= option, 4553
CLM option, 4553
CUMULATIVE option, 4553
DATA= option, 4553
FITSTAT option, 4554
OUT= option, 4554
OUTROC= option, 4554
PRIOR= option, 4554
PRIOREVENT= option, 4554
ROCEPS= option, 4554
LOGISTIC procedure, TEST statement, 4557
PRINT option, 4557
LOGISTIC procedure, UNITS statement, 4557
DEFAULT= option, 4558
LOGISTIC procedure, WEIGHT statement, 4558
NORMALIZE option, 4559
LOGISTIC procedure, CLASS statement, 4510
CPREFIX= option, 4510
DESCENDING option, 4510
LPREFIX= option, 4510
MISSING option, 4510
ORDER= option, 4510
PARAM= option, 4511
REF= option, 4512
TRUNCATE option, 4512
LOGISTIC procedure, CODE statement, 4513
LOGISTIC procedure, EFFECT statement, 4516
LOGISTIC procedure, EFFECTPLOT statement, 4518
LOGISTIC procedure, ESTIMATE statement, 4519
LOGISTIC procedure, EXACT statement, 4520
ALPHA= option, 4521
CLTYPE= option, 4521
ESTIMATE option, 4521
JOINT option, 4521
JOINTONLY option, 4521
MIDPFACTOR= option, 4521
ONESIDED option, 4521
OUTDIST= option, 4522
LOGISTIC procedure, EXACTOPTIONS statement,
4522
LOGISTIC procedure, LSMEANS statement, 4526
LOGISTIC procedure, LSMESTIMATE statement,
4527
LOGISTIC procedure, MODEL statement
ABSFCONV option, 4523
FCONV= option, 4523
NOLOGSCALE option, 4524
XCONV= option, 4525
LOGISTIC procedure, PROC LOGISTIC statement
EXACTONLY option, 4498
LOGISTIC procedure, SLICE statement, 4555
LOGISTIC procedure, STORE statement, 4555
LOGISTIC procedure, STRATA statement, 4555
CHECKDEPENDENCY= option, 4556
INFO option, 4556
MISSING option, 4556
NOSUMMARY option, 4556
LOWER= option
OUTPUT statement (LOGISTIC), 4548
LPREFIX= option
CLASS statement (LOGISTIC), 4510
LSMEANS statement
LOGISTIC procedure, 4526
LSMESTIMATE statement
LOGISTIC procedure, 4527
MAXFUNCTION= option
MODEL statement (LOGISTIC), 4537
MAXITER= option
MODEL statement (LOGISTIC), 4538
MAXSTEP= option
MODEL statement (LOGISTIC), 4538
MIDPFACTOR= option
EXACT statement (LOGISTIC), 4521
MISSING option
CLASS statement (LOGISTIC), 4510
STRATA statement (LOGISTIC), 4556
MODEL statement
LOGISTIC procedure, 4529
MULTIPASS option
PROC LOGISTIC statement, 4499
NAMELEN= option
PROC LOGISTIC statement, 4499
NLOPTIONS statement
LOGISTIC procedure, 4544
NOCHECK option
MODEL statement (LOGISTIC), 4538
NOCOV option
PROC LOGISTIC statement, 4499
NODESIGNPRINT= option
MODEL statement (LOGISTIC), 4538
NODUMMYPRINT= option
MODEL statement (LOGISTIC), 4538
NOFIT option
MODEL statement (LOGISTIC), 4538
NOINT option
MODEL statement (LOGISTIC), 4538
NOLOGSCALE option
MODEL statement (LOGISTIC), 4524, 4538
NOODDSRATIO option
MODEL statement (LOGISTIC), 4538
NOOFFSET option
ROC statement (LOGISTIC), 4551
NOOR option
MODEL statement (LOGISTIC), 4538
NOPRINT option
PROC LOGISTIC statement, 4499
NORMALIZE option
WEIGHT statement (LOGISTIC), 4559
NOSUMMARY option
STRATA statement (LOGISTIC), 4556
ODDSRATIO statement
LOGISTIC procedure, 4544
OFFSET= option
MODEL statement (LOGISTIC), 4539
ONESIDED option
EXACT statement (LOGISTIC), 4521
ORDER= option
CLASS statement (LOGISTIC), 4510
MODEL statement, 4530
PROC LOGISTIC statement, 4499
OUT= option
OUTPUT statement (LOGISTIC), 4548
SCORE statement (LOGISTIC), 4554
OUTDESIGN= option
PROC LOGISTIC statement, 4499
OUTDESIGNONLY option
PROC LOGISTIC statement, 4500
OUTDIST= option
EXACT statement (LOGISTIC), 4522
OUTEST= option
PROC LOGISTIC statement, 4500
OUTMODEL= option
PROC LOGISTIC statement, 4500
OUTPUT statement
LOGISTIC procedure, 4546
OUTROC= option
MODEL statement (LOGISTIC), 4539
SCORE statement (LOGISTIC), 4554
PARAM= option
CLASS statement (LOGISTIC), 4511
PARMLABEL option
MODEL statement (LOGISTIC), 4539
PCORR option
MODEL statement (LOGISTIC), 4539
PEVENT= option
MODEL statement (LOGISTIC), 4539
PLCL option
MODEL statement (LOGISTIC), 4539
PLCONV= option
MODEL statement (LOGISTIC), 4539
ODDSRATIO statement (LOGISTIC), 4545
PLMAXITER= option
ODDSRATIO statement (LOGISTIC), 4545
PLOTS option
PROC LOGISTIC statement, 4500
PLRL option
MODEL statement (LOGISTIC), 4539
PLSINGULAR= option
ODDSRATIO statement (LOGISTIC), 4546
PPROB= option
MODEL statement (LOGISTIC), 4539
PREDICTED= option
OUTPUT statement (LOGISTIC), 4548
PREDPROBS= option
OUTPUT statement (LOGISTIC), 4548
PRINT option
TEST statement (LOGISTIC), 4557
PRIOR= option
SCORE statement (LOGISTIC), 4554
PRIOREVENT= option
SCORE statement (LOGISTIC), 4554
PROC LOGISTIC statement, see LOGISTIC
procedure
REF= option
CLASS statement (LOGISTIC), 4512
REFERENCE option
ROCCONTRAST statement (LOGISTIC), 4551
REFERENCE= option
MODEL statement, 4530
RESCHI= option
OUTPUT statement (LOGISTIC), 4549
RESDEV= option
OUTPUT statement (LOGISTIC), 4549
RESLIK= option
OUTPUT statement (LOGISTIC), 4549
RIDGING= option
MODEL statement (LOGISTIC), 4540
RISKLIMITS option
MODEL statement (LOGISTIC), 4540
ROC statement
LOGISTIC procedure, 4551
ROCCONTRAST statement
LOGISTIC procedure, 4551
ROCEPS= option
MODEL statement (LOGISTIC), 4540
SCORE statement (LOGISTIC), 4554
ROCOPTIONS option
PROC LOGISTIC statement, 4503
RSQUARE option
MODEL statement (LOGISTIC), 4540
SCALE= option
MODEL statement (LOGISTIC), 4540
SCORE statement
LOGISTIC procedure, 4552
SELECTION= option
MODEL statement (LOGISTIC), 4541
SEQUENTIAL option
MODEL statement (LOGISTIC), 4541
SIMPLE option
PROC LOGISTIC statement, 4504
SINGULAR= option
CONTRAST statement (LOGISTIC), 4515
MODEL statement (LOGISTIC), 4542
SLENTRY= option
MODEL statement (LOGISTIC), 4542
SLICE statement
LOGISTIC procedure, 4555
SLSTAY= option
MODEL statement (LOGISTIC), 4542
START= option
MODEL statement (LOGISTIC), 4542
STB option
MODEL statement (LOGISTIC), 4542
STDRESCHI= option
OUTPUT statement (LOGISTIC), 4549
STDRESDEV= option
OUTPUT statement (LOGISTIC), 4549
STDXBETA= option
OUTPUT statement (LOGISTIC), 4549
STOP= option
MODEL statement (LOGISTIC), 4542
STOPRES option
MODEL statement (LOGISTIC), 4543
STORE statement
LOGISTIC procedure, 4555
STRATA statement
LOGISTIC procedure, 4555
TECHNIQUE= option
MODEL statement (LOGISTIC), 4543
TEST statement
LOGISTIC procedure, 4557
TRUNCATE option
CLASS statement (LOGISTIC), 4512
PROC LOGISTIC statement, 4504
UNEQUALSLOPES option
MODEL statement (LOGISTIC), 4543
UNITS statement, LOGISTIC procedure, 4557
UPPER= option
OUTPUT statement (LOGISTIC), 4549
WALDCL option
MODEL statement (LOGISTIC), 4543
WALDRL option
MODEL statement (LOGISTIC), 4540
WEIGHT statement
LOGISTIC procedure, 4558
XBETA= option
OUTPUT statement (LOGISTIC), 4549
XCONV= option
MODEL statement (LOGISTIC), 4525, 4544
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