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:
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 53.9.1. Notice that neither the A*B interaction nor the B main effect is significant.
Model Information | |
---|---|
Data Set | WORK.ONE |
Response Variable | Y |
Number of Response Levels | 2 |
Frequency Variable | F |
Model | binary logit |
Optimization Technique | Fisher's scoring |
Number of Observations Read | 8 |
---|---|
Number of Observations Used | 8 |
Sum of Frequencies Read | 528 |
Sum of Frequencies Used | 528 |
Response Profile | ||
---|---|---|
Ordered Value |
Y | Total Frequency |
1 | 1 | 191 |
2 | 2 | 337 |
Model Convergence Status |
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Convergence criterion (GCONV=1E-8) satisfied. |
Model Fit Statistics | ||
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Criterion | Intercept Only |
Intercept and Covariates |
AIC | 693.061 | 691.914 |
SC | 697.330 | 708.990 |
-2 Log L | 691.061 | 683.914 |
Testing Global Null Hypothesis: BETA=0 | |||
---|---|---|---|
Test | Chi-Square | DF | Pr > ChiSq |
Likelihood Ratio | 7.1478 | 3 | 0.0673 |
Score | 6.9921 | 3 | 0.0721 |
Wald | 6.9118 | 3 | 0.0748 |
Analysis of Maximum Likelihood Estimates | |||||
---|---|---|---|---|---|
Parameter | DF | Estimate | Standard Error |
Wald Chi-Square |
Pr > ChiSq |
Intercept | 1 | -1.0074 | 0.2436 | 17.1015 | <.0001 |
A | 1 | 0.6069 | 0.2903 | 4.3714 | 0.0365 |
B | 1 | 0.1929 | 0.3254 | 0.3515 | 0.5533 |
A*B | 1 | -0.1883 | 0.3933 | 0.2293 | 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-of-fit tests have degrees of freedom, where is the number of subpopulations and is the number of model parameters. In the preceding model, , 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 and zero degrees of freedom for the tests.
proc logistic data=One; freq F; model Y=A / scale=none aggregate=(A B); run;
The goodness-of-fit tests in Output 53.9.2 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.
Deviance and Pearson Goodness-of-Fit Statistics | ||||
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Criterion | Value | DF | Value/DF | Pr > ChiSq |
Deviance | 0.3541 | 2 | 0.1770 | 0.8377 |
Pearson | 0.3531 | 2 | 0.1765 | 0.8382 |