Dear SteveDenham, I run the data according to example # 40.5. In any run I found the value of zero was estimated for gp=1. The error in Log file and some part of the results are below: 1095 ; 1096 proc genmod; 1097 class gp time dog; 1098 model comfort=gp|time / dist=multinomial type1; 1099 repeated subject=dog (GP)/ corr=ind corrw; 1100 run; WARNING: TYPE1 tests are not available with the REPEATED statement. NOTE: PROC GENMOD is modeling the probabilities of levels of comfort having LOWER Ordered Values in the response profile table. One way to change this to model the probabilities of HIGHER Ordered Values is to specify the DESCENDING option in the PROC statement. NOTE: Algorithm converged. NOTE: The scale parameter was held fixed. NOTE: The working correlation table is not created for this model. WARNING: The generalized Hessian matrix is not positive definite. Iteration will be terminated. ERROR: Error in parameter estimate covariance computation. ERROR: Error in estimation routine. NOTE: The SAS System stopped processing this step because of errors. NOTE: PROCEDURE GENMOD used (Total process time): real time 1.07 seconds Standard Wald 95% Confidence Chi- Parameter DF Estimate Error Limits Square Pr > ChiSq Intercept1 1 -1.2915 0.8530 -2.9632 0.3803 2.29 0.1300 Intercept2 1 0.3581 0.8312 -1.2710 1.9871 0.19 0.6666 Intercept3 1 6.7907 1.5366 3.7791 9.8023 19.53 <.0001 gp 0 1 -0.6143 1.2264 -3.0180 1.7895 0.25 0.6165 gp 1 0 0.0000 0.0000 0.0000 0.0000 . . time 0 1 26.0945 121587.6 -238281 238333.4 0.00 0.9998 time 15 1 2.1050 1.4560 -0.7486 4.9587 2.09 0.1482 time 30 1 0.4672 1.2108 -1.9059 2.8404 0.15 0.6996 time 45 1 -0.1838 1.2766 -2.6859 2.3183 0.02 0.8855 time 60 1 -1.5133 1.3929 -4.2433 1.2168 1.18 0.2773 time 120 1 -1.5133 1.3929 -4.2433 1.2168 1.18 0.2773 time 180 1 -0.1838 1.2766 -2.6859 2.3183 0.02 0.8855 time 240 1 -1.5133 1.3929 -4.2433 1.2168 1.18 0.2773 time 300 1 -1.5133 1.3929 -4.2433 1.2168 1.18 0.2773 time 360 1 2.1050 1.4560 -0.7486 4.9587 2.09 0.1482 time 480 1 -0.1838 1.2766 -2.6859 2.3183 0.02 0.8855 time 600 1 -1.1565 1.4358 -3.9707 1.6577 0.65 0.4206 time 720 1 0.4672 1.2108 -1.9059 2.8404 0.15 0.6996 time 1440 0 0.0000 0.0000 0.0000 0.0000 . . gp*time 0 0 1 0.6143 171950.8 -337017 337018.0 0.00 1.0000 gp*time 0 15 1 -8.2847 2.3706 -12.9309 -3.6384 12.21 0.0005 gp*time 0 30 1 -7.3382 2.3360 -11.9167 -2.7598 9.87 0.0017 gp*time 0 45 1 -5.9958 2.2579 -10.4212 -1.5704 7.05 0.0079 gp*time 0 60 1 -3.5833 2.3657 -8.2200 1.0534 2.29 0.1299 gp*time 0 120 1 -1.4468 2.5517 -6.4481 3.5544 0.32 0.5707 gp*time 0 180 1 -2.7763 2.4974 -7.6712 2.1185 1.24 0.2663 gp*time 0 240 1 -1.4468 2.5517 -6.4481 3.5544 0.32 0.5707 gp*time 0 300 1 -1.4468 2.5517 -6.4481 3.5544 0.32 0.5707 gp*time 0 360 1 -5.0652 2.5993 -10.1596 0.0293 3.80 0.0513 gp*time 0 480 1 1.2664 1.9098 -2.4768 5.0096 0.44 0.5073 gp*time 0 600 1 1.1565 1.9261 -2.6186 4.9316 0.36 0.5482 gp*time 0 720 1 -1.3663 1.8842 -5.0593 2.3268 0.53 0.4684 gp*time 0 1440 0 0.0000 0.0000 0.0000 0.0000 . . gp*time 1 0 0 0.0000 0.0000 0.0000 0.0000 . . gp*time 1 15 0 0.0000 0.0000 0.0000 0.0000 . . gp*time 1 30 0 0.0000 0.0000 0.0000 0.0000 . . gp*time 1 45 0 0.0000 0.0000 0.0000 0.0000 . . Kind regards
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