You would need to turn on the ODS Graphics to get the LINES plot. The second output might be from when the ODS Graphics is turned off. This note has more detailed information about this -- http://support.sas.com/kb/63810   Hope this helps, Jill   ... View more

I do not understand your data -- is it a crossover design with repeated measures? Or just a repeated measures data? If you can share your data that would be helpful.   The statement -- random Intercept phase/sub=case; specifies 2 columns in the Z matrix for each subject -- a column of 1 (intercept) and a column for Phase. The Estimated G matrix is not positive definite message indicates that the model might not work for your data.     The statement -- random Intercept/sub=case type=cs residual; models the correlations in the (linearized) residuals. It is similar to the REPEATED statement in PROC MIXED. So there is no G-side random effect, therefore no Z matrix. The syntax above specifies INTERCEPT as the repeated effect, which might not be appropriate.   The statements - random phase/sub=case type=cs residual; random intercept/sub =case; specify a random intercept model as well as a CS correlated "residuals". This is likely an overspecified model and therefore encountered some convergence issue.   Again, if you can share your data (even with the fake y values) I might be able to provide more assistance.   Thanks, Jill ... View more

This usage note provides various ways of scoring a new data set -- http://support.sas.com/kb/33307   Here is an example of Approach 3. Augmenting the training data set for your case: 1. create a scoring data set, for example, NEW, that contains the fixed values for other covariates and the plotting values for the ones you are interested in plotting. Make sure the dependent variable has missing values in this data set; 2. combine the original data set with this scoring data set (NEW) with a SET statement in a SAS data step. 3. Use PROC MIXED to fit your model on this combined data set. Make sure you use the OUTP= option in the MODEL statement. The observations from step 2 have missing values for the dependent variable, therefore will not be used in the model fitting. However, they will get predicted values. You will then use PROC SGPLOT to plot these observations.   Alternatively, you might use the data set NEW as the scoring data in PROC PLM to get the predicted values, then use PROC SGPLOT to plot it. This is basically approach 1 in the usage note linked above. Hope this helps, Jill   ... View more

Actually I see the E option output that might represents the coefficients used in your ESTIMATE statements. The estimates are not the same as what PROC PLM plots. The effects in the E option output table are cut off due to long names so it is not easy to understand the effects, but it seems to me that some average of some effects are computed, which is not the case for the PROC PLM slicefit plot.   ... View more

I do not see your ESTIMATE statements in PROC MIXED. Please include that in your program. Thanks, Jill   ... View more

Yes, that is correct. I am not sure what sensitivity analysis you plan to perform. But to depict an interaction effect from a model, I would probably get predicted vales of y at fixed vales of other covariates then use PROC SGPLOT to plot the interaction / slopes. Thanks, Jill    ... View more

It is not correct to say that PROC PLM does not account for other effects in the model.  PROC PLM is for post-fitting analysis. The parameter estimates are from the model you specify. So the predicted values are computed based on the parameter estimates that account for all effects in the model. The plot "fixes" the values for other covariates, as shown in the plot (it tells you which values the other covariates are set), versus your PROC SGPLOT code in which the values of other covariates are whatever in your data set, they are not fixed. I would probably use PROC PLM to depict the interaction effects in your model.   Thanks, Jill ... View more

Regarding your PROC SGPLOT: FOBS does not seem to be in the model, so it might not be a plot to depict the interaction term in the model. It does show how the predicted values change over LENGTH2 for different groups of FOBS. And the predicted values are computed accounting for all other effects in the model. Regarding your fit plot from PROC PLM: It shows how the predicted values change over Length2 at a fixed value of other covariates/effects in the model.   Two output plots look different because the predicted values being plotted are computed differently.   Do you just wanted to plot an interaction term? Then if the two variables involved in the interaction are class variables, you might use INTERACTION in the EFFECTPLOT statement in PROC PLM; if both are continuous variables, then you might use CONTOUR in the EFFECTPLOT statement in PROC PLM; if one is a CLASS variable one is a continuous variable, then you might use SLICEFIT in the EFFECTPLOT statement. Please see the documentation below for more details: https://go.documentation.sas.com/doc/en/pgmsascdc/v_037/statug/statug_plm_syntax03.htm   Hope this helps, Jill     ... View more

Your REPEATED statement does not really add anything more to the model than the one without the REPEATED statement.   If you wanted to model the correlations in the data, you might want to use something like -- repeated / subject=id type=cs;   This can also be accomplished by using a RANDOM statement -- random int / subject=id;   You just need one of the two, but not both, in your PROC MIXED program.   I hope this helps, Jill ... View more

@PaigeMiller No, the results would not be the same due to the modeling of the correlations in the multivariate responses with the R-side random effect. ... View more

You can use something like the following to fit a multivariate logistic regression model in PROC GLIMMIX:   subject y variable $ x 1 1 y1 3 1 0 y2 3 2 0 y1 2 2 1 y2 2 .....   proc glimmix data=yourdata; class subject variable; model y=x varriable x*variable / dist=binary link=logit; random _residual_ / type=un subject=subject; run;   You might add a RANDOM statement to model the multilevel part of your data.   Hope this helps, Jill   ... View more