Hi, Could you provide us a copy of your log and output, so that we can review your code and any notes and errors? ... View more

" how to interpret the output for a random effect." Just interpret random effect as fixed effect.   "there is no probability of the effect like in the fixed effect," You can get estimated coefficient with SOLUTION= option.   "How can I know if there is a significance? " You could use option COVTEST to check whether the random effect is significant or not.   proc mixed data=sashelp.class covtest; /*option covtest check random effect is significant or not*/ class age; model height=weight/solution ; /*option solution= get estimated coefficient of fixed effect*/ random int/subject=age solution; /*option solution= get estimated coefficient of random effect */ run;   Here random effect AGE got P value 0.2017 , which means AGE is NOT significant .You can use only Fixed effect in model. ... View more

The community would need more information on the problem you face and on the data you have, the model you want to run, and on what the output table you quote would have in it. ... View more

These exception errors in my experience fall into 3 classes   1) requesting a combination of options that don't make real world sense and that the programmers at SAS didn't provide a check for because "no one will ever do that" 2) pathological data. This means data for a specific purpose that misbehaves. The specific analysis and data determines what that may be. I suspect that is why @StatsMan asked for the number of levels for the strata variable. Many levels may make it harder to deal with. 3) System problems. This can be anything from bad installation media so one program file is corrupted (I had this happen so that the DATA step wouldn't run once), or something relying on external SAS files is incompatible such as Java, Microsoft Office (32 vs 64 bit can still be an issue) or similar.   But diagnosing almost anything like this is a tech support issue. ... View more

@bhr-q wrote: my question is to get the plot using ODS graphics You are using ODS graphics. As you mentioned in your initial post, you had to "turn on ODS graphics" (using the ODS GRAPHICS statement) and this is indeed the prerequisite for the PLOTS= option to work. Otherwise, the warning WARNING: You must enable ODS graphics before requesting plots. would be written to the log. The PLOTS= option of PROC CORR was introduced in SAS version 9, as were ODS graphics.   ... View more

Have you considered using PROC GLIMMIX, as in the following:   proc glimmix data=MAST_long_VAS; class PPID Time; model BC_stress = Time Time*Time / solution; random _residual_ / subject=PPID type=ar(1); run; In large part, this is adapted from the example - Example 22.10 Correlation and Covariance Matrices in Using the Output Delivery System documentation.   SteveDenham   ... View more

The following usage note provides sample programs for obtaining subject-specific intercept and slope -- http://support.sas.com/kb/37109 The following note shows how to plot these subject-specific models -- http://support.sas.com/kb/37110   Hope this helps, Jill ... View more

Sorry to not respond earlier. I tried your solution as well as the one I mentioned. The algorithm didn't converge with either one of them. I don't think I have enough observations to be able to use the model. I hope to have another opportunity to dive into this again, but with less sparse data. Thanks for your help statsman!   ... View more

I would caution you about any inferences regarding drug or treatment. The design I see completely confounds drug*treatment with pen, and for a lot of folks that would mean that pen is the experimental unit, and animals are observational units measured on each of the pens.  You might be able to use the following, but I provide no guarantees. I moved to the spatial power covariance structure because I believe the residuals will be correlated with those closer in time more highly correlated. The autoregressive error structures (including ANTE and ARMA) treat the time points as being equally spaced in time. I also fit pen as an effect, rather than trt or drug. You can get LSMEAN-like estimates for the various levels of each by using LSMESTIMATE statements to manipulate the pen lsmeans. I added the /e option to the LSMEANS statement to facilitate construction of the LSMESTIMATES:   proc mixed data= XX; class animal pen day; model BW = pen|day initial_BW ddfm=kr; random intercept / subject = animal; repeated days / subject = animal type = sp(pow)(t); /* t is a continuous version of 'day' */ lsmeans pen|day /e adjust = tukey; /* Add a section of LSMESTIMATE code here to calculate trt and drug means and differences */ run; SteveDenham   ... View more

Thank you for all this feedback! let me review this carefully and bounce it off the team to see what we can do. ... View more

Thanks! It works! ... View more

From the on-line help for Proc Anova in the Overview section at the start: Overview: ANOVA Procedure   The ANOVA procedure performs analysis of variance (ANOVA) for balanced data from a wide variety of experimental designs. In analysis of variance, a continuous response variable, known as a dependent variable, is measured under experimental conditions identified by classification variables, known as independent variables.   The variation in the response is assumed to be due to effects in the classification, with random error accounting for the remaining variation. Emphasis added. Traditional ANOVA in my personal opinion had exactly one advantage: traditional calculations could be done by hand. ( And I've done them that way because the computers I had available in the 1970's didn't have appropriate software).     ... View more

You can explore the Kronecker product structures for TYPE= on the REPEATED statement. That is a handy construct for two-dimension repeated measures. You can set up an unstructured covariance between the eyes of a patient and CS, AR(1), or (on occasion) UN for observations within an eye. The model you have now is used quite often as well. You could try adding covariance within an eye as an extra random effect, with random int eye / subject=ptid; That RANDOM statement correlates observations from the same patient and further correlates observations from the same eye within a patient. You could fit AR(1) as a structure on the REPEATED statement you have now with this RANDOM statement.  ... View more

Mr. Google was my friend today. Searching on "Kenward roger approximation" led to this: https://documentation.sas.com/doc/en/statug/15.2/statug_glimmix_details40.htm    From this you will see that the denominator degrees of freedom is a solution to an equation, rather than the (n-1) or (n-1)(m-1) you think of as the design denominator degrees of freedom. If they happen to come out to the same value, it is because the data are balanced and the covariance matrices associated do not result in any shrinkage of the standard errors.   So if the KR ddfm values are not completely inappropriate (such as ddf =1 in a repeated measures design), they represent a less biased approach to the analysis. Even better for controlling bias would be KR2.   SteveDenham ... View more