About jiltao

jiltao · ‎05-21-2021

What you wanted is there in the LSMEANS DIFF output, but because of the large amount of output, it is not easy to spot it. You might use the following LSMEANS statement to see it more easily -- lsmeans race_combined /diff;

jiltao · ‎05-21-2021

The LSMEANS statement computes the least square means for each level of the fixed effect. Does that not work for you?

jiltao · ‎05-18-2021

Another thought: You might want to recode the values for year. I assume the year values are in the thousands? That can produce very small slope estimates and cause some convergence issues. See if you can record the values to, for example, 1, 2, 3, 4, etc.

jiltao · ‎05-18-2021

Your PROC MIXED program did not converge, and that is why your OUTP= data set is empty. I wonder if your response variable has very large values. If so you might want to rescale it so the values are within a reasonable range. If you can send in your SAS data set I might take a look to see what I can recommend.

jiltao · ‎05-17-2021

Please also send the Output from your PROC MIXED program. Thanks!

jiltao · ‎05-16-2021

The correlation is generally the same from subject to subject. What is your PROC MIXED program?

jiltao · ‎05-16-2021

The specified model might not be appropriate for your data and therefore have caused convergence issues. If you can send in data that would be helpful.

jiltao · ‎05-14-2021

You can use the VCORR option in the RANDOM statement to get the correlation matrix for V, or the RCORR option in the REPEATED statement to get the correlation matrix for R.

jiltao · ‎05-14-2021

First of all, the slope variance estimate from PROC MIXED is for the subject slope "effect". You would need to compute the variance for ssdev rather than sscoeff with PROC UNIVARIATE if you want to check on the consistency of the variance estimate. Second of all, you are not going to get the same variance estimates by these two approaches. The SSDEV estimates are BLUPS and have the "shrinkage" property. The variance from PROC UNIVARIATE should be close, but is likely to be smaller, and that is expected -- the two variances are not the same thing.

jiltao · ‎05-14-2021

For this model -- random intercept year / subject=rfa_id type=un; can you send in the Log (including the program and messages) and Output?

jiltao · ‎05-13-2021

If you want to fit a random intercept model, you might want to use -- random intercept / subject=rfa_id; If you want to fit a random intercept and slope model, you might want to use -- random intercept year / subject=rfa_id type=un; I would not use type=CS for a random coefficients model. Are you saying that the random intercept model (model 1 above) worked but the random intercept and slope model (model 2 above) did not? What exactly happened for the model 2 above?

jiltao · ‎05-12-2021

You can add the LSMEANS statement in your PROC MIXED program. For example, lsmeans race_combined / diff;

jiltao · ‎05-04-2021

As I pointed out previously, your ESTIMATE statements do not test the same thing as your interaction effect. Are you trying to write the ESTIMATE statement to reproduce the interaction effect? What do you mean by "conditional effects"?

jiltao · ‎05-03-2021

The subplot and the A*B interaction are confounded. So you either model the A*B interaction and do not model any random effects, or model the SUBPLOT as the random effect but no A*B interaction, as what you have already done. I assume the properties are nested within the subplot, correct? If so, I cannot think of other ways to model this data.

jiltao · ‎05-03-2021

Can you send us your data set?

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