LSmeans Proc mixed

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New Contributor
Posts: 2

LSmeans Proc mixed

Hi

I´m relatve unexperienced using proc mixed.
My problem is, that running lsmeans in my reduced model results in the message Non-est. instead of an estimate.

Don´t understand why.
Can anyone help, please ??

Model:
Repeated measurement of the variable region at mri_id, wbvcalc as covariante.

Proc Mixed Data=kerstin1 method=ml covtest scoring=2;
Class mri_id sex0F1M region hemi agegroup ;
Model response = region sex0f1m hemi age WBVCalc
region * age
region * wbvcalc
/noint solution ;
repeated / Subject=mri_id type=cs group = region ;
lsmeans region / pdiff;
Run;
Valued Guide
Valued Guide
Posts: 673

Re: LSmeans Proc mixed

Probably you have interaction means with missing cells. You might have to remove some interactions.
Respected Advisor
Posts: 2,655

Re: LSmeans Proc mixed

You should read Milliken and Johnson's Analysis of Messy Data.

Probably the best approach is to fit a means model, and address the tests of effects through the use of contrast and estimate statements.

Another approach, find out what combinations of the independent variables are missing. If you can combine levels so that there are no empty cells in the highest level interaction in your model, and it still addresses your objectives, then you are home free.

Also, check that the covariates age and wbvcalc are not completely confounded with region.

Good luck,

Steve Denham Added thought:
Last, you say that you are fitting a "reduced model". I would guess that you have dropped some terms that were "non-significant." Is that really a good idea? Were those terms part of the study design, so that they have meaning, even if not significant? And most importantly, are the least squares means estimable in the "full model"?




Message was edited by: SteveDenham
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