when I run heterogeneous variance model using proc glimmix to test homogeneity of variance assumption I get this DATA a; input id 1 batch 1 ref 5-7 new 9-11; diff=new-ref; datalines; 1 116 119 2 115 121 3 103 121 4 135 130 5 108 116 6 113 122 7 115 117 8 127 124 ; keep batch ref new diff; *wide-format data to long-format data; DATA b; SET a; ARRAY trials[2] ref new; DO TIME = 1 TO 2; y = trials[TIME]; trial = TIME-1; OUTPUT; *wihtout this time=3 values overwrite all; END; KEEP batch y trial; proc print data=c (OBS=12) NOOBS; run; *run glm; PROC GLIMMIX data=b plots=residualpanel; CLASS trial (ref=first); MODEL y=trial /solution ddfm=KR; random _residual_ /group=trial type=VC; covtest /wald; LSMEANS trial /alpha=0.05 cl diff plot=diffplot; run; Covariance Parameter Estimates Cov Parm Group Estimate Standard Error Z Value Pr > Z Residual (VC) trial 1 19.3571 10.3468 1.87 0.0307 Residual (VC) trial 0 103.43 55.2849 1.87 0.0307 my question is: why z and p values for variance components are fixed to be equal to each other? is this some sort of model constraints? If the sample do not pass the homogeneity variance test so I end up keeping heterogenous variance in the model, can i use and interpret these p-values? Thanks! Best, Tom
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