Hello.DR.Wicklin, First i appreciate you for all your knowledge that you are sharing with us. I am applying proc mixed with only repeat statement and the covariance structure is sp(power). my question is about sas output. I am confused about the first covparm estimate of the output table, which is sp(pow). what is its relation with rho. To be clear, i attached the output table and my sas code below. Thanks for any help. ... View more

thanks your solution solved my problem.   ... View more

why it gives me error that  Matrix Y has not been set to a value?bellow is the changed code. proc iml; FixedVar = "_X1":"_X48"; use All; read all var FixedVar into X; close All; use SolutionF; read all var {Estimate} into beta; close; eta = X*beta;                      use CovParms; read all var {Estimate} into var; close;                  varCS=var[1]; varR=var[2];             /* Variance of residuals*/ k=15; s=17; b=varCS*j(k,k,1 )+varR*I(k); R=b; do i=2 to s; R=block(R,b); end; R = I(s) @ b;*print R; zer = j(1,k*s,0);*print zer; call randseed(1234); eps= j(nrow(x) , 500); eps=randnormal(1 ,zer ,R); Y = eta +eps; * print Y; yNames = "y1": ("y"+strip(char(500)));           create Simdat from Y[c=yNames];  append from Y;  close; quit; ... View more

Hello , i have a question about the estimating sampling distribution of the variance of random effects of the data(varCS and varR)  that i was simulating using  repeated measures data having fixed effect and random error effects.i used proc univariate to see the sampling distributions but why my estimations  are not close to the variance of CS and residuals ? R is a compound symmetry matrix. bellow is my code proc iml; FixedVar = "_X1":"_X48"; use All; read all var FixedVar into X; close All; use SolutionF; read all var {Estimate} into beta; close; eta = X*beta;                      use CovParms; read all var {Estimate} into var; close;                  varCS=var[1]; varR=var[2];             /* Variance of residuals*/ k=15; s=17; b=varCS*j(k,k,1 )+varR*I(k); R=b; do i=2 to s; R=block(R,b); end; R = I(s) @ b;*print R; zer = j(1,k*s,0);*print zer; call randseed(1234); eps= j(nrow(x) , 500); call randgen(eps, "normal" ,zer ,R); Y = eta +eps; * print Y; yNames = "y1": ("y"+strip(char(500)));           create Simdat from Y[c=yNames];  append from Y;  close; quit;     ... View more

Thanks for your response. For examine and compare the distribution of AIC what procedure should I use? Proc univariate or proc means? ... View more

appreciate for your smart response. ... View more

Dr.Wicklin appreciate for your great answer.it helped me to solve my problem.again thanks a lot for sharing your knowledge with us. ... View more

Hello.Dr.Wicklin, I want to construct block diagonal matrix as covariance matrix for errors. Is there  any way to create this matrix directly by using the R matrix that is in ods output ?below is my code .it gives me error. appreciate for any help.   proc mixed data=mydata ;  class  Subject times responder ;  model response=times|responder/ solution outpm=outpm;  repeated times / type=CS subject=Subject(Responder)  R;   ods select    R ;   ods output  R=R ;  quit;   proc iml; k=3; s=5; R=R  /*first block*/ do i=2 to s; R=block(R,R); end; R = I(s) @ R;         ... View more