I've been told that this is a non-trivial problem, and I should post it here.
Here is a link to the LaTeXed version of the problem description and the model (in matrix form) that I would like to fit:
http://farm4.static.flickr.com/3483/3834750144_e7d9560902_o.jpg
My primary interest is estimating G, or equivalently D, as shown in the link above.
My variables in SAS are:
- effect (the responses, or treatment effects I want to model)
- endp (class variable to represent the two endpoints corresponding to alpha and beta treatment effects)
- center (class variable to represent the trial where the treatment effects were measured)
To get the specific model and covariance structures shown in the link above, I have tried the following (among many others):
proc mixed data=shihco maxiter=500 update info covtest noclprint;
class center endp;
model effect=endp /noint s;
random endp / subject = center type=un g;
repeated endp /group=center type=un r;
parms /parmsdata=parms hold =4 to &nobs;
ods output covparms=covparm;
run;
I am using the parms statement to provide initial values for the elements of G, and fixed values for the elements of R (estimated previously).
When I fit this model under different variations of the above, either there is no convergence, or the Hessian is not positive definite. I've tried a variety of covariance structures for G. The only one that yields no errors is type=UNR under the random statement; however, I do not trust these G estimates.
Should I try changing something else? Am I even using the correct options here in random/repeated to get the model form and covariances that I want? Any help is appreciated.
Message was edited by: phdstudent