I've configured two repeated measures models, one using PROC MIXED and the other using PROX GLIMMIX, as consistently as possible to the best of my understanding. The experiment is a factorial, RCBD.
Trt_Amend_App and Trt_CC are experimental treatments with 8 and 2 levels, respectively.
ID_S are sites or locations (2)
Block has 4 levels
Year_Time is the time component with 7 levels.
ID_S_P is the plot ID or subject
proc mixed data=df plots=studentpanel;
class Trt_Amend_App Trt_CC Year_Time ID_S Block ID_S_P;
model Soil_PAN_LN = Trt_Amend_App | Trt_CC | ID_S | Year_Time;
repeated Year_Time / type=un sub=ID_S_P;
random Block(ID_S);
run;
proc glimmix data=df plots=studentpanel method=rspl;
class Trt_Amend_App Trt_CC Year_Time ID_S Block ID_S_P;
model Soil_PAN_LN = Trt_Amend_App | Trt_CC | ID_S | Year_Time;
random Year_Time / residual type=un subject=ID_S_P;
random Block(ID_S);
/*nloptions maxiter=500; /* Set this to avoid "did not yield a valid objective function error"*/
/*nloptions technique=nrridg;*/
run;
The within-subject correlations are complex, and unstructured yields the best (lowest) information criteria results (AIC, BIC). The MIXED model doesn't execute when using compound symmetry, giving the "WARNING: Stopped because of infinite likelihood" error.
I'm curious as to why I cannot get the GLIMMIX model to execute, and why I get the error "The initial estimates did not yield a valid objective function." every time that I attempt to run it. To address this I've attempted increasing the iterations (via INITITER and MAXITER, the latter of which is shown commented out). I've also tried changing the optimization technique to NRRIDG, which I've read is the default for PROC MIXED but not PROC GLIMMIX. None of that has worked, and I get the valid objective function error each time. I considered passing PARMS based on results from MIXED, but that seems unpractical given the amount of covariance parameters with unstructured correlation (28). Why might GLIMMIX be failing where MIXED works? Also, it occurred to me that within-subject measurements may be so irregular that attempting to model within-subject correlation may be pointless. How might one verify whether there is any reason to use repeated measures period? When executing the MIXED model with ar(1) structure, the within-subject correlation is less than the estimate for residual, but not very low (see picture). I could use some practical advice in this regard. Thanks for reading!
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