The covariance structure type=sp(pow) time for my glimmix model (repeated date*period/subj=animal(farm) and the subject effect additional to the random farm effect in the random statement) seems to work fine (smaller -2 Res LogL and AIC ....closer to zero, respectively, than with cs), the same for type=UN@AR(1) for my mixed model;
but it seems to me that my repeated structure is not correctly modeled as the dimensions table in my output(glimmix) tells me:
Subjects = 1, and max Obs per subject = 234 while I have 26 subjects and 9 observations (3 repetitions per period) per subject
and the model information table states that
variance matrix = not blocked;
Dimensions of G and R-side effects seem to be ok.
similarly, in my output for the mixed model with
the residual variance method = Parameter in the model information and
subject=1 , max obs per subject = 234 in the dimensions table;
So what's wrong with my model?
proc glimmix:
proc glimmix data=xxxx;
class genotype farm animal period date;
model x =genotype period genotype*period mean_bg / dist=negbin solution;
random farm animal(farm);
random date(period) / subject=animal(farm) type=sp(pow)(time) residual;
run;
I use the univariate format (one row per experimental unit at each timepoint) as emphasized in Littell et al, J. Anim. Sci. 1998. 76:1216–1231 for proc mixed/repeated measurements
and other sources e.g. in
http://www.sportsci.org/resource/stats/threetrials.html
I assume it's the same for proc glimmix.
In the SAS glimmix documentation (keyword: processing by subjects) I found the statement that "if a random statement does not have a subject=effect (as I do have in my model: random = farm), processing by subjects is not possible unless the random effect is a pure R-side overdispersion effect".
As far as I understand, processing by subject is a question of how glimmix mathematically processes data but not a question of correctly modeling covariance structures, right ? if not, do I have to choose a subject for farm ???
Anyone a clou about this ?
I also want to look at the significance of my random farm effect by omitting it from the model (in mixed as well as in glimmix) above, but SAS doesn't give a result in the output for the Likelihood Ratio Test.
Is the Likelihood Ratio Test not possible with other effects in the random statement (e.g. the subject effect required by UN@AR(1) and sp(pow)(time)) ?
Many thanks,
Karin