Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear.
My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM, random=list(experiment=pdBlocked(list(pdIdent(~randf-1),pdIdent(~randm-1)))), family=binomial, data=sala.data). Where data neame=sala.glmm which contain y is response wsf is fixed effect wsm is fixed effects randf is random effect random is random effect The data contain three experiments at the same time. The previous cod is work but it does not give me accurate result especially for the random effects. For experiment I wrote this code experiment <- factor(c(rep(1,120),rep(2,120),rep(3,120))) because I have three experiments at the same time, but if I change the experiment to e.g experiment <- factor(c(rep(1,360))) is still give answer but is not the right answer. So, I am accusing my specification of the experiment (group). If you have any suggestion pleas let me know. E-mail:[EMAIL PROTECTED] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
