# [R] glmm in R

Diego Rubolini diego.rubolini at unipv.it
Thu Aug 26 16:50:34 CEST 2004

```Dear all,

I'm new to R and to the list, and I have a problem which I'm unable to solve.

Consider the following simple simulated data frame:

simul<-data.frame(Group=factor(c(rep(1,4),rep(2,2),rep(3,6),rep(4,8),rep(5,4))),Pair=factor(rep(1:12,rep(2,12))),Y=rep(c(0,1),12),X1=c(9,10,8,9,1,5,2,7,0,3,7,9,5,4,6,8,4,8,10,4,1,6,2,3),X2=c(1,3,2,5,2,4,0,1,3,7,8,10,3,4,5,5,4,8,10,12,0,3,3,4),Day=(c(1,1,2,2,2,2,1,1,2,2,3,3,2,2,4,4,5,5,7,7,1,1,2,2)))

I'd like to fit a logistic mixed model with two random effects, one is a
subject level random effect (Group) and another is a pair-level random
effect (Pair), which is nested within Group. Furthermore, I have a Day
variable (a time variable), which has the same value for each Y pair. As
far as I could understand, the syntax for the nested random effect should

random = ~ 1|Pair/Group

I used GLMM (from lme4) and glmPQL (from MASS) functions using the
following model specifications:

fit <- GLMM(Y ~ X1+X2+Day+X1*Day+X2*Day, family = binomial, data =
simul,  random = ~ 1|Pair/Group)
summary(fit)

fit1 <- glmmPQL(Y ~ X1+X2+Day+X1*Day+X2*Day, family = binomial, data =
simul,  random = ~ 1|Pair/Group)
summary(fit1)

My questions are (in order of importance):

1) is the formulation correct for the nested random effect?

2) why do the results of the two packages give different std. errors and
P-values for parameter estimates, which are indeed very similar? glmmPQL
tend to give greater errors and higher P's than GLMM. Further, it takes
longer to produce the output. They both implement PQL estimation, I
believe. Which function is best? I believe it should be that with smaller se's.

3) I wish also to model Day as a random intercept in Group (but not in Pair
nested in Group), but I do not know if this is possible. Does anybody have
any suggestion?

4) X2 and Day are correlated by nature. Is this giving any problem for
parameter estimates?

resolved my case (you know who you are :-)). I've also been told that PQL
estimates are flawed, and that I should better turn to other software (e.g.
SAS PROC NLMIXED).

Thank you all for your attention,

Diego

_________________________________________________

Diego Rubolini
Dipartimento di Biologia Animale
Università degli Studi di Pavia
Piazza Botta 9
I - 27100 Pavia
Italy
Email: diego.rubolini at unipv.it

URL: http://www.unipv.it/webbio/labweb/ecoeto/

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