Dear colleagues,

Happy Halloween.

When I try fitting glmm under lme4 using the following code:

x=as.factor(rep(c("1","2","3", "4", "5", "6"),each=2))
y=c(14, 18, 10, 47, 13, 24, 10, 12,  1,  0,  6,  6)
offset=c(5.505332,5.645447, 5.549076, 5.886104, 6.023448, 6.177944, 6.077642, 
6.186209,
5.030438, 5.117994, 5.170484, 5.493061)
eu=gl(12,1)
try=data.frame(x,y,eu,offset)
fit <- glmer(y ~ x + (1 | eu) + offset(log(adjust)),
              family = poisson, data =try)


A message pops up, saying "Number of levels of a grouping factor for the random 
effects
is *equal* to n, the number of observations". 


Question1: can I still use the fit statistics? (Should the levels be less than 
the number of observations? But I can't help it. My data are like that)

Question2:


When running the glmm function under the repeated package written by James 
Lindsey, I encountered the problem of trying to decide on the value of points, 
ie. Gauss Hermite integration numbers. Different points resulted in quite 
different fit statistics. Here is my code:


fit2<-glmm(y~x, family=poisson, offset=offset, nest=gl(12,1),points=4,data=try)

Is it true that the more points, the better? A book says points=20 entails 
decent approximation, but there’s an error msg when I increased my point number 
to 10. It says “Product of probabilities is too small to calculate.” May I seek 
your advice on this issue (what point should I be using)? Are there good 
references you would recommend so that I can understand better understand Gauss 
Hermite integration? BTW, glmer under lme4 uses adapted Gauss-Hermite 
integration too.


Thanks so much. Bon week-end. -- jie
 
 Jie Li
Department of Statistics
1219 Snedecor Hall
Iowa State University
Ames, IA 50010
lij@iastate.edu
(515) 294-5790



      
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