Hi,
 
I am using lmer to analyze habitat selection in wolverines using the
following model:
 
(me.fit.of <-
lmer(USED~1+STEP+ALT+ALT2+relM+relM:ALT+(1|ID)+(1|ID:TRKPT2),data=vdata,
control=list(usePQL=TRUE),family=poisson,method="Laplace"))
 
Here, the habitat selection is calaculated using a so-called discrete
choice model where each used location has a certain number of
alternatives which the animal could have chosen. These sets of locations
are captured using the TRKPT2 random grouping. However, these sets are
also clustered over the different individuals (ID). USED is my binary
dependent variable which is 1 for used locations and zero for unused
locations. The other are my predictors.
 
I would like to predict the model fit at different values of the
predictors, but does anyone know whether it is possible to do this? I
have looked around at the R-sites and in help but it seems that there
doesn't exist a predict function for lmer???
 
I hope someone can help me with this; point me to the right functions or
tell me to just forget it....
 
Thanks in advance!
 
Cheers Roel
 
Roel May
Norwegian Institute for Nature Research
Tungasletta 2, NO-7089 Trondheim, Norway


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