R help - Feb 2012 - prediction for linear mixed model

Hi, I am wondering if we can make prediction on a linear mixed model by lmer()
from lme4 package? Specifically I am fitting a very simple glmer() with binomial
family distribution, and want to see if I can get the predicted probability like
that in regular logistic regression?
 
fit<-glmer(y~x+(1|id),dat,family=binomial)
 
where y is the response variable (0, 1), and x is a continuous variable.
 
I would like to get a predicted probability at a given value of x.
 
Many thanks
 
John
	[[alternative HTML version deleted]]

array chip <arrayprofile <at> yahoo.com> writes:

> Hi, I am wondering if we can make prediction on a linear mixed model
>  by lmer() from lme4 package?
> Specifically I am fitting a very simple glmer() with binomial family
> distribution, and want to see if I can get the predicted probability like
> that in regular logistic regression?
> fit<-glmer(y~x+(1|id),dat,family=binomial)
> where y is the response variable (0, 1), and x is a continuous variable.
> I would like to get a predicted probability at a given value of x.
  See (1) code on http://glmm.wikidot.com/faq for this precise task,
and (2) the 'predict' method in the bleeding-edge development version
(lme4Eigen). 

  This question is probably slightly better suited for the
r-sig-mixed-models <at> r-project.org mailing list.