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mixed effect-models

5 messages · Xavi, Brian Ripley, Thomas Lumley +1 more

Xavi
# 
Hello,
?
I believe that in R, it is not possible to analyze mixed effect-models
when the distribucion is not gaussian (p.e. binomial or poisson), isn't?
?
Somebody can suggest me alternative? 
?
thanks
? 
xavi
?



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Brian Ripley
# 
Sure you can!

See glmmPQL in package MASS, glmm in package glmmGibbs, glmm in one of
Jim Lindsey's packages, ....

There is even a discussion of this in MASS4 secion 10.4.
On Mon, 21 Oct 2002, Xavi wrote:

            

      
      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Thomas Lumley
    

    

      
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On Mon, 21 Oct 2002, Xavi wrote:

        

            

      
      
      
    

        
It depends on exactly what you mean.

 - Jim Lindsey's packages will fit (at least) random intercept models

 - For binomial or Poisson models with reasonably large means (perhaps 4
or so) the PQL approximation used by glmmPQL in the MASS package is pretty
good.

            

      
      
      
    

        
Again, it depends on why you want to fit mixed-effects models. You may be
able to fit marginal models (GEE) instead.

If you really want to fit mixed models with multiple random effects to
binary data you probably need SAS PROC NLMIXED or a Bayesian solution
(or HLM or MLWiN might be able to do it by now).

	-thomas


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      Feng Zhang
    

    

      
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So in R there is no package for mixed models with multiple random effects to
Binary data?

Thanks.

----- Original Message -----
From: "Thomas Lumley" <tlumley at u.washington.edu>
To: "Xavi" <xpuig at dsss.scs.es>
Cc: <r-help at stat.math.ethz.ch>
Sent: Monday, October 21, 2002 9:03 AM
Subject: Re: [R] mixed effect-models

            

      
      
      
    

        
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http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html

            

      
      
      
    

        
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      Brian Ripley
    

    

      
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On Mon, 21 Oct 2002, Feng Zhang wrote:

        

            

      
      
      
    

        
R-help has already been told about two in answer to the original question.

GLMMs for binary data are a tricky inference problem and all the software
I know of has drawbacks but real problems do get solved using it.