[R] HGLM in R (was: writing a package for generalized linear mixed models)
paradis at isem.univ-montp2.fr
Wed May 8 18:03:51 CEST 2002
I wonder if someone has tried to implement the hierarchical generalized
linear model (HGLM) approach of Lee and Nelder (JRSSB, 1996, 58: 619-56) in R.
Thanks in advance.
At 17:18 01/04/02 +0100, ripley at stats.ox.ac.uk wrote:
>On Mon, 1 Apr 2002, Jason Liao wrote:
>> Happy new month, everyone!
>> I am planning to write a NIH grant proposal to study ways to speed
>> Monte Carlo based maximum likelihood algorithm for hierarchical models
>> with a focus on generalized linear mixed models (GLM with random
>> effects). I thought it would be nice and also increase the chance of
>> funding if I could produce an R package in the process. I understand
>> that Prof. Pinheiro ang Bates have produced LME for linear mixed models
>> and NLME for non-linear mixed models. But these do not fit logistic
>> mixed models or Poisson mixed models. SAS Proc NLMIXED can fit simple
>GLME (beta, S-PLUS only) does.
>> logistic or Poisson mixed models but the syntax is not specific for
>> generalized mixed models. There can be only one level of random
>> effects. STATA version 7 can fit random intercept models but not more.
>> There are also some standalone programs such as MIXOR by Don Hendeker
>> of Chicago. But it is hard to use a stnadalone program for data
>> analysis efficiently because you have to convert the data set and you
>> lose all the familiar tools for data transformation and graphics.
>> I would appeciate your comments on the following points:
>> 1. Is there a strong need for a package for generalized linear mixed
>> models? Could someone have already written or in the process of writing
>See package GLMMgibbs on CRAN, function glmmPQL in package MASS and
>function glmm (only a random intercept) in one of Jim Lindsey's packages.
>GLMMs are half a chapter in the upcoming fourth edition of Venables &
>> 3. How big is the undertaking? I have some R code for GLMM that runs at
>> an acceptable speed. I can see that some part can benifit from
>> converting to C or Fortran. I am not familiar with R's interface with C
>> and Fortran. I do not know either how to make the package available for
>> different platforms. Will the multi-platform issue become easier if I
>> stay with 100% pure R?
>100% pure R would be unacceptably slow.
>I would rate this as a research problem, and a major undertaking.
>GLMMgibbs is an existence proof, but not able to handle many quite simple
>Brian D. Ripley, ripley at stats.ox.ac.uk
>Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
>University of Oxford, Tel: +44 1865 272861 (self)
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