[R] writing a package for generalized linear mixed modesl

Jason Liao jg_liao at yahoo.com
Mon Apr 1 16:49:24 CEST 2002

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
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

2. Will production of a package as part of the algorithm research
increase the chance of funding from NIH?

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?


Jason G. Liao, Ph.D.
Division of Biometrics
University of Medicine and Dentistry of New Jersey
335 George Street, Suite 2200
New Brunswick, NJ 08903-2688
phone (732) 235-9748, fax (732) 235-9777


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