R help - Apr 2002 - writing a package for generalized linear mixed modesl

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
one?

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
http://www.geocities.com/jg_liao

__________________________________________________

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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
> one?
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 &
Ripley.
> 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
problems.

-- 
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)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !)  To: r-help-request at
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Hi,

I think that "reglm"  fits GLM with random effects in S (see
http://www.statsci.org/s/reglm.html).
In fact I was looking for a R equivalent of reglm, but I didn't find it.

Juli


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
> one?
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 &
Ripley.
> 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
problems.

--
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)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

r-help mailing list -- Read
http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !)  To: r-help-request at
stat.math.ethz.ch

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-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !)  To: r-help-request at
stat.math.ethz.ch
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