[R] R package that has (much) the same capabilities as SAS v9 PROC GENMOD

[Thomas Lumley]

On Tue, 5 Apr 2005, Simon Blomberg wrote:

> The questioner clearly wants generalized linear mixed models. lmer in package 
> lme4 may be more appropriate. (Prof. Bates is a co-author.). glmmPQL should 
> do the same job, though, but with less accuracy.

Actually, I think the questioner wants GEE, from geepack or yags.  SAS has 
an excellent glmm implementation, but it's through PROC NLMIXED rather 
than GENMOD, which does marginal models.

 	-thomas

> Simon.
>
>> check glm()
>> 
>> On Apr 4, 2005 6:46 PM, William M. Grove <grove001 at umn.edu> wrote:
>>>  I need capabilities, for my data analysis, like the Pinheiro & Bates
>>>  S-Plus/R package nlme() but with binomial family and logit link.
>>> 
>>>  I need multiple crossed, possibly interacting fixed effects (age cohort 
>>> of
>>>  twin when entered study, sex of twin, sampling method used to acquire 
>>> twin
>>>  pair, and twin zygosity), a couple of random effects other than the 
>>> cluster
>>>  variable, and the ability to have a variable of the sort that P&B call
>>>  "outer" to the clustering variable---zygosity.  Dependent variables are 
>>> all
>>>  parental (mom, dad separately of course) psychiatric diagnoses.
>>> 
>>>  In my data, twin pair ID is the clustering variable; correlations are
>>>  expected to be exchangeable but substantially different between members 
>>> of
>>>  monozygotic twin pairs and members of dizygotic twin pairs.  Hence, in my
>>>  analyses, the variable that's "outer" to twin pair is monozygotic vs.
>>>  dizygotic which of course applies to the whole pair.
>>> 
>>>  nlme() does all that but requires quasi-continuous responses, according 
>>> to
>>>  the preface/intro of P&B's mixed models book and what I infer from online
>>>  help (i.e., no family= or link= argument).
>>> 
>>>  The repeated() library by Lindsey seems to handle just one nested random
>>>  effect, or so I believe I read while scanning backlogs of the R-Help 
>>> list.
>>> 
>>>  glmmPQL() is in the ballpark of what I need, but once again seems to lack
>>>  the "outer" variable specification that nlme() has, and which PROC GENMOD
>>>  also has---and which I need.
>>> 
>>>  I read someplace of yags() that apparently uses GEE to estimate 
>>> parameters
>>>  of nonlinear models including GLIMs/mixed models, just the way PROC 
>>> GENMOD
>>>  (and many another program) does.  But on trying to install it (either
>>>  v4.0-1.zip or v4.0-2.tar.gz from Carey's site, or Ripley's Windows port)
>>>  from a local, downloaded zip file (or tar.gz file converted to zip file), 
>>> I
>>>  always get an error saying:
>>>   > Error in file(file, "r") : unable to open connection
>>>   > In addition: Warning message:
>>>   > cannot open file `YAGS/DESCRIPTION'
>>>  with no obvious solution.
>>> 
>>>  So I can't really try it out to see if it does what I want.
>>> 
>>>  You may ask:  Why not just use GENMOD and skip the R hassles?  Because I
>>>  want to embed the GLIM/mixed model analysis in a stratified resampling
>>>  bootstrapping loop.  Very easy to implement in R, moderately painful to 
>>> do
>>>  in SAS.
>>> 
>>>  Can anybody give me a lead, or some guidance, about getting this job done
>>>  in R?  Thanks in advance for your help.
>>> 
>>>  Regards,
>>> 
>>>  Will Grove      | Iohannes Paulus PP. II, xxx
>>>  Psychology Dept. |
>>>  U. of Minnesota  |
>>>  -----------------+
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>> 
>> 
>> --
>> WenSui Liu, MS MA
>> Senior Decision Support Analyst
>> Division of Health Policy and Clinical Effectiveness
>> Cincinnati Children Hospital Medical Center
>> 
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>
>
> -- 
> Simon Blomberg, B.Sc.(Hons.), Ph.D, M.App.Stat.
> Visiting Fellow
> School of Botany & Zoology
> The Australian National University
> Canberra ACT 0200
> Australia
>
> T: +61 2 6125 8057  email: Simon.Blomberg at anu.edu.au
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>
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Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle