[R] R package that has (much) the same capabilities as SAS v9 PROC GENMOD
Thomas Lumley
tlumley at u.washington.edu
Tue Apr 5 16:26:01 CEST 2005
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
>>
>> ______________________________________________
>> R-help at stat.math.ethz.ch mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide!
>> http://www.R-project.org/posting-guide.html
>
>
> --
> 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
> F: +61 2 6125 5573
>
> CRICOS Provider # 00120C
>
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>
Thomas Lumley Assoc. Professor, Biostatistics
tlumley at u.washington.edu University of Washington, Seattle
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