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 <[EMAIL PROTECTED]> 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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