Message: 4
Date: Mon, 28 Mar 2005 12:06:25 +0100
From: JEB Halliday <s0454869 at sms.ed.ac.uk>
Subject: [R] glmmPQL questions
To: r-help at stat.math.ethz.ch
Message-ID: <1112007985.4247e531657c5 at sms.ed.ac.uk>
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I am looking a risk factors for disease in cattle and am
interested in modelling
farm and sampling cluster as random effects (My outcome is
positive or negative
at the level of the farm). I am using R version 2.0.1 on a Mac and have
identified glmmPQL as hopefully the correct function to use. I have run a
couple of models using this but was hoping that you might be able
to answer a
few questions.
e.g. model<-glmmPQL(farmstatus~cattlenumber,random~1|farm,binomial)
I am pretty new to both R and stats so if these questions are
very simple and I
am just missing something, suggestions about good texts on GLMM
in R would be
great.
First up, what is the best way to constrain the model to only
look at certain
levels of a multi-level factor e.g. a categorisation of cattle
number where all
points of high influence
(as determined using: summary(influence.measures(model)) )
are confined to the largest class (D) and I want to run the model
which just
looks at levels A,B and C? (or only months May-September..)
I was also wondering about the best way to force specified
variables to remain
in the model when running e.g. stepwise selection of interaction terms?
Finally, is there is a recognised method for dealing with missing
values in
these models?
and as a minor point the models do not run unless i specify the
data= part of
the syntax and as this is apparently an optional piece of
information I was
wondering why this is required when all of my variables are in
the same data
frame (and even when this data frame is attached?)
Any help would be greatly appreciated
Jo Halliday
MSc student
University of Edinburgh
s0454869 at sms.ed.ac.uk