[R] Repeated measures

Dan Bebber danbebber at forestecology.co.uk
Thu Oct 7 13:35:00 CEST 2004

Hi Sean,

I'm not sure I quite understand your question. Am I right in thinking that:
state = a binomial dependent variable
measure = a continuous predictor

If so, perhaps you could try using glmmPQL (Generalized Linear Mixed Models
fitted by Penalized Quasi-Likelihood) in library MASS.
The model would include random intercepts for each individual, have binomial
errors, and some kind of continuous autoregressive error structure (I
expect), and would look something like

results<-glmmPQL(fixed=state~measure,random=~1|individual, family=binomial,

If I've got the wrong end of the stick, my apologies.

Dan Bebber

Department of Plant Sciences
University of Oxford
South Parks Road
Oxford OX1 3RB
Tel. 01865 275000


Message: 11
Date: Wed, 6 Oct 2004 08:07:38 -0400
From: Sean Davis <sdavis2 at mail.nih.gov>
Subject: [R] Repeated measures
To: r-help <r-help at stat.math.ethz.ch>
Message-ID: <5125203F-1790-11D9-97DA-000A95D7BA10 at mail.nih.gov>
Content-Type: text/plain; charset=US-ASCII; format=flowed

I have a data set in which I have 5000 repeated measures on 6 subjects 
over time (varying intervals, but measurements for all individuals are 
at the same times).  There are two states, a "resting" state (the 
majority of the time), and a perturbed state.  I have a continuous 
measurement at each time point for each of the individuals.  I would 
like to determine the "state" for each individual at each time point.  
It looks to me like I should be able to do this with the "hidden" 
command from the "repeated" package 
(http://popgen0146uns50.unimaas.nl/~jlindsey/rcode.html), but I have 
found it a bit confusing to get started.  The distributions in the two 
states are approximately normal with differences in centrality and 
possibly variance (but I can start by assuming similar variances).


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