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). Thanks, Sean
5,000 repeated measures! You might have problems fitting a "old school" univariate or multivariate ANOVA RM model. Can you group some of the measurements or you actually want to make inferences at each time point? Anyway, try using a newer method, like lme from the library lme4. The book by Pinheiro and Bates "Mixed-Effects Models in S and S-Plus" describes several examples of RM analysis using their package. Good luck! Francisco>From: Sean Davis <sdavis2 at mail.nih.gov> >To: r-help <r-help at stat.math.ethz.ch> >Subject: [R] Repeated measures >Date: Wed, 6 Oct 2004 08:07:38 -0400 > >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). > >Thanks, >Sean > >______________________________________________ >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
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, correlation=corCar1(args...),data=your.data) 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 UK 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). Thanks, Sean