[R-sig-ME] Some very basic questions about modelling heteroscedasticity of within subjects variance in repeated measures obserational studies
th|erry@onke||nx @end|ng |rom |nbo@be
Mon Sep 9 21:25:04 CEST 2019
I think you want each individual to have a different residual variance. You
can do this with lme() using weight = varIdent(~1|individual). Note that
you'll need a lot data for each individual to get sensible results.
There is a chapter on this in Pinheiro and Bates (2000) Mixed-Effects
Models in S and S-PLUS and probably also in Zuur et al (2009) Mixed Effects
Models and Extensions in Ecology with RMixed-Effects Models in S and S-PLUS
ir. Thierry Onkelinx
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Op ma 9 sep. 2019 om 20:54 schreef Chris Evans <chrishold using psyctc.org>:
> I'm a psychotherapist not a theoretical nor a professional statistician so
> apologies if some of these questions are embarrassingly stupid. I have done
> some searching for pertinent publications and answers but I'm no finding
> answers, perhaps I may be using entirely the wrong search terms/questions.
> I took a vow over a decade ago to stick with R for all my stats and don't
> intend to break it. I'm slowly getting my head around mostly linear mixed
> models and mostly for changes on continuous measures in therapies mostly
> from naturalistic datasets from services, but just now also of some
> non-help-seeking general population samples. So far I've been using lmer
> from the lme4 package and lme and nlme from the nlme package. As I don't
> have the resource of colleagues with real skills in this area I've been
> trying to check my assumptions and analyses by modelling and seeing if
> analyses of models fit what I expect. Finding the simstudy package recently
> looks as if it will help with this and replace my otherwise clumsy models.
> My particular interest is in what I'm calling "heteroscedasticity of
> within subjects variance in repeated measures", I think it could also be
> called "allowing a random variance term within individuals" or something
> like that. I have good theoretical and some (quite old but robust looking
> pre-mixed models) empirical work that suggests that such a random effect
> may be present in most of my datasets. I know how to model random offsets
> (centred or not), random slopes and even piecewise random and nonlinear
> slopes (all of which are realistic in clinical datasets). However, I'm
> really unclear how I should model a random variance term, if I'm explaining
> myself clearly. I found some discussion off [
> ] but the only software implementations seemed to refer to
> MLWin and though I have great respect for MLWin and have used it in the
> past, I would really like to keep my vow to do everything in R.
> 1) Can anyone point me to work within the mixed models tradition that
> looks as random within subjects variance?
> 2) Can anyone point me to work using R tools that explores this?
> Many thanks in advance to all, and, while I'm here, huge thanks to the
> people who have written all these tools and so often give incredibly
> helpful and thoughtful Emails here from which I've learned much of what I
> do know about his area,
> Chris Evans <chris using psyctc.org> Visiting Professor, University of
> Sheffield <chris.evans using sheffield.ac.uk>
> I do some consultation work for the University of Roehampton <
> chris.evans using roehampton.ac.uk> and other places
> but <chris using psyctc.org> remains my main Email address. I have a work web
> site at:
> and a site I manage for CORE and CORE system trust at:
> I have "semigrated" to France, see:
> That page will also take you to my blog which started with earlier joys in
> France and Spain!
> If you want to book to talk, I am trying to keep that to Thursdays and my
> diary is at:
> Beware: French time, generally an hour ahead of UK.
> R-sig-mixed-models using r-project.org mailing list
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