Message-ID: <1117202767.4297294fd6e37@webmail.utoronto.ca>
Date: 2005-05-27T14:06:07Z
From: Hadassa Brunschwig
Subject: longitudinal survey data
In-Reply-To: <Pine.A41.4.61b.0505261305140.303294@homer12.u.washington.edu>
Thank you for your reply.
Does that mean that in order to take in account the repeated measures I denote
these as another cluster in R?
Dassy
Quoting Thomas Lumley <tlumley at u.washington.edu>:
> On Thu, 26 May 2005 h.brunschwig at utoronto.ca wrote:
>
> >
> > Dear R-Users!
> >
> > Is there a possibility in R to do analyze longitudinal survey data
> (repeated
> > measures in a survey)? I know that for longitudinal data I can use lme()
> to
> > incorporate the correlation structure within individual and I know that
> there is
> > the package survey for analyzing survey data. How can I combine both? I
> am
> > trying to calculate design-based estimates. However, if I use svyglm() from
> the
> > survey package I would ignore the correlation structure of the repeated
> measures.
> >
>
> You *can* fit regression models to these data with svyglm(). Remember that
> from a design-based point of view there is no such thing as a correlation
> structure of repeated measures -- only the sampling is random, not the
> population data.
>
>
> If you *want* to fit mixed models (eg because you are interested in
> estimating variance components, or perhaps to gain efficiency) then it's
> quite a bit trickier. You can't just use the sampling weights in lme().
> You can correct for the biased sampling if you put the variables that
> affect the weights in as predictors in the model. Cluster sampling could
> perhaps then be modelled as another level of random effect.
>
>
> -thomas
>
> Thomas Lumley Assoc. Professor, Biostatistics
> tlumley at u.washington.edu University of Washington, Seattle
>