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 <[EMAIL PROTECTED]>: > On Thu, 26 May 2005 [EMAIL PROTECTED] 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 > [EMAIL PROTECTED] University of Washington, Seattle > ______________________________________________ R-help@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
