Re: [R] longitudinal survey data
On 5/26/05, Thomas Lumley [EMAIL PROTECTED] wrote: 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. I've been struggeling with case weights (in the case of unequal selection probabilities) in mixed effects models. Those are not possible in lme(). Isn't it, however, possible to use case weights in glmmPQL from MASS? Koen Pelleriaux Sociologist University of Antwerp __ 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
Re: [R] longitudinal survey data
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
Re: [R] longitudinal survey data
On Fri, 27 May 2005 [EMAIL PROTECTED] wrote: 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? Yes, but unless you have multistage finite population corrections to put in the design object only the first stage of clustering affects the results, so you may not need to bother. -thomas __ 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
Re: [R] longitudinal survey data
Sorry, still confused. If I dont have fpc's ready in my dataset (calculate myself?) that means that R will use the weight of an individual for each of his repeated observations. But is that then still correct? The cluster individual is ignored and each observation of an individual has the same weight. Thanks a lot. Dassy Quoting Thomas Lumley [EMAIL PROTECTED]: On Fri, 27 May 2005 [EMAIL PROTECTED] wrote: 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? Yes, but unless you have multistage finite population corrections to put in the design object only the first stage of clustering affects the results, so you may not need to bother. -thomas __ 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
Re: [R] longitudinal survey data
On Fri, 27 May 2005 [EMAIL PROTECTED] wrote: Sorry, still confused. If I dont have fpc's ready in my dataset (calculate myself?) that means that R will use the weight of an individual for each of his repeated observations. But is that then still correct? The cluster individual is ignored and each observation of an individual has the same weight. Well, it depends to some extent on what inferences you are making, but yes, you probably do want each observation to have the same weight. Suppose you have 4 measurements on each person, and you are working with a simple random sample of 1000 people from a population of 1,000,000. If you had done these 4 measurements on the whole population you would have 4,000,000 measurements, so the 4000 measurements you have are 1/1000 of the population. This is the same weighting as if you had a single measurement person person, giving 1000 measurements in the sample and 1,000,000 in the population. If different individuals have different numbers of measurements then things get a bit trickier. It depends then on why there are different numbers of measurements.If they are the result of non-response you might want to rescale the weights at later time points to give the right population totals. If they are part of the sampling design then the design will specify what to do with them. -thomas __ 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
Re: [R] longitudinal survey data
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
