Hi there, I'm new to some of these more advanced regression techniques and also new to R. This looks like a great forum.
I am trying to examine the association with membership in a group and some different variables, most of which are (approximately) normally distributed. Would just do an ANOVA, but I have correlated data. Everyone in my dataset has a sibling in the sample. The correlation is a nuisance for the purpose of what I'm interested in. I know that some individuals have used linear mixed models and modeled the nuisance correlation as a random effect. My question is -- I've seen quite a few people write about using the GLM with the "survey" package, which can apparently also handle correlated data, and it seems like there is really detailed documentation for the package, which is a must for me, as I'm still learning. What are the advantages/disadvantages to using this survey package (and specifying a normal distribution) instead of a linear mixed model package? I'm interested in practical issues with implementation in R, as well as statistical concerns that I should be aware of... If LMM is better, which package to people recommend? -- View this message in context: http://r.789695.n4.nabble.com/Survey-package-GLM-vs-Linear-Mixed-Models-tp4636207.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
