Please always reply to the list as well as there always might be someone faster/better answering (or it could be that I am wrong, so someone might correct me)
Indeed Pinheiro/Bates assume gaussian error terms... but I am not really sure whether you meant that with "non normally distributed respond variable" resp. "with non-normal data" however: "/ Mixed-effects models: / The recommended nlme <http://cran.r-project.org/src/contrib/Descriptions/nlme.html> package, associated with Pinheiro and Bates, / Mixed-Effects Models in S and S-PLUS / (Springer, 2000), fits linear and nonlinear mixed-effects models, commonly used in the social sciences for hierarchical and longitudinal data. Generalized linear mixed-effects models may be fit by the glmmPQL function in the MASS package, and by the lmer function in the Matrix <http://cran.r-project.org/src/contrib/Descriptions/Matrix.html> package (related to the lme4 <http://cran.r-project.org/src/contrib/Descriptions/lme4.html> package, which largely supersedes nlme <http://cran.r-project.org/src/contrib/Descriptions/nlme.html> for / linear / mixed models). Also see the lmeSplines <http://cran.r-project.org/src/contrib/Descriptions/lmeSplines.html> and lmm <http://cran.r-project.org/src/contrib/Descriptions/lmm.html> packages." [ http://cran.r-project.org/src/contrib/Views/SocialSciences.html ] Lina Jansen schrieb: > > > 2006/10/17, Stefan Grosse <[EMAIL PROTECTED] > <mailto:[EMAIL PROTECTED]>>: > > Interesting packages for you might be the nlme and lme4 packages > and as > a book Pinheiro/Bates, "Mixed-Effects Models in S and S-Plus" > > > Thank you for the answer. I am always unsure concerning the > non-normality. Can I use the nlme and lme4 with non-normal data? > First, I thought they would work like an ANOVA but with random and > fixed effects. ______________________________________________ [email protected] 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.
