Hi, you can try reading http://cran.r-project.org/web/packages/SASmixed/vignettes/Usinglmer.pdf
If I am not mistaken, the repeated measures over time and nesting of individuals in communities as you describe them would be captured by: reg=lmer(y~cond+timecat+cond*timecat+(timecat|community/subject)) summary(reg) The random effects have the following structure: (1|subject) = random intercept (timecat|subject) = random intercept and slope (1|community/subject) = subject nested in community The random effect above is a combination of nesting and random intercept and slope. However, the question which random effects to select is a modeling question and thus ultimately the ressearchers responsibility. Best, Daniel ------------------------- cuncta stricte discussurus ------------------------- -----Ursprüngliche Nachricht----- Von: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Im Auftrag von Alan Kelly Gesendet: Tuesday, June 23, 2009 4:55 AM An: r-help@r-project.org Betreff: [R] nested cross-sectional design using lmer or nlme Dear all, I'd appreciate some advice on the following problem. I'm attempting to analyse a nested cross-sectional design in which an intervention was offered to a series of randomly selected (small) communities, so the unit of randomisation is the community. All available individuals in each community were interviewed before the intervention and again at follow-up post-intervention. The set of available individuals at baseline and at follow-up were far from identical (a common feature of such designs). Similarly, a series of control communities were interviewed. This type of design is used in epidemiological studies particularly in intervention designed to alter lifestyle factors. Such designs tend to be highly unbalanced Murray et al. discuss the appropriate analysis of such studies (Analysis of data from group-randomized trials with repeat observations on the same groups, Stats in Med. 17, 1581-1600). They offer three examples of SAS code - one of which is as follow: proc mixed; class cond unit timecat; model y=cond timecat cond*timecat/ddfm=res; random int timecat/subject=unit(cond); run; cond is 0/1 corresponding to control/intervention timecat is 0/1 corresponding to baseline/follow-up unit is 1 to 39 and identifies the communities. and y is a continuous score I've read the random statement as cond nested within unit and crossed (?) by timecat. Unfortunately I'm not familiar with SAS code. I would expect random effects for unit and timecat X unit. I would much appreciate any suggestions on how to code the above in lmer or nlme. Alan Kelly Trinity College Dublin > ______________________________________________ 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. ______________________________________________ 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.