quote: > I am using nlme for data from nested design. That is, "tows" are nested > within "trip", "trips" nested within "vessel", and "vessels" nested > within "season". I also have several covariates, say "tow_time", > "latitude" and "depth" > My model is > y = season + tow_time + latitude + depth + vessel(season) + > trip(season, vessel) + e > In SAS, the program would be > proc mixed NOCLPRINT NOITPRINT data=obtwl.x; > class vessel trip tow season depth; > model y = season depth latitude /solution; <----------fixed effects > random vessel(season) trip(season vessel); > run; > My question is: How this nested mixed-effects model can be > fitted in R-> "nlme"?
> I do not know about SAS but I would guess that your model should be > fitted > as something like: > > lme (fixed= y ~ season + tow_time + latitude + depth, > random= ~ 1 | season/vessel/trip) > > Maybe you should do some reading in the book by Pinheiro & Bates? > They explain well how to set up models. I would create a grouped data variable, to avoid having season a both a random and fixed effect: your.data$SV<-getGroups(your.data, form=~1|season/vessel, level=2) the effect is to create a variable that groups vessels %in% season. BTW, according to your coding of the data, this stem is not always necessary. HTH Federico Calboli -- ================================= Federico C. F. Calboli Dipartimento di Biologia Via Selmi 3 40126 Bologna Italy tel (+39) 051 209 4187 fax (+39) 051 209 4286 f.calboli at ucl.ac.uk fcalboli at alma.unibo.it ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html