I am trying to learn how to use nlme by working on a simple example. I attach the data from a toy example I made up which is similar to my real problem. (My grasp of fixed/random effects is still a bit tenuous) It is a longitudinal study of the effect of two treatments: A and B. The data were created by: A: y<-12/(1+exp((2-time)/.5)),y<-8/(1+exp((2-time)/.5)) B: y<-22/(1+exp((2-time)/.5)), y<-18/(1+exp((2-time)/.5)) fixed effect for param a = 10,10, 20,20, random effects= +2, -2, +2, -2 fixed effect for param b=2, fixed effect for c = .5 normal noise with sd=1 was added to all y values. I do: df<-read.table(file="papers/alex/junk.dat",header=TRUE) attach(df) fullfit<-nlsList(y~SSlogis(Time,Asym,xmid,scal)|Subject,data=df) and get the separate fits of the logistic to each subject's data. 4 curves and 4 sets of param values (12 params total for the model). How do I get the fit of the model which is the true one as stated above: Asym for group A (fixed effect) Asym for group B (fixed effect) Asym diff from group A mean for subject 1 (random effect) Asym diff from group A mean for subject 2 (random effect) Asym diff from group B mean for subject 3 (random effect) Asym diff from group B mean for subject 4 (random effect) xmid (fixed effect) scal (fixed effect) = 8 params total I would like 95% CIs for params and in particular a CI for the diff in Asym for groups A and B (or a hypo test). If I had an additional grouping factor (e.g. Sex), how would I do it? (I would have enough subjects to make it worthwhile--I actually have 53). Thanks very much for any help. BTW I have not tried to use groupedData object because I think that's mainly useful for the fancy trellis graphics, which we don't have in R. I know there was a recent announcement about lattice, but I'm not at all sure that would work with nlme at this point... Bill Simpson -------------- next part -------------- "Subject" "Group" "Time" "y" 1 a 1 1.803407 1 a 2 3.790964 1 a 3 6.885437 1 a 4 6.304922 1 a 5 6.423945 2 a 1 1.685487 2 a 2 4.775235 2 a 3 12.058397 2 a 4 12.500626 2 a 5 12.602189 3 b 1 1.532756 3 b 2 9.866473 3 b 3 16.928385 3 b 4 17.762473 3 b 5 18.493848 4 b 1 2.975398 4 b 2 13.158969 4 b 3 19.589508 4 b 4 22.389456 4 b 5 21.381210