On Dec 13, 2007 4:15 PM, Ilona Leyer <[EMAIL PROTECTED]> wrote: > Dear All, > I want to analyse treatment effects with time series > data: I measured e.g. leaf number (five replicate > plants) in relation to two soil pH - after 2,4,6,8 > weeks. I used mixed effects models, but some analyses > didn´t work. It seems for me as if this is a randomly > occurring problem since sometimes the same model works > sometimes not. > > An example: > > names(test) > [1] "rep" "treat" "leaf" "week" > > library (lattice) > > library (nlme) > > test<-groupedData(leaf~week|rep,outer=~treat,test) > > model<-lme(leaf~treat,random=~leaf|rep) > Error in lme.formula(leaf~ treat, random = ~week|rep)
Really!? You gave lme a model with random = ~ leaf | rep (and no data specification) and it tried to fit a model with random = ~ week | rep? Are you sure that is an exact transcript? > : > nlminb problem, convergence error code = 1; > message = iteration limit reached without convergence > (9) > Has anybody an idea to solve this problem? Oh, I have lots of ideas but without a reproducible example I can't hope to decide what might be the problem. It appears that the model may be over-parameterized. Assuming that there are 4 different values of week then ~ week | rep requires fitting 10 variance-covariance parameters. That's a lot. The error code indicates that the optimizer is taking ______________________________________________ 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.
