I think my problem is that I can't >> incorporate the 'lake' variable in a fixed-effect interaction because it is >> only has one binary observation. But I don't know what to do to be able to >> fit this model. Any help would be greatly appreciated! >> -Sean > > In principle you should be able to fit this model, but the error message > is telling you that there are numeric problems -- it may just be that > your data are a little too sparse in some direction.
Yes. Consider the collinearity between Age and Year, i.e. for a given cohort (mos or all captured by "fishID" ?) they are essentially the same variable with different units. So I would suspect the problem is you are over fitting those. A few suggestions: > > * try centering Age, or re-introducing the intercept, to see if you > can get something to work. > * You _might_ try the development version of lme4 (lme4Eigen, on r-forge) > * plot your data to see if you see anything odd about the data > * perhaps try making Year a fixed effect -- 4 levels is fairly small > for a random effect > * Ask further questions on the r-sig-mixed-models mailing list. > > Ben Bolker > Add one more to those: make sure your random effects are indeed crossed. If they are nested (without knowing anything about your data, just given their names that's a possibility), you could try nlme::lme. Elai > ______________________________________________ > 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.
