Sean Godwin <sean.godwin <at> gmail.com> writes: > > Hi all, > > I am fairly new to mixed effects models and lmer, so bear with me. > > Here is a subset of my data, which includes a binary variable (lake (TOM or > JAN)), one other fixed factor (Age) and a random factor (Year). > lake FishID Age Increment Year > 1 TOM 1 1 0.304 2007 > 2 TOM 1 2 0.148 2008 > 3 TOM 1 3 0.119 2009 > 4 TOM 1 4 0.053 2010 > 5 JAN 2 1 0.352 2009 > 6 JAN 2 2 0.118 2010 > > The model I'm trying to fit is: > m1 <- lmer(Increment ~ 0 + Age + Age*lake + (1|Year) + (1|Year:Age) + > (1|FishID),lakedata) > > The error message I get is: *"Error in mer_finalize(ans) : Downdated X'X is > not positive definite, 27."* > * > * > >From reading up on the subject, 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. 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 ______________________________________________ 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.
