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 [[alternative HTML version deleted]] ______________________________________________ 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.