Kevin Wright <kw.stat <at> gmail.com> writes: > > Generally, the only way to estimate f1:f2 is if you have all combinations of > data present for these two factors.
Well, he said it was unbalanced, he didn't say how unbalanced -- i.e. it's not clear (to me) whether there are any completely missing cells or not ... > On Wed, Oct 5, 2011 at 2:00 PM, Brad Davis <bhdavis1978 <at> > gmail.com> wrote: > > > > I'm having some difficulty with lme. I am currently trying to run the > > following simple model > > > > anova(lme(x ~ f1 + f2 + f1:f2, data=m, random=~1|r1)) [which you could also specify as ~f1*f2] > > Which is currently producing the error > > > > Error in MEEM(object, conLin, control$niterEM) : > > Singularity in backsolve at level 0, block 1 > > > > x is a numeric vector containing 194 observations. f1 is a factor vector > > containing two levels, and f2 is a different factor vector containing 5 > > different levels. R1 is a another factor vector containing 13 different > > levels, and it is again, unbalanaced. f1, f2 and r1 are unbalanced, but I > > can't do anything about it. The data comes from wild-caught samples and > > not > > from a nice, neat experiment. If I change the model specification slightly > > removing the interaction term (e.g. anova(lme(x ~ f1 + f2, data=m, > > random=~1|r1)) ), then lme proceeds without producing any errors. I have a couple of suggestions: (1) try lmer (it will at least work differently, and might work better) (2) try expanding your model out to a one-way design -- lme(x~interaction(f1,f2),data=m,random=~1|r1) Follow-ups should probably be sent to r-sig-mixed-mod...@r-project.org ______________________________________________ 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.
