Generally, the only way to estimate f1:f2 is if you have all combinations of data present for these two factors.
Sometimes it makes sense to include f1:f2 as a random effect in the model (which does NOT need balanced data) but that is something you have to decide. Kevin On Wed, Oct 5, 2011 at 2:00 PM, Brad Davis <bhdavis1...@gmail.com> wrote: > Hi all, > > 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 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. > > Thanks, > Brad Davis > > [[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. > [[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.
