Difficulty with lme
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-models at r-project.org