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 <bhdavis1978@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
>
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>
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