# [R] lme4, error in mer_finalize(ans)

Douglas Bates bates at stat.wisc.edu
Fri Dec 5 23:17:26 CET 2008

```On Fri, Dec 5, 2008 at 3:44 PM, B. Meijering <B.Meijering at student.rug.nl> wrote:
> Using lmer() on my data results in an error. The problem, I think, is my
> model specification. However, lm() works ok.
> I recreated this error with a more simple dataset. (See code below.)

> # word and letter recognition data
> # two within factors:
> # word length: 4, 5, 6 letters
> # letter position: 1-4 (in 4-letter words), 1-5 (in 5-letter words), 1-6 (in
> 6-letter words)
> # one dependent variable:
> # reaction time

> # first fit a linear model with letter position nested in word length
> lm(rt ~ length + length:pos, data=df)

> # fit a mixed effects model, with subj (participant) as random effect
> lmer(rt ~ length + length:pos + (1 | subj), data=df)

> Using lmer() results in an error: Error in mer_finalize(ans) : Downdated X'X
> is not positive definite, 13. I don't experience any problems using lm().
> Does anyone know where things go wrong?

That, admittedly obscure, error message relates to the fixed-effects
specification rt ~ length + length:pos being rank deficient.  If you
look at the summary of the linear model fit you will see that there
are 3 coefficients that are not determined because of singularities.
The lm function detects the singularities and fits a lower-rank model.
The lmer function is not as sophisticated.  It just detects the
singularities and quits.

The length and the position are confounded.

> xtabs(~ len + pos, df)
pos
len 1 2 3 4 5 6
4 2 2 2 2 0 0
5 2 2 2 2 2 0
6 2 2 2 2 2 2

(By the way, I changed the name of the length variable to len as
typing "length" makes me expect the function called length.)

Even when you remove this confounding by creating the len:pos
interaction separately as a factor, you will still get singularities
because there is only one len:pos combination for len = 6.

You will need to think of a way of parameterizing the fixed effects
without the singularities.  You can check for singularities in the
summary of the lm fit.

```