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 # make artificial data length <- c(rep(4,4), rep(5,5), rep(6,6)) # independent variable "word length" length <- factor(c(rep(length, 2))) pos <- c(1:4, 1:5, 1:6) # independent variable "letter position" pos <- factor(c(rep(pos, 2))) rt <- c(rnorm(15, 200, sd=10), rnorm(15, 300, sd=15)) # dependent variable "reaction time" df <- data.frame(subj=factor(c(rep(1:2, each=15))), length=length, pos=pos, rt=rt) # to use lmer from lme4 package library(lme4) # 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? ~ Ben Meijering
lme4, error in mer_finalize(ans)
2 messages · Ben Meijering, Douglas Bates
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.