R help - Mar 2008 - strange results from binomial lmer?

I'm running lmer repeatedly on artificial data with two fixed factors
(called
'gender' and 'stress') and one random factor
('speaker'). Gender is a
between-speaker variable, stress is a within-speaker variable, if that matters.
Each dataset has 100 rows from each of 20 speakers, 2000 rows in all.

About 5% of the time I get a strange result, where the lmer() model with BOTH
fixed factors and the random factor ('gs_s') comes out MUCH worse
compared to
the models with ONE fixed factor and the random factor ('g_s' and
's_s'), and
also compared to the glm() model with both fixed factors and no random factor
('gs').

This doesn't make much sense to me.

I've placed a dataset on the Web that exhibits this behavior, as follows:

dat <- read.csv("http://www.ling.upenn.edu/~johnson4/strange.csv")

gs <- glm(outcome~gender+stress,binomial,dat)
g_s <- lmer(outcome~gender+(1|speaker),dat,binomial)
s_s <- lmer(outcome~stress+(1|speaker),dat,binomial)
gs_s <- lmer(outcome~gender+stress+(1|speaker),dat,binomial)

logLik(gs)         #  -1344 (df=3)
logLik(g_s)        #  -1342 (df=3)
logLik(s_s)        #  -1314 (df=3)
logLik(gs_s)       # -11823 (df=4)

This seems like an error of some kind. The glm() model with both fixed effects
is well-behaved, but lmer() seems to be going haywire when confronted with the
same situation plus the random effect.

Could anyone advise me how to stop this from happening, and/or explain why it
is?

Thanks very much,
Daniel

johnson4 at babel.ling.upenn.edu wrote:
> I'm running lmer repeatedly on artificial data with two fixed factors
(called
> 'gender' and 'stress') and one random factor
('speaker'). Gender is a
> between-speaker variable, stress is a within-speaker variable, if that
matters.
> Each dataset has 100 rows from each of 20 speakers, 2000 rows in all.
> 
> About 5% of the time I get a strange result, where the lmer() model with
BOTH
> fixed factors and the random factor ('gs_s') comes out MUCH worse
compared to
> the models with ONE fixed factor and the random factor ('g_s' and
's_s'), and
> also compared to the glm() model with both fixed factors and no random
factor
> ('gs').
> 
> This doesn't make much sense to me.
> 
> I've placed a dataset on the Web that exhibits this behavior, as
follows:
> 
> dat <-
read.csv("http://www.ling.upenn.edu/~johnson4/strange.csv")
> 
> gs <- glm(outcome~gender+stress,binomial,dat)
> g_s <- lmer(outcome~gender+(1|speaker),dat,binomial)
> s_s <- lmer(outcome~stress+(1|speaker),dat,binomial)
> gs_s <- lmer(outcome~gender+stress+(1|speaker),dat,binomial)
> 
> logLik(gs)         #  -1344 (df=3)
> logLik(g_s)        #  -1342 (df=3)
> logLik(s_s)        #  -1314 (df=3)
> logLik(gs_s)       # -11823 (df=4)
> 
> This seems like an error of some kind. The glm() model with both fixed
effects
> is well-behaved, but lmer() seems to be going haywire when confronted with
the
> same situation plus the random effect.
What version of the `lme4' package are you using? (Including the output 
of `sessionInfo()' would have helped here, as suggested by the posting 
guide.)

Using version 0.999375-8 of the `lme4' package, I get

 > logLik(gs)         #  -1344 (df=3)
'log Lik.' -1344.320 (df=3)
 > logLik(g_s)        #  -1342 (df=3)
'log Lik.' -1341.794 (df=3)
 > logLik(s_s)        #  -1314 (df=3)
'log Lik.' -1314.395 (df=3)
 > logLik(gs_s)       # -11823 (df=4)
'log Lik.' -1312.270 (df=4)
> 
> Could anyone advise me how to stop this from happening, and/or explain why
it
> is?
You're likely using some obsolete version of `lme4'. Try downloading a 
development snapshot from R-Forge 
(http://r-forge.r-project.org/projects/lme4/).


HTH,
Henric


> 
> Thanks very much,
> Daniel
> 
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