Mixed modelers,

I have a dataset (mydataset) where subjects' (Subject) responses (Correct)
to a stimulus (Stimulus) was tested over time (Months). There were 8
stimuli, 2 of which had focus A and 6 had focus B (Focus).  At each time
point, subjects were tested multiple times for each stimuli.  Subject,
Stimulus, and Focus are all factors.

I have run model as follows:

(m1 <- glmer(Correct ~ Months*Stimulus + (1|Subject), family=binomial,
data=mydataset))
(m2 <- glmer(Correct ~ Months*Stimulus + (1|Subject) + (0 + Months
|Subject), family=binomial, data=mydataset))
(m3 <- glmer(Correct ~ Months*Stimulus + (1 + Months |Subject),
family=binomial, data=mydataset))
(m4 <- glmer(Correct ~ Months*Stimulus + (1 + Months + Stimulus |Subject),
family=binomial, data=mydataset))
(m5 <- glmer(Correct ~ Months*Stimulus + (1 + Months + Stimulus + Months *
Stimulus |Subject), family=binomial, data=mydataset))

I would like to test Focus to see if, when controlling for Focus, the effect
of Stimulus goes away.

(m1f <- glmer(Correct ~ Months*Stimulus*Focus + (1|Subject),
family=binomial, data=mydataset))

However, I get the following error:

Error in asMethod(object) : matrix is not symmetric [1,2]
In addition: Warning message:
In mer_finalize(ans) : gr cannot be computed at initial par (65)

I believe this is a case of multicolinearity between Focus and Stimulus.
Would that be a correct assumption?  What options do I have in order to
examine the relationship between Correct and Stimulus/Focus in a
mixed-effects situation.

I'm using version 0.999375-35 of lme4.

Thanks!

Luke

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