Hello list, I have two data sets with 22 subjects of one classification,
and 20 subjects of another
response times were measured on each of these subjects in response to 20
items.
Initially I had thought about examining this data in a mixed-effects
model where the items (20 of them) were the fixed effects and the
subjects were the random effects, something like
lme(responsetime ~item, data=blah,blah,random=~subject);.
I have two problems, one I have more random effects (42 20 in one group,
22 in another) than observations and the sheer size of the data set is
causing a error in the lme routine
[
"Problem in .C("mixed_loglik",: Unable to obtain requested dynamic
memory, while calling subroutine mixed_loglik
Use traceback() to see the call stack" ]
One thought is I could group the 42 subjects into their two diagnostic
groups which would reduce the random effects in the model, but I'm
afraid that would gloss over the important issue of variability within
groups. Any help?
thanks, wayne
|