I am running a mixed effects model with two random effects that have ~500
and ~1400 factor levels respectively.
For a continuous outcome, the computation time using lme4 is workable.
However for a binary outcome the computation time increases 4-80 fold
compared to a similar model for a continuous outcome. I tend to stop
computations if they've been running more than 8 hours, so I don't have a
max time estimate)
At least one of the fixed effects is also a 6-level factor. I attempted to
treat this as a sparse matrix, but lmer() doesn't seem to allow for this
type of matrix in the model.
Are there any suggestions on what I can do (other than simplify the model)
to improve the computation time for a binary outcome?
Also, could people comment on the speed of MCMCglmm vs lme4? Perhaps I could
go this route if it will prove to be substantially quicker for a binary
outcome.
Thank you to Douglas Bates for suggesting I post here. I think i'll be able
to find more help using lme4 here than on the normal R-help.
~~~~~~~~~~~~~~~~~~~
-Robin Jeffries
Dr.P.H. Candidate in Biostatistics
UCLA School of Public Health
rjeffries@ucla.edu
530-624-0428
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