1. I don't know SAS. What's FIML? Is that like imputing missing
values? If yes, why do you want to do that? That was a great thing to
do when you needed balance in a data set in order to be able to get an
answer. However, there is much less rationale for that today. "lme"
uses maximum likelihood or restricted maximum likelihood and does not
impute missing values to get an answer. If you want to impute the
values, you can use "predict.lme".
2. Are you familiar with Pinheiro and Bates (2000) Mixed-Effects
Models in S and S-Plus (Springer)? Doug Bates is among the leading
experts in the world on the best ways to approach this kind of problem.
He and his graduate students, including Jose Pinheiro, developed and
wrote "lme", and continue making seminal contributions in this area. I
have been amply rewarded for the time I have spent with this and other
things he has written.
hope this helps. spencer graves
Frank Lawrence wrote:
I did not receive a reply the first time I posted this question so I am
re-submitting it.
I notice that lme does not tolerate missing data on the response variable in
the same way that SAS Proc Mixed handles missing data. Is there a way to
invoke FIML in lme?
Respectfully,
Frank Lawrence
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