Answering on a mail from
>From Keith Wong
Date Sun 04 Jul 2004 - 17:21:36 EST
Subject [R] Random intercept model with time-dependent
covariates, results different from SAS
Hi all
I've got a question about the degrees of freedom in a mixed model,
calculated with lme
Thank you Prof Ripley and Dr Bebber for the helpful responses to my post on
4 July 2004, and references to further reading.
To close the thread, I summarize the answer to my question. The different
results between SAS and R arose from more than one cause.
1. SAS incorrectly assumed that Group w
Hello,
I have been struggling with a similar problem, i.e. fitting an LME model to
unbalanced repeated measures data.
I found "Linear Mixed Models" by John Fox
(http://socserv2.socsci.mcmaster.ca/jfox/Books/Companion/appendix-mixed-mode
ls.pdf)
quite helpful.
Fox gives examples which are unbalanced
Thank you for the very prompt response. I only included a small part of the
output to make the message brief. I'm sorry it did not provide enough detail to
answer my question. I have appended the summary() and anova() outputs to the
two models I fitted in R.
Quoting Prof Brian Ripley <[EMAIL PROTE
Looking at the significance of a main effect (group) in the presence of an
interaction (time:group) is hard to interpret, and in your case is I think
not even interesting. (The `main effect' probably represents difference
in intercept for the time effect, that is the group difference at the last
t
Dear list-members
I am new to R and a statistics beginner. I really like the ease with which I can
extract and manipulate data in R, and would like to use it primarily. I've
been learning by checking analyses that have already been run in SAS.
In an experiment with Y being a response variable,
