On Wed, 5 Mar 2003 11:14:37 +0900 Hiroto Miyoshi <[EMAIL PROTECTED]> wrote:
> Dear R-users > > I need your help. > I have a data set which was collected from > an experiment of one between- and one > within-subject design. And the response > data is coded by success(1)/failure(0). > > The experiment had two groups of subjects: > The one was experimental, and the > other, control. The experimental group > got a task training, and both groups of subjects > were tested twice, once before the training > and once after the training. at the same time. > I like to examine the effect of training by > detecting an interaction effect of the group and > tests. > Now, it seems glm is not appropriate to this > situation since it does not deal with stratified > errors. > > Could you lead me to appropriate functions? > Sincerely > ----------------------- > Hiroto Miyoshi > [EMAIL PROTECTED] > There are several ways to go. GEE is one, random effects models another. One other approach is to install the Hmisc and Design packages (http://hesweb1.med.virginia.edu/biostat/s) and do (assume id is the unique subject identifier): f <- lrm(y ~ x1 + x2*x3 + ..., x=T, y=T) # working independence model g <- robcov(f, id) # cluster sandwich variance adjustment h <- bootcov(f, id, B=100) # cluster bootstrap adjustment summary(g) # etc. -- Frank E Harrell Jr Prof. of Biostatistics & Statistics Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences U. Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat ______________________________________________ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
