Stanislav Aggerwal <stan.aggerwal <at> gmail.com> writes: > > I have searched the r-help archive and saw only one > unanswered post related > to mine.
Take a look at the r-sig-mixed-models (@r-project.org) mailing list and archive ... > > My design is as follows. > > - y is Bernoulli response > - x1 is continuous variable > - x2 is categorical (factor) variable with two levels > > The experiment is completely within subjects. That is, each subject > receives each combination of x1 and x2. > > This is a repeated measures logistic regression set-up. > The experiment will > give two ogives for p(y==1) vs x1, one for level1 and one > for level2 of x2. > The effect of x2 should be that for level2 compared to level1, the ogive > should have a shallower slope and increased intercept. > I am struggling with finding the model using lme4. Here is a guess at it: > > glmer(y~x1*x2 +(1|subject),family=binomial) > So far as I understand it, the 1|subject part says > that subject is a random > effect. But I do not really understand the notation or > how to specify that x1 and x2 are repeated measures variables. > In the end I want a model that > includes a random effect for subjects, and gives estimated slopes and > intercepts for level1 and level2. I believe you want glmer(y~x1*x2 +(x1*x2|subject),family=binomial,data=...) (I strongly recommend including the data= argument in your call) This will give a population-level estimate of intercept (log-odds in group 1 at x1=0) treatment effect on intercept (log-odds(level2,x1=0)-log-odds(level1,x=0)) log-odds slope in level 1 difference in slopes as well as among-individual variances in all four of these parameters, and covariances among all the parameters (i.e. a 4x4 variance-covariance matrix for these parameters). For binary data and estimating 4 fixed + 10 RE parameters (i.e., variances and covariances), you're going to need a lot of data -- very conservatively, 140 total observations. It may help to center your x1 variable. see http://glmm.wikidot.com/faq (especially http://glmm.wikidot.com/faq#modelspec), and the r-sig-mixed-models mailing list. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.