Thanks very much Ben for your extremely helpful response. I have loads of data so this worked fine.
cheers, Stan On Saturday, July 27, 2013, Ben Bolker wrote: > 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 <javascript:;> 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. > [[alternative HTML version deleted]] ______________________________________________ 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.
