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]]

