I have a repeated-measures experiment where the dependent variable is a percentage, and I have multiple factors as independent variables. I'd like to use
glmer from the R package
lme4 to treat it as a logistic regression problem (by specifying
family=binomial) since it seems to accommodate this setup directly.
My data looks like this:
> head(data.xvsy) foldnum featureset noisered pooldur dpoolmode auc 1 0 mfcc-ms nr0 1 mean 0.6760438 2 1 mfcc-ms nr0 1 mean 0.6739482 3 0 melspec-maxp nr075 1 max 0.8141421 4 1 melspec-maxp nr075 1 max 0.7822994 5 0 chrmpeak-tpor1d nr075 1 max 0.6547476 6 1 chrmpeak-tpor1d nr075 1 max 0.6699825
and here's the R command that I was hoping would be appropriate:
glmer(auc~1+featureset*noisered*pooldur*dpoolmode+(1|foldnum), data.xvsy, family=binomial)
The problem with this is that the command complains about my dependent variable not being integers:
In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!
and the analysis of this (pilot) data gives weird answers as a result.
I understand why the
binomial family expects integers (yes-no counts), but it seems it should be OK to regress percentage data directly. How to do this?