Not glm, it should be glmer in lme4 package. Ronggui
On 22 March 2010 22:31, René Mayer <ma...@psychologie.tu-dresden.de> wrote: > Dear R community, > > I've data-set with reaction times and count data (answers - yes, no) of N > subjects under conditions A, B. > For the analysis reaction time I used aov. > > fit.rt = aov(rt ~ A * B + Error(subjects/(A*B)), data = m ) > > But how do I analyze the frequencies correctly? > > example fable of frequencies from one subject: > > , , = A1 > > B1 B2 B3 > yes 31 36 19 > no 22 27 10 > , , = A2 > > B1 B2 B3 > yes 22 27 10 > no 31 36 19 > > Is a generalized linear model the right method? > How do I specify the same model for the count data (frequencies) in glm? > > is this right: glm(count~A*B*answer+(1|subject),family=poisson)? > > Regards, René > > ______________________________________________ > 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. > -- Wincent Ronggui HUANG Doctoral Candidate Dept of Public and Social Administration City University of Hong Kong http://asrr.r-forge.r-project.org/rghuang.html ______________________________________________ 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.
