On 18 Apr 2004 at 13:47, Christophe Pallier wrote: You should probably look into glmmPQL (package MASS) or GLMM (package lme4).
Kjetil Halvorsen > Hello, > > I routinely use aov and and the Error term to perform analyses of > variance of experiments with 'within-subject' factors. I wonder > whether a notion like 'multistratum models' exists for glm models when > performing a logit analysis (without being 100% sure whether this > would make sense). > > I have data of an experiment where the outcome is a categorical > variable: > > 20 individuals listened to 80 synthetic utterances (distributed in 4 > types) and were ask classify them into four categories. (The variables > in the data.frame are 'subject', 'sentence', 'type', and 'response') > > Here is the table of counts table(type,response): > > response > type a b c d > a 181 166 42 11 > b 69 170 72 89 > c 90 174 75 61 > d 14 125 53 208 > > > There are several questions of interest, such as, for example: > > - are responses distibuted in the same way for the different types? > > - are the numbers of 'a' responses for the 'b' and 'c' types > significantly different? > > - is the proportion of 'd' over 'a' responses different for the 'b' > and 'c' categories? > > ... > > (I want to make inferences for the population of potential subjects on > the one hand, and on the population of potential sentences on the > other hand). > > If the responses were continuous, I would just run two one-way anovas: > one with the factor type over the means by subject*type, and the other > with the factor type over the means by sentences (in type). And use > t.test to compare between different pairs of types. > > Now, as the answers are categorical, I am not sure about the correct > approach and how to use R to perform such an analysis. > > I could treat response as a factor, and use percentages of responses > per subject in each cell of response*type, and run an anova on > that...[ aov(percentage~response*type+Error(subject/(response*type))] > But it seems incorrect to me to use the response of the subject as an > independent variable (though I do not have a forceful argument). > > Simple Chi-square tests are not the answer either, as a given subject > contributed several times (80) to the counts in the table above. > > My reading of MASS and of several other books suggest the use of > logit/multinomial models when the response is categorical. But in all > the examples provided, the units of analysis contribute only one > measurement. Should I include the subject and sentences factors in the > formula? But then they would be treated as fixed-factors in the > analysis, would they not? > > > Any suggestion is welcome. > > Christophe Pallier > www.pallier.org > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
