[R-sig-ME] How to use mixed-effects models on multinomial data

[Linda Mortensen]

Dear list members,
 
In the past, I have used the lmer function to model data sets with crossed random effects (i.e., of subjects and items) and with either a continuous response variable (reaction times) or a binary response variable (correct vs. incorrect response). For the reaction time data, I use the formula:
lmer(response ~ predictor1 * predictor2 ....  + (1 + predictor1 * predictor2 .... | subject) + (1 + predictor1 * predictor2 .... | item), data)
And for the binomial data, I use the formula: 
lmer(response ~ predictor1 * predictor2 ....  + (1 + predictor1 * predictor2 .... | subject) + (1 + predictor1 * predictor2 .... | item), data, family="binomial").
 
I'm currently working on a data set for which the response variable is number of correct items with accuracy ranging from 0 to 5. So, here the response variable is not binomial but multinomial. I want to stay within the mixed-effects model framework, but am not sure how to modify the lmer function formula so that it will work on ordered multinomial data. I am not even sure whether this function can handle this kind of data at all.
I have tried to model the same data using the DPolmm function in the DPpackage, but this function doesn't seem to accept two random effect terms, at least it produces an error message when I enter "random = ..." twice. 
 
Does anyone know which function to use here? Any advice is very much appreciated. 
 
If this mailing list does not deal with inquiries of this kind, I apologise, but would appreciate if someone would re-direct me to another more suitable list. Thanks.   
 
Linda 
 
 
Linda Mortensen
Post-doctoral research fellow
Department of Psychology
University of Copenhagen 
Øster Farimagsgade 2A
1353 Copenhagen K
Denmark
Tel.: +45 3532 4889
E-mail: linda.mortensen at psy.ku.dk