[R-sig-ME] Running multinomial models with random effects

[MORAN LOPEZ, TERESA]

Hi Florian,
thanks a lot for your help. I have been reading De Boeck and Partchev  
paper and I have some questions.
First of all I am having some problems when applying dendrify function.
My dataframe has the following structure:
   RISK       Area CHOICE
1 MONTE      Sopie      2
2 MONTE      Sopie      2
3 MONTE      Sopie      2
4 MONTE Anchurones      3
5 MONTE Anchurones      1
6 MONTE Anchurones      2

being 1 (both acorns choice, 2 chosing the big one and 3 chosing the  
small one)
Following your instructions I mapped my tree as:
mapping <- cbind(c(0, 1, 1), c(NA, 0, 1))
Then I remove the first two columns in order to implement dendrify function
dendrify(jay[,-(1:2)],mapping)
The following error arise:
Error: is.matrix(mat) is not TRUE

I have not been able to find the problem. I have  followed tutorial  
package and paper instructions. mat=(jay[,-(1:2)], has only one column  
since I only have one item and each row corresponds to one choice event.
Any suggestions?




Quoting :

> Alternatively, you could fit a "tree-based" mixed logit model
> based on continuation ratio logits. See a recent JSS paper by De
> Boeck and Partchev (2012, http://www.jstatsoft.org/v48/c01/).
>
> The idea is to convert the three-level response into a binary one
> using a decision tree. In one possible tree, the first node is
> indifferent w.r.t. size (response both) vs. picky (response small
> or big). The second node is small given picky vs. big given
> picky.
>
> You need to extend your data by a two-level node variable. The
> new response is then binary. The model can be fit using glmer().
>
> Best, Florian
>
> ---
> Florian Wickelmaier
> Department of Psychology
> University of Tuebingen
> Schleichstr. 4, 72076 Tuebingen, Germany
>
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