Hi everyone!

I'm analizing data from a study where subjects were trained by four different methods and exposed to three different kinds of stimuli in a forced-choice task. The subject's responses could be either correct or incorrect. I'm considering "response" as a categorical depedent variable and running a mixed logit regression with lmer. My independent variables are "group of training", with four levels (between-subjects) and "type of stimuli", with three levels (within subjects). I introduced group, type of stimuli and their interaction as fixed factors, and subject ID as a random factor. According to lmer output, both the interaction  and the fixed effects are significant. But the output table compares all the levels within a fixed factor to a reference category, as well as the level combinations of the interaction (for instance, I can tell that stimulus types 1 and 2 significantly differ from stimulus type 0, but I can't tell if they are different from each
 other). 

Does anyone know if there's a way to run this kind of contrasts with lmer?


As I'm new to this kind of test, I imagine that there might be some mistake in the codification of the variables. My depedent variable "response" is coded as 0 for errors and 1 for correct answers in the database. Stimulus type and training group are codified with categorical labels (I also tried with numbers, but it made no difference).

Furthermore, I tried to run a generalized mixed model with a logit transformations in SPSS19. It offered an option to compare estimated marginal means with pairwise comparisons (applying a Bonferroni correction) and provided F-tests of significance for the fixed effects and the interactions. Is it correct to apply this procedures in a mixed logit model? Because I couldn't find any reference of it outside the SPSS software.


I have read Florian Jaeger's 2008 paper and Baayen's book for language data analysis with R, but I still can't figure out what am I doing wrong.

I apologise for my ignorance and thank you in advance for your kind attention. Even though I'm quite new to R, I find the software quite helpful, and I'd really like to learn this procedure and get it right.

Thanks again!
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