[R-lang] Generalized linear mixed models

[Roger Levy]

Kathryn Campbell-Kibler wrote:
> Hi all,
> 
> I've recently been exploring beyond my established comfort zone with
> mixed models, and am looking for some correction or reassurance.  I am
> working with experimental data on social perceptions of linguistic
> variation.  I've got two types of dependent variables: ratings on a 6
> point scale (e.g. not at all intelligent-very intelligent), which I've
> been treating as linear variables and binary variables, based on
> whether a given term was selected as a good description of a speaker
> (e.g. hardworking).
 >
 > ...
 >
> lmer(intellect~speaker*ining*(pleasant_mood+mood_arousal)+(1|subject_id)+(1|recording),
> data=whitenoise)

Hi Kathryn,

One other point I neglected to mention.  Technically it is not really 
correct to treat data on a 6-point scale with a linear model, because 
the error in your data cannot be normally distributed.  This problem 
will probably be worst in cases where the predicted response rate is 
close to the extreme values, where the distribution is likely to be 
skewed.  Ordinal regression would probably be the most natural approach, 
but the bad news is that I believe there is no current means within R to 
include mixed effects in an ordinal regression model.


Best

Roger


-- 

Roger Levy                      Email: rlevy at ucsd.edu
Assistant Professor             Phone: 858-534-7219
Department of Linguistics       Fax:   858-534-4789
UC San Diego                    Web:   http://ling.ucsd.edu/~rlevy