[R] multistratum glm?
kjetil@entelnet.bo
kjetil at entelnet.bo
Wed Apr 21 23:49:03 CEST 2004
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
>
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