[R-sig-ME] Modeling truncated counts with glmer

[Thierry Onkelinx]

Dear João,

A binomial distribution seems more relevant to me.

glmer(cbind(correctPair, incorrectPair) ~ I((abruf - 1)^2) * treatment +
(1|subjectNumber), data=data, family = binomial)

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2017-01-23 8:46 GMT+01:00 João C P Santiago <joao.santiago op uni-tuebingen.de>
:

> Hi,
>
> In my experiment 20 participants did a word-pairs learning task in two
> conditions (repeated measures):
> 40 pairs of nouns are presented on a monitor, each for 4s and with an
> interval of 1s. The words of each pair were moderately semantically related
> (e.g., brain, consciousness and solution, problem). Two different word
> lists were used for the subject’s two experimental conditions, with the
> order of word lists balanced across subjects and conditions. The subject
> had unlimited time to recall the appropriate response word, and did three
> trials in succession for each list:
>
> Condition 1, List A > T1, T2, T3
> Condition 2, List B > T1, T2, T3
>
> No feedback was given as to whether the remembered word was correct or not.
>
> I've seen some people go at this with anova, others subtract the total
> number of correct pairs in one condition from the other per subject and run
> a t-test. Since this is count data, a generalized linear model should be
> more appropriate, right?
>
> head(data)
>   subjectNumber expDay      bmi treatment tones       hour abruf
> correctPair incorrectPair
>           <dbl>  <chr>    <dbl>    <fctr> <dbl>     <time> <dbl>
>  <dbl>         <dbl>
> 1             1     N2 22.53086   Control     0 27900 secs     1
> 26            14
> 2             1     N2 22.53086   Control     0 27900 secs     2
> 40             0
> 3             1     N2 22.53086   Control     0 27900 secs     3
> 40             0
> 4             2     N1 22.53086   Control     0 27900 secs     1
> 22            18
> 5             2     N1 22.53086   Control     0 27900 secs     2
> 33             7
> 6             2     N1 22.53086   Control     0 27900 secs     3
> 36             4
>
>
>
> I fitted a model with glmer.nb(correctPair ~ I((abruf - 1)^2) * treatment
> + (1|subjectNumber), data=data). The residuals don't look so good to me
> http://imgur.com/a/AJXGq and the model is fitting values above 40, which
> will never happen in real life (not sure if this is important).
>
> I'm interested in knowing if there is any difference between conditions
> (are the values at timepoint (abruf) 1 different? do people remember less
> in one one condition than in the other (different number of pairs at
> timepoint 3?)
>
>
> If the direction I'm taking is completely wrong please let me know.
>
> Best,
> Santiago
>
>
>
> --
> João C. P. Santiago
> Institute for Medical Psychology & Behavioral Neurobiology
> Center of Integrative Neuroscience
> University of Tuebingen
> Otfried-Mueller-Str. 25
> 72076 Tuebingen, Germany
>
> Phone: +49 7071 29 88981
> Fax: +49 7071 29 25016
>
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