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

[João C P Santiago]

Thank you! Could you be a bit more specific as to why? I will most  
likely encounter similar data in the future and I want to know how to  
think about it.

Fitting the model with abruf as a factor resulted in a better fit, but  
that answers a different question right? Namely how different is the  
intercept at a timepoint in comparison with the main level (abruf 0 in  
my code)?

Best

Quoting Thierry Onkelinx <thierry.onkelinx at inbo.be>:

> 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 at 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
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models



-- 
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