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

[Thierry Onkelinx]

It looks like you participants performed a known number of trials which
resulted in either success or failure. The binomial distribution models
exactly that. The model fit would be the probability of success.

Once you have the relevant distribution, you can set the relevant
covariates. Which and in which form (linear, polynomial, factor) depends on
the hypotheses which are relevant for your experiment.

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 10:01 GMT+01:00 João C P Santiago <joao.santiago op uni-tuebingen.de
>:

> 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 op 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 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
>>>
>>> _______________________________________________
>>> R-sig-mixed-models op 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
>
>

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