[R-sig-ME] Modeling truncated counts with glmer
João C P Santiago
joao.santiago at uni-tuebingen.de
Wed Feb 1 14:22:56 CET 2017
Thank you for your input! Only now did I go back to this model.
I'm having some doubts about the meaning of the intercept from my
binomial model. Here's the complete output:
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: cbind(correctPair, incorrectPair) ~ I(abruf - 1) * treatment +
version + (1 | subjectNumber)
Data: .
AIC BIC logLik deviance df.resid
691.4 708.4 -339.7 679.4 119
Scaled residuals:
Min 1Q Median 3Q Max
-3.2676 -0.7861 -0.0428 0.9417 2.7483
Random effects:
Groups Name Variance Std.Dev.
subjectNumber (Intercept) 0.7135 0.8447
Number of obs: 125, groups: subjectNumber, 21
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.07376 0.20096 -0.367 0.714
I(abruf - 1) 1.30891 0.06904 18.958 <2e-16 ***
treatmentStimulation 0.06116 0.09961 0.614 0.539
versionB -0.08709 0.07222 -1.206 0.228
I(abruf - 1):treatmentStimulation 0.03342 0.09727 0.344 0.731
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) I(b-1) trtmnS versnB
I(abruf-1) -0.235
trtmntStmlt -0.254 0.482
versionB -0.189 -0.029 0.037
I(-1):trtmS 0.164 -0.681 -0.689 0.030
abruf has values c(1,2,3) so by -1 it starts at a more meaningful point.
My question is: is the intercept the ratio of success/no success on
abruf 0, treatment control and version A? If so why is it
statistically speaking 1 on the log scale? The number of successes
increases from abruf 1 to 3 (as seen by the estimate of the model and
plots).
It's the first time I'm dealing with such complex models. Thank you
for your patience and time.
Best
J Santiago
Quoting Thierry Onkelinx <thierry.onkelinx at inbo.be>:
> 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 at 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 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
>>
>>
--
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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