[R-sig-ME] Modeling truncated counts with glmer
Thierry Onkelinx
thierry.onkelinx at inbo.be
Wed Feb 1 15:02:26 CET 2017
Dear João,
The intercept is -0.07376 on the **logit** scale. That is 0.48 on the
original scale. Use plogis(-0.07376) to transform from logit to original
scale. Your interpretation of the intercept is correct.
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
2017-02-01 14:22 GMT+01:00 João C P Santiago <joao.santiago op uni-tuebingen.de
>:
> 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 op 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 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
>>>
>>>
>>>
>
>
> --
> 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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