[R-sig-ME] Random slopes for 2 variables and random intercept for 1 variable

[Thierry Onkelinx]

Dear Shad,

Your question isn't very clear. You'll need to tell use which random slopes
you want to add to the model.

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

2016-04-27 11:17 GMT+02:00 Shadiya Al Hashmi <saah500 op york.ac.uk>:

> Thanks Thierry but I need the random effects in the model since I am
> working within a generalized mixed effects model. That's why I used glmer.
>
> The reason why I didn't include the random effects in the model is that I
> wasn't sure of how to translate the slopes and intercepts of the variables.
>
> Two ways I could think of, however, are as follows.
>
> maxmodal<- glmer(match ~ Listgp + length + context + gender + age + freq.
> + (0-Listgp|listener), (1+length|listener)+(1+context|listener),
> (1+Listgp|stimulus), (0-length|stimulus), (0-context|stimulus), data =
> msba, family = "binomial", control = glmerControl(optimizer =
> "bobyqa"), nAGQ =1)
>
> maxmodal<- glmer(match ~ Listgp + length + context + gender + age + freq.
> + (1-Listgp|listener), (1+length|listener)+(1+context|listener),
> (1+Listgp|stimulus), (1-length|stimulus), (1-context|stimulus), data =
> msba, family = "binomial", control = glmerControl(optimizer =
> "bobyqa"), nAGQ =1)
>
>
> However, I am not sure either is the right way to go about it.
>
> Best wishes,
>
> Shad
>
> On 27 April 2016 at 11:45, Thierry Onkelinx <thierry.onkelinx op inbo.be>
> wrote:
>
>> Dear Shadiya,
>>
>> glmer() requires at least one random effect. You can use glm() to fit the
>> model without random effects.
>>
>> 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
>>
>> 2016-04-27 10:38 GMT+02:00 Shadiya Al Hashmi <saah500 op york.ac.uk>:
>>
>>> Good morning,
>>>
>>>
>>> I have a data design which includes 3 factors of interest/*experimental
>>> manipulations* (using Barr et. al’s (2013) terminology); namely *Listgp*
>>> (listener group: T[monolinguals], TA [bilinguals] and TQ [Turkish
>>> speakers
>>> who know Arabic through reading Quran]), *length* (long and short vowels)
>>> and *context (emphatics, pharyngeals, plain and q)*.
>>>
>>>
>>>
>>> *Listgp* is a *between-listener* (subject) and *within-stimulus* (item)
>>> variable [(1|listener), (1+Listgp|stimulus)] while both *length* and
>>> *context* are *within-listener* and *between-stimulus* variables
>>> [(1+length|listener), (1|stimulus) and (1+context|listener),
>>> (1|stimulus)].
>>>
>>>
>>>
>>> My question is, how can I code this in the following maximal model
>>> lacking
>>> the random effects (for the time being?
>>>
>>>
>>>
>>> maxmodal<- glmer(match ~ Listgp + length + context + gender + age +
>>> freq.,
>>> data = msba, family = "binomial", control = glmerControl(optimizer =
>>> "bobyqa"), nAGQ =1)
>>>
>>>
>>>
>>> Here is more information on the variables involved.
>>>
>>> *DV/Y (response):* match
>>>
>>> *Random effects:* listener and stimulus
>>>
>>> *Fixed effects/predictors:* a) *By-listener predictors*+ b) *by-stimulus
>>> predictors: *
>>>
>>> *a) By-listener predictors:* (*3*)
>>>
>>> *1. Factors: (2)*
>>>
>>> -*Listgp* (listener group): effect of interest (T: monolingual Turkish
>>> speakers, TA: bilingual Turkish speakers and TQ: Turkish speakers who
>>> know
>>> Arabic through reading Quran).)
>>>
>>> -*gender* (female and male)
>>>
>>>
>>>
>>> *2. Continuous predictors (1)*
>>>
>>> -*age *(age of listeners at the time of experiment)
>>>
>>>
>>>
>>> *b) By-stimulus predictors: (3)*
>>>
>>> *1. Factors: (2)_*
>>>
>>> -* context *(stimulus context: emphatic, pharyngeal, plain and q)
>>>
>>> -*length *(stimulus length: long and short)
>>>
>>> *2. Continuous predictors: (1)*
>>>
>>> -*freq*. (stimulus frequency as per arabiCorpus)
>>>
>>> Number of obs: 1224, groups:  listener, 51; stimulus, 24
>>>
>>>
>>>
>>>
>>>
>>> Appreciating your kind input.
>>>
>>> --
>>> Shad
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models op r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>>
>
>
>
>

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