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

[Thierry Onkelinx]

Adding (Listgp|stimulus) + (length + context|listener) to the formula would
add these slopes and their correlations.

Other options are possible. e.g. (0 + Listgp|stimulus) + (0 +
length|listener) + (0 + context |listener). Please have a look at
http://glmm.wikidot.com/faq and look for "Model specification etc." (0 +
x|group) is explained on that webpage.

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 11:32 GMT+02:00 Shadiya Al Hashmi <saah500 op york.ac.uk>:

> Dear Thierry,
>
> Apologies if my question wasn't clear.
>
> I need to add a slope for Listgp per stimulus, a slope for length per
> listener and a slope for context per listener.
>
> Listgp per listener is intercept only, length and context per stimulus per
> listener are both intercept only.
>
> Hope this clarifies things.
>
> Best wishes,
>
> Shad
>
> Sent from my iPhone
>
> On Apr 27, 2016, at 12:21 PM, Thierry Onkelinx <thierry.onkelinx op inbo.be>
> wrote:
>
> 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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