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

[Shadiya Al Hashmi]

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 at 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 at 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 at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>
>

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