Random slopes for 2 variables and random intercept for 1 variable

On Apr 27, 2016, at 12:21 PM, Thierry Onkelinx <thierry.onkelinx at 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 at 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 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

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