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 at 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 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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