[R-sig-ME] Random effects in multinomial regression in R?
Doran, Harold
HDor@n @end|ng |rom @|r@org
Fri Mar 22 23:23:45 CET 2019
This article describes the issue, but in the context of linear models. The underlying issue remains even within the GLMM, however.
https://amstat.tandfonline.com/doi/full/10.1080/2330443X.2014.955228#.XJVf0nN7m8U
My software, METRICS, implements variations of the EiV model described, but again, only for linear models
https://shiny.airast.org/METRICS/
From: Souheyla GHEBGHOUB <souheyla.ghebghoub using gmail.com<mailto:souheyla.ghebghoub using gmail.com>>
Date: Friday, March 22, 2019 at 4:39 PM
To: AIR <hdoran using air.org<mailto:hdoran using air.org>>
Cc: Ren� <bimonosom using gmail.com<mailto:bimonosom using gmail.com>>, "r-sig-mixed-models using r-project.org<mailto:r-sig-mixed-models using r-project.org>" <r-sig-mixed-models using r-project.org<mailto:r-sig-mixed-models using r-project.org>>
Subject: Re: [R-sig-ME] Random effects in multinomial regression in R?
Dear Doran,
Could you explain more this point to me, please?
Thank you,
Souheyla
On Fri, 22 Mar 2019, 21:19 Doran, Harold, <HDoran using air.org<mailto:HDoran using air.org>> wrote:
Yes, but conditioning on the pre-test means you are using a variable
measured with error and the estimates you obtain and now inconsistent, and
that�s a pretty big sin.
On 3/22/19, 3:49 PM, "Souheyla GHEBGHOUB" <souheyla.ghebghoub using gmail.com<mailto:souheyla.ghebghoub using gmail.com>>
wrote:
>Dear Ren�,
>
>Thank you for your feedback to me. You are right, dropping the pretest
>from
>covariate if I predict change definitely makes sense to me! But the fact
>that i need to control for the starting levels of participants makes it
>obligatory for me to chose the second way, which is predicting posttest
>instead of change to have pretest scores controlled for.
>
>You also chose (1+group | word) , which is new to me. Does it intend to
>assume the effect of group to vary across words, which is something
>applicable to my data, right?
>I will discuss all this with my supervisor, and may reply here again in
>few
>days if you do not mind.
>Thank you very much
>Souheyla
>University of York
>
>
>On Fri, 22 Mar 2019 at 13:42, Ren� <bimonosom using gmail.com<mailto:bimonosom using gmail.com>> wrote:
>
>> Hi Souheyla,
>>
>> it seems to me that you will run into problems with your coding of
>>change
>> (gain, no gain and decline) because the 'change' is by
>> definition/calculation depending on the predictor pretest.
>> See, according to your coding scheme:
>> Change = decline can only occur if pretest=1 (not by pretest=0).
>> Change = gain can only occur if pretest = 0 (not by pretest=1)
>> Change = No Gain can occur if pretest= 1 or 0
>> In other words:
>> If pretest = 1 then the possible outcomes can be decline or no gain
>> If pretest = 0 then the possible outcomes can be gain or no gain
>>
>> And if the model result shows you then that the pre-test is
>>significantly
>> related to p(change-outcome), I guess there is no surprise in it, is it?
>>
>> So the first solution to this would be simply kicking the pre-test
>> predictor out of the model completely, and predict:
>> mod1 <- brm(Change ~ Group + (1|Subject) + (1+Group|Word),...)
>> (Btw.: actually the first Hierarchical Bayes Model question I see on the
>> mixed-effects mailing list :))
>>
>> Attempt for a further clarification on which random slopes would reflect
>> the model's design:
>> If you have a within-subjects design, by-subject random slopes are
>> possible for the within-subject variable (e.g. if there are two sets of
>> words/lists [e.g. abstract vs. concrete words] for each participant, and
>> you test whether there is a performance-difference between these
>> word-lists, then you can implement by-subject random slopes for words,
>> because each participant has seen both sets.) If each participant has
>>seen
>> only one list (i.e. between subjects design) by subject random slopes
>>for
>> words are not appropriate, because there is no 'slope' by participant
>>(i.e.
>> by definition, having a slope requires at least two observations...).
>>This
>> is always a good rule of thumb without thinking about it too heavily :)
>> Ans as you see: you can define a random slope for words:
>>(1+Group|Word),
>> because each word has been presented in each group (i.e. there can be a
>> slope for each word). And intuitively speaking the Treatment-effect can
>> vary depending on the stimuli you use, and the slope makes sense. (You
>>also
>> see in this example that the treatment effect can also vary by subjects,
>> but in fact, this subject effect variation IS EQUAL to the effect you
>>want
>> to test, and having by subject group random slopes would eliminate the
>> fixed effect...)
>>
>> Anyway, there is a second possibility to define your model, depending on
>> how you want to interpret it. In the previous model you can say
>>something
>> about the type-of-change likelihoods depending on the treatment group.
>>But
>> you could implement the model as binomial as well (i.e. logistic
>>regression)
>>
>> mod2 <- brm(posttest ~ pretest*Group + (1|Subject) + (1+Group|Word),...)
>>
>> And what you would expect here would be an interaction between pre-test
>> and Group. For instance; if pretest=0 & treatment 1 then posttest larger
>> than with pretest=0 & treatment 2; but not when pretest=1 (because this
>>is
>> a plausible no gain situation). And so on...
>> (And in this model there are no also no further random slopes hidden in
>> your design :))
>> Hope this helps.
>>
>> Best, Ren�
>>
>>
>> Am Do., 21. M�rz 2019 um 14:01 Uhr schrieb Souheyla GHEBGHOUB <
>> souheyla.ghebghoub using gmail.com<mailto:souheyla.ghebghoub using gmail.com>>:
>>
>>> Dear Philip,
>>>
>>> I understand , here is the structure of my data in case it could help.
>>>
>>> I have 3 groups of participants (control, treatment1, treatment2). Each
>>> group was tested twice, once before treatment (pretest) and once after
>>> treatment (posttest).
>>> In each test, they were tested on knowledge of 28 words, scores are
>>> dichotomous (0 = unknown , 1 = known). Tests are the same.
>>>
>>> I calculated change from pretest to posttest :
>>> if pretest 0 and posttest 0 = no gain
>>> if pretest 1 and posttest 1 = no gain
>>> if pretest 0 and posttest 1 = gain
>>> if pretest 1 and posttest 0 = decline
>>> So I ended up with a dependent variable called Change with 3 levels
>>> (no_gain, gain, decline) and I tried to predict it using Group and
>>>Pretest
>>> as covariates using multinomial logit model. mod0 <- brm(Change ~
>>>Pretest
>>> +
>>> Group) I would like to add random effects for subjects but don't know
>>> what's the best form when Time factor is absent.
>>>
>>> I hope other statisticians who read this could help
>>> Thank you
>>> Souheyla
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org<mailto:R-sig-mixed-models using r-project.org> mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>
> [[alternative HTML version deleted]]
>
>_______________________________________________
>R-sig-mixed-models using r-project.org<mailto:R-sig-mixed-models using r-project.org> mailing list
>https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models
mailing list