[R-sig-ME] Random effects in multinomial regression in R?

Souheyla GHEBGHOUB @ouhey|@@ghebghoub @end|ng |rom gm@||@com
Fri Mar 22 20:49:45 CET 2019

Dear  René,

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
University of York

On Fri, 22 Mar 2019 at 13:42, René <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, René
> Am Do., 21. März 2019 um 14:01 Uhr schrieb Souheyla GHEBGHOUB <
> 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
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