[R-sig-ME] Random effects in multinomial regression in R?
b|mono@om @end|ng |rom gm@||@com
Fri Mar 22 14:42:46 CET 2019
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
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.
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
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