[R-sig-ME] Random effects in multinomial regression in R?
Souheyla GHEBGHOUB
@ouhey|@@ghebghoub @end|ng |rom gm@||@com
Fri Mar 22 22:39:06 CET 2019
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> 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
> that¹s a pretty big sin.
>
> On 3/22/19, 3:49 PM, "Souheyla GHEBGHOUB" <souheyla.ghebghoub using gmail.com>
> wrote:
>
> >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
> >Souheyla
> >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
> >>>
> >>> [[alternative HTML version deleted]]
> >>>
> >>> _______________________________________________
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> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>
> >>
> >
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> >
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