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

[Souheyla GHEBGHOUB]

Thank you both. I will look into this and see :)

Best,
Souheyla

On Fri, 22 Mar 2019, 22:02 Uanhoro, James, <uanhoro.1 using buckeyemail.osu.edu>
wrote:

> In standard regression models, the assumption is predictor variables are
> measured without error. Test scores will have measurement error, hence
> Doran's comment when test scores are used as covariates. See: Hausman, J.
> (2001). Mismeasured Variables in Econometric Analysis: Problems from the
> Right and Problems from the Left. *Journal of Economic Perspectives*, *15*(4),
> 57–67. https://doi.org/10.1257/jep.15.4.57
> I will note that many practitioners ignore this issue, and it is quite
> common to use predictors measured with error. Consider the number of times
> people use polychotomized income measures, or SES measures as predictors,
> or some other "construct".
> On Mar 22 2019, at 5:39 pm, Souheyla GHEBGHOUB <
> souheyla.ghebghoub using gmail.com> wrote:
>
> 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
>
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