[R-sig-ME] Multilevel logistic regression guessing parameter

[Dominik Ćepulić]

Dear Conor and Jonathan, thanks for your replies!


Maybe I am wrong but I believe that there is a difference when applying
logistic regression  when you ask an open question, without any answers
given, and the answer may be right or wrong (so dependent variable is
dichotomous) and
when you ask a question but offer two answers among which one of them is
correct. That has to be taken into account somehow when doing logistic
regression, according to my viewpoint.

Am I missing something?

On Fri, May 12, 2017 at 1:12 PM, Conor Michael Goold <conor.goold at nmbu.no>
wrote:

> Hi Dominik,
>
> I meant to write equation 4 in appendix 1 of the hyperlinked paper.
>
> Conor
>
>
> ________________________________________
> From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on
> behalf of Conor Michael Goold <conor.goold at nmbu.no>
> Sent: Friday, May 12, 2017 11:32 AM
> To: Paul Buerkner; Dominik Ćepulić
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Multilevel logistic regression guessing parameter
>
> Hi Dominik,
>
> I may have misunderstood your problem, but I don't understand why you want
> to constrain the probability of success to be 0.5 at the lowest. If
> participants are choosing their answers completely randomly, their average
> probability of choosing the correct response (i.e. a 1 if the responses are
> coded 0 = incorrect and 1 = correct) across tasks may be around 0.5, but it
> seems completely plausible that the average probability of choosing the
> correct response could be between 0 and 1, and this propensity for a
> correct answer could vary between participants. This seems like a normal
> application of logistic regression. Sorry if I am missing something!
>
> If you have reason to believe that participants do just guess sometimes,
> which may result in some 'outlying' data points (i.e. correct or incorrect
> responses where we may not expect them), as others have said, this can be
> included in a Bayesian model. John Kruschke has an example in his book
> Doing Bayesian Data Analysis (using JAGS) and also in this paper (see
> equation 3 in appendix 4): http://journal.sjdm.org/14/
> 14721a/jdm14721a.html
>
> Best regards
> Conor
> ________________________________________
> From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on
> behalf of Paul Buerkner <paul.buerkner at gmail.com>
> Sent: Friday, May 12, 2017 11:04 AM
> To: Dominik Ćepulić
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Multilevel logistic regression guessing parameter
>
> Hi Dominik,
>
> I mean that brms uses Stan (http://mc-stan.org/) for the model fitting,
> but
> you don't need to worry about that. I am confident that brms will allow you
> to fit the model you have in mind.
>
> Best,
> Paul
>
> 2017-05-12 10:55 GMT+02:00 Dominik Ćepulić <dcepulic at gmail.com>:
>
> > Dear everybody, thank you for your ideas and messages!
> >
> > First, Philipp, yes, you are right. We have a simple two-choice
> > recognition task. Participants were learning some stimuli,  and after
> some
> > the recognition phase started. Always one stimuli per screen, and they
> have
> > to say whether it is one of the learnt ones or not. B is therefore coded
> as
> > response1 and response2 and afterwards coded in correct/incorrect.
> > The problem that might have appeared is that some distractors may have
> > been very similar to some well learnt items, and were simultaneously
> paired
> > with a poorly learnt target. That might produce the effect of correctness
> > below 0.5 We searched for such tasks and deleted them from further
> analysis.
> >
> > My problem is that when I try to plot probability functions (x -
> predictor
> > variable, y - Accuracy from 0 to 1) for domains, they go below 0.5 which
> > doesn´t make sense, as this was a two-choice task. Their lower asymptote
> > should be on 0.5 not on 0. That´s why I am asking.
> >
> > @Paul: Thanks for recommendation, but what do you mean by "Stan under the
> > hood"? I basically need a typical multilevel logistic regression (with
> > random effects for 2 crossed levels) but with lower asymptote being 0.5
> and
> > not 0.
> >
> > I will take a look at the functions!
> >
> > Best,
> > Dominik
> >
> > On Fri, May 12, 2017 at 9:36 AM, Paul Buerkner <paul.buerkner at gmail.com>
> > wrote:
> >
> >> Hi Dominik,
> >>
> >> in addition to what Jake said, you can do this with the brms package
> >> (using Stan under the hood). After installing brms, you can learn how to
> >> fit such models in the "brms_nonlinear" vignette: Type
> >> vignette("brms_nonlinear") in R.
> >>
> >> Best,
> >> Paul
> >>
> >> 2017-05-11 13:00 GMT+02:00 Dominik Ćepulić <dcepulic at gmail.com>:
> >>
> >>> I  have a following situation:
> >>>
> >>> I want to predict variable B (which is dichotomous) from variable A
> >>> (continous) controlling for random effects on the level of a) Subjects;
> >>> b)
> >>> Tasks.
> >>>
> >>> A -> B (1)
> >>>
> >>> The problem is that when I use model to predict the values of B from A,
> >>> values below probability of 0.5 get predicted, and in my case that
> >>> doesn´t
> >>> make sense, because, if you guess at random, the probability of correct
> >>> answer on B would be 0.5.
> >>>
> >>> I want to know how I can constrain the model (1) in lme4 so that it
> >>> doesn´t
> >>> predict values lower than 0.5 in variable B.
> >>>
> >>> Thank you,
> >>>
> >>> Dominik!
> >>>
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> >>>
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> >>
> >>
> >>
> >
>
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