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

Dominik Ćepulić dcepulic at gmail.com
Fri May 12 19:32:18 CEST 2017

Ok, I was also thinking about an IRT package before, but had problems
understanding how I could conceptualize my problem insde IRT framework.
Regarding that issue, this are really basic questions, but I really haven´t
found any concrete answer to them:
1) I always got the impression that IRT is used when observed variables are
used to predict latent trait measured by them. In my experiment that is not
the case - observed reaction times predict observed accuracies. How can I
set predictors and criterion variables in IRT framework?

2) As I understood IRT, you always get several parameters for a certain
task (depending on which IRT model you are using). That means that for a
certain stimuli I would get e.g. 3 parameters. My issue is that I am not
interested in single stimuli, because I have over 500 of them - I am
interested in a domain certain group of stimuli describe. I.e To me it is
not important to know the parameters for a single exemplar but for a whole
category which is measured by 64 exemplars. How can I calculate category
parameters with an IRT package?

I know this might not be the questions best suited for this forum, but if
anyone has some guidelines, I would be very grateful.

Besides that, do you have any suggestions for an IRT package that´s well
suited for my problem?


On Fri, May 12, 2017 at 4:57 PM, Doran, Harold <HDoran at air.org> wrote:

> Dominik
> It in fact is well known issue that the lower asymptote of the 3 parameter
> model you are essentially working with can be different than .5. The
> empirical results are suggesting that there is some information in the item
> itself, perhaps in one if the distractors, or in the item stem itself that
> leads responders to have a probability of response that differs from .5.
> You might consider going straight to some IRT packages in R or
> IRT-specific software instead
> -----Original Message-----
> From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org]
> On Behalf Of Dominik Cepulic
> Sent: Friday, May 12, 2017 4:55 AM
> To: Paul Buerkner <paul.buerkner at gmail.com>
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Multilevel logistic regression guessing parameter
> 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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