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

Paul Buerkner paul.buerkner at gmail.com
Fri May 12 11:04:03 CEST 2017

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.


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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