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

[Doran, Harold]

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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