________________________________________
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 ?epuli?
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 ?epuli?
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 ?epuli? <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 ?epuli? <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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