Logistic modelling with guessing parameter

Peter Harrison <pharr011 at ...> writes:

Hello Ben and Ken,

Thanks very much for your useful responses!

Ben - thanks for your hint re non-finite values in PIRLS, I'll bear that 

in mind for the future. I added

non-finite warning checks to the link functions etc. and nothing 

came up this time, unfortunately.

You're right, all of the groups show complete separation - I hadn't 

thought too carefully about this but

realise now that it will be a problem. I think I might have to merge the

musical tracks into larger groups,

perhaps by metre, genre, etc.

Thanks a lot for the great graph, too!

Ken - thanks for the heads-up about using the link directly from 

the psyphy package, that definitely

simplifies things! Sorry, the code I gave and you ran is 

an example that does work: responses ~ accuracy +

(1|p_ID). The model I was having problems with 

was when I added the "audio_name" predictor, i.e.

responses ~ accuracy + audio_name + (1|p_ID).

Something that you can try is the mafc.cauchit link 
that would impose a less steep slope because of
the heavy tails of the Cauchy. This might be less
sensitive to complete separation sort of like a
regularization of the psychometric function slope.
I have used this link to correct for the bias introduced
by lapses at the upper asymptote instead of introducing
a lapse parameter which is not obvious how to do
otherwise with glmer.

Best wishes,
Peter

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