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how to adjust link function in logistic regression to predict the proportion of correct responses in 2AFC task?
2 messages · Ken Knoblauch, Brian Ripley
On Sat, 16 Dec 2006, Ken Knoblauch wrote:
In theory, the probability of correct responses ranges between 0.5 and 1 here, but in practice it is frequent to find cases where the observed proportion of correct responses is a little less. The number of trials is limited, after all. The inverse of this link function generates a Nan when this occurs. Is that a problem? and if so, how can it be dealt with here?
No. Links apply to fitted values, not observed proportions, and R link functions have a validity function to ensure they are used correctly.
Thank you.
I have used the gnlr function in Lindsey's gnlm package for this problem in
the past, but glm would be simpler, it seems to me.
@Article{pmid16817511,
Author="Yssaad-Fesselier, Rosa and Knoblauch, Kenneth",
Title="{{M}odeling psychometric functions in {R}}",
Journal="Behav Res Methods",
Year="2006",
Volume="38",
Number="1",
Pages="28--41",
Month="Feb"
}
On Sat, 16 Dec 2006, baud-bovy.gabriel at hsr.it wrote:
I have would like to use logistic regression to analyze the percentage of correct responses in a 2 alternative forced choice task. The question is whether one needs to take into account the fact expected probabilities for the percentage of correct responses ranges between 0.5 and 1 in this case.
Yes.
Second, how can one implement a link function of the type f(x) = (1+exp(x)/(1+exp(x)))/2 in R?
Looking at make.link() should give you enough to go on.
Third, can it be also done with gee and/or glmm?
For gee, you need to change the C-level internals. (I've done this in the far past for S-PLUS but not for R.) It would be easier to use yags (but I think you still need to dive into the internals). What 'glmm' did you have in mind? Looks like e.g. glmmML and glmmPQL will work with the new link. [...] Someone may have been here already: e.g. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1434755 -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
-- Ken Knoblauch Inserm U371 Institut Cellule Souche et Cerveau D?partement Neurosciences Int?gratives 18 avenue du Doyen L?pine 69500 Bron France tel: +33 (0)4 72 91 34 77 fax: +33 (0)4 72 91 34 61 portable: +33 (0)6 84 10 64 10 http://www.lyon.inserm.fr/371/
Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595