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
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--
Brian D. Ripley, [EMAIL PROTECTED]
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
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