If the data asymptote at 0 and 1, then you can use glm with the binomial family with either the logistic or probit links. If the data are from an n-alternative forced choice procedure or if the data do not asymptote at 0 and 1 for some reason or other, then you need to try other procedures. Two possibilities are to use the PsychoFun package available here
http://www.kyb.tuebingen.mpg.de/~kuss and described in Kuss, M., F. Jäkel and F.A. Wichmann: Bayesian inference for psychometric functions. Journal of Vision 5(5), 478-492 (2005) or tools from some of Jim Lindsey's packages, described here Yssaad-Fesselier R, Knoblauch K. Modeling psychometric functions in R. Behav Res Methods. 2006 Feb;38(1):28-41. HTH ken > Gamer, Matthias <gamer <at> uni-mainz.de> writes: > > > Specifically, I have data from a psychometric function relating the > > frequency a subject's binary response (stimulus present / absent) to > the > > strength of a physical stimulus. Such data is often modeled using a > > cumulative gaussian function. > > Well, more often by a logistic function, and there are quite a few > tools > powerful for doings this around, for example glm, or lmer/lme4, > glmmPQL/MASS, > glmmML/glmmML . The latter three are the tools of choice when you have > within > subject repeats, as it's standard in psychophysics. See > http://finzi.psych.upenn.edu/R/Rhelp02a/archive/33737.html for a > comparison. > > If you really want a cumlative gaussian, you can misuse drfit/drfit, > which is > primarily for dose/response curves and ld50 determination. I think > there is a > fitdistr/MASS example around (somewhere in the budworms chapter), but > I don't > have the book at hand currently. > > Dieter [[alternative text/enriched version deleted]]
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