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Hi, I want to fit some psychophysical data with cumulative gaussians. There is quite a convenient toolbox for matlab called 'psignifit' (formerly known as 'psychofit'). It allows the lower bound of the sigmoid to vary slightly from zero, aswell as the upper bound to vary from one. with these two free parameters, the fitted function is less sensitive to noisy data and outliers.

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

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 and how to adjust the link function accordingly in R (see details below). Gabriel Subjects were asked