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
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.
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.
