Generation Of Free Running List Of Lapse Rate And Guess Rate For Psychometric Curve Fitting (scipy)

As a new user to the curve fitting function from scipy and a relatively new user of python, I am a little confused as to what *popt and p0 exactly generates (with reference to this

Solution 1:

p0 is the starting point for the fit procedure. popt is the resulting best-fit values of the parameters.

Note that curve_fit assumes that the signature of your function if f(x, *parameters): the first argument is an independent variable for which you have xdata, and the rest are parameters that you want optimized.

In your first example, sigmoidscaled takes four arguments, and you provide a length-three list for p0. This way, the fitting starts with x0 = 1; k = 1; lapse = -10.

In your second example, sigmoidscaled takes five arguments, meaning you're fitting four parameters for which you need initial values.

Quick check:

In [22]: p0 = [1, 1, -10, 0]    # add the 4th element

In [23]: popt, pcov = curve_fit(sigmoidscaled, xdata, ydata, p0, maxfev = 3000)

In [24]: popt
Out[24]: array([ -1.97865387e+01,   3.31731590e-01,  -1.03275740e-01,
        -1.05595226e+03])