Python - R square and absolute sum of squares obtainable by scipy.optimize curve_fit?

Question

I am fitting curves using curve_fit. Is there a way to read out the coefficient of determination and the absolute sum of squares? Thanks, Woodpicker

Answer 1

According to doc, optimization with curve_fit gives you

Optimal values for the parameters so that the sum of the squared error of f(xdata, *popt) - ydata is minimized

Then, use optimize.leastsq

import scipy.optimize
p,cov,infodict,mesg,ier = optimize.leastsq(
        residuals,a_guess,args=(x,y),full_output=True,warning=True)

with this for residuals:

def residuals(a,x,y):
    return y-f(x,a)

residuals is the method returning difference between true output data y and model output, with f the model, a the parameter(s), x the input data.

Method optimize.leastsq is returning a lot of information you can use to compute RSquared and RMSE by yourself. For RSQuared, you can do

ssErr = (infodict['fvec']**2).sum()
ssTot = ((y-y.mean())**2).sum()
rsquared = 1-(ssErr/ssTot )

More details on what is infodict['fvec']

In [48]: optimize.leastsq?
...
      infodict -- a dictionary of optional outputs with the keys:
                  'fvec' : the function evaluated at the output