I'm using Pyextremes to fit a Generalized Pareto Distribution (GPD) to my data. Then I used the learned parameters to generate a corresponding distribution object in SciPy. However, I noticed that the return value plot generated during the diagnostic plot (model.plot_diagnostic) does not match the plot produced by SciPy, and I am trying to understand what I might be missing.
# Extracting model parameters from Pyextremes
loc = model.distribution.fixed_parameters['floc']
c= model._fit_parameters['c']
scale = model._fit_parameters['scale']
Modeling GPD in Scipy.
genpareto(c=c, loc=loc, scale=scale)
Plot generated by Pyextremes
Plot generated by Scipy.
I'm using Pyextremes to fit a Generalized Pareto Distribution (GPD) to my data. Then I used the learned parameters to generate a corresponding distribution object in SciPy. However, I noticed that the return value plot generated during the diagnostic plot (model.plot_diagnostic) does not match the plot produced by SciPy, and I am trying to understand what I might be missing.
Modeling GPD in Scipy.
Plot generated by Pyextremes
Plot generated by Scipy.
