how to calculate the R value properly for discrete data

[dddyx]

Hi Jeff,
Thanks for your great work !
I'm in trouble calculating the R-value for discrete data.
My code is:

import  powerlaw
theoretical_distribution=powerlaw.Power_Law(xmin=2,parameters=[10],discrete=True) 
simulated_data=theoretical_distribution.generate_random(10000)
fit = powerlaw.Fit(simulated_data,discrete=True)

R, p = fit.distribution_compare('power_law', 'exponential', normalized_ratio=True) 

the results are:

R= -1.1743540614477388
p= 0.24025323758420514

Does this mean that the distribution of the data tends to be more exponential? Or is there something wrong with my understanding
Thanks again for the tool!

[aredjil]

As far as I understand, according to the original paper POWER-LAW DISTRIBUTIONS IN EMPIRICAL DATA you have to calculate the goodness-of-fit between the data and the power law according to the method described section 3 of the previously mentioned paper (As far as I know I don't think there is a method implement in the power law package to perform the goodness-of-fit, as possibility is to write your own function to generate synthetic data, compute the distances and then count the number of distances larger than the original one). if the p-value is greater than $0.1$, the power law is a plausible hypothesis for the data. If that's the case, then depending on the magnitude and sign of the likelihood ratio the claim for the power law as a plausible hypothesis that explains the data is either strengthened or weakened. I hope this helps :)