Supplement 1. Matlab files for fitting power-law exponents using different methods.
datasetposted on 2016-08-05, 08:00 authored by Ethan P. White, Brian J. Enquist, Jessica L. Green
cdf_pareto.m -- Matlab file for fitting the CDF of the Pareto distribution cdf_power.m -- Matlab file for fitting the CDF of the Power distribution linbin_estimator.m -- Matlab file for fitting linearly binned data logbin_estimator.m -- Matlab file for fitting normalized logarithmicly binned data mle_discretepareto.m -- Matlab file for maximum likelihood estimation of the discrete Pareto distribution mle_pareto.m -- Matlab file for maximum likelihood estimation of the Pareto distribution mle_power.m -- Matlab file for maximum likelihood estimation of the Power Function distribution mle_truncpareto.m -- Matlab file for maximum likelihood estimation of the truncated Pareto distribution allfiles.zip -- Download all files at once
The accompanying Matlab files perform each of the different fitting methods described in the original paper. Depending on the method there may be several different files to allow the fitting of the different distributions described in the original paper. All files take a vector data that is list of each observed value of x, as well as a series of arguments that are unique to each combination of method and distribution. Detailed descriptions of these arguments are provided in a comment header at the beginning of each file. All files are also heavily commented for clarity. For non-Matlab users, m-files can be opened using any standard text editor (note: % is the comment symbol).