statsmodels.stats.gof.gof_binning_discrete(rvs, distfn, arg, nsupp=20)[source]

get bins for chisquare type gof tests for a discrete distribution


rvs : array

sample data

distname : string

name of distribution function

arg : sequence

parameters of distribution

nsupp : integer

number of bins. The algorithm tries to find bins with equal weights. depending on the distribution, the actual number of bins can be smaller.


freq : array

empirical frequencies for sample; not normalized, adds up to sample size

expfreq : array

theoretical frequencies according to distribution

histsupp : array

bin boundaries for histogram, (added 1e-8 for numerical robustness)


The results can be used for a chisquare test

(chis,pval) = stats.chisquare(freq, expfreq)

originally written for scipy.stats test suite, still needs to be checked for standalone usage, insufficient input checking may not run yet (after copy/paste)

refactor: maybe a class, check returns, or separate binning from
test results
todo :
optimal number of bins ? (check easyfit), recommendation in literature at least 5 expected observations in each bin

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