statsmodels.stats.gof.gof_binning_discrete#

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

Get bins for chisquare type gof tests for a discrete distribution

Parameters:

rvsndarray: sample data
distfndistribution instance: Discrete distribution function to be tested; needs a, b and cdf attributes/methods.
argsequence: parameters of distribution
nsuppint: number of bins. The algorithm tries to find bins with equal weights. depending on the distribution, the actual number of bins can be smaller.

Returns:

freqndarray: empirical frequencies for sample; not normalized, adds up to sample size
expfreqndarray: theoretical frequencies according to distribution
histsuppndarray: bin boundaries for histogram, (added 1e-8 for numerical robustness)

Notes

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