stats.gof.chisquare_effectsize()

statsmodels.stats.gof.chisquare_effectsize

statsmodels.stats.gof.chisquare_effectsize(probs0, probs1, correction=None, cohen=True, axis=0) [source]

effect size for a chisquare goodness-of-fit test

Parameters:

probs0 : array_like probabilities or cell frequencies under the Null hypothesis probs1 : array_like probabilities or cell frequencies under the Alternative hypothesis probs0 and probs1 need to have the same length in the axis dimension. and broadcast in the other dimensions Both probs0 and probs1 are normalized to add to one (in the axis dimension). correction : None or tuple (nobs, df) If None, then the effect size is the chisquare statistic divide by the number of observations. If the correction is a tuple (nobs, df), then the effectsize is corrected to have less bias and a smaller variance. However, the correction can make the effectsize negative. In that case, the effectsize is set to zero. Pederson and Johnson (1990) as referenced in McLaren et all. (1994) cohen : bool If True, then the square root is returned as in the definition of the effect size by Cohen (1977), If False, then the original effect size is returned. axis : int If the probability arrays broadcast to more than 1 dimension, then this is the axis over which the sums are taken.

Returns:

effectsize : float effect size of chisquare test