[SciPy-User] Power

[josef.pktd at gmail.com]

see for example http://www.statmethods.net/stats/power.html

some almost one liners coming soon to statsmodels

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import numpy as np
from scipy import stats


def chisquare_power(effect_size, nobs, n_bins, alpha=0.05, ddof=0):
    '''power of chisquare goodness of fit test

    effect size is sqrt of chisquare statistic divided by nobs

    Parameters
    ----------
    effect_size : float
        This is the deviation from the Null of the normalized chi_square
        statistic .
    nobs : int or float
        number of observations
    n_bins : int (or float)
        number of bins, or points in the discrete distribution
    alpha : float in (0,1)
        significance level of the test, default alpha=0.05

    Returns
    -------
    power : float
        power of the test at given significance level at effect size

    Notes
    -----
    This function also works vectorized if all arguments broadcast.


    '''
    crit = stats.chi2.isf(alpha, n_bins - 1 - ddof)
    power = stats.ncx2.sf(crit, n_bins - 1 - ddof, effect_size**2 * nobs)
    return power

def chisquare_effectsize(probs0, probs1):
    '''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 shape.
        Both probs0 and probs1 are normalized to add to one.

    Returns
    -------
    effectsize : float
        effect size of chisquare test

    '''
    probs0 = np.asarray(probs0, float)
    probs1 = np.asarray(probs1, float)
    probs0 = probs0 / probs0.sum(0)
    probs1 = probs1 / probs1.sum(0)

    return np.sqrt(((probs1 - probs0)**2 / probs0).sum(0))
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Josef
    Brain: We must prepare for tomorrow night.
    Pinky: Why? What are we going to do tomorrow night?
    Brain: The same thing we do every night, Pinky - try to take over the world!