mvpa2.clfs.stats.Nonparametric

class mvpa2.clfs.stats.Nonparametric(dist_samples, correction='clip')

Non-parametric 1d distribution – derives cdf based on stored values.

Introduced to complement parametric distributions present in scipy.stats.

Methods

cdf(x)	Returns the cdf value at x.
fit(dist_samples)
rcdf(x)	Returns cdf of reversed distribution (i.e.

Parameters:

dist_samples : ndarray

Samples to be used to assess the distribution.

correction : {‘clip’} or None, optional

Determines the behavior when .cdf is queried. If None – no correction is made. If ‘clip’ – values are clipped to lie in the range [1/(N+2), (N+1)/(N+2)] (simply because non-parametric assessment lacks the power to resolve with higher precision in the tails, so ‘imagery’ samples are placed in each of the two tails).

Methods

cdf(x)	Returns the cdf value at x.
fit(dist_samples)
rcdf(x)	Returns cdf of reversed distribution (i.e.

cdf(x)

Returns the cdf value at x.

static fit(dist_samples)

rcdf(x)

Returns cdf of reversed distribution (i.e. if integrating from right tail)

Necessary for hypothesis testing in the right tail. It is really just a 1 - cdf(x) + pmf(x) == sf(x)+pmf(x) for a discrete distribution