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.
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).
- 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
