mvpa2.misc.transformers.DistPValue

Inheritance diagram of DistPValue
class mvpa2.misc.transformers.DistPValue(sd=0, distribution='rdist', fpp=None, nbins=400, **kwargs)

Converts values into p-values under vague and non-scientific assumptions

Notes

Available conditional attributes:

  • nulldist_number+: Number of features within the estimated null-distribution
  • positives_recovered+: Number of features considered to be positives and which were recovered

(Conditional attributes enabled by default suffixed with +)

Attributes

descr Description of the object if any

Methods

__call__(x)
reset()

L2-Norm the values, convert them to p-values of a given distribution.

Parameters:

sd : int

Samples dimension (if len(x.shape)>1) on which to operate

distribution : string

Which distribution to use. Known are: ‘rdist’ (later normal should be there as well)

fpp : float

At what p-value (both tails) if not None, to control for false positives. It would iteratively prune the tails (tentative real positives) until empirical p-value becomes less or equal to numerical.

nbins : int

Number of bins for the iterative pruning of positives

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

descr : str

Description of the instance

WARNING: Highly experimental/slow/etc: no theoretical grounds have been

presented in any paper, nor proven

Attributes

descr Description of the object if any

Methods

__call__(x)
reset()