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Package mvpa :: Package misc :: Module transformers :: Class DistPValue

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Class DistPValue

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

Nested Classes

Inherited from state.ClassWithCollections: __metaclass__

Instance Methods

__init__(self, sd=0, distribution='rdist', fpp=None, nbins=400, **kwargs)
L2-Norm the values, convert them to p-values of a given distribution.

source code

__call__(self, x)

source code

Inherited from state.ClassWithCollections: __getattribute__, __new__, __repr__, __setattr__, __str__, reset

Inherited from object: __delattr__, __format__, __hash__, __reduce__, __reduce_ex__, __sizeof__, __subclasshook__

Class Variables

nulldist_number = StateVariable(enabled= True, doc= "Number of...

positives_recovered = StateVariable(enabled= True, doc= "Numbe...

Inherited from state.ClassWithCollections: _DEV__doc__, descr

Properties

Inherited from object: __class__

Method Details

init(self, sd=0, distribution='rdist', fpp=None, nbins=400, **kwargs)
(Constructor)

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

WARNING: Highly experimental/slow/etc: no theoretical grounds have been presented in any paper, nor proven

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

Overrides: object.__init__

Class Variable Details

nulldist_number

Value:

StateVariable(enabled= True, doc= "Number of features within the estim
ated null-distribution")

positives_recovered

Value:

StateVariable(enabled= True, doc= "Number of features considered to be
 positives and which were recovered")

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