HypothesisTest_PartialSpearman

HypothesisTest_PartialSpearman(firstSample, secondSample, selection, level=0.05)

Test whether two sample have no rank correlation.

Available usages:

HypothesisTest_PartialSpearman(firstSample, secondSample, selection)

HypothesisTest_PartialSpearman(firstSample, secondSample, selection, level)

Parameters
firstSample2-d sequence of float

First tested sample, of dimension n \geq 1.

secondSample2-d sequence of float

Second tested sample, of dimension 1.

selectionsequence of integers, maximum integer value < n

List of indices selecting which subsets of the first sample will successively be tested with the second sample through the Spearman test.

levelpositive float < 1

Threshold p-value of the test (= first kind risk), it must be < 1, equal to 0.05 by default.

Returns
testResultTestResult

Structure containing the result of the test.

See also

HypothesisTest_Spearman, HypothesisTest_FullSpearman

Notes

The Partial Spearman Test is used to check hypothesis of no rank correlation between two samples: firstSample of dimension n and secondSample of dimension 1. The parameter selection enables to select specific subsets of marginals of firstSample to be tested.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> func = ot.SymbolicFunction(['x'], ['x', 'x^2', 'x^3', 'sin(5*x)'])
>>> testedSample = func(sample)
>>> test_result = ot.HypothesisTest.PartialSpearman(testedSample, sample, [0,3])
>>> print(test_result)
[class=TestResult name=Unnamed type=Spearman binaryQualityMeasure=false p-value threshold=0.05 p-value=0 statistic=1.79769e+308 description=[],class=TestResult name=Unnamed type=Spearman binaryQualityMeasure=true p-value threshold=0.05 p-value=0.570533 statistic=-0.569502 description=[]]