# 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: firstSample : 2-d sequence of float First tested sample, of dimension . secondSample : 2-d sequence of float Second tested sample, of dimension 1. selection : sequence of integers, maximum integer value List of indices selecting which subsets of the first sample will successively be tested with the second sample through the Spearman test. level : positive float Threshold p-value of the test (= first kind risk), it must be , equal to 0.05 by default. testResult : TestResult Structure containing the result of the test.

Notes

The Partial Spearman Test is used to check hypothesis of no rank correlation between two samples: firstSample of dimension 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 description=[],class=TestResult name=Unnamed type=Spearman binaryQualityMeasure=true p-value threshold=0.05 p-value=0.570533 description=[]]