HypothesisTest_FullSpearman

HypothesisTest_FullSpearman(firstSample, secondSample, level=0.05)

Test whether two samples have no rank correlation.

Available usages:

HypothesisTest.FullSpearman(firstSample, secondSample)

HypothesisTest.FullSpearman(firstSample, secondSample, level)

Parameters:
firstSample : 2-d sequence of float

Sample of dimension n \geq 1.

secondSample : 2-d sequence of float

Sample of dimension 1.

level : positive float < 1

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

Returns:
testResult : TestResultCollection

Collection of TestResult of size n, one result per component of the first sample.

Notes

The Full Spearman Test is used to check the hypothesis of no rank correlation between two samples: firstSample of dimension n and secondSample of dimension 1. The test is done marginal by marginal on the first sample.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> func = ot.SymbolicFunction(['x'], ['x', 'x^2'])
>>> testedSample = func(sample)
>>> test_result = ot.HypothesisTest.FullSpearman(testedSample, sample, 0.05)
>>> 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=0.442067 description=[]]