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
firstSample2-d sequence of float

Sample of dimension n \geq 1.

secondSample2-d sequence of float

Sample of dimension 1.

levelpositive float < 1

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

Returns
testResultTestResultCollection

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

See also

HypothesisTest_Spearman, HypothesisTest_PartialSpearman

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 statistic=1.79769e+308 description=[],class=TestResult name=Unnamed type=Spearman binaryQualityMeasure=true p-value threshold=0.05 p-value=0.442067 statistic=-0.774521 description=[]]