FullSpearman

FullSpearman(firstSample, secondSample, level=0.05)

Test whether two samples have no rank correlation.

Parameters:
firstSample2-d sequence of float

Sample of dimension n \geq 1.

secondSample2-d sequence of float

Sample of dimension 1.

levelpositive float < 1, optional

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

openturns.HypothesisTest.Spearman
openturns.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[1])
class=TestResult name=Unnamed type=Spearman binaryQualityMeasure=true p-value threshold=0.05 p-value=0.442067 statistic=-0.774521 description=[]
>>> no_rank_correlation = test_result[1].getBinaryQualityMeasure()