HypothesisTest_FullSpearman(firstSample, secondSample, level=0.95)

Test whether two discrete samples are not monotonous.

Available usages:

HypothesisTest.FullSpearman(firstSample, secondSample)

HypothesisTest.FullSpearman(firstSample, secondSample, level)

fisrtSample : 2-d sequence of float

First tested sample, of dimension n \geq 1.

secondSample : 2-d sequence of float

Second tested sample, of dimension 1.

level : positive float < 1

Threshold p-value of the test (= 1 - first type risk), it must be < 1, equal to 0.95 by default.

testResult : TestResult

Structure containing the result of the test.


The Full Spearman Test is used to check hypothesis of non monotonous relation between two samples: firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the numerical sample extracted from firstSample (i^{th} coordinate of each point of the numerical sample), FullSpearman performs the independence Spearman test simultaneously on all firstSample[i] and secondSample.


>>> 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)
>>> 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=0.44348 description=[]]