PartialSpearman¶
- PartialSpearman(firstSample, secondSample, selection, level=0.05)¶
Test whether two sample have no rank correlation.
- Parameters:
- firstSample2-d sequence of float
First tested sample, of dimension .
- secondSample2-d sequence of float
Second tested sample, of dimension 1.
- indicessequence of integers, maximum integer value
Indices selecting which subsets of the first sample will successively be tested with the second sample through the Spearman test.
- levelpositive float , optional
Threshold p-value of the test (= first kind risk), it must be , equal to 0.05 by default.
- Returns:
- testResult
TestResultCollection
Collection of
TestResult
for each selected component.
- testResult
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[1]) class=TestResult name=Unnamed type=Spearman binaryQualityMeasure=true p-value threshold=0.05 p-value=0.570533 statistic=-0.569502 description=[] >>> no_rank_correlation = test_result[1].getBinaryQualityMeasure()