# HypothesisTest_PartialSpearman¶

`HypothesisTest_PartialSpearman`(firstSample, secondSample, selection, level=0.05)

Test whether two sample have no rank correlation.

Available usages:

HypothesisTest_PartialSpearman(firstSample, secondSample, selection)

HypothesisTest_PartialSpearman(firstSample, secondSample, selection, level)

Parameters
firstSample2-d sequence of float

First tested sample, of dimension .

secondSample2-d sequence of float

Second tested sample, of dimension 1.

selectionsequence of integers, maximum integer value List of indices selecting which subsets of the first sample will successively be tested with the second sample through the Spearman test.

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

Returns
testResult`TestResult`

Structure containing the result of the test.

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)
[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=0.570533 statistic=-0.569502 description=[]]
```