HypothesisTest_PartialPearson¶

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

Test whether two discrete samples are independent.

Available usages:

HypothesisTest.PartialPearson(firstSample, secondSample, selection)

HypothesisTest.PartialPearson(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 Pearson 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 Pearson Test is used to check the independence between two samples: firstSample of dimension n and secondSample of dimension 1. The parameter selection enables to select specific subsets of the firstSample to be tested.

Examples

```>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distCol = [ot.Normal(), ot.Normal(), ot.Normal(), ot.Normal()]
>>> S = ot.CorrelationMatrix(4)
>>> S[0, 3] = 0.9
>>> copula = ot.NormalCopula(S)
>>> distribution = ot.ComposedDistribution(distCol, copula)
>>> sample = distribution.getSample(30)
>>> firstSample = sample[:, :3]
>>> secondSample = sample[:, 3]
>>> test_result = ot.HypothesisTest.PartialPearson(firstSample, secondSample, [0, 2])
>>> print(test_result)
[class=TestResult name=Unnamed type=Pearson binaryQualityMeasure=false p-value threshold=0.05 p-value=1.17002e-10 statistic=9.91178 description=[],class=TestResult name=Unnamed type=Pearson binaryQualityMeasure=true p-value threshold=0.05 p-value=0.19193 statistic=-1.33717 description=[]]
```