HypothesisTest_FullPearson(firstSample, secondSample, level=0.05)¶
Test whether two discrete samples are independent.
HypothesisTest.FullPearson(firstSample, secondSample, level)
- firstSample2-d sequence of float
First tested sample, of dimension .
- secondSample2-d sequence of float
Second tested sample, of dimension 1.
- levelpositive float
Threshold p-value of the test (= first kind risk), it must be , equal to 0.05 by default.
Structure containing the result of the test.
The Full Pearson Test is the independence Pearson test between 2 samples : firstSample of dimension n and secondSample of dimension 1. If firstSample[i] is the sample extracted from firstSample ( coordinate of each point of the sample), FullPearson performs the independence Pearson test simultaneously on firstSample[i] and secondSample. For all i, it is supposed that the couple (firstSample[i] and secondSample) is issued from a gaussian vector.
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> distCol = [ot.Normal()] * 3 >>> S = ot.CorrelationMatrix(3) >>> S[0, 2] = 0.9 >>> copula = ot.NormalCopula(S) >>> distribution = ot.ComposedDistribution(distCol, copula) >>> sample = distribution.getSample(30) >>> firstSample = sample[:, :2] >>> secondSample = sample[:, 2] >>> test_result = ot.HypothesisTest.FullPearson(firstSample, secondSample) >>> print(test_result) [class=TestResult name=Unnamed type=Pearson binaryQualityMeasure=false p-value threshold=0.05 p-value=7.23...e-14 statistic=13.61 description=,class=TestResult name=Unnamed type=Pearson binaryQualityMeasure=true p-value threshold=0.05 p-value=0.895124 statistic=-0.133027 description=]