LinearModelTest_PartialRegression¶

LinearModelTest_PartialRegression
(firstSample, secondSample, selection, level=0.05)¶ Test whether two discrete samples are independent.
Available usages:
LinearModelTest.PartialRegression(firstSample, secondSample, selection)
LinearModelTest.PartialRegression(firstSample, secondSample, selection, level)
 Parameters
 firstSample2d sequence of float
First tested sample, of dimension .
 secondSample2d sequence of float
Second tested sample, of dimension 1.
 selectionsequence of int, maximum integer value
List of indices selecting which subsets of the first sample will successively be tested with the second sample through the regression test.
 levelpositive float
Threshold pvalue of the test (= first kind risk), it must be , equal to 0.05 by default.
 Returns
 testResultsCollection of
TestResult
Results for each component of the linear model including intercept.
 testResultsCollection of
Notes
The Partial Regression Test is used to assess the linearity between a subset of components of firstSample and secondSample. The parameter selection enables to select specific subsets of the firstSample to be tested.
Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> dim = 3 >>> distCol = [ot.Normal()] * dim >>> S = ot.CorrelationMatrix(dim) >>> S[0, dim  1] = 0.99 >>> copula = ot.NormalCopula(S) >>> distribution = ot.ComposedDistribution(distCol, copula) >>> sample = distribution.getSample(30) >>> firstSample = sample[:, :2] >>> secondSample = sample[:, 2] >>> selection = [1] >>> test_result = ot.LinearModelTest.PartialRegression(firstSample, secondSample, selection) >>> print(test_result[1]) class=TestResult name=Unnamed type=Regression binaryQualityMeasure=true pvalue threshold=0.05 pvalue=0.579638 statistic=0.560438 description=[]