FullRegression¶
- FullRegression(firstSample, secondSample, level=0.05)¶
Test whether two discrete samples are not linear.
Available usages:
LinearModelTest.FullRegression(firstSample, secondSample)
LinearModelTest.FullRegression(firstSample, secondSample, level)
- Parameters:
- 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.
- Returns:
- testResultsCollection of
TestResult
Results for each component of the linear model including intercept.
- testResultsCollection of
Notes
The Full Regression Test is used to check the quality of the linear regression model between two 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), FullRegression performs the linear regression test on all firstSample[i] and secondSample. The linear regression test tests if the linear regression model between two scalar samples is not significant. It is based on the deviation analysis of the regression.
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.JointDistribution(distCol, copula) >>> sample = distribution.getSample(30) >>> firstSample = sample[:, :2] >>> secondSample = sample[:, 2] >>> test_result = ot.LinearModelTest.FullRegression(firstSample, secondSample) >>> print(test_result) [class=TestResult name=Unnamed type=Regression binaryQualityMeasure=true p-value threshold=0.05 p-value=0.605 statistic=-0.52335 description=[],class=TestResult name=Unnamed type=Regression binaryQualityMeasure=false p-value threshold=0.05 p-value=9.70282e-27 statistic=44.256 description=[],class=TestResult name=Unnamed type=Regression binaryQualityMeasure=true p-value threshold=0.05 p-value=0.11352 statistic=1.63564 description=[]]