LinearModelTest_LinearModelBreuschPagan

LinearModelTest_LinearModelBreuschPagan(\*args)

Test the homoskedasticity of the linear regression model residuals.

Available usages:

LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample)

LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample, linearModelResult)

LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample, level)

LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample, linearModelResult, level)

Parameters
firstSample2-d sequence of float

First tested sample, of dimension 1.

secondSample2-d sequence of float

Second tested sample, of dimension 1.

linearModelResultLinearModelResult

A linear model. If not provided, it is built using the given samples.

levelpositive float < 1

Threshold p-value of the test (= first kind risk), it must be < 1, equal to 0.05 by default.

Returns
testResultTestResult

Structure containing the result of the test.

See also

LinearModelTest_LinearModelHarrisonMcCabe, LinearModelTest_LinearModelResidualMean
LinearModelTest_LinearModelDurbinWatson

Notes

The LinearModelTest class is used through its static methods in order to evaluate the quality of the linear regression model between two samples. The linear regression model between the scalar variable Y and the n-dimensional one \vect{X} = (X_i)_{i \leq n} is as follows:

\tilde{Y} = a_0 + \sum_{i=1}^n a_i X_i + \epsilon

where \epsilon is the residual.

The Breusch-Pagan test checks the heteroskedasticity of the residuals. A linear regression model is fitted on the squared residuals. The statistic is computed using the Studendized version with the chi-squared distribution. If the binary quality measure is false, then the homoskedasticity hypothesis can be rejected with respect to the given level.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> func = ot.SymbolicFunction('x', '2 * x + 1')
>>> firstSample = sample
>>> secondSample = func(sample) + ot.Normal().getSample(30)
>>> test_result = ot.LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=BreuschPagan binaryQualityMeasure=true p-value threshold=0.05 p-value=0.700772 statistic=0.14767 description=[]