LinearModelTest_LinearModelFisher

LinearModelTest_LinearModelFisher(*args)

Test the nullity of the linear regression model coefficients.

Available usages:

LinearModelTest.LinearModelFisher(firstSample, secondSample)

LinearModelTest.LinearModelFisher(firstSample, secondSample, level)

LinearModelTest.LinearModelFisher(firstSample, secondSample, linearModelResult)

LinearModelTest.LinearModelFisher(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_LinearModelResidualMean

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, supposed to follow the standard Normal distribution.

The LinearModelFisher test checks the nullity of the regression linear model coefficients (Fisher distribution is used).

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.LinearModelFisher(firstSample, secondSample)
>>> print(test_result.getPValue())
5.1...e-12