Test the nullity of the linear regression model coefficients.
LinearModelTest.LinearModelFisher(firstSample, secondSample, level)
LinearModelTest.LinearModelFisher(firstSample, secondSample, linearModel)
LinearModelTest.LinearModelFisher(firstSample, secondSample, linearModel, level)
fisrtSample : 2-d sequence of float
First tested sample, of dimension 1.
secondSample : 2-d sequence of float
Second tested sample, of dimension 1.
A linear model. If not provided, it is built using the given samples.
level : positive float
Threshold p-value of the test (= 1 - first type risk), it must be , equal to 0.95 by default.
Structure containing the result of the test.
The LinearModelTest class is used through its static methods in order to evaluate the quality of the linear regression model between two samples (see
LinearModel). The linear regression model between the scalar variable and the -dimensional one is as follows:
where 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).
>>> 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) class=TestResult name=Unnamed type=Fisher binaryQualityMeasure=false p-value threshold=0.05 p-value=1 description=