libopenturns-dev 1.9-5 / usr / include / openturns / swig / LinearModelTest_doc.i

%feature("docstring") OT::LinearModelTest::LinearModelAdjustedRSquared
"Test the quality of the linear regression model Test.

Based on the adjusted :math:`R^2` indicator.

**Available usages**:

    LinearModelTest.LinearModelAdjustedRSquared(*firstSample, secondSample*)

    LinearModelTest.LinearModelAdjustedRSquared(*firstSample, secondSample, level*)

    LinearModelTest.LinearModelAdjustedRSquared(*firstSample, secondSample, linearModel*)

    LinearModelTest.LinearModelAdjustedRSquared(*firstSample, secondSample, linearModel, level*)


Parameters
----------
fisrtSample : 2-d sequence of float
    First tested sample, of dimension 1.
secondSample : 2-d sequence of float
    Second tested sample, of dimension 1.
linearModel : :class:`~openturns.LinearModel`
    A linear model. If not provided, it is built using the given samples.
level : positive float :math:`< 1`
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.

Returns
-------
testResult : :class:`~openturns.TestResult`
    Structure containing the result of the test.

See Also
--------
LinearModelTest_LinearModelRSquared, LinearModelTest_LinearModelFisher,
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
(see :class:`~openturns.LinearModel`). The linear regression model between the
scalar variable :math:`Y` and the :math:`n`-dimensional one
:math:`\\\\vect{X} = (X_i)_{i \\\\leq n}` is as follows:

.. math::

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

where :math:`\\\\epsilon` is the residual, supposed to follow the standard Normal
distribution.

The LinearModelAdjustedRSquared test checks the quality of the linear
regression model. It evaluates the indicator :math:`R^2` adjusted (regression
variance analysis) and compares it to a 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.LinearModelAdjustedRSquared(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=AdjustedRSquared binaryQualityMeasure=false p-value threshold=0.95 p-value=0.815998 description=[]
"

// ---------------------------------------------------------------------

%feature("docstring") OT::LinearModelTest::LinearModelFisher
"Test the nullity of the linear regression model coefficients.

**Available usages**:

    LinearModelTest.LinearModelFisher(*firstSample, secondSample*)

    LinearModelTest.LinearModelFisher(*firstSample, secondSample, level*)

    LinearModelTest.LinearModelFisher(*firstSample, secondSample, linearModel*)

    LinearModelTest.LinearModelFisher(*firstSample, secondSample, linearModel, level*)


Parameters
----------
fisrtSample : 2-d sequence of float
    First tested sample, of dimension 1.
secondSample : 2-d sequence of float
    Second tested sample, of dimension 1.
linearModel : :class:`~openturns.LinearModel`
    A linear model. If not provided, it is built using the given samples.
level : positive float :math:`< 1`
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.

Returns
-------
testResult : :class:`~openturns.TestResult`
    Structure containing the result of the test.

See Also
--------
LinearModelTest_LinearModelRSquared, LinearModelTest_LinearModelAdjustedRSquared,
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
(see :class:`~openturns.LinearModel`). The linear regression model between the
scalar variable :math:`Y` and the :math:`n`-dimensional one
:math:`\\\\vect{X} = (X_i)_{i \\\\leq n}` is as follows:

.. math::

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

where :math:`\\\\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)
class=TestResult name=Unnamed type=Fisher binaryQualityMeasure=false p-value threshold=0.05 p-value=1 description=[]
"

// ---------------------------------------------------------------------

%feature("docstring") OT::LinearModelTest::LinearModelRSquared
"Test the quality of the linear regression model based on the :math:`R^2` indicator.

**Available usages**:

    LinearModelTest.LinearModelRSquared(*firstSample, secondSample*)

    LinearModelTest.LinearModelRSquared(*firstSample, secondSample, level*)

    LinearModelTest.LinearModelRSquared(*firstSample, secondSample, linearModel*)

    LinearModelTest.LinearModelRSquared(*firstSample, secondSample, linearModel, level*)


Parameters
----------
fisrtSample : 2-d sequence of float
    First tested sample, of dimension 1.
secondSample : 2-d sequence of float
    Second tested sample, of dimension 1.
linearModel : :class:`~openturns.LinearModel`
    A linear model. If not provided, it is built using the given samples.
level : positive float :math:`< 1`
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.

Returns
-------
testResult : :class:`~openturns.TestResult`
    Structure containing the result of the test.

See Also
--------
LinearModelTest_LinearModelAdjustedRSquared,  LinearModelTest_LinearModelFisher, 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
(see :class:`~openturns.LinearModel`). The linear regression model between the
scalar variable :math:`Y` and the :math:`n`-dimensional one
:math:`\\\\vect{X} = (X_i)_{i \\\\leq n}` is as follows:

.. math::

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

where :math:`\\\\epsilon` is the residual, supposed to follow the standard Normal
distribution.

The LinearModelRSquared test checks the quality of the linear
regression model. It evaluates the indicator :math:`R^2` (regression
variance analysis) and compares it to a 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.LinearModelRSquared(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=RSquared binaryQualityMeasure=false p-value threshold=0.95 p-value=0.822343 description=[]
"

// ---------------------------------------------------------------------

%feature("docstring") OT::LinearModelTest::LinearModelResidualMean
"Test zero mean value of the residual of the linear regression model.

**Available usages**:

    LinearModelTest.LinearModelResidualMean(*firstSample, secondSample*)

    LinearModelTest.LinearModelResidualMean(*firstSample, secondSample, level*)

    LinearModelTest.LinearModelResidualMean(*firstSample, secondSample, linearModel*)

    LinearModelTest.LinearModelResidualMean(*firstSample, secondSample, linearModel, level*)


Parameters
----------
fisrtSample : 2-d sequence of float
    First tested sample, of dimension 1.
secondSample : 2-d sequence of float
    Second tested sample, of dimension 1.
linearModel : :class:`~openturns.LinearModel`
    A linear model. If not provided, it is built using the given samples.
level : positive float :math:`< 1`
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.

Returns
-------
testResult : :class:`~openturns.TestResult`
    Structure containing the result of the test.

See Also
--------
LinearModelTest_LinearModelAdjustedRSquared,  LinearModelTest_LinearModelFisher, LinearModelTest_LinearModelRSquared, LinearModelTest_LinearModelHarrisonMcCabe

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

.. math::

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

where :math:`\\\\epsilon` is the residual, supposed to follow the standard Normal
distribution.

The LinearModelResidualMean Test checks, under the hypothesis of a gaussian
sample, if the mean of the residual is equal to zero. It is based on the Student
test (equality of mean for two gaussian samples).

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.LinearModelResidualMean(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=ResidualMean binaryQualityMeasure=true p-value threshold=0.05 p-value=1 description=[]
"

// ---------------------------------------------------------------------

%feature("docstring") OT::LinearModelTest::LinearModelHarrisonMcCabe
"Test the homoskedasticity of the linear regression model residuals.

**Available usages**:

    LinearModelTest.LinearModelHarrisonMcCabe(*firstSample, secondSample*)

    LinearModelTest.LinearModelHarrisonMcCabe(*firstSample, secondSample, linearModel*)

    LinearModelTest.LinearModelHarrisonMcCabe(*firstSample, secondSample, level, breakPoint, simulationSize*)

    LinearModelTest.LinearModelHarrisonMcCabe(*firstSample, secondSample, linearModel, level, breakPoint, simulationSize*)

Parameters
----------
fisrtSample : 2-d sequence of float
    First tested sample, of dimension 1.
secondSample : 2-d sequence of float
    Second tested sample, of dimension 1.
linearModel : :class:`~openturns.LinearModel`
    A linear model. If not provided, it is built using the given samples.
level : positive float :math:`< 1`
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.
breakPoint : positive float :math:`< 1`
    Percentage of data to be taken as breakPoint in the variances. It must be
    :math:`< 1`, equal to 0.5 by default.
simulationSize : positive int
    Size of the sample used to compute the p-value. Default is 1000.

Returns
-------
testResult : :class:`~openturns.TestResult`
    Structure containing the result of the test.

See Also
--------
LinearModelTest_LinearModelBreuschPagan, 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
(see :class:`~openturns.LinearModel`). The linear regression model between the
scalar variable :math:`Y` and the :math:`n`-dimensional one
:math:`\\\\vect{X} = (X_i)_{i \\\\leq n}` is as follows:

.. math::

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

where :math:`\\\\epsilon` is the residual.

The Harrison-McCabe test checks the heteroskedasticity of the residuals. The
breakpoint in the variances is set by default to the half of the sample. The
p-value is estimed using simulation. 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.LinearModelHarrisonMcCabe(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=HarrisonMcCabe binaryQualityMeasure=true p-value threshold=0.95 p-value=0.142 description=[]
"

// ---------------------------------------------------------------------

%feature("docstring") OT::LinearModelTest::LinearModelBreuschPagan
"Test the homoskedasticity of the linear regression model residuals.

**Available usages**:

    LinearModelTest.LinearModelBreuschPagan(*firstSample, secondSample*)

    LinearModelTest.LinearModelBreuschPagan(*firstSample, secondSample, linearModel*)

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

    LinearModelTest.LinearModelBreuschPagan(*firstSample, secondSample, linearModel, level*)

Parameters
----------
fisrtSample : 2-d sequence of float
    First tested sample, of dimension 1.
secondSample : 2-d sequence of float
    Second tested sample, of dimension 1.
linearModel : :class:`~openturns.LinearModel`
    A linear model. If not provided, it is built using the given samples.
level : positive float :math:`< 1`
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.

Returns
-------
testResult : :class:`~openturns.TestResult`
    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
(see :class:`~openturns.LinearModel`). The linear regression model between the
scalar variable :math:`Y` and the :math:`n`-dimensional one
:math:`\\\\vect{X} = (X_i)_{i \\\\leq n}` is as follows:

.. math::

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

where :math:`\\\\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=0.95 p-value=0.700772 description=[]
"


// ---------------------------------------------------------------------

%feature("docstring") OT::LinearModelTest::LinearModelDurbinWatson
"Test the autocorrelation of the linear regression model residuals.

**Available usages**:

    LinearModelTest.LinearModelDurbinWatson(*firstSample, secondSample*)

    LinearModelTest.LinearModelDurbinWatson(*firstSample, secondSample, hypothesis, level*)

    LinearModelTest.LinearModelDurbinWatson(*firstSample, secondSample, linearModel*)

    LinearModelTest.LinearModelDurbinWatson(*firstSample, secondSample, linearModel, hypothesis, level*)

Parameters
----------
fisrtSample : 2-d sequence of float
    First tested sample, of dimension 1.
secondSample : 2-d sequence of float
    Second tested sample, of dimension 1.
linearModel : :class:`~openturns.LinearModel`
    A linear model. If not provided, it is built using the given samples.
hypothesis : string
    Hypothesis H0 for the residuals. It can be : 'Equal' to 0, 'Less' than 0 or
    'Greater' than 0. Default is set to 'Equal' to 0.
level : positive float :math:`< 1`
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.

Returns
-------
testResult : :class:`~openturns.TestResult`
    Structure containing the result of the test.

See Also
--------
LinearModelTest_LinearModelBreuschPagan, LinearModelTest_LinearModelHarrisonMcCabe, 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
(see :class:`~openturns.LinearModel`). The linear regression model between the
scalar variable :math:`Y` and the :math:`n`-dimensional one
:math:`\\\\vect{X} = (X_i)_{i \\\\leq n}` is as follows:

.. math::

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

where :math:`\\\\epsilon` is the residual.

The Durbin-Watson test checks the autocorrelation of the residuals. It is possible
to test is the autocorrelation is equal to 0, and less or greater than 0. The
p-value is computed using a normal approximation with mean and variance of the
Durbin-Watson test statistic. If the binary quality measure is false, then the
given autocorrelation 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.LinearModelDurbinWatson(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=DurbinWatson binaryQualityMeasure=true p-value threshold=0.95 p-value=0.653603 description=[Hypothesis test: autocorrelation equals 0.]
"