LinearModelResult¶

class LinearModelResult(*args)¶

Result of a LinearModelAlgorithm.

Parameters

inputSample2-d sequence of float: The input sample of a model.
basisBasis: Functional basis to estimate the trend.
designMatrix: The design matrix $X$ .
outputSample2-d sequence of float: The output sample of a model.
metaModelFunction: The meta model.
trendCoefficientssequence of float: The trend coeffients associated to the linearmodel.
formulastr: The formula description.
coefficientsNamessequence of str: The coefficients names of the basis.
sampleResiduals2-d sequence of float: The residual errors.
standardizedSampleResiduals2-d sequence of float: The normalized residual errors.
diagonalGramInversesequence of float: The diagonal of the Gram inverse matrix.
leveragessequence of float: The leverage score.
cookDistancessequence of float: The cook’s distances.
sigma2float: The unbiased noise variance.

See also

LinearModelAlgorithm

Methods

`getAdjustedRSquared`(self)	Accessor to the Adjusted R-squared test.
`getBasis`(self)	Accessor to the basis.
`getClassName`(self)	Accessor to the object’s name.
`getCoefficients`(self)	Accessor to the coefficients of the linear model of the trend.
`getCoefficientsNames`(self)	Accessor to the coefficients names.
`getCoefficientsStandardErrors`(self)	Accessor to the coefficients of standard error.
`getCookDistances`(self)	Accessor to the cook’s distances.
`getDegreesOfFreedom`(self)	Accessor to the degrees of freedom.
`getDiagonalGramInverse`(self)	Accessor to the diagonal gram inverse matrix.
`getFittedSample`(self)	Accessor to the fitted sample.
`getFormula`(self)	Accessor to the formula.
`getId`(self)	Accessor to the object’s id.
`getInputSample`(self)	Accessor to the input sample.
`getLeverages`(self)	Accessor to the leverages.
`getMetaModel`(self)	Accessor to the metamodel.
`getModel`(self)	Accessor to the model.
`getName`(self)	Accessor to the object’s name.
`getNoiseDistribution`(self)	Accessor to the noise distribution, ie the underlying distribution of the residual.
`getOutputSample`(self)	Accessor to the output sample.
`getRSquared`(self)	Accessor to the R-squared test.
`getRelativeErrors`(self)	Accessor to the relative errors.
`getResiduals`(self)	Accessor to the residuals.
`getSampleResiduals`(self)	Accessor to the residuals.
`getShadowedId`(self)	Accessor to the object’s shadowed id.
`getStandardizedResiduals`(self)	Accessor to the standardized residuals.
`getVisibility`(self)	Accessor to the object’s visibility state.
`hasName`(self)	Test if the object is named.
`hasVisibleName`(self)	Test if the object has a distinguishable name.
`setMetaModel`(self, metaModel)	Accessor to the metamodel.
`setModel`(self, model)	Accessor to the model.
`setName`(self, name)	Accessor to the object’s name.
`setRelativeErrors`(self, relativeErrors)	Accessor to the relative errors.
`setResiduals`(self, residuals)	Accessor to the residuals.
`setShadowedId`(self, id)	Accessor to the object’s shadowed id.
`setVisibility`(self, visible)	Accessor to the object’s visibility state.

__init__(self, \*args)¶: Initialize self. See help(type(self)) for accurate signature.

getAdjustedRSquared(self)¶

Accessor to the Adjusted R-squared test.

Returns

adjustedRSquaredfloat

getBasis(self)¶

Accessor to the basis.

Returns

basisBasis: The basis which had been passed to the constructor.

getClassName(self)¶

Accessor to the object’s name.

Returns

class_namestr: The object class name (object.__class__.__name__).

getCoefficients(self)¶

Accessor to the coefficients of the linear model of the trend.

Returns

betaPoint

getCoefficientsNames(self)¶

Accessor to the coefficients names.

Returns

coefficientsNamesDescription

getCoefficientsStandardErrors(self)¶

Accessor to the coefficients of standard error.

Returns

standardErrorsPoint

getCookDistances(self)¶

Accessor to the cook’s distances.

Returns

cookDistancesPoint

getDegreesOfFreedom(self)¶

Accessor to the degrees of freedom.

Returns

dofint

getDiagonalGramInverse(self)¶

Accessor to the diagonal gram inverse matrix.

Returns

diagonalGramInversePoint

getFittedSample(self)¶

Accessor to the fitted sample.

Returns

outputSampleSample

getFormula(self)¶

Accessor to the formula.

Returns

condensedFormulastr

getId(self)¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getInputSample(self)¶

Accessor to the input sample.

Returns

inputSampleSample: The Xsample which had been passed to the constructor.

getLeverages(self)¶

Accessor to the leverages.

Returns

leveragesPoint

getMetaModel(self)¶

Accessor to the metamodel.

Returns

metaModelFunction: Metamodel.

getModel(self)¶

Accessor to the model.

Returns

modelFunction: Physical model approximated by a metamodel.

getName(self)¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getNoiseDistribution(self)¶

Accessor to the noise distribution, ie the underlying distribution of the residual.

Returns

noiseDistributionDistribution

getOutputSample(self)¶

Accessor to the output sample.

Returns

outputSampleSample: The Ysample which had been passed to the constructor.

getRSquared(self)¶

Accessor to the R-squared test.

Returns

rSquaredfloat

getRelativeErrors(self)¶

Accessor to the relative errors.

Returns

relativeErrorsPoint: The relative errors defined as follows for each output of the model: $\displaystyle \frac{\sum_{i=1}^N (y_i - \hat{y_i})^2}{N \Var{\vect{Y}}}$ with $\vect{Y}$ the vector of the $N$ model’s values $y_i$ and $\hat{y_i}$ the metamodel’s values.

getResiduals(self)¶

Accessor to the residuals.

Returns

residualsPoint: The residual values defined as follows for each output of the model: $\displaystyle \frac{\sqrt{\sum_{i=1}^N (y_i - \hat{y_i})^2}}{N}$ with $y_i$ the $N$ model’s values and $\hat{y_i}$ the metamodel’s values.

getSampleResiduals(self)¶

Accessor to the residuals.

Returns

sampleResidualsSample

getShadowedId(self)¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getStandardizedResiduals(self)¶

Accessor to the standardized residuals.

Returns

standardizedResidualsSample

getVisibility(self)¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

hasName(self)¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasVisibleName(self)¶

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool: True if the name is not empty and not the default one.

setMetaModel(self, metaModel)¶

Accessor to the metamodel.

Parameters

metaModelFunction: Metamodel.

setModel(self, model)¶

Accessor to the model.

Parameters

modelFunction: Physical model approximated by a metamodel.

setName(self, name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setRelativeErrors(self, relativeErrors)¶

Accessor to the relative errors.

Parameters

relativeErrorssequence of float: The relative errors defined as follows for each output of the model: $\displaystyle \frac{\sum_{i=1}^N (y_i - \hat{y_i})^2}{N \Var{\vect{Y}}}$ with $\vect{Y}$ the vector of the $N$ model’s values $y_i$ and $\hat{y_i}$ the metamodel’s values.

setResiduals(self, residuals)¶

Accessor to the residuals.

Parameters

residualssequence of float: The residual values defined as follows for each output of the model: $\displaystyle \frac{\sqrt{\sum_{i=1}^N (y_i - \hat{y_i})^2}}{N}$ with $y_i$ the $N$ model’s values and $\hat{y_i}$ the metamodel’s values.

setShadowedId(self, id)¶

Accessor to the object’s shadowed id.

Parameters

idint: Internal unique identifier.

setVisibility(self, visible)¶

Accessor to the object’s visibility state.

Parameters

visiblebool: Visibility flag.

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LinearModelResult¶