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.

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.