LinearModelFactory¶

class
LinearModelFactory
(*args)¶ Class used to create a linear model from numerical samples.
Available usages:
LinearModelFactory()See also
Notes
This class is used in order to create a linear model from numerical samples. The linear regression model between the scalar variable and the dimensional one writes as follows:
where is the residual, supposed to follow the standard Normal distribution.
Each coefficient is evaluated from both samples and and is accompagnied by a confidence interval and a pvalue (which tests if they are significantly different from 0.0).
This class enables to test the quality of the model. It provides only numerical tests. If is scalar, a graphical validation test exists, that draws the residual couples , where the residual is evaluated on the samples : with . The method is
VisualTest_DrawLinearModelResidual
.Methods
build
(*args)Build the linear model from numerical samples. 
__init__
(*args)¶ Initialize self. See help(type(self)) for accurate signature.

build
(*args)¶ Build the linear model from numerical samples.
Available usages:
build(Xsample, Ysample)
build(Xsample, Ysample, level)
Parameters: Xsample : 2d sequence of float
Input sample, of dimension .
Ysample : 2d sequence of float
Output sample, of dimension 1.
level : positive float
The level value of the confidence intervals of each coefficient of the linear model, equal to 0.95 by default.
Returns: linearModel :
LinearModel
The linear model built from the samples : , where is the random residual with zero mean.
See also
Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> distribution = ot.Normal() >>> Xsample = distribution.getSample(30) >>> func = ot.SymbolicFunction(['x'], ['2 * x + 1']) >>> Ysample = func(Xsample) + ot.Normal().getSample(30) >>> LMF = ot.LinearModelFactory() >>> linearModel = LMF.build(Xsample, Ysample) >>> print(linearModel) LinearModel name=Unnamed regression=[1.1802,2.0034] confidence intervals=[0.887852, 1.47256] [1.70439, 2.3024] pValues=[1.87486e07,5.10531e12]
