CorrelationAnalysis

class CorrelationAnalysis(*args)

Correlation analysis methods.

Provides elementary sensitivity analysis methods to determine the influence of each component of a random vector over a single one-dimensional variable.

Parameters:

firstSample2-d list of float

Values taken by a random vector.

secondSample2-d list of float

Values taken by a single one-dimensional random variable.

See also

LinearModelAlgorithm

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> firstSample = ot.Normal(3).getSample(100)
>>> model = ot.SymbolicFunction(['x', 'y', 'z'], ['x + 3 * y - 5 * z'])
>>> secondSample = model(firstSample)
>>> correlationAnalysis = ot.CorrelationAnalysis(firstSample, secondSample)
>>> cor = correlationAnalysis.computeLinearCorrelation()

Methods

computeKendallTau()	Correlation evaluation based on Kendall's coefficient.
computeLinearCorrelation()	Correlation evaluation based on the linear (Pearson) correlation coefficient.
computePCC()	Correlation evaluation based on the Partial Correlation Coefficient.
computePRCC()	Correlation evaluation based on the Partial Rank Correlation Coefficient.
computeSRC()	Correlation evaluation based on the Standard Regression Coefficient.
computeSRRC()	Correlation evaluation based on the Standard Rank Regression Coefficient.
computeSpearmanCorrelation()	Correlation evaluation based on the Spearman correlation coefficient.
computeSquaredSRC([normalize])	Correlation evaluation based on the square of the Standard Regression Coefficient.
getClassName()	Accessor to the object's name.
getName()	Accessor to the object's name.
hasName()	Test if the object is named.
setName(name)	Accessor to the object's name.

computePearsonCorrelation

__init__(*args)

computeKendallTau()

Correlation evaluation based on Kendall’s coefficient.

See the Sample computeKendallTau() method for details.

Returns:

coefPoint

Kendall’s coefficients evaluated between the secondSample and each coordinate of the firstSample.

computeLinearCorrelation()

Correlation evaluation based on the linear (Pearson) correlation coefficient.

Refer to Pearson correlation coefficient.

Returns:

coefPoint

The linear (Pearson) coefficients evaluated between the secondSample and each coordinate of the firstSample.

computePCC()

Correlation evaluation based on the Partial Correlation Coefficient.

Refer to Uncertainty ranking: PCC and PRCC.

Returns:

coefPoint

The PCC coefficients evaluated between the secondSample and each coordinate of the firstSample.

computePRCC()

Correlation evaluation based on the Partial Rank Correlation Coefficient.

Refer to Uncertainty ranking: PCC and PRCC.

Returns:

coefPoint

The PRCC coefficients evaluated between the secondSample and each coordinate of the firstSample.

computeSRC()

Correlation evaluation based on the Standard Regression Coefficient.

Refer to Uncertainty ranking: SRC and SRRC.

Returns:

coefPoint

The SRC evaluated between the secondSample and each coordinate of the firstSample.

computeSRRC()

Correlation evaluation based on the Standard Rank Regression Coefficient.

Refer to Uncertainty ranking: SRC and SRRC.

Returns:

coefPoint

The SRRC evaluated between the secondSample and each coordinate of the firstSample.

computeSpearmanCorrelation()

Correlation evaluation based on the Spearman correlation coefficient.

Refer to Spearman correlation coefficient.

Returns:

coefPoint

The Spearman coefficients evaluated between the secondSample and each coordinate of the firstSample.

computeSquaredSRC(normalize=False)

Correlation evaluation based on the square of the Standard Regression Coefficient.

Refer to Uncertainty ranking: SRC and SRRC.

Parameters:

normalizebool

If True, coefficients sum to one. Default value is False.

Returns:

coefPoint

The squared SRC evaluated between the secondSample and each coordinate of the firstSample.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

Examples using the class

Estimate correlation coefficients

Estimate correlation coefficients

Compute squared SRC indices confidence intervals

Compute squared SRC indices confidence intervals

Example of sensitivity analyses on the wing weight model

Example of sensitivity analyses on the wing weight model