Package elki.math

Class PearsonCorrelation

java.lang.Object

elki.math.PearsonCorrelation

public class PearsonCorrelation
extends java.lang.Object

Class to compute the Pearson correlation coefficient (PCC) also known as Pearson product-moment correlation coefficient (PPMCC).

This computes Var(X), Var(Y) and Cov(X, Y), all of which can be obtained from this class. If you need more than two variables, use CovarianceMatrix which uses slightly more memory (by using arrays) but essentially does the same.

This method used a numerically more stable approach than the popular \( E[XY]-E[X]E[Y] \) based version.

Since:

0.5.0

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field	Description
private double	sumWe	Weight sum.
private double	sumX	Current mean for X and Y.
private double	sumXX	Aggregation for squared residuals - we are not using sum-of-squares!
private double	sumXY	Aggregation for squared residuals - we are not using sum-of-squares!
private double	sumY	Current mean for X and Y.
private double	sumYY	Aggregation for squared residuals - we are not using sum-of-squares!

Constructor Summary

Constructors

Constructor	Description
PearsonCorrelation()	Constructor.

Method Summary

Modifier and Type	Method	Description
static double	coefficient(double[] x, double[] y)	Compute the Pearson product-moment correlation coefficient.
static double	coefficient(NumberVector x, NumberVector y)	Compute the Pearson product-moment correlation coefficient for two NumberVectors.
double	getCorrelation()	Get the Pearson correlation value.
double	getCount()	Get the number of points the average is based on.
double	getMeanX()	Return mean of X
double	getMeanY()	Return mean of Y
double	getNaiveCovariance()	Get the covariance of X and Y (not taking sampling into account)
double	getPopulationStddevX()	Return standard deviation using the non-sample variance
double	getPopulationStddevY()	Return standard deviation using the non-sample variance
double	getPopulationVarianceX()	Return the naive variance (not taking sampling into account)
double	getPopulationVarianceY()	Return the naive variance (not taking sampling into account)
double	getSampleCovariance()	Get the covariance of X and Y (with sampling correction)
double	getSampleStddevX()	Return standard deviation
double	getSampleStddevY()	Return standard deviation
double	getSampleVarianceX()	Return sample variance.
double	getSampleVarianceY()	Return sample variance.
void	put(double x, double y)	Put a single value into the correlation statistic.
void	put(double x, double y, double w)	Put a single value into the correlation statistic.
void	reset()	Reset the value.
static double	weightedCoefficient(double[] x, double[] y, double[] weights)	Compute the Pearson product-moment correlation coefficient.
static double	weightedCoefficient(NumberVector x, NumberVector y, double[] weights)	Compute the Pearson product-moment correlation coefficient for two NumberVectors.
static double	weightedCoefficient(NumberVector x, NumberVector y, NumberVector weights)	Compute the Pearson product-moment correlation coefficient,

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

sumXX

private double sumXX

Aggregation for squared residuals - we are not using sum-of-squares!

sumYY

private double sumYY

Aggregation for squared residuals - we are not using sum-of-squares!

sumXY

private double sumXY

Aggregation for squared residuals - we are not using sum-of-squares!

sumX

private double sumX

Current mean for X and Y.

sumY

private double sumY

Current mean for X and Y.

sumWe

private double sumWe

Weight sum.

Constructor Detail

PearsonCorrelation

public PearsonCorrelation()

Constructor.

Method Detail

put

public void put(double x,
                double y,
                double w)

Put a single value into the correlation statistic.

Parameters:

x - Value in X

y - Value in Y

w - Weight

put

public void put(double x,
                double y)

Put a single value into the correlation statistic.

Parameters:

x - Value in X

y - Value in Y

getCorrelation

public double getCorrelation()

Get the Pearson correlation value.

Returns:

Correlation value

getCount

public double getCount()

Get the number of points the average is based on.

Returns:

number of data points

getMeanX

public double getMeanX()

Return mean of X

Returns:

mean

getMeanY

public double getMeanY()

Return mean of Y

Returns:

mean

getNaiveCovariance

public double getNaiveCovariance()

Get the covariance of X and Y (not taking sampling into account)

Returns:

Covariance

getSampleCovariance

public double getSampleCovariance()

Get the covariance of X and Y (with sampling correction)

Returns:

Covariance

getPopulationVarianceX

public double getPopulationVarianceX()

Return the naive variance (not taking sampling into account)

Note: often you should be using getSampleVarianceX() instead!

Returns:

variance

getSampleVarianceX

public double getSampleVarianceX()

Return sample variance.

Returns:

sample variance

getPopulationStddevX

public double getPopulationStddevX()

Return standard deviation using the non-sample variance

Note: often you should be using getSampleStddevX() instead!

Returns:

standard deviation

getSampleStddevX

public double getSampleStddevX()

Return standard deviation

Returns:

standard deviation

getPopulationVarianceY

public double getPopulationVarianceY()

Return the naive variance (not taking sampling into account)

Note: often you should be using getSampleVarianceY() instead!

Returns:

variance

getSampleVarianceY

public double getSampleVarianceY()

Return sample variance.

Returns:

sample variance

getPopulationStddevY

public double getPopulationStddevY()

Return standard deviation using the non-sample variance

Note: often you should be using getSampleStddevY() instead!

Returns:

stddev

getSampleStddevY

public double getSampleStddevY()

Return standard deviation

Returns:

stddev

reset

public void reset()

Reset the value.

coefficient

public static double coefficient(double[] x,
                                 double[] y)

Compute the Pearson product-moment correlation coefficient.

Parameters:

x - first data array

y - second data array

Returns:

the Pearson product-moment correlation coefficient for x and y

coefficient

public static double coefficient(NumberVector x,
                                 NumberVector y)

Compute the Pearson product-moment correlation coefficient for two NumberVectors.

Parameters:

x - first NumberVector

y - second NumberVector

Returns:

the Pearson product-moment correlation coefficient for x and y

weightedCoefficient

public static double weightedCoefficient(double[] x,
                                         double[] y,
                                         double[] weights)

Compute the Pearson product-moment correlation coefficient.

Parameters:

x - first data array

y - second data array

weights - Weights

Returns:

the Pearson product-moment correlation coefficient for x and y

weightedCoefficient

public static double weightedCoefficient(NumberVector x,
                                         NumberVector y,
                                         double[] weights)

Compute the Pearson product-moment correlation coefficient for two NumberVectors.

Parameters:

x - first NumberVector

y - second NumberVector

weights - Weights

Returns:

the Pearson product-moment correlation coefficient for x and y

weightedCoefficient

public static double weightedCoefficient(NumberVector x,
                                         NumberVector y,
                                         NumberVector weights)

Compute the Pearson product-moment correlation coefficient,

Parameters:

x - first NumberVector

y - second NumerVector

weights - Weights

Returns:

the Pearson product-moment correlation coefficient for x and y