Apache Commons Math 3.6.1 · org.apache.commons.math3.stat.correlation.Covariance

Computes covariances for pairs of arrays or columns of a matrix.

The constructors that take RealMatrix or double[][] arguments generate covariance matrices. The columns of the input matrices are assumed to represent variable values.

The constructor argument biasCorrected determines whether or not computed covariances are bias-corrected.

Unbiased covariances are given by the formula

cov(X, Y) = Σ[(x_i - E(X))(y_i - E(Y))] / (n - 1) where E(X) is the mean of X and E(Y) is the mean of the Y values.

Non-bias-corrected estimates use n in place of n - 1

Field Summary

Modifier and Type	Field and Description
private final RealMatrix	covarianceMatrix covariance matrix
private final int	n Number of observations (length of covariate vectors)

Constructor Summary

Access	Constructor and Description
public	Covariance() Create a Covariance with no data
public	Covariance(double[][] rectangular array with columns representing covariates data, boolean true means covariances are bias-corrected biasCorrected) Create a Covariance matrix from a rectangular array whose columns represent covariates.
public	Covariance(double[][] rectangular array with columns representing covariates data) Create a Covariance matrix from a rectangular array whose columns represent covariates.
public	Covariance(RealMatrix matrix with columns representing covariates matrix, boolean true means covariances are bias-corrected biasCorrected) Create a covariance matrix from a matrix whose columns represent covariates.
public	Covariance(RealMatrix matrix with columns representing covariates matrix) Create a covariance matrix from a matrix whose columns represent covariates.

Method Summary

Modifier and Type	Method and Description
private void	checkSufficientData(final RealMatrix matrix to check matrix) Throws MathIllegalArgumentException if the matrix does not have at least one column and two rows.
protected RealMatrix	Returns: covariance matrix computeCovarianceMatrix(RealMatrix input matrix (must have at least one column and two rows) matrix, boolean determines whether or not covariance estimates are bias-corrected biasCorrected) Compute a covariance matrix from a matrix whose columns represent covariates.
protected RealMatrix	Returns: covariance matrix computeCovarianceMatrix(RealMatrix input matrix (must have at least one column and two rows) matrix) Create a covariance matrix from a matrix whose columns represent covariates.
protected RealMatrix	Returns: covariance matrix computeCovarianceMatrix(double[][] input array (must have at least one column and two rows) data, boolean determines whether or not covariance estimates are bias-corrected biasCorrected) Compute a covariance matrix from a rectangular array whose columns represent covariates.
protected RealMatrix	Returns: covariance matrix computeCovarianceMatrix(double[][] input array (must have at least one column and two rows) data) Create a covariance matrix from a rectangular array whose columns represent covariates.
public double	Returns: returns the covariance for the two arrays covariance(final double[] first data array xArray, final double[] second data array yArray, boolean if true, returned value will be bias-corrected biasCorrected) Computes the covariance between the two arrays.
public double	Returns: returns the covariance for the two arrays covariance(final double[] first data array xArray, final double[] second data array yArray) Computes the covariance between the two arrays, using the bias-corrected formula.
public RealMatrix	Returns: covariance matrix getCovarianceMatrix() Returns the covariance matrix
public int	Returns: number of observations getN() Returns the number of observations (length of covariate vectors)

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

covarianceMatrix	back to summary
private final RealMatrix covarianceMatrix covariance matrix

n	back to summary
private final int n Number of observations (length of covariate vectors)

Constructor Detail

Covariance	back to summary
public Covariance() Create a Covariance with no data

Covariance back to summary

Covariance	back to summary
public Covariance(double[][] data, boolean biasCorrected) throws MathIllegalArgumentException, NotStrictlyPositiveException Create a Covariance matrix from a rectangular array whose columns represent covariates. The `biasCorrected` parameter determines whether or not covariance estimates are bias-corrected. The input array must be rectangular with at least one column and two rows. Parameters data:double[][] rectangular array with columns representing covariates biasCorrected:boolean true means covariances are bias-corrected Exceptions MathIllegalArgumentException: if the input data array is not rectangular with at least two rows and one column. NotStrictlyPositiveException: if the input data array is not rectangular with at least one row and one column.

public Covariance(double[][] data, boolean biasCorrected) throws MathIllegalArgumentException, NotStrictlyPositiveException

Create a Covariance matrix from a rectangular array whose columns represent covariates.

The biasCorrected parameter determines whether or not covariance estimates are bias-corrected.

The input array must be rectangular with at least one column and two rows.

Parameters

data:double[][]: rectangular array with columns representing covariates
biasCorrected:boolean: true means covariances are bias-corrected

Exceptions

MathIllegalArgumentException:: if the input data array is not rectangular with at least two rows and one column.
NotStrictlyPositiveException:: if the input data array is not rectangular with at least one row and one column.

Covariance	back to summary
public Covariance(double[][] data) throws MathIllegalArgumentException, NotStrictlyPositiveException Create a Covariance matrix from a rectangular array whose columns represent covariates. The input array must be rectangular with at least one column and two rows Parameters data:double[][] rectangular array with columns representing covariates Exceptions MathIllegalArgumentException: if the input data array is not rectangular with at least two rows and one column. NotStrictlyPositiveException: if the input data array is not rectangular with at least one row and one column.

Covariance

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public Covariance(double[][] data) throws MathIllegalArgumentException, NotStrictlyPositiveException

Create a Covariance matrix from a rectangular array whose columns represent covariates.

The input array must be rectangular with at least one column and two rows

Parameters

data:double[][]: rectangular array with columns representing covariates

Exceptions

MathIllegalArgumentException:: if the input data array is not rectangular with at least two rows and one column.
NotStrictlyPositiveException:: if the input data array is not rectangular with at least one row and one column.

Covariance back to summary

Covariance	back to summary
public Covariance(RealMatrix matrix, boolean biasCorrected) throws MathIllegalArgumentException Create a covariance matrix from a matrix whose columns represent covariates. The `biasCorrected` parameter determines whether or not covariance estimates are bias-corrected. The matrix must have at least one column and two rows Parameters matrix:RealMatrix matrix with columns representing covariates biasCorrected:boolean true means covariances are bias-corrected Exceptions MathIllegalArgumentException: if the input matrix does not have at least two rows and one column

public Covariance(RealMatrix matrix, boolean biasCorrected) throws MathIllegalArgumentException

Create a covariance matrix from a matrix whose columns represent covariates.

The biasCorrected parameter determines whether or not covariance estimates are bias-corrected.

The matrix must have at least one column and two rows

Parameters

matrix:RealMatrix: matrix with columns representing covariates
biasCorrected:boolean: true means covariances are bias-corrected

Exceptions

MathIllegalArgumentException:: if the input matrix does not have at least two rows and one column

Covariance	back to summary
public Covariance(RealMatrix matrix) throws MathIllegalArgumentException Create a covariance matrix from a matrix whose columns represent covariates. The matrix must have at least one column and two rows Parameters matrix:RealMatrix matrix with columns representing covariates Exceptions MathIllegalArgumentException: if the input matrix does not have at least two rows and one column

Covariance

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public Covariance(RealMatrix matrix) throws MathIllegalArgumentException

Create a covariance matrix from a matrix whose columns represent covariates.

The matrix must have at least one column and two rows

Parameters

matrix:RealMatrix: matrix with columns representing covariates

Exceptions

MathIllegalArgumentException:: if the input matrix does not have at least two rows and one column

Method Detail

checkSufficientData	back to summary
private void checkSufficientData(final RealMatrix matrix) throws MathIllegalArgumentException Throws MathIllegalArgumentException if the matrix does not have at least one column and two rows. Parameters matrix:RealMatrix matrix to check Exceptions MathIllegalArgumentException: if the matrix does not contain sufficient data to compute covariance

checkSufficientData

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private void checkSufficientData(final RealMatrix matrix) throws MathIllegalArgumentException

Throws MathIllegalArgumentException if the matrix does not have at least one column and two rows.

Parameters

matrix:RealMatrix: matrix to check

Exceptions

MathIllegalArgumentException:: if the matrix does not contain sufficient data to compute covariance

computeCovarianceMatrix	back to summary
protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) throws MathIllegalArgumentException Compute a covariance matrix from a matrix whose columns represent covariates. Parameters matrix:RealMatrix input matrix (must have at least one column and two rows) biasCorrected:boolean determines whether or not covariance estimates are bias-corrected Returns:RealMatrix covariance matrix Exceptions MathIllegalArgumentException: if the matrix does not contain sufficient data

computeCovarianceMatrix

back to summary

protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) throws MathIllegalArgumentException

Compute a covariance matrix from a matrix whose columns represent covariates.

Parameters

matrix:RealMatrix: input matrix (must have at least one column and two rows)
biasCorrected:boolean: determines whether or not covariance estimates are bias-corrected

Returns:RealMatrix

covariance matrix

Exceptions

MathIllegalArgumentException:: if the matrix does not contain sufficient data

computeCovarianceMatrix back to summary

computeCovarianceMatrix	back to summary
protected RealMatrix computeCovarianceMatrix(RealMatrix matrix) throws MathIllegalArgumentException Create a covariance matrix from a matrix whose columns represent covariates. Covariances are computed using the bias-corrected formula. Parameters matrix:RealMatrix input matrix (must have at least one column and two rows) Returns:RealMatrix covariance matrix Exceptions MathIllegalArgumentException: if matrix does not contain sufficient data See Also `Covariance`

protected RealMatrix computeCovarianceMatrix(RealMatrix matrix) throws MathIllegalArgumentException

Create a covariance matrix from a matrix whose columns represent covariates. Covariances are computed using the bias-corrected formula.

Parameters

matrix:RealMatrix: input matrix (must have at least one column and two rows)

Returns:RealMatrix

covariance matrix

Exceptions

MathIllegalArgumentException:: if matrix does not contain sufficient data

See Also

Covariance

computeCovarianceMatrix	back to summary
protected RealMatrix computeCovarianceMatrix(double[][] data, boolean biasCorrected) throws MathIllegalArgumentException, NotStrictlyPositiveException Compute a covariance matrix from a rectangular array whose columns represent covariates. Parameters data:double[][] input array (must have at least one column and two rows) biasCorrected:boolean determines whether or not covariance estimates are bias-corrected Returns:RealMatrix covariance matrix Exceptions MathIllegalArgumentException: if the data array does not contain sufficient data NotStrictlyPositiveException: if the input data array is not rectangular with at least one row and one column.

computeCovarianceMatrix

back to summary

protected RealMatrix computeCovarianceMatrix(double[][] data, boolean biasCorrected) throws MathIllegalArgumentException, NotStrictlyPositiveException

Compute a covariance matrix from a rectangular array whose columns represent covariates.

Parameters

data:double[][]: input array (must have at least one column and two rows)
biasCorrected:boolean: determines whether or not covariance estimates are bias-corrected

Returns:RealMatrix

covariance matrix

Exceptions

MathIllegalArgumentException:: if the data array does not contain sufficient data
NotStrictlyPositiveException:: if the input data array is not rectangular with at least one row and one column.

computeCovarianceMatrix back to summary

computeCovarianceMatrix	back to summary
protected RealMatrix computeCovarianceMatrix(double[][] data) throws MathIllegalArgumentException, NotStrictlyPositiveException Create a covariance matrix from a rectangular array whose columns represent covariates. Covariances are computed using the bias-corrected formula. Parameters data:double[][] input array (must have at least one column and two rows) Returns:RealMatrix covariance matrix Exceptions MathIllegalArgumentException: if the data array does not contain sufficient data NotStrictlyPositiveException: if the input data array is not rectangular with at least one row and one column. See Also `Covariance`

protected RealMatrix computeCovarianceMatrix(double[][] data) throws MathIllegalArgumentException, NotStrictlyPositiveException

Create a covariance matrix from a rectangular array whose columns represent covariates. Covariances are computed using the bias-corrected formula.

Parameters

data:double[][]: input array (must have at least one column and two rows)

Returns:RealMatrix

covariance matrix

Exceptions

MathIllegalArgumentException:: if the data array does not contain sufficient data
NotStrictlyPositiveException:: if the input data array is not rectangular with at least one row and one column.

See Also

Covariance

covariance	back to summary
public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) throws MathIllegalArgumentException Computes the covariance between the two arrays. Array lengths must match and the common length must be at least 2. Parameters xArray:double[] first data array yArray:double[] second data array biasCorrected:boolean if true, returned value will be bias-corrected Returns:double returns the covariance for the two arrays Exceptions MathIllegalArgumentException: if the arrays lengths do not match or there is insufficient data

covariance

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public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected) throws MathIllegalArgumentException

Computes the covariance between the two arrays.

Array lengths must match and the common length must be at least 2.

Parameters

xArray:double[]: first data array
yArray:double[]: second data array
biasCorrected:boolean: if true, returned value will be bias-corrected

Returns:double

returns the covariance for the two arrays

Exceptions

MathIllegalArgumentException:: if the arrays lengths do not match or there is insufficient data

covariance	back to summary
public double covariance(final double[] xArray, final double[] yArray) throws MathIllegalArgumentException Computes the covariance between the two arrays, using the bias-corrected formula. Array lengths must match and the common length must be at least 2. Parameters xArray:double[] first data array yArray:double[] second data array Returns:double returns the covariance for the two arrays Exceptions MathIllegalArgumentException: if the arrays lengths do not match or there is insufficient data

covariance

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public double covariance(final double[] xArray, final double[] yArray) throws MathIllegalArgumentException

Computes the covariance between the two arrays, using the bias-corrected formula.

Array lengths must match and the common length must be at least 2.

Parameters

xArray:double[]: first data array
yArray:double[]: second data array

Returns:double

returns the covariance for the two arrays

Exceptions

MathIllegalArgumentException:: if the arrays lengths do not match or there is insufficient data

getCovarianceMatrix	back to summary
public RealMatrix getCovarianceMatrix() Returns the covariance matrix Returns:RealMatrix covariance matrix

getCovarianceMatrix

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public RealMatrix getCovarianceMatrix()

Returns the covariance matrix

Returns:RealMatrix: covariance matrix

getN	back to summary
public int getN() Returns the number of observations (length of covariate vectors) Returns:int number of observations

getN

back to summary

public int getN()

Returns the number of observations (length of covariate vectors)

Returns:int: number of observations

public Class Covariance

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Field Detail

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Method Detail