com.numericalmethod.suanshu.stats.descriptive.covariance

Class SampleCovariance

java.lang.Object

com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

com.numericalmethod.suanshu.stats.descriptive.covariance.SampleCovariance

All Implemented Interfaces:

Matrix, MatrixAccess, MatrixRing, MatrixTable, Densifiable, AbelianGroup<Matrix>, Monoid<Matrix>, Ring<Matrix>, Table, DeepCopyable

public class SampleCovariance
extends DenseMatrix

This class computes the Covariance matrix of a matrix, where the (i, j) entry is the covariance of the i-th column and j-th column of the matrix.

The R equivalent function is cov.

Constructor Summary

Constructors

Constructor and Description
SampleCovariance(Matrix A) Construct the covariance matrix of a matrix.
SampleCovariance(Matrix A, boolean unbiased) Construct the covariance matrix of a matrix.

Method Summary

Methods inherited from class com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

add, deepCopy, equals, get, getColumn, getColumn, getRow, getRow, hashCode, minus, multiply, multiply, nCols, nRows, ONE, opposite, scaled, set, setColumn, setRow, t, toDense, toString, ZERO

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

SampleCovariance

public SampleCovariance(Matrix A)

Construct the covariance matrix of a matrix. By default, the sample covariance matrix is unbiased.

Parameters:

A - a matrix

SampleCovariance

public SampleCovariance(Matrix A,
                        boolean unbiased)

Construct the covariance matrix of a matrix.

Parameters:

A - a matrix

unbiased - true if the estimate is unbiased