Namespaces

Types in MathNet.Numerics.Statistics

Type Correlation

Namespace MathNet.Numerics.Statistics

A class with correlation measures between two datasets.

Static Functions

Public Static Functions

Double[] Auto(Double[] x)

Auto-correlation function (ACF) based on FFT for all possible lags k.
Parameters
Double[] x

Data array to calculate auto correlation for.

Return
Double[]

An array with the ACF as a function of the lags k.

Double[] Auto(Double[] x, int kMax, int kMin)

Auto-correlation function (ACF) based on FFT for lags between kMin and kMax.
Parameters
Double[] x

The data array to calculate auto correlation for.

int kMax

Max lag to calculate ACF for must be positive and smaller than x.Length.

int kMin

Min lag to calculate ACF for (0 = no shift with acf=1) must be zero or positive and smaller than x.Length.

Return
Double[]

An array with the ACF as a function of the lags k.

Double[] Auto(Double[] x, Int32[] k)

Auto-correlation function based on FFT for lags k.
Parameters
Double[] x

The data array to calculate auto correlation for.

Int32[] k

Array with lags to calculate ACF for.

Return
Double[]

An array with the ACF as a function of the lags k.

double Pearson(IEnumerable<double> dataA, IEnumerable<double> dataB)

Computes the Pearson Product-Moment Correlation coefficient.
Parameters
IEnumerable<double> dataA

Sample data A.

IEnumerable<double> dataB

Sample data B.

Return
double

The Pearson product-moment correlation coefficient.

Matrix<T> PearsonMatrix(Double[][] vectors)

Matrix<T> PearsonMatrix(IEnumerable<Double[]> vectors)

Computes the Pearson Product-Moment Correlation matrix.
Parameters
IEnumerable<Double[]> vectors

Enumerable of sample data vectors.

Return
Matrix<T>

The Pearson product-moment correlation matrix.

double Spearman(IEnumerable<double> dataA, IEnumerable<double> dataB)

Computes the Spearman Ranked Correlation coefficient.
Parameters
IEnumerable<double> dataA

Sample data series A.

IEnumerable<double> dataB

Sample data series B.

Return
double

The Spearman ranked correlation coefficient.

Matrix<T> SpearmanMatrix(Double[][] vectors)

Matrix<T> SpearmanMatrix(IEnumerable<Double[]> vectors)

Computes the Spearman Ranked Correlation matrix.
Parameters
IEnumerable<Double[]> vectors

Enumerable of sample data vectors.

Return
Matrix<T>

The Spearman ranked correlation matrix.

double WeightedPearson(IEnumerable<double> dataA, IEnumerable<double> dataB, IEnumerable<double> weights)

Computes the Weighted Pearson Product-Moment Correlation coefficient.
Parameters
IEnumerable<double> dataA

Sample data A.

IEnumerable<double> dataB

Sample data B.

IEnumerable<double> weights

Corresponding weights of data.

Return
double

The Weighted Pearson product-moment correlation coefficient.