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C++ API

Function File: eigval = pca (S)
Function File: [eigval, eigvec] = pca (S)
Function File: [eigval, eigvec, ts] = pca (S)
Function File: […] = pca (S, paramName, paramValue, …)

Performs a global principal component analysis (PCA). It gives the eigenvalues of the covariance matrix and depending on the flag w settings the eigenvectors, projections of the input time series.

Input

S

This function always assumes that each time series is along the longer dimension of matrix S. It also assumes that every dimension (counting along the shorter dimension) of S is considered a component of the time series.

Parameters

m

Defines embedding dimension. Since all of the data in S is analysed there is no need for setting the number of columns to be read (as is the case in TISEAN ’pca’) [default = 1].

d

Delay must be scalar integer [default = 1].

q

Determines the properties of TS. When parameter w is set then q determines the projection dimension. Otherwise it determines the number of components written to output [default = full dimension/all components].

Switch

w

If w is set then TS is a projection of the time series onto the first q eigenvectors (global noise reduction). If w is not set then TS is a transformation of the time series onto the eigenvector basis. The number of projection dimension/components printed is determined by parameter q.

Output

eigval

The calculated eigenvalues.

eigvec

The eigenvectors. The vectors are alligned with the longer dimension of S.

ts

If w is set then this variable holds the projected time series onto the first q eigenvectors. If w is not set then TS is the transformed time series onto the eigenvector basis (number of components == parameter q).

Algorithms

The algorithms for this functions have been taken from the TISEAN package.

Package: tisean