Description of the program: pca

This program performs a global principal component analysis (PCA). It gives the eigenvalues of the covariance matrix and depending on the -W flag eigenvectors, projections... of the input time series.

Usage:

pca [Options]

Everything not being a valid option will be interpreted as a potential datafile name. Given no datafile at all, means read stdin. Also - means stdin

Possible options are:

Option Description Default
-l# number of data to be used whole file
-x# number of lines to be ignored 0
-c# columns to be read 1
-m#,# no. of input columns,embedding dimension 2,1
-d# delay 1
-W# 0: Just write the eigenvalues
1: Write the eigenvectors. The columns of the output matrix are the eigenvectors.
2: Transformation of the time series onto the eigenvector basis. The number of components printed is determined by the -q flag.
3: Project the time series onto the first -q eigenvectors (global noise reduction). 		0
-q# depending on -W:
-W2: Number of components written
-W3: Projection dimension 		full dimension
-o[#] output file name -o without argument means 'datafile'.pca
(or stdin.pca if data were read from stdin)
without -o means stdout
-V# verbosity level
  0: only panic messages
  1: add input/output messages 		1
-h show these options none

Description of the Output:

The output consists of the eigenvalues and depending on the -W flag of the eigenvectors, the new components or the projected time series.
View the C-sources.
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