Scilab Website | Contribute with GitLab | Mailing list archives | ATOMS toolboxes
Scilab Online Help
5.3.3 - English

Change language to:
Français - 日本語 - Português

Please note that the recommended version of Scilab is 2025.0.0. This page might be outdated.
See the recommended documentation of this function

Scilab help >> Statistics > show_pca

show_pca

Visualization of principal components analysis results

Calling Sequence

show_pca(lambda,facpr,N)

Arguments

lambda

is a p x 2 numerical matrix. In the first column we find the eigenvalues of V, where V is the correlation p x p matrix and in the second column are the ratios of the corresponding eigenvalue over the sum of eigenvalues.

facpr

are the principal factors: eigenvectors of V. Each column is an eigenvector element of the dual of R^p.

N

Is a 2x1 integer vector. Its coefficients point to the eigenvectors corresponding to the eigenvalues of the correlation matrix p by p ordered by decreasing values of eigenvalues. If N. is missing, we suppose N=[1 2]..

Description

This function visualize the pca results.

Examples

a=rand(100,10,'n');
[lambda,facpr,comprinc] = pca(a);
show_pca(lambda,facpr)

See Also

  • pca — Computes principal components analysis with standardized variables
  • princomp — Principal components analysis

Authors

Carlos Klimann

Bibliography

Saporta, Gilbert, Probabilites, Analyse des Donnees et Statistique, Editions Technip, Paris, 1990.

<< samwr Statistics st_deviation >>

Copyright (c) 2022-2024 (Dassault Systèmes)
Copyright (c) 2017-2022 (ESI Group)
Copyright (c) 2011-2017 (Scilab Enterprises)
Copyright (c) 1989-2012 (INRIA)
Copyright (c) 1989-2007 (ENPC)
with contributors
Last updated:
Wed Oct 05 12:09:57 CEST 2011