- Manuel Scilab
- Statistiques
- cdfbet
- cdfbin
- cdfchi
- cdfchn
- cdff
- cdffnc
- cdfgam
- cdfnbn
- cdfnor
- cdfpoi
- cdft
- median
- st_deviation
- center
- wcenter
- cmoment
- correl
- covar
- ftest
- ftuneq
- geomean
- harmean
- iqr
- labostat
- mad
- mean
- meanf
- moment
- msd
- mvvacov
- nancumsum
- nand2mean
- nanmax
- nanmean
- nanmeanf
- nanmedian
- nanmin
- nanstdev
- nansum
- nfreq
- pca
- perctl
- princomp
- quart
- regress
- sample
- samplef
- samwr
- show_pca
- stdevf
- strange
- tabul
- thrownan
- trimmean
- variance
- variancef

Please note that the recommended version of Scilab is 6.0.2. This page might be outdated.

See the recommended documentation of this function

# pca

Computes principal components analysis with standardized variables

### Calling Sequence

[lambda,facpr,comprinc] = pca(x)

### Arguments

- x
is a nxp (n individuals, p variables) real matrix. Note that

`pca`

center and normalize the columns of`x`

to produce principal components analysis with standardized variables.- 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`

.- comprinc
are the principal components. Each column (c_i=Xu_i) of this n x n matrix is the M-orthogonal projection of individuals onto principal axis. Each one of this columns is a linear combination of the variables x1, ...,xp with maximum variance under condition

`u'_i M^(-1) u_i=1`

### Description

This function performs several computations known as "principal component analysis".

The idea behind this method is to represent in an approximative manner a cluster of n individuals in a smaller dimensional subspace. In order to do that, it projects the cluster onto a subspace. The choice of the k-dimensional projection subspace is made in such a way that the distances in the projection have a minimal deformation: we are looking for a k-dimensional subspace such that the squares of the distances in the projection is as big as possible (in fact in a projection, distances can only stretch). In other words, inertia of the projection onto the k dimensional subspace must be maximal.

Warning, the graphical part of the old version of
`pca`

has been removed. It can now be performed
using the show_pca
function.

### Authors

Carlos Klimann

### Bibliography

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

## Comments

Add a comment:Please login to comment this page.