Man Scilab

pca
Scilab Function

pca - Principal components analysis

Calling Sequence

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

Parameters

Description

This function performs several computations known as "principal component analysis". It includes drawing of "correlations circle", i.e. in the horizontal axis the correlation values r(c1;xj) and in the vertical r(c2;xj). It is an extension of the pca function.

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.

Author

Carlos Klimann

Bibliography

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

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