pca — Computes principal components analysis with standardized variables
[lambda,facpr,comprinc] = pca(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.
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.
are the principal factors: eigenvectors of
V. Each column is an eigenvector element of the
dual of R^p.
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
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 as been removed. It can now be performed
using the show_pca
function.