psvd (version 1.0-0)

calcPCA: Perform principal component analysis

Description

Given a data matrix, the function allows to perform principal component analysis using a power method to get the eigendecomposition.

Usage

calcPCA(X, r, eta, itmax, err, normed, mySeed)

Value

This function returns a data frame containing 5 components

values

Eigenvalues

vectors

Matrix with the eigenvectors.

iter

The number of iterations used in the eigendecomposition.

li

Projection of rows in the r principal components space.

co

Projection of columns in the r principal components space.

Arguments

X

Data matrix of size (m,n), m >= n.

r

Number of principal components, default: r=2.

eta

Power method tuning parameter, default: eta=10.

itmax

Maximum number of iteration in the power method, default: itmax=200.

err

Tolerance level in the power method, default: err=1e-8.

normed

TRUE (default) or FALSE for PCA using standardized data or not.

mySeed

An integer allowing to reproduce results from two different runs, default: mySeed=50.

Details

X is usually a data matrix .

Examples

data(iris)
X <- as.matrix(iris[,1:4])
rownames(X) <- iris[,5]
res <- calcPCA(X, r=3)
res$values
pcol <- c(rep("cyan",50), rep("red",50), rep("blue",50))
plot(res$li[,1], res$li[,3], col = pcol)