pca: Principal Component Analysis

In s-u/pcurve: Principal curve analysis

Description

Calculates principal components from a matrix

Usage

pca(mat, cent = TRUE, scle = FALSE)

Arguments

mat	a numeric matrix.
cent	a logical value referring to center argument in scale.
scle	a logical value referring to scale argument in scale.

Value

a list containing

pcs	a matrix of principal component loadings
d	a matrix containing the singular value (eigenvalue) of each principal component on its diagonal
v	a matrix of eigenvectors

Author(s)

R port by Chris Walsh [e-mail] from S+ library by Glenn De'ath [e-mail].

Examples

         data(soilspec)
         species <- sqrt(soilspec[,2:9])
         specpca <- pca(species)
         eqscplot(specpca$pcs[,1], specpca$pcs[,2], type = "n",
              xlab = "Principal component 1",
              ylab = "Principal component 2") 
         text(specpca$pcs[,1], specpca$pcs[,2], 
              soilspec$site) 
         mtext(paste("Grassland communities in 45 sites"))

s-u/pcurve documentation built on May 28, 2019, 10:48 a.m.