pca: Principal Component Analysis

In jdstorey/fastats: Foundations of Applied Statistics course package

Description

Implements the Principal Component Analysis (PCA) algorithm

Usage

pca(x, space = c("rows", "columns"), nsv = NULL, center = TRUE,
  scale = FALSE)

Arguments

x	An n x p matrix.
space	Specifies which space to do the projections. Either "row" or "column", default is "row".
nsv	Optional. An integer specifying the number of PCs.
center	If TRUE then matrix will be mean centered. Default is TRUE.
scale	If TRUE then matrix will be normalized. Default is FALSE

Value

A list of object type "pca" containing:

pc	A matrix containing the principal components.
loading	A matrix containing the loadings.
pve	A vector containing the percent variation explained by each PC.

Examples

# import data
X <- matrix(rnorm(100 * 10), ncol = 10)

out_pca <- pca(X)

scree_plot(out_pca)

jdstorey/fastats documentation built on May 16, 2019, 9:29 a.m.