epPCA: epPCA: Principal Component Analysis (PCA) via ExPosition.

In ExPosition: Exploratory Analysis with the Singular Value Decomposition

Description

Principal Component Analysis (PCA) via ExPosition.

Usage

epPCA(DATA, scale = TRUE, center = TRUE, DESIGN = NULL, make_design_nominal = TRUE, 
	graphs = TRUE, k = 0)

Arguments

DATA	original data to perform a PCA on.
scale	a boolean, vector, or string. See expo.scale for details.
center	a boolean, vector, or string. See expo.scale for details.
DESIGN	a design matrix to indicate if rows belong to groups.
make_design_nominal	a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.
graphs	a boolean. If TRUE (default), graphs and plots are provided (via epGraphs)
k	number of components to return.

Details

epPCA performs principal components analysis on a data matrix.

Value

See corePCA for details on what is returned.

Author(s)

Derek Beaton

References

Abdi, H., and Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459.
Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912.

See Also

corePCA, epMDS, epGPCA

Examples

	data(words)
	pca.words.res <- epPCA(words$data)

Example output

Loading required package: prettyGraphs
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
dev.new(): using pdf(file="Rplots4.pdf")

ExPosition documentation built on May 1, 2019, 7:06 p.m.