linear_PCA: Principal Component Analysis

In Rdimtools: Dimension Reduction and Estimation Methods

Principal Component Analysis

Description

do.pca performs a classical principal component analysis \insertCitepearson_liii_1901Rdimtools using RcppArmadillo package for faster and efficient computation.

Usage

do.pca(X, ndim = 2, ...)

Arguments

X	an (n\times p) matrix whose rows are observations and columns represent independent variables.
ndim	an integer-valued target dimension.
...	extra parameters including cor mode of eigendecomposition. FALSE for decomposing covariance matrix (default), and TRUE for correlation matrix. preprocess an additional option for preprocessing the data. Default is "center". See also aux.preprocess for more details.

Value

a named Rdimtools S3 object containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

vars

a vector containing variances of projected data onto principal components.

projection

a (p\times ndim) whose columns are basis for projection.

trfinfo

a list containing information for out-of-sample prediction.

algorithm

name of the algorithm.

Author(s)

Kisung You

References

\insertAllCited

Examples

## use iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
lab   = as.factor(iris[subid,5])

## try covariance & correlation decomposition
out1 <- do.pca(X, ndim=2, cor=FALSE)
out2 <- do.pca(X, ndim=2, cor=TRUE)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(out1$Y, col=lab, pch=19, main="correlation decomposition")
plot(out2$Y, col=lab, pch=19, main="covariance decomposition")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.