lolR (version 2.1)

lol.project.pca: Principal Component Analysis (PCA)

Description

A function that performs PCA on data.

Usage

lol.project.pca(X, r, xfm = FALSE, xfm.opts = list(), robust = FALSE, ...)

Arguments

X

[n, d] the data with n samples in d dimensions.

r

the rank of the projection.

xfm

whether to transform the variables before taking the SVD.

• FALSEapply no transform to the variables.

• 'unit'unit transform the variables, defaulting to centering and scaling to mean 0, variance 1. See scale for details and optional arguments to be passed with xfm.opts.

• 'log'log-transform the variables, for use-cases such as having high variance in larger values. Defaults to natural logarithm. See log for details and optional arguments to be passed with xfm.opts.

• 'rank'rank-transform the variables. Defalts to breaking ties with the average rank of the tied values. See rank for details and optional arguments to be passed with xfm.opts.

• c(opt1, opt2, etc.)apply the transform specified in opt1, followed by opt2, etc.

xfm.opts

optional arguments to pass to the xfm option specified. Should be a numbered list of lists, where xfm.opts[[i]] corresponds to the optional arguments for xfm[i]. Defaults to the default options for each transform scheme.

robust

whether to perform PCA on a robust estimate of the covariance matrix or not. Defaults to FALSE.

...

trailing args.

Value

A list containing the following:

A

[d, r] the projection matrix from d to r dimensions.

d

the eigen values associated with the eigendecomposition.

Xr

[n, r] the n data points in reduced dimensionality r.

Details

For more details see the help vignette: vignette("pca", package = "lolR")

Examples

# NOT RUN {
library(lolR)
data <- lol.sims.rtrunk(n=200, d=30)  # 200 examples of 30 dimensions
X <- data$X; Y <- data$Y
model <- lol.project.pca(X=X, r=2)  # use pca to project into 2 dimensions
# }