lol.project.pls: Partial Least-Squares (PLS)

In lolR: Linear Optimal Low-Rank Projection

Description

A function for implementing the Partial Least-Squares (PLS) Algorithm.

Usage

lol.project.pls(X, Y, r, ...)

Arguments

X	[n, d] the data with n samples in d dimensions.
Y	[n] the labels of the samples with K unique labels.
r	the rank of the projection.
...	trailing args.

Value

A list containing the following:

A	[d, r] the projection matrix from d to r dimensions.
ylabs	[K] vector containing the K unique, ordered class labels.
centroids	[K, d] centroid matrix of the K unique, ordered classes in native d dimensions.
priors	[K] vector containing the K prior probabilities for the unique, ordered classes.
Xr	[n, r] the n data points in reduced dimensionality r.
cr	[K, r] the K centroids in reduced dimensionality r.

Details

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

Author(s)

Eric Bridgeford

Examples

library(lolR)
data <- lol.sims.rtrunk(n=200, d=30)  # 200 examples of 30 dimensions
X <- data$X; Y <- data$Y
model <- lol.project.pls(X=X, Y=Y, r=5)  # use pls to project into 5 dimensions

Example output

Registered S3 methods overwritten by 'robust':
  method              from      
  plot.covfm          fit.models
  print.covfm         fit.models
  summary.covfm       fit.models
  print.summary.covfm fit.models
rlm is already registered in the fit.models registry
covfm is already registered in the fit.models registry

lolR documentation built on July 8, 2020, 7:35 p.m.