lol.project.pls: Partial Least-Squares (PLS)
In neurodata/lol: Linear Optimal Low-Rank Projection

Description Usage Arguments Value Details Author(s) Examples

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

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

`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.

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`.

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

Eric Bridgeford

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