kpls_nipals: Non linear kernel PLS algorithm

In mlesnoff/rnirs: Dimension reduction, Regression and Discrimination for Chemometrics

Non linear kernel PLS algorithm

Description

Function kpls_nipals fits a NIPALS KPLS (Rosipal & Trejo, 2001).

The kernel Gram matrice is internally centered before the analyses, but the data are not column-wise scaled (there is no argument scale in the function). If needed, the user has to do the scaling before using the function .

Row observations can eventually be weighted (using argument weights).

Usage

kpls_nipals(X, Y, ncomp, kern = kpol, weights = NULL,
                         tol = .Machine$double.eps^0.5, maxit = 100, ...)

Arguments

X	A n x p matrix or data frame of observations.
Y	A n x q matrix or data frame, or a vector of length n, of responses.
ncomp	The number of scores (= components = latent variables) to consider.
kern	A function defining the considered kernel (Default to kpol). See kpol for syntax, and other available kernel functions.
weights	A vector of length n defining a priori weights to apply to the observations. Internally, weights are "normalized" to sum to 1. Default to NULL (weights are set to 1 / n).
tol	Tolerance level for stopping the NIPALS iterations.
maxit	Maximum number of NIPALS iterations.
...	Optionnal arguments to pass in the kernel function defined in kern.

Value

A list of outputs (see examples), such as:

T	KPLS scores (n x ncomp).

References

Rosipal, R., Trejo, L.J., 2001. Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research 2, 97-123.

Examples

n <- 8
p <- 4
set.seed(1)
X <- matrix(rnorm(n * p, mean = 10), ncol = p)
y1 <- 100 * rnorm(n)
y2 <- 100 * rnorm(n)
Y <- cbind(y1, y2)
set.seed(NULL)

kpls_nipals(X, Y, ncomp = 5)

mlesnoff/rnirs documentation built on April 24, 2023, 4:17 a.m.