kplsr: KPLSR Models

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

KPLSR Models

Description

NIPALS Kernel PLSR algorithm described in Rosipal & Trejo (2001).

The algorithm is slow for n >= 500.

Usage

kplsr(X, Y, weights = NULL, nlv, kern = "krbf",
     tol = .Machine$double.eps^0.5, maxit = 100, ...)

## S3 method for class 'Kplsr'
transform(object, X, ..., nlv = NULL)  

## S3 method for class 'Kplsr'
coef(object, ..., nlv = NULL)  

## S3 method for class 'Kplsr'
predict(object, X, ..., nlv = NULL)  

Arguments

X	For main function: Training X-data (n, p). — For auxiliary functions: New X-data (m, p) to consider.
Y	Training Y-data (n, q).
weights	Weights (n, 1) to apply to the training observations. Internally, weights are "normalized" to sum to 1. Default to NULL (weights are set to 1 / n).
nlv	The number(s) of LVs to calculate.
kern	Name of the function defining the considered kernel for building the Gram matrix. See krbf for syntax, and other available kernel functions.
object	A fitted model, output of a call to the main function.
tol	Tolerance level for stopping the NIPALS iterations.
maxit	Maximum number of NIPALS iterations.
...	Optional arguments to pass in the kernel function defined in kern (e.g. gamma for krbf).

Value

See the examples.

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 <- 6 ; p <- 4
Xtrain <- matrix(rnorm(n * p), ncol = p)
ytrain <- rnorm(n)
Ytrain <- cbind(y1 = ytrain, y2 = 100 * ytrain)
m <- 3
Xtest <- Xtrain[1:m, , drop = FALSE] 
Ytest <- Ytrain[1:m, , drop = FALSE] ; ytest <- Ytest[1:m, 1]

nlv <- 2
fm <- kplsr(Xtrain, Ytrain, nlv = nlv, kern = "krbf", gamma = .8)
transform(fm, Xtest)
transform(fm, Xtest, nlv = 1)
coef(fm)
coef(fm, nlv = 1)

predict(fm, Xtest)
predict(fm, Xtest, nlv = 0:nlv)$pred

pred <- predict(fm, Xtest)$pred
msep(pred, Ytest)

nlv <- 2
fm <- kplsr(Xtrain, Ytrain, nlv = nlv, kern = "kpol", degree = 2, coef0 = 10)
predict(fm, Xtest, nlv = nlv)

####### Example of fitting the function sinc(x)
####### described in Rosipal & Trejo 2001 p. 105-106 

x <- seq(-10, 10, by = .2)
x[x == 0] <- 1e-5
n <- length(x)
zy <- sin(abs(x)) / abs(x)
y <- zy + rnorm(n, 0, .2)
plot(x, y, type = "p")
lines(x, zy, lty = 2)
X <- matrix(x, ncol = 1)

nlv <- 2
fm <- kplsr(X, y, nlv = nlv)
pred <- predict(fm, X)$pred
plot(X, y, type = "p")
lines(X, zy, lty = 2)
lines(X, pred, col = "red")

mlesnoff/rchemo documentation built on April 15, 2023, 1:25 p.m.