kplsrda: KPLSR-DA models

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

KPLSR-DA models

Description

Discrimination (DA) based on kernel PLSR (KPLSR)

The training variable y (univariate class membership) is transformed to a dummy table containing nclas columns, where nclas is the number of classes present in y. Each column is a dummy variable (0/1). Then, a kernel PLSR (KPLSR) is run on the X-data and the dummy table, returning predictions of the dummy variables. For a given observation, the final prediction is the class corresponding to the dummy variable for which the prediction is the highest.

Usage

kplsrda(X, y, weights = NULL, nlv, kern = "krbf", ...)

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

Arguments

X	For main functions: Training X-data (n, p). — For auxiliary functions: New X-data (m, p) to consider.
y	Training class membership (n). Note: If y is a factor, it is replaced by a character vector.
weights	Weights (n) to apply to the training observations for the PLS2. 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 functions.
...	Optional arguments to pass in the kernel function defined in kern (e.g. gamma for krbf).

Value

See the examples.

Examples

n <- 50 ; p <- 8
Xtrain <- matrix(rnorm(n * p), ncol = p)
ytrain <- sample(c(1, 4, 10), size = n, replace = TRUE)
#ytrain <- sample(c("a", "10", "d"), size = n, replace = TRUE)
m <- 5
Xtest <- Xtrain[1:m, ] ; ytest <- ytrain[1:m]

nlv <- 2
fm <- kplsrda(Xtrain, ytrain, nlv = nlv)
names(fm)
predict(fm, Xtest)

pred <- predict(fm, Xtest)$pred
err(pred, ytest)

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

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

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