krrda: KRR-DA models

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

KRR-DA models

Description

Discrimination (DA) based on kernel ridge regression (KRR).

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 ridge regression (KRR) 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

krrda(X, y, weights = NULL, lb = 1e-5, kern = "krbf", ...)

## S3 method for class 'Krrda'
predict(object, X, ..., lb = NULL) 

Arguments

X	For the 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).
lb	A value of regularization parameter lambda. If lb = 0, a pseudo-inverse is used in the RR.
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]

lb <- 1
fm <- krrda(Xtrain, ytrain, lb = lb)
names(fm)
predict(fm, Xtest)

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

predict(fm, Xtest, lb = 0:2)
predict(fm, Xtest, lb = 0)

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