screen.earth.backwardprune: Non-linear regression screening algorithm

In saraemoore/SLScreenExtra: A Collection of Additional Feature Selection Algorithms and Utilities for SuperLearner

Non-linear regression screening algorithm

Description

Performs feature selection via "Multivariate Adaptive Regression Splines"/ "Fast MARS" using earth's implementation.

Usage

screen.earth.backwardprune(..., pMethod = "backward", nFold = 0)

Arguments

...	Arguments passed on to screen.earth.
pMethod	Pruning method. Default: "backward".
nFold	Number of cross-validation folds. Default: 0 (cross-validation disabled).

Examples

data(iris)
Y <- as.numeric(iris$Species=="setosa")
X <- iris[,-which(colnames(iris)=="Species")]
screen.earth.backwardprune(Y, X, binomial())

data(mtcars)
Y <- mtcars$mpg
X <- mtcars[,-which(colnames(mtcars)=="mpg")]
screen.earth.backwardprune(Y, X, gaussian())

# based on examples in SuperLearner package
set.seed(1)
n <- 250
p <- 20
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
X <- data.frame(X)
Y <- X[, 1] + sqrt(abs(X[, 2] * X[, 3])) + X[, 2] - X[, 3] + rnorm(n)

library(SuperLearner)
sl = SuperLearner(Y, X, family = gaussian(), cvControl = list(V = 2),
                  SL.library = list(c("SL.glm", "All"),
                                    c("SL.glm", "screen.earth.backwardprune")))
sl
sl$whichScreen

saraemoore/SLScreenExtra documentation built on Nov. 4, 2023, 9:31 p.m.