predict.hdsvm: Make Predictions from a `hdsvm` Object

Description

Produces fitted values for new predictor data using a fitted `hdsvm()` object.

Usage

# S3 method for hdsvm
predict(object, newx, s = NULL, type = c("class", "loss"), ...)

Value

Returns a vector or matrix of predicted values corresponding to the specified `lambda` values.

Arguments

object: Fitted `hdsvm()` object from which predictions are to be derived.
newx: Matrix of new predictor values for which predictions are desired. This must be a matrix and is a required argument.
s: Values of the penalty parameter `lambda` for which predictions are requested. Defaults to the entire sequence used during the model fit.
type: Type of prediction required. Type `"class"` produces the predicted binary class labels and type `"loss"` returns the fitted values. Default is "class".
...: Not used.

Details

This function generates predictions at specified `lambda` values from a fitted `hdsvm()` object. It is essential to provide a new matrix of predictor values (`newx`) at which these predictions are to be made.

Examples

Run this code

set.seed(315)
n <- 100
p <- 400
x1 <- matrix(rnorm(n / 2 * p, -0.25, 0.1), n / 2)
x2 <- matrix(rnorm(n / 2 * p, 0.25, 0.1), n / 2)
x <- rbind(x1, x2)
beta <- 0.1 * rnorm(p)
prob <- plogis(c(x %*% beta))
y <- 2 * rbinom(n, 1, prob) - 1
lam2 <- 0.01
fit <- hdsvm(x, y, lam2=lam2)
preds <- predict(fit, newx = tail(x), s = fit$lambda[3:5])