predict.nc.hdsvm: Make Predictions from a 'nc.hdsvm' Object

In hdsvm: Fast Algorithm for Support Vector Machine

Make Predictions from a 'nc.hdsvm' Object

Description

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

Usage

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

Arguments

object	Fitted 'nc.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 'nc.hdsvm()' object. It is essential to provide a new matrix of predictor values ('newx') at which these predictions are to be made.

Value

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

See Also

nc.hdsvm, coef.nc.hdsvm

Examples

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
lambda <- 10^(seq(1,-4, length.out = 30)) 
nc.fit <- nc.hdsvm(x = x, y = y, lambda = lambda, lam2 = lam2, pen = "scad")
nc.preds <- predict(nc.fit, newx = tail(x), s = nc.fit$lambda[3:5])

hdsvm documentation built on April 12, 2025, 1:27 a.m.