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

In hdsvm: Fast Algorithm for Support Vector Machine

Make Predictions from a 'cv.hdsvm' Object

Description

Generates predictions using a fitted 'cv.hdsvm()' object. This function utilizes the stored 'hdsvm.fit' object and an optimal value of 'lambda' determined during the cross-validation process.

Usage

## S3 method for class 'cv.hdsvm'
predict(
  object,
  newx,
  s = c("lambda.1se", "lambda.min"),
  type = c("class", "loss"),
  ...
)

Arguments

object	A fitted 'cv.hdsvm()' object from which predictions are to be made.
newx	Matrix of new predictor values for which predictions are desired. This must be a matrix and is a required argument.
s	Specifies the value(s) of the penalty parameter 'lambda' at which predictions are desired. The default is 's = "lambda.1se"', representing the largest value of 'lambda' such that the cross-validation error estimate is within one standard error of the minimum. Alternatively, 's = "lambda.min"' can be used, corresponding to the minimum of the cross-validation error estimate. If 's' is numeric, these are taken as the actual values of 'lambda' to use for predictions.
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.

Value

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

See Also

cv.hdsvm, coef.cv.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
cv.fit <- cv.hdsvm(x, y, lam2 = 0.01)
predict(cv.fit, newx = x[50:60, ], s = "lambda.min")

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