hdsvm (version 1.0.2)

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

Description

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

Usage

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

Value

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

Arguments

object

A fitted `cv.nc.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.

See Also

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