predict.cv.cpernet: Make predictions from a cv.cpernet object

In SALES: The (Adaptive) Elastic Net and Lasso Penalized Sparse Asymmetric Least Squares (SALES) and Coupled Sparse Asymmetric Least Squares (COSALES) using Coordinate Descent and Proximal Gradient Algorithms

Make predictions from a cv.cpernet object

Description

This function makes predictions from a cross-validated cpernet model, using the fitted cv.cpernet object, and the optimal value chosen for lambda.

Usage

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

Arguments

object	fitted cv.cpernet object.
newx	matrix of new values for x at which predictions are to be made. Must be a matrix. See documentation for predict.cpernet.
s	value(s) of the penalty parameter lambda at which predictions are to be made. Default is the value s = "lambda.1se" stored on the CV object. Alternatively s = "lambda.min" can be used. If s is numeric, it is taken as the value(s) of lambda to be used.
...	not used. Other arguments to predict.

Details

This function makes it easier to use the results of cross-validation to make a prediction.

Value

The object returned depends the ... argument which is passed on to the predict method for cpernet objects.

Author(s)

Yuwen Gu and Hui Zou

Maintainer: Yuwen Gu <[e-mail]>

See Also

cv.cpernet, coef.cv.cpernet, plot.cv.cpernet

Examples

set.seed(1)
n <- 100
p <- 400
x <- matrix(rnorm(n * p), n, p)
y <- rnorm(n)
tau <- 0.30
pf <- abs(rnorm(p))
pf2 <- abs(rnorm(p))
w <- 2.0
lambda2 <- 1
m2.cv <- cv.cpernet(y = y, x = x, w = w, tau = tau, eps = 1e-8,
                    pf.mean = pf, pf.scale = pf2,
                    standardize = FALSE, lambda2 = lambda2)
as.vector(predict(m2.cv, newx = x, s = "lambda.min"))

SALES documentation built on Aug. 16, 2022, 1:05 a.m.