coef.cv.ernet: Get coefficients from a cv.ernet 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

Get coefficients from a cv.ernet object

Description

This function gets coefficients from a cross-validated ernet model, using the fitted cv.ernet object, and the optimal value chosen for lambda.

Usage

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

Arguments

object	fitted cv.ernet object.
s	value(s) of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.1se" stored on the CV object, it is the largest value of lambda such that error is within 1 standard error of the minimum. Alternatively s="lambda.min" can be used, it is the optimal value of lambda that gives minimum cross validation error cvm. 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 get coefficients or make coefficient predictions.

Value

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

Author(s)

Yuwen Gu and Hui Zou

Maintainer: Yuwen Gu <[e-mail]>

See Also

cv.ernet, predict.cv.ernet

Examples

set.seed(1)
n <- 100
p <- 400
x <- matrix(rnorm(n * p), n, p)
y <- rnorm(n)
tau <- 0.90
pf <- abs(rnorm(p))
pf2 <- abs(rnorm(p))
lambda2 <- 1
m1.cv <- cv.ernet(y = y, x = x, tau = tau, eps = 1e-8, pf = pf,
                  pf2 = pf2, standardize = FALSE, intercept = FALSE,
                  lambda2 = lambda2)
as.vector(coef(m1.cv, s = "lambda.min"))

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