cv.ernet: Cross-validation for ernet

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

Cross-validation for ernet

Description

Does k-fold cross-validation for ernet, produces a plot, and returns a value for lambda. This function is based on the cv function from the glmnet package.

Usage

cv.ernet(
  x,
  y,
  lambda = NULL,
  pred.loss = "loss",
  nfolds = 5,
  foldid,
  tau = 0.5,
  ...
)

Arguments

x	x matrix as in ernet.
y	response variable y as in ernet.
lambda	optional user-supplied lambda sequence; default is NULL, and ernet chooses its own sequence.
pred.loss	loss function used to calculate cross-validation error. The only option now is "loss", which is the asymmetric squared error loss (ASEL).
nfolds	number of folds. Default value is 5. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowed is 3.
foldid	an optional vector of values between 1 and nfolds, identifying what fold each observation is in. If supplied, nfolds will be supressed.
tau	the asymmetry coefficient τ used in the asymmetric squared error loss.
...	other arguments that can be passed to ernet.

Details

The function runs ernet nfolds+1 times; the first to get the lambda sequence, and the remainder to compute the fit with each of the folds removed. The average error and standard deviation over the folds are computed.

Value

an object of class cv.ernet is returned, which is a list with the ingredients of the cross-validation fit.

lambda	the values of lambda used in the fits.
cvm	the mean cross-validated error - a vector of length length(lambda).
cvsd	estimate of standard error of cvm.
cvupper	upper curve = cvm+cvsd.
cvlower	lower curve = cvm-cvsd.
nzero	number of non-zero coefficients at each lambda.
name	a text string indicating type of measure (for plotting purposes).
ernet.fit	a fitted ernet object for the full data.
lambda.min	The optimal value of lambda that gives minimum cross validation error cvm.
lambda.1se	The largest value of lambda such that error is within 1 standard error of the minimum.

Author(s)

Yuwen Gu and Hui Zou

Maintainer: Yuwen Gu <[e-mail]>

See Also

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)

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