cv.l0ara: cross-validation for l0ara

In wguo1990/l0ara: Sparse Generalized Linear Model with L0 Approximation for Feature Selection

Description

Does k-fold cross-validation for l0ara, produces a plot, and returns the optimal lambda

Usage

cv.l0ara(x, y, family, lam, measure, nfolds, maxit, eps, seed)

Arguments

x	Input matrix as in l0ara.
y	Response variable as in l0ara.
family	Response type as in l0ara.
lam	A user supplied lambda sequence in descending or asecending order. This function does not fit models. To fit a model with given lam value, use l0ara.
measure	Loss function used for corss validation. measurer="mse" or "mae" for all models. "measure"="class" or "measure"="auc" only for logsitic regression.
nfolds	Number of folds. Default value is 10. Smallest value is 3.
maxit	Maximum number of passes over the data for lambda. Default value is 1e3.
eps	Convergence threshold. Default value is 1e-4.
seed	Seed of random number generator.

Details

This function calls l0ara nfolds times, each time leaving out 1/nfolds of the data. The cross-validation error is based on etiher mean square error (mse) or mean absolute error (mae).

Value

An object with S3 class "cv.l0ara" containing:

cv.error	The mean cross validated error for given lambda sequence
cv.std	The estimates of standard error of cv.error
lam.min	The lambda gives min cv.error
lambda	The lambda used
measure	Type of measure
family	Model used
x	Design matrix
y	Response variable
name	Full name of the measure

Author(s)

Wenchuan Guo <[e-mail]>, Shujie Ma <[e-mail]>, Zhenqiu Liu <[e-mail]>

See Also

l0ara, coef.cv.l0ara, plot.cv.l0ara methods.

Examples

#' # Linear regression
# Generate design matrix and response variable
n <- 100
p <- 40
x <- matrix(rnorm(n*p), n, p)
beta <- c(1,0,2,3,rep(0,p-4))
noise <- rnorm(n)
y <- x%*%beta+noise
lam <- c(0.1, 0.3, 0.5)
fit <- cv.l0ara(x, y, family="gaussian", lam, measure = "mse")

wguo1990/l0ara documentation built on Feb. 6, 2020, 2:12 p.m.