coef.cv.nnGarrote: Coefficients for cv.nnGarrote Object

In nnGarrote: Non-Negative Garrote Estimation with Penalized Initial Estimators

Description

coef.cv.nnGarrote returns the coefficients for a cv.nnGarrote object.

Usage

## S3 method for class 'cv.nnGarrote'
coef(object, optimal.only = TRUE, ...)

Arguments

object	An object of class cv.nnGarrote
optimal.only	A boolean variable (TRUE default) to indicate if only the coefficient of the optimal split are returned.
...	Additional arguments for compatibility.

Value

A matrix with the coefficients of the cv.nnGarrote object.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

cv.nnGarrote

Examples

# Setting the parameters
p <- 500
n <- 100
n.test <- 5000
sparsity <- 0.15
rho <- 0.5
SNR <- 3
set.seed(0)
# Generating the coefficient
p.active <- floor(p*sparsity)
a <- 4*log(n)/sqrt(n)
neg.prob <- 0.2
nonzero.betas <- (-1)^(rbinom(p.active, 1, neg.prob))*(a + abs(rnorm(p.active)))
true.beta <- c(nonzero.betas, rep(0, p-p.active))
# Two groups correlation structure
Sigma.rho <- matrix(0, p, p)
Sigma.rho[1:p.active, 1:p.active] <- rho
diag(Sigma.rho) <- 1
sigma.epsilon <- as.numeric(sqrt((t(true.beta) %*% Sigma.rho %*% true.beta)/SNR))

# Simulate some data
library(mvnfast)
x.train <- mvnfast::rmvn(n, mu=rep(0,p), sigma=Sigma.rho)
y.train <- 1 + x.train %*% true.beta + rnorm(n=n, mean=0, sd=sigma.epsilon)
x.test <- mvnfast::rmvn(n.test, mu=rep(0,p), sigma=Sigma.rho)
y.test <- 1 + x.test %*% true.beta + rnorm(n.test, sd=sigma.epsilon)

# Applying the NNG with Ridge as an initial estimator
nng.out <- cv.nnGarrote(x.train, y.train, intercept=TRUE,
                        initial.model=c("LS", "glmnet")[2],
                        lambda.nng=NULL, lambda.initial=NULL, alpha=0,
                        nfolds=5)
nng.predictions <- predict(nng.out, newx=x.test)
mean((nng.predictions-y.test)^2)/sigma.epsilon^2
coef(nng.out)

nnGarrote documentation built on Oct. 7, 2021, 9:06 a.m.