nnGarrote: Non-negative Garrote Estimator

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

Description

nnGarrote computes the non-negative garrote estimator.

Usage

nnGarrote(
  x,
  y,
  intercept = TRUE,
  initial.model = c("LS", "glmnet")[1],
  lambda.nng = NULL,
  lambda.initial = NULL,
  alpha = 0
)

Arguments

x	Design matrix.
y	Response vector.
intercept	Boolean variable to determine if there is intercept (default is TRUE) or not.
initial.model	Model used for the groups. Must be one of "LS" (default) or "glmnet".
lambda.nng	Shinkage parameter for the non-negative garrote. If NULL(default), it will be computed based on data.
lambda.initial	The shinkrage parameter for the "glmnet" regularization. If NULL (default), optimal value is chosen by cross-validation.
alpha	Elastic net mixing parameter for initial estimate. Should be between 0 (default) and 1.

Value

An object of class nnGarrote.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

coef.nnGarrote, predict.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 <- nnGarrote(x.train, y.train, intercept=TRUE,
                     initial.model=c("LS", "glmnet")[2],
                     lambda.nng=NULL, lambda.initial=NULL, alpha=0)
nng.predictions <- predict(nng.out, newx=x.test)
nng.coef <- coef(nng.out)

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