Description Usage Arguments Value Author(s) See Also Examples
View source: R/predict.cv.nnGarrote.R
predict.cv.nnGarrote
returns the prediction for cv.nnGarrote for new data.
1 2 |
object |
An object of class cv.nnGarrote |
newx |
A matrix with the new data. |
optimal.only |
A boolean variable (TRUE default) to indicate if only the coefficient of the optimal split are returned. |
... |
Additional arguments for compatibility. |
A matrix with the predictions of the cv.nnGarrote
object.
Anthony-Alexander Christidis, anthony.christidis@stat.ubc.ca
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | # 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)
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