splitSelect (version 1.0.3)

predict.cv.splitSelect: Predictions for cv.splitSelect object

Description

predict.cv.splitSelect returns the prediction for cv.splitSelect for new data.

Usage

# S3 method for cv.splitSelect
predict(object, newx, optimal.only = TRUE, ...)

Arguments

object

An object of class cv.splitSelect.

newx

A matrix with the new data.

optimal.only

A boolean variable (TRUE default) to indicate if only the predictions of the optimal split are returned.

...

Additional arguments for compatibility.

Value

A matrix with the predictions of the cv.splitSelect object.

See Also

cv.splitSelect

Examples

# NOT RUN {
# Setting the parameters
p <- 4
n <- 30
n.test <- 5000
beta <- rep(5,4)
rho <- 0.1
r <- 0.9
SNR <- 3
# Creating the target matrix with "kernel" set to rho
target_cor <- function(r, p){
  Gamma <- diag(p)
  for(i in 1:(p-1)){
    for(j in (i+1):p){
      Gamma[i,j] <- Gamma[j,i] <- r^(abs(i-j))
    }
  }
  return(Gamma)
}
# AR Correlation Structure
Sigma.r <- target_cor(r, p)
Sigma.rho <- target_cor(rho, p)
sigma.epsilon <- as.numeric(sqrt((t(beta) %*% Sigma.rho %*% beta)/SNR))
# Simulate some data
x.train <- mvnfast::rmvn(30, mu=rep(0,p), sigma=Sigma.r)
y.train <- 1 + x.train %*% 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 %*% beta + rnorm(n.test, sd=sigma.epsilon)

# Generating the coefficients for a fixed split
# }
# NOT RUN {
split.out <- cv.splitSelect(x.train, y.train, G=2, use.all=TRUE,
                            fix.partition=list(matrix(c(2,2), 
                                               ncol=2, byrow=TRUE)), 
                            fix.split=NULL,
                            intercept=TRUE, group.model="glmnet", alphas=0)
predict(split.out, newx=x.test)
# }
# NOT RUN {
# }