predict.splitSelect: Predictions for splitSelect object

In splitSelect: Best Split Selection Modeling for Low-Dimensional Data

Description

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

Usage

## S3 method for class 'splitSelect'
predict(object, newx, ...)

Arguments

object	An object of class splitSelect.
newx	A matrix with the new data.
...	Additional arguments for compatibility.

Value

A matrix with the predictions of the splitSelect object.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

splitSelect

Examples

# 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

split.out <- 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)

splitSelect documentation built on Nov. 9, 2021, 9:07 a.m.