splitSelect_coef {splitSelect}

Split Selection for Regression - Coefficients Generation

Description

splitSelect_coef generates the coefficients for a particular split of variables into groups.

Usage

splitSelect_coef(
  x,
  y,
  variables.split,
  intercept = TRUE,
  family = c("gaussian", "binomial")[1],
  group.model = c("glmnet", "LS", "Logistic")[1],
  lambdas = NULL,
  alphas = 0
)

Arguments

x

Design matrix.

y

Response vector.

variables.split

A vector with the split of the variables into groups as values.

intercept

Boolean variable to determine if there is intercept (default is TRUE) or not.

family

Description of the error distribution and link function to be used for the model. Must be one of "gaussian" or "binomial".

group.model

Model used for the groups. Must be one of "glmnet" or "LS".

lambdas

The shinkrage parameters for the "glmnet" regularization. If NULL (default), optimal values are chosen.

alphas

Elastic net mixing parameter. Should be between 0 (default) and 1.

Value

A vector with the regression coefficients for the split.

Author(s)

Anthony-Alexander Christidis, [e-mail]

Examples

# Setting the parameters
p <- 6
n <- 30
n.test <- 5000
group.beta <- -3
beta <- c(rep(1, 2), rep(group.beta, p-2))
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
splitSelect_coef(x.train, y.train, variables.split=matrix(c(1,2,1,2,1,2), nrow=1))