% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/srlars.R
\name{srlars}
\alias{srlars}
\title{Fast and Scalable Cellwise-Robust Ensemble (FSCRE)}
\usage{
srlars(
  x,
  y,
  n_models = 5,
  tolerance = 1e-08,
  max_predictors = NULL,
  x_preprocess = c("ddc", "none"),
  y_preprocess = c("wrap", "robust_z", "none"),
  cor_estimator = c("wrap", "pearson"),
  cv_preprocess = c("global", "foldwise"),
  cv_fit = c("ls", "huber"),
  cv_loss = c("huber", "trimmed", "mse"),
  cv_folds = 5,
  compute_coef = TRUE
)
}
\arguments{
\item{x}{Design matrix (n x p).}

\item{y}{Response vector (n x 1).}

\item{n_models}{Number of models in the ensemble (K). Default is 5.}

\item{tolerance}{Relative improvement tolerance for stopping (tau). Default is 1e-8.}

\item{max_predictors}{Maximum total number of variables to select across all models. Default is n * n_models.}

\item{x_preprocess}{Character. "ddc" (default) for cellwise cleaning, or "none".}

\item{y_preprocess}{Character. "wrap" (default) for univariate robustification, "robust_z", or "none".}

\item{cor_estimator}{Character. "wrap" (default) for robust PSD correlation, or "pearson".}

\item{cv_preprocess}{Character. "global" (default) or "foldwise" (to prevent data leakage).}

\item{cv_fit}{Character. "ls" (default) or "huber" for the inner arbiter fitting method.}

\item{cv_loss}{Character. "huber" (default), "trimmed", or "mse" for arbiter scoring.}

\item{cv_folds}{Integer. Number of cross-validation folds. Default is 5.}

\item{compute_coef}{Logical. If TRUE, fits the final robust MM-models. Default is TRUE.}
}
\value{
An object of class \code{srlars} containing the selected variables and coefficients.
}
\description{
\code{srlars} performs the FSCRE algorithm for robust variable selection and regression.
}
\examples{
# Required libraries
library(mvnfast)
library(cellWise)
library(robustbase)

# Simulation parameters
n <- 50
p <- 100
rho.within <- 0.8
rho.between <- 0.2
p.active <- 20
group.size <- 5
snr <- 3
contamination.prop <- 0.1

# Setting the seed
set.seed(0)

# Block correlation structure
sigma.mat <- matrix(0, p, p)
sigma.mat[1:p.active, 1:p.active] <- rho.between
for(group in 0:(p.active/group.size - 1))
  sigma.mat[(group*group.size+1):(group*group.size+group.size),
  (group*group.size+1):(group*group.size+group.size)] <- rho.within
diag(sigma.mat) <- 1

# Simulation of beta vector
true.beta <- c(runif(p.active, 0, 5)*(-1)^rbinom(p.active, 1, 0.7), rep(0, p - p.active))

# Setting the SD of the variance
sigma <- as.numeric(sqrt(t(true.beta) \%*\% sigma.mat \%*\% true.beta)/sqrt(snr))

# Simulation of uncontaminated data
x <- mvnfast::rmvn(n, mu = rep(0, p), sigma = sigma.mat)
colnames(x) <- paste0("V", 1:p)
y <- x \%*\% true.beta + rnorm(n, 0, sigma)

# Cellwise contamination
contamination_indices <- sample(1:(n * p), round(n * p * contamination.prop))
x_train <- x
x_train[contamination_indices] <- runif(length(contamination_indices), -10, 10)

# FSCRE Ensemble model
ensemble_fit <- srlars(x_train, y,
                       n_models = 5,
                       tolerance = 1e-4,
                       x_preprocess = "ddc",
                       y_preprocess = "wrap",
                       cor_estimator = "wrap",
                       cv_preprocess = "global",
                       cv_fit = "ls",
                       cv_loss = "huber",
                       compute_coef = TRUE)

# Check selected variables
print(ensemble_fit$active.sets)

}
\seealso{
\code{\link{coef.srlars}}, \code{\link{predict.srlars}}
}
\author{
Anthony-Alexander Christidis, \email{anthony.christidis@stat.ubc.ca}
}
