srlars (version 2.0.0)

coef.srlars: Coefficients for srlars Object

Description

coef.srlars returns the averaged coefficients for a srlars object.

Usage

# S3 method for srlars
coef(object, model_index = NULL, ...)

Value

A numeric vector containing the averaged intercept (first element) and slope coefficients.

Arguments

object

An object of class srlars.

model_index

Indices of the sub-models to include in the ensemble average. Default is NULL, which includes all models.

...

Additional arguments for compatibility.

Author

Anthony-Alexander Christidis,

See Also

srlars, predict.srlars

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)
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 = 0.01,
                       robust = TRUE,
                       compute_coef = TRUE)

# Ensemble coefficients
# Default: Average over all models
ensemble_coefs <- coef(ensemble_fit)

# Sensitivity (Recall)
active_selected <- which(ensemble_coefs[-1] != 0)
true_active <- which(true.beta != 0)
recall <- length(intersect(active_selected, true_active)) / length(true_active)
print(paste("Recall:", recall))

# Precision
if(length(active_selected) > 0){
  precision <- length(intersect(active_selected, true_active)) / length(active_selected)
} else {
  precision <- 0
}
print(paste("Precision:", precision))