coef.srlars {srlars}

Coefficients for srlars Object

Description

coef.srlars returns the coefficients for a srlars object.

Usage

## S3 method for class 'srlars'
coef(object, group_index = NULL, ...)

Arguments

object

An object of class srlars

group_index

Groups included in the ensemble. Default setting includes all the groups.

...

Additional arguments for compatibility.

Value

The coefficients for the srlars object.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

srlars

Examples

# Required library
library(mvnfast)

# Simulation parameters
n <- 50
p <- 500
rho.within <- 0.8
rho.between <- 0.2
p.active <- 100
group.size <- 25
snr <- 3
contamination.prop <- 0.2

# 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)

# Contamination of data
contamination_indices <- 1:floor(n*contamination.prop)
k_lev <- 2
k_slo <- 100
x_train <- x
y_train <- y
beta_cont <- true.beta
beta_cont[true.beta!=0] <- beta_cont[true.beta!=0]*(1 + k_slo)
beta_cont[true.beta==0] <- k_slo*max(abs(true.beta))
for(cont_id in contamination_indices){

  a <- runif(p, min = -1, max = 1)
  a <- a - as.numeric((1/p)*t(a) %*% rep(1, p))
  x_train[cont_id,] <- mvnfast::rmvn(1, rep(0, p), 0.1^2*diag(p)) +
    k_lev * a / as.numeric(sqrt(t(a) %*% solve(sigma.mat) %*% a))
  y_train[cont_id] <- t(x_train[cont_id,]) %*% beta_cont
}

# Ensemble models
ensemble_fit <- srlars(x_train, y_train,
                       n_models = 5,
                       model_saturation = c("fixed", "p-value")[1],
                       alpha = 0.05, model_size = n - 1,
                       robust = TRUE,
                       compute_coef = TRUE,
                       en_alpha = 1/4)

# Ensemble coefficients
ensemble_coefs <- coef(ensemble_fit, group_index = 1:ensemble_fit$n_models)
sens_ensemble <- sum(which((ensemble_coefs[-1]!=0)) <= p.active)/p.active
spec_ensemble <- sum(which((ensemble_coefs[-1]!=0)) <= p.active)/sum(ensemble_coefs[-1]!=0)

# Simulation of test data
m <- 2e3
x_test <- mvnfast::rmvn(m, mu = rep(0, p), sigma = sigma.mat)
y_test <- x_test %*% true.beta + rnorm(m, 0, sigma)

# Prediction of test samples
ensemble_preds <- predict(ensemble_fit, newx = x_test,
                          group_index = 1:ensemble_fit$n_models,
                          dynamic = FALSE)
mspe_ensemble <- mean((y_test - ensemble_preds)^2)/sigma^2