tests/testthat/test_srlars.R

In srlars: Split Robust Least Angle Regression

# --------------------------------------------------
# Test Script - Output from cv.SplitGLM Function
# --------------------------------------------------

# Required libraries
library(mvnfast)
library(srlars)

# Context of test script
context("Verify output of srlars function.")

# There should be an error if we want to compute the IF TS, and no returns are provided
test_that("Error in the srlars function.", {

  # Simulation parameters
  n <- 50
  p <- 500
  rho <- 0.5
  rho.inactive <- 0.2
  group.size <- 25
  p.active <- 100
  snr <- 1
  contamination.prop <- 0.2

  # Setting the seed
  set.seed(0)

  # Block Correlation
  sigma.mat <- matrix(0, p, p)
  sigma.mat[1:p.active, 1:p.active] <- rho.inactive
  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
  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 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)

  # 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
  }

  # srlars models
  srlars_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)
  srlars_preds <- predict(srlars_fit, newx = x_test,
                          group_index = 1:srlars_fit$n_models,
                          dynamic = FALSE)
  srlars_coefs <- coef(srlars_fit, group_index = 1:srlars_fit$n_models)

  expect_vector(srlars_coefs)

})