tests/testthat/test_LassoShooting.fit.R

In hdm: High-Dimensional Metrics

context("Test LassoShooting.fit")
library(hdm)
library(testthat)

DPG_lassoShooting <- function(n, p, px, lambda0 = 110, min = 0.85, max = 1.15){
  
X <- matrix(rnorm(n*p), ncol=p)
beta <- c(rep(2,px), rep(0,p-px))
y <- X %*% beta + rnorm(n)
loadings <- runif(p, min = min, max = max)
lambda <- lambda0 * loadings

list(X = X, y = y, beta = beta, lambda = lambda, lambda0 = lambda0, loadings = loadings)
}


set.seed(2)
ret <- DPG_lassoShooting(200, 100, 10, 110)
X <- ret$X
y <- ret$y
beta <- ret$beta
lambda <- ret$lambda
rm(ret)


test_that("LassoShooting.fit - Input check x, y and lambda",{
  expect_is(LassoShooting.fit(X, y, lambda), "list")
  expect_is(LassoShooting.fit(X, as.vector(y), lambda), "list")
  expect_is(LassoShooting.fit(X[, 1, drop = FALSE], y, lambda), "list")
  expect_is(LassoShooting.fit(X[, 1, drop = FALSE], as.vector(y), lambda), "list")
})


test_that("LassoShooting.fit - Input check control, XX, Xy and beta start",{
  expect_is(LassoShooting.fit(X, y, lambda, control = list(maxIter = 150, optTol = 10^(-4), zeroThreshold = 10^(-5))), "list")
  expect_is(LassoShooting.fit(X, y, lambda, XX = (t(X) %*% X) * 0.8), "list")
  expect_is(LassoShooting.fit(X, y, lambda, Xy = (t(X) %*% y) * 0.8), "list")
  expect_is(LassoShooting.fit(X, y, lambda, beta.start = rep(1,100)), "list")
})