test_that("crossval_compute returns residuals (w/o instruments)", {
  # Simulate small dataset
  X <- matrix(rnorm(100*100), 100, 100) # Simulate features
  y <- 1 + X %*% (10*runif(100) * (runif(100) < 0.05)) + rnorm(100)
  # Define arguments
  test_sample <- sample(1:length(y), 33)
  learner <- list(fun = ols)
  # Compute cross-validation instance
  oos_resid <- crossval_compute(test_sample, learner,
                                y, X, Z = NULL)
  # Check output with expectations
  expect_equal(length(oos_resid), 33)
})#TEST_THAT

test_that("crossval returns residuals by learner (w/o instruments)", {
  # Simulate small dataset
  X <- cbind(1, matrix(rnorm(100*99), 100, 99)) # Simulate features
  nonzero_X <- (runif(100) < 0.05)
  y <- X %*% (10*runif(100) * nonzero_X) + rnorm(100)
  # Define arguments
  learners <- list(list(fun = ols),
                 list(fun = ols),
                 list(fun = ols,
                      assign_X = which(nonzero_X)))
  # Compute cross-validation instance
  cv_res <- crossval(y, X, Z = NULL,
                     learners,
                     cv_folds = 3,
                     silent = T)
  # Check output with expectations
  expect_equal(dim(cv_res$oos_resid), c(length(y), length(learners)))
})#TEST_THAT

test_that("crossval returns residuals by learner in correct order", {
  # Simulate small dataset
  n <- 147
  X <- matrix(rnorm(n * 10), n, 10)
  y <- rowSums(X[, 1:3]) + rnorm(n)
  # split data to two folds and compute residuals manually
  subsample_list <- generate_subsamples(n, 2)
  oos_res_cv <- matrix(0, n, 2)
  for(i in seq_along(subsample_list)) {
    idx_i <- subsample_list[[i]]
    # ols 1
    ols_fit <- ols(y[-idx_i], X[-idx_i, 1:5])
    oos_res_cv[idx_i, 1] <- y[idx_i] -
      ddml:::predict.ols(ols_fit, X[idx_i, 1:5])
    # ols 1
    ols_fit <- ols(y[-idx_i], X[-idx_i, 1:10])
    oos_res_cv[idx_i, 2] <- y[idx_i] -
      ddml:::predict.ols(ols_fit, X[idx_i, 1:10])
  }#FOR
  # Compute cross-validation with crossval using the same subsamples
  cv_res <- crossval(y, X,
                     learners = list(list(fun = ols,
                                          assign_X = 1:5),
                                     list(fun = ols,
                                          assign_X = 1:10)),
                     cv_subsamples = subsample_list,
                     silent = T)

  # Check output with expectations
  expect_equal(round(cv_res$oos_resid[, 1], 3), round(oos_res_cv[, 1], 3))
  expect_equal(round(cv_res$oos_resid[, 2], 3), round(oos_res_cv[, 2], 3))
})#TEST_THAT

test_that("crossval returns residuals by learner (w/ instruments)", {
  # Simulate small dataset
  X <- cbind(1, matrix(rnorm(100*39), 100, 39))
  Z <- matrix(rnorm(100*10), 100, 10)
  D <-  X %*% runif(40) + Z %*% c(1, runif(9)) + rnorm(100)
  # Define arguments
  learners <- list(list(fun = ols),
                 list(fun = ols),
                 list(fun = mdl_glmnet))
  # Compute cross-validation instance
  cv_res <- crossval(D, X, Z,
                     learners,
                     cv_folds = 3,
                     silent = T)
  # Check output with expectations
  expect_equal(all(round(cv_res$oos_resid[, 1], 3) ==
                     round(cv_res$oos_resid[, 2], 3)), TRUE)
})#TEST_THAT