test_that("ddml_plm computes with a single model", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D_tld <- X %*% runif(40) + rnorm(nobs) D <- 1 * (D_tld > mean(D_tld)) y <- D + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(what = mdl_glmnet, args = list(alpha = 0.5)) ddml_plm_fit <- ddml_plm(y, D, X, learners = learners, cv_folds = 3, sample_folds = 3, silent = T) # Check output with expectations expect_equal(length(ddml_plm_fit$coef), 1) })#TEST_THAT test_that("ddml_plm computes with an ensemble procedure", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D <- X %*% runif(40) + rnorm(nobs) y <- D + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(list(fun = mdl_glmnet, args = list(alpha = 0.5)), list(fun = ols)) # Compute DDML PLM estimator ddml_plm_fit <- ddml_plm(y, D, X, learners = learners, ensemble_type = "ols", shortstack = FALSE, cv_folds = 3, sample_folds = 3, silent = T) # Check output with expectations expect_equal(length(ddml_plm_fit$coef), 1) })#TEST_THAT test_that("ddml_plm computes with multiple ensemble procedures", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D <- X %*% runif(40) + rnorm(nobs) y <- D + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(list(fun = mdl_glmnet, args = list(alpha = 0.5)), list(fun = ols)) # Compute DDML PLM estimator ddml_plm_fit <- ddml_plm(y, D, X, learners, ensemble_type = c("ols", "nnls", "nnls1", "singlebest", "average"), shortstack = FALSE, cv_folds = 3, sample_folds = 3, silent = T) # Check output with expectations expect_equal(length(ddml_plm_fit$coef), 5) })#TEST_THAT test_that("ddml_plm computes with multiple ensemble procedures & sparse mats", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D <- X %*% runif(40) + rnorm(nobs) y <- D + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(list(fun = mdl_glmnet, args = list(alpha = 0.5)), list(fun = ols)) # Compute DDML PLM estimator ddml_plm_fit <- ddml_plm(y, D, as(X, "sparseMatrix"), learners, ensemble_type = c("ols", "nnls", "nnls1", "singlebest", "average"), shortstack = FALSE, cv_folds = 3, sample_folds = 3, silent = T) # Check output with expectations expect_equal(length(ddml_plm_fit$coef), 5) })#TEST_THAT test_that("ddml_plm computes w/ an ensemble procedure & shortstacking", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D <- X %*% runif(40) + rnorm(nobs) y <- D + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(list(fun = mdl_glmnet, args = list(alpha = 0.5)), list(fun = ols)) # Compute DDML PLM estimator ddml_plm_fit <- ddml_plm(y, D, X, learners = learners, ensemble_type = "ols", shortstack = TRUE, cv_folds = 3, sample_folds = 3, silent = T) # Check output with expectations expect_equal(length(ddml_plm_fit$coef), 1) })#TEST_THAT test_that("ddml_plm computes w/ multiple ensemble procedures & shortstacking", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D <- X %*% runif(40) + rnorm(nobs) y <- D + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(list(fun = mdl_glmnet, args = list(alpha = 0.5)), list(fun = ols)) # Compute DDML PLM estimator ddml_plm_fit <- ddml_plm(y, D, X, learners, ensemble_type = c("ols", "nnls", "nnls1", "singlebest", "average"), shortstack = TRUE, cv_folds = 3, sample_folds = 3, silent = T) # Check output with expectations expect_equal(length(ddml_plm_fit$coef), 5) })#TEST_THAT test_that("summary.ddml_plm computes with a single model", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D_tld <- X %*% runif(40) + rnorm(nobs) D <- 1 * (D_tld > mean(D_tld)) y <- D + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(what = mdl_glmnet, args = list(alpha = 0.5)) ddml_plm_fit <- ddml_plm(y, D, X, learners = learners, cv_folds = 3, sample_folds = 3, silent = T) capture_output({inf_res <- summary(ddml_plm_fit)}) # Check output with expectations expect_equal(length(inf_res), 8) })#TEST_THAT test_that("summary.ddml_plm computes with multiple ensemble procedures", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D <- X %*% runif(40) + rnorm(nobs) y <- D + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(list(fun = mdl_glmnet, args = list(alpha = 0.5)), list(fun = ols)) # Compute DDML PLM estimator ddml_plm_fit <- ddml_plm(y, D, X, learners, ensemble_type = c("ols", "nnls", "nnls1", "singlebest", "average"), shortstack = FALSE, cv_folds = 3, sample_folds = 3, silent = T) capture_output({inf_res <- summary(ddml_plm_fit, type = "HC1")}) # Check output with expectations expect_equal(length(inf_res), 8 * 5) })#TEST_THAT test_that("ddml_plm computes with an ensemble procedure and multivariate D", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D <- cbind(X %*% runif(40) + rnorm(nobs), rnorm(nobs)) y <- rowSums(D) + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(list(fun = mdl_glmnet, args = list(alpha = 0.5)), list(fun = ols)) # Compute DDML PLM estimator ddml_plm_fit <- ddml_plm(y, D, X, learners = learners, ensemble_type = "ols", shortstack = FALSE, cv_folds = 3, sample_folds = 3, silent = T) # Check output with expectations expect_equal(length(ddml_plm_fit$coef), 2) })#TEST_THAT test_that("ddml_plm computes with multiple ensemble types and multivariate D", { # Simulate small dataset nobs <- 200 X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39)) D <- cbind(X %*% runif(40) + rnorm(nobs), rnorm(nobs)) y <- rowSums(D) + X %*% runif(40) + rnorm(nobs) # Define arguments learners <- list(list(fun = mdl_glmnet, args = list(alpha = 0.5)), list(fun = ols)) # Compute DDML PLM estimator ddml_plm_fit <- ddml_plm(y, D, X, learners, ensemble_type = c("ols", "nnls", "nnls1", "singlebest", "average"), shortstack = FALSE, cv_folds = 3, sample_folds = 3, silent = T) # Check output with expectations expect_equal(length(ddml_plm_fit$coef), 10) })#TEST_THAT