test_that("Binary treatment", { skip_if_not_installed("rootSolve") skip_if_not_installed("cobalt") skip_if_not_installed("brglm2") skip_if_not_installed("logistf") eps <- if (capabilities("long.double")) 1e-5 else 1e-1 test_data <- readRDS(test_path("fixtures", "test_data.rds")) expect_no_condition({ W0 <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", include.obj = TRUE) }) expect_M_parts_okay(W0) expect_true(is.numeric(W0$ps)) # quick expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", quick = TRUE, include.obj = TRUE) }) expect_M_parts_okay(W) expect_equal(W$weights, W0$weights) expect_false(is_null(W$obj)) expect_false(is_null(W0$obj)) expect_failure(expect_equal(W$obj, W0$obj)) # Estimands expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATT") }) expect_M_parts_okay(W) expect_equal(W$weights[W$treat == 1], rep(1, sum(W$treat == 1)), tolerance = eps) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATC") }) expect_M_parts_okay(W) expect_equal(W$weights[W$treat == 0], rep(1, sum(W$treat == 0)), tolerance = eps) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATO") }) expect_M_parts_okay(W) expect_equal(unname(cobalt::col_w_smd(W$covs, W$treat, W$weights)), rep(0, 12), tolerance = eps) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATM") }) expect_M_parts_okay(W) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATOS") }) expect_M_parts_okay(W) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", link = "probit") }) expect_M_parts_okay(W) # brglm2 expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", link = "br.logit", epsilon = 1e-10) }) expect_M_parts_okay(W) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", link = "br.probit", type = "AS_median", epsilon = 1e-10) }) expect_M_parts_okay(W) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", link = "br.logit", type = "correction", epsilon = 1e-10) }) expect_null(attr(W, "Mparts")) # logistf expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", link = "flic") }) expect_null(attr(W, "Mparts")) # s.weights expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", s.weights = "SW") }) expect_equal(test_data$SW, W$s.weights) expect_M_parts_okay(W) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X5 + X6, data = test_data, method = "glm", estimand = "ATE", s.weights = "SW", link = "log") }) expect_M_parts_okay(W) # No warning for non-integer #successes expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", link = "br.logit", s.weights = "SW", epsilon = 1e-10) }) expect_M_parts_okay(W) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", link = "flac", s.weights = "SW") }) expect_equal(test_data$SW, W$s.weights) #Stabilization expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", include.obj = TRUE, stabilize = TRUE) }) expect_M_parts_okay(W) expect_null(attr(W, "Mparts", exact = TRUE)) expect_false(is_null(attr(W, "Mparts.list", exact = TRUE))) expect_equal(cobalt::col_w_smd(W$covs, W$treat, W$weights), cobalt::col_w_smd(W0$covs, W0$treat, W0$weights), tolerance = eps) expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", include.obj = TRUE, stabilize = ~X1) }) expect_M_parts_okay(W) expect_failure(expect_equal(cobalt::col_w_smd(W$covs, W$treat, W$weights), cobalt::col_w_smd(W0$covs, W0$treat, W0$weights))) #Non-full rank expect_no_condition({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + I(1 - X5) + I(X9 * 2), data = test_data, method = "glm", estimand = "ATE", include.obj = TRUE) }) expect_equal(W$weights, W0$weights) # Separation set.seed(123) test_data$Xx <- rbinom(nrow(test_data), 1, .01 + .99 * test_data$A) expect_warning({ W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + Xx, data = test_data, method = "glm", estimand = "ATE", include.obj = TRUE) }, "Propensity scores numerically equal to 0 or 1 were estimated") # expect_failure(expect_M_parts_okay(W)) test_data$Xx <- NULL }) test_that("Ordinal treatment", { skip_if_not_installed("rootSolve") eps <- if (capabilities("long.double")) 1e-5 else 1e-1 test_data <- readRDS(test_path("fixtures", "test_data.rds")) test_data$Ao <- ordered(findInterval(test_data$Ac, quantile(test_data$Ac, seq(0, 1, length.out = 5)), all.inside = TRUE)) expect_no_condition({ W0 <- weightit(Ao ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", include.obj = TRUE) }) expect_M_parts_okay(W0) # expect_no_condition({ # W <- weightit(Ao ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, # data = test_data, method = "glm", estimand = "ATE", # link = "br.logit", parallel = TRUE, # include.obj = TRUE) # }) # expect_failure(expect_M_parts_okay(W)) expect_no_condition({ W <- weightit(Ao ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9, data = test_data, method = "glm", estimand = "ATE", multi.method = "weightit", include.obj = TRUE) }) expect_M_parts_okay(W) })