context("Factorial Design")
test_that("Factorial", {
my_population <- declare_model(N = 2000, noise = rnorm(N))
my_potential_outcomes <- declare_potential_outcomes(
Y_Z_T1 = noise,
Y_Z_T2 = noise + 0.2,
Y_Z_T3 = noise + 0.2,
Y_Z_T4 = noise + 0.6
)
my_assignment <- declare_assignment(Z = complete_ra(N, num_arms = 4))
my_inquiry <- declare_inquiry(interaction = mean(Y_Z_T4 - Y_Z_T3) - mean(Y_Z_T2 - Y_Z_T1))
my_estimator <- declare_estimator(Y ~ Z1 + Z2 + Z1 * Z2,
.method = lm_robust,
term = "Z1:Z2"
)
my_measurement <- declare_measurement(Y = reveal_outcomes(Y ~ Z))
my_design <-
my_population +
my_potential_outcomes +
my_inquiry +
my_assignment +
declare_step(dplyr::mutate,
Z1 = as.numeric(Z %in% c("T2", "T4")),
Z2 = as.numeric(Z %in% c("T3", "T4"))
) +
my_measurement +
my_estimator
expect_equal(my_design %>% draw_data() %>% nrow(), 2000)
expect_equal(my_design %>% run_design() %>% names(), c("inquiry", "estimand", "estimator", "term", "estimate",
"std.error", "statistic", "p.value", "conf.low", "conf.high",
"df", "outcome"))
diagnosis <- diagnose_design(my_design, sims = 2, bootstrap_sims = FALSE)
expect_equal(diagnosis %>% get_simulations %>% dim, c(2, 14))
expect_equal(diagnosis %>% get_diagnosands %>% dim, c(1, 13))
})
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