Nothing
tests/testthat/test-maxEquivTest-output-IU.R
In EquiTrends: Equivalence Testing for Pre-Trends in Difference-in-Differences Designs
test_that("maxEquivTest return for matrix input and standard variance-covariance matrix",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- plm_test$vcov
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.38987476186682140655, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
test_that("maxEquivTest return for HC-type variance-covariance matrix",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- plm::vcovHC(plm_test, type="HC1", method = "white1")
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="HC")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.37412870289644517552, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="HC")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov="HC")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov = "HC")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
test_that("maxEquivTest return for HAC-type variance-covariance matrix",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- plm::vcovHC(plm_test, type="HC3", method = "arellano")
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="HAC")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.39576374871198399807, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="HAC")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov="HAC")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov = "HAC")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
test_that("maxEquivTest return for CL-type variance-covariance matrix without cluster vector",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- clubSandwich::vcovCR(plm_test, cluster="ID", type="CR0")
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
alpha = alpha,
type = "IU", vcov="CL")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.39537894825602459825, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
alpha = alpha,
type = "IU", vcov="CL")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
data = sim_data,
alpha = alpha,
type = "IU", vcov="CL")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
data = sim_data,
alpha = alpha,
type = "IU", vcov = "CL")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
test_that("maxEquivTest return for CL-type variance-covariance matrix with cluster vector",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- clubSandwich::vcovCR(plm_test, cluster= cluster_data, type="CR0")
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), drop = FALSE]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="CL")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.36172344249370752545, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="CL")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov="CL")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov = "CL")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
# Print functions:
test_that("maxEquivTest print for standard variance-covariance matrix",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.05
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- plm_test$vcov
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU")
expect_snapshot(print(maxEquivTest_results))
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU")
expect_snapshot(print(maxEquivTest_results2))
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU")
expect_snapshot(print(maxEquivTest_results3))
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU")
expect_snapshot(print(maxEquivTest_results4))
})
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test_that("maxEquivTest return for matrix input and standard variance-covariance matrix",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- plm_test$vcov
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.38987476186682140655, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
test_that("maxEquivTest return for HC-type variance-covariance matrix",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- plm::vcovHC(plm_test, type="HC1", method = "white1")
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="HC")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.37412870289644517552, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="HC")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov="HC")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov = "HC")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
test_that("maxEquivTest return for HAC-type variance-covariance matrix",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- plm::vcovHC(plm_test, type="HC3", method = "arellano")
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="HAC")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.39576374871198399807, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="HAC")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov="HAC")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov = "HAC")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
test_that("maxEquivTest return for CL-type variance-covariance matrix without cluster vector",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- clubSandwich::vcovCR(plm_test, cluster="ID", type="CR0")
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
alpha = alpha,
type = "IU", vcov="CL")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.39537894825602459825, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
alpha = alpha,
type = "IU", vcov="CL")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
data = sim_data,
alpha = alpha,
type = "IU", vcov="CL")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
data = sim_data,
alpha = alpha,
type = "IU", vcov = "CL")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
test_that("maxEquivTest return for CL-type variance-covariance matrix with cluster vector",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.1
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- clubSandwich::vcovCR(plm_test, cluster= cluster_data, type="CR0")
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), drop = FALSE]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="CL")
expect_equal(class(maxEquivTest_results), "maxEquivTestIU")
expect_equal(maxEquivTest_results$equiv_threshold_specified, FALSE)
expect_equal(maxEquivTest_results$significance_level, alpha)
expect_equal(maxEquivTest_results$num_individuals, 500)
expect_equal(maxEquivTest_results$num_periods, 5)
expect_equal(maxEquivTest_results$base_period, 5)
expect_equal(maxEquivTest_results$minimum_equiv_threshold, 0.36172344249370752545, tolerance = 1e-6)
expect_equal(maxEquivTest_results$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results$placebo_coefficients), 4)
expect_equal(maxEquivTest_results$placebo_coefficients, placebo_coefs)
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU", vcov="CL")
expect_equal(class(maxEquivTest_results2), "maxEquivTestIU")
expect_equal(maxEquivTest_results2$equiv_threshold_specified, TRUE)
expect_equal(maxEquivTest_results2$significance_level, alpha)
expect_equal(maxEquivTest_results2$num_individuals, 500)
expect_equal(maxEquivTest_results2$num_periods, 5)
expect_equal(maxEquivTest_results2$base_period, 5)
expect_equal(maxEquivTest_results2$equiv_threshold, 0.2)
expect_equal(maxEquivTest_results2$placebo_coefficients_se, beta_se, tolerance = 1e-6)
expect_equal(length(maxEquivTest_results2$placebo_coefficients), 4)
expect_equal(maxEquivTest_results2$placebo_coefficients, placebo_coefs)
expect_equal(maxEquivTest_results2$reject_null_hypothesis, FALSE)
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov="CL")
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU", vcov = "CL")
expect_equal(maxEquivTest_results3, maxEquivTest_results)
expect_equal(maxEquivTest_results4, maxEquivTest_results2)
})
# Print functions:
test_that("maxEquivTest print for standard variance-covariance matrix",{
sim_data <- readRDS(test_path("fixtures", "test_data.rds"))
# The data in vector/matrix form:
Y_data <- sim_data$Y
ID_data <- sim_data$ID
G_data <- sim_data$G
period_data <- sim_data$period
X_data <- sim_data[, c("X_1", "X_2")]
cluster_data <- sim_data$cluster
# For the matrix input version of the function:
# - No equivalence threshold:
pre_treatment_period <- 1:5
base_period <- 5
alpha <- 0.05
# Do the procedure by hand for comparison:
subdata <- sim_data[,c("ID", "period", "Y", "placebo_1", "placebo_2", "placebo_3", "placebo_4", "X_1", "X_2")]
test_formula <- as.formula(Y ~ X_1 + X_2 + placebo_1 + placebo_2 + placebo_3 + placebo_4)
plm_test <- plm::plm(test_formula, data=subdata, effect="twoways", model="within", index=c("ID","period"))
placebo_coefs <- plm_test$coefficients[c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
vcov_mat <- plm_test$vcov
subcov_mat <- vcov_mat[c("placebo_1", "placebo_2", "placebo_3", "placebo_4"), c("placebo_1", "placebo_2", "placebo_3", "placebo_4")]
beta_var <- diag(subcov_mat)
# Calculating the standard errors
beta_se <- sqrt(beta_var)
maxEquivTest_results <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU")
expect_snapshot(print(maxEquivTest_results))
# Equivalence threshold specified:
maxEquivTest_results2 <- maxEquivTest(Y = Y_data, ID= ID_data, G = G_data,
period = period_data, X=X_data,
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = cluster_data,
alpha = alpha,
type = "IU")
expect_snapshot(print(maxEquivTest_results2))
# with index input:
maxEquivTest_results3 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X= c(5,6),
equiv_threshold = NULL,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU")
expect_snapshot(print(maxEquivTest_results3))
# Equivalence threshold specified:
maxEquivTest_results4 <- maxEquivTest(Y = 1, ID= 2, G = 4,
period = 3, X=c(5,6),
equiv_threshold = 0.2,
pretreatment_period = pre_treatment_period,
base_period = base_period,
cluster = 7, data = sim_data,
alpha = alpha,
type = "IU")
expect_snapshot(print(maxEquivTest_results4))
})
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