Nothing
test_that("matching_l1 functions as expected.", {
skip_on_cran()
#data.table::setDTthreads(1)
set.seed(721)
s_data <- generate_syn_data(sample_size=200,
outcome_sd = 10,
gps_spec = 1,
cova_spec = 1)
s_data$id <- seq_along(1:nrow(s_data))
m_xgboost <- function(nthread = 4,
ntrees = 35,
shrinkage = 0.3,
max_depth = 5,
...) {SuperLearner::SL.xgboost(
nthread = nthread,
ntrees = ntrees,
shrinkage=shrinkage,
max_depth=max_depth,
...)}
assign("m_xgboost", m_xgboost, envir = .GlobalEnv)
data_with_gps_1 <- estimate_gps(
.data = s_data,
.formula = w ~ cf1 + cf2 + cf3 + cf4 + cf5 + cf6,
sl_lib = c("m_xgboost"),
gps_density = "normal")
m_d <- data_with_gps_1
exposure_col_name = "w"
gps_mx <- compute_min_max(m_d$.data[["gps"]])
w_mx <- compute_min_max(m_d$.data[[exposure_col_name]])
val <- matching_fn(w = 10,
dataset = m_d$.data,
exposure_col_name = exposure_col_name,
e_gps_pred = as.vector(m_d$.data$e_gps_pred),
e_gps_std_pred = as.vector(m_d$.data$e_gps_std_pred),
w_resid = as.vector(m_d$.data$w_resid),
gps_mx = gps_mx,
w_mx = w_mx,
dist_measure = "l1",
gps_density = "kernel",
delta_n = 1,
scale = 0.5,
nthread = 1)
expect_equal(nrow(val), 11)
expect_equal(length(val), 2)
})
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