if( as.numeric(sessionInfo()$R.version$major) >= 4 ){
library(psychmeta)
load("data_ma_r.rda")
test_that("multi-construct bare-bones - example 1", {
expect_equal(
ma_r(
rxyi = rxyi, n = n, rxx = rxxi, ryy = ryyi,
construct_x = x_name, construct_y = y_name, sample_id = sample_id,
moderators = moderator, data = data_r_meas_multi
),
ma_r_example_one
)
})
test_that("multiple individual-correction - example 2", {
expect_equal(
ma_r(
ma_method = "ic", rxyi = rxyi, n = n, rxx = rxxi, ryy = ryyi,
construct_x = x_name, construct_y = y_name, sample_id = sample_id,
moderators = moderator, data = data_r_meas_multi
),
ma_r_example_two
)
})
test_that("curate artifact distributions and compute multiple artifact-distribution - example 3", {
expect_equal(
ma_r(
ma_method = "ad", ad_type = "int", rxyi = rxyi, n = n, rxx = rxxi, ryy = ryyi,
correct_rr_x = FALSE, correct_rr_y = FALSE,
construct_x = x_name, construct_y = y_name, sample_id = sample_id,
clean_artifacts = FALSE, impute_artifacts = FALSE,
moderators = moderator, data = data_r_meas_multi
),
ma_r_example_three
)
})
test_that("pre-specified artifact distributions from previous meta-analyses - example 4", {
expect_equal(
ma_r(
ma_method = "ad", rxyi = rxyi, n = n,
correct_rr_x = FALSE, correct_rr_y = FALSE,
construct_x = x_name, construct_y = y_name, sample_id = sample_id,
clean_artifacts = FALSE, impute_artifacts = FALSE,
moderators = moderator, data = data_r_meas_multi,
supplemental_ads =
list(
X = list(
mean_qxi = 0.8927818, var_qxi = 0.0008095520, k_qxi = 40,
mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha"
),
Y = list(
mean_qxi = 0.8941266, var_qxi = 0.0009367234, k_qxi = 40,
mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha"
),
Z = list(
mean_qxi = 0.8962108, var_qxi = 0.0007840593, k_qxi = 40,
mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha"
)
)
),
ma_r_example_four
)
})
test_that("manual artifact information - artifact-distribution meta-analysis - example 5", {
expect_equal(
ma_r(
ma_method = "ad", rxyi = rxyi, n = n,
correct_rr_x = FALSE, correct_rr_y = FALSE,
construct_x = x_name, construct_y = y_name, sample_id = sample_id,
clean_artifacts = FALSE, impute_artifacts = FALSE,
moderators = moderator, data = data_r_meas_multi,
supplemental_ads = ad_list
),
ma_r_example_five
)
})
test_that("Passing artifact information with the 'supplemental_ads' argument - example 6", {
expect_equal(
ma_r(
ma_method = "ad", rxyi = rxyi, n = n,
correct_rr_x = FALSE, correct_rr_y = FALSE,
construct_x = x_name, construct_y = y_name,
moderators = moderator, sample_id = sample_id, data = data_r_meas_multi,
supplemental_ads = list(
X = list(rxxi = rxxi, n_rxxi = n_rxxi, wt_rxxi = n_rxxi),
Y = list(rxxi = ryyi, n_rxxi = n_ryyi, wt_rxxi = n_ryyi),
Z = list(rxxi = rzzi, n_rxxi = n_rzzi, wt_rxxi = n_rzzi)
)
),
ma_r_example_six
)
})
test_that("use_all_arts = TRUE, artifacts from studies without valid correlations - example 7", {
m <- expect_warning(
ma_r(
ma_method = "ad", rxyi = rxyi, n = n, rxx = rxxi, ryy = ryyi,
correct_rr_x = FALSE, correct_rr_y = FALSE,
construct_x = x_name, construct_y = y_name,
sample_id = sample_id, moderators = moderator,
use_all_arts = TRUE, data = dat
)
)
expect_equal(
m,
ma_r_example_seven
)
})
test_that("control_intercor with same-construct convergent correlation data", {
dat <- data.frame(
sample_id = c(1, 2, 2, 2),
n = c(100, 50, 50, 50),
r_xy = c(.07, .38, .82, .83),
x_name = c("faultline_strength", "diversity", "diversity", "diversity"),
y_name = c("faultline_strength", "diversity", "faultline_strength", "faultline_strength"),
rel_x = 1, rel_y = 1
)
m <- ma_r(
ma_method = "ad",
data = dat,
rxyi = r_xy,
n = n,
sample_id = sample_id,
collapse_method = "composite",
construct_x = x_name, construct_y = y_name,
rxx = rel_x, ryy = rel_y
)
expect_equal(
get_metatab(m)[[2]][[1]]$mean_rho,
c(0.07035354, 0.96234864, 0.38387755)
)
})
}
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