Nothing
context("Results structures")
test_that("make_results_row()"
, {
testthat::skip_on_cran()
EQN_FILE <- system.file("extdata", "prospective_memory.eqn", package = "MPTmultiverse")
DATA_FILE <- system.file("extdata", "smith_et_al_2011.csv", package = "MPTmultiverse")
data <- read.csv(DATA_FILE, fileEncoding = "UTF-8-BOM")
data <- data[c(1:5, 113:118),]
COL_CONDITION <- "WM_EX"
data[[COL_CONDITION]] <- as.character(factor(
data[[COL_CONDITION]]
, levels = 1:2
, labels = c("low_WM", "high_WM")
))
data$id <- as.character(1:nrow(data))
capture_output(mpt_options("test"))
object <- MPTmultiverse:::make_results_row(
model = EQN_FILE
, dataset = DATA_FILE
, pooling = "partial"
, package = "TreeBUGS"
, method = "trait"
, data = data
# , parameters = c("test1", "test2", "test3")
, id = "id"
, condition = "WM_EX"
, core = c("C1", "C2")
)
# check column names
expect_identical(
object = colnames(object)
, expected = c(
"model"
, "dataset"
, "pooling"
, "package"
, "method"
, "est_group"
, "est_indiv"
, "est_rho"
, "test_between"
, "test_within"
, "gof"
, "gof_group"
, "gof_indiv"
, "fungibility"
, "test_homogeneity"
, "convergence"
, "estimation"
, "options"
)
)
# Extract column classes
column_classes <- lapply(X = object[, -c(1:5)], FUN = function(x) {
lapply(X = x[[1]], FUN = class)
})
expect_identical(
column_classes$est_group
, expected = list(
condition = "character"
, parameter = "character"
, core = "logical"
, est = "numeric"
, se = "numeric"
, ci_0.025 = "numeric"
, ci_0.1 = "numeric"
, ci_0.9 = "numeric"
, ci_0.975 = "numeric"
)
)
expect_identical(
column_classes$est_group
, expected = list(
condition = "character"
, parameter = "character"
, core = "logical"
, est = "numeric"
, se = "numeric"
, ci_0.025 = "numeric"
, ci_0.1 = "numeric"
, ci_0.9 = "numeric"
, ci_0.975 = "numeric"
)
)
expect_identical(
column_classes$est_indiv
, expected = list(
id = "character"
, condition = "character"
, parameter = "character"
, core = "logical"
, est = "numeric"
, se = "numeric"
, ci_0.025 = "numeric"
, ci_0.1 = "numeric"
, ci_0.9 = "numeric"
, ci_0.975 = "numeric"
, identifiable = "logical"
)
)
expect_identical(
column_classes$est_rho
, expected = list(
parameter1 = "character"
, parameter2 = "character"
, core1 = "logical"
, core2 = "logical"
, condition = "character"
, est = "numeric"
, se = "numeric"
, p = "numeric"
, ci_0.025 = "numeric"
, ci_0.1 = "numeric"
, ci_0.9 = "numeric"
, ci_0.975 = "numeric"
)
)
expect_identical(
column_classes$test_between
, expected = list(
parameter = "character"
, core = "logical"
, condition1 = "character"
, condition2 = "character"
, est_diff = "numeric"
, se = "numeric"
, p = "numeric"
, ci_0.025 = "numeric"
, ci_0.1 = "numeric"
, ci_0.9 = "numeric"
, ci_0.975 = "numeric"
)
)
expect_identical(
column_classes$test_within
, expected = list(
condition = "character"
, parameter1 = "character"
, parameter2 = "character"
, core1 = "logical"
, core2 = "logical"
, est = "numeric"
, se = "numeric"
, statistic = "numeric"
, df = "numeric"
, p = "numeric"
, ci_0.025 = "numeric"
, ci_0.1 = "numeric"
, ci_0.9 = "numeric"
, ci_0.975 = "numeric"
)
)
expect_identical(
column_classes$gof
, expected = list(
type = "character"
, focus = "character"
, stat_obs = "numeric"
, stat_pred = "numeric"
, stat_df = "numeric"
, p = "numeric"
)
)
expect_identical(
column_classes$gof_group
, expected = list(
condition = "character"
, type = "character"
, focus = "character"
, stat_obs = "numeric"
, stat_pred = "numeric"
, stat_df = "numeric"
, p = "numeric"
)
)
expect_identical(
column_classes$gof_indiv
, expected = list(
id = "character"
, condition = "character"
, type = "character"
, focus = "character"
, stat_obs = "numeric"
, stat_pred = "numeric"
, stat_df = "numeric"
, p = "numeric"
)
)
expect_identical(
column_classes$fungibility
, expected = list(
parameter1 = "character"
, parameter2 = "character"
, core1 = "logical"
, core2 = "logical"
, condition = "character"
, correlation = "numeric"
)
)
expect_identical(
column_classes$test_homogeneity
, expected = list(
condition = "character"
, chisq = "numeric"
, df = "numeric"
, p = "numeric"
)
)
expect_identical(
column_classes$estimation
, expected = list(
condition = "character"
, time_difference = "difftime"
)
)
# column `convergence` is initialized as an empty list, so missing here
expect_identical(
column_classes$options
, expected = list(
bootstrap_samples = "numeric"
, n.optim = "numeric"
, n.chains = "numeric"
, n.iter = "numeric"
, n.adapt = "numeric"
, n.burnin = "numeric"
, n.thin = "numeric"
, Rhat_max = "numeric"
, Neff_min = "numeric"
, extend_max = "numeric"
, n.PPP = "numeric"
, prior.beta = "character"
, ci_size = "list"
, max_ci_indiv = "numeric"
)
)
}
)
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