Nothing
context("test-infer_bf")
con <- rnorm(100)
exp <- rnorm(100)
test_that("infer_bf yields correct S4 class", {
expect_is(
infer_bf(x = con,
y = exp,
ni_margin = 0.5),
"baymedrNonInferiority"
)
expect_is(
infer_bf(n_x = 100,
n_y = 100,
mean_x = 10,
mean_y = 10,
sd_x = 5,
sd_y = 5,
ni_margin = 0.5),
"baymedrNonInferiority"
)
})
test_that("infer_bf yields numeric Bayes factor", {
expect_true(
is.numeric(infer_bf(x = con,
y = exp,
ni_margin = 0.5)@bf)
)
expect_true(
is.numeric(infer_bf(n_x = 100,
n_y = 100,
mean_x = 10,
mean_y = 10,
sd_x = 5,
sd_y = 5,
ni_margin = 0.5)@bf)
)
})
test_that("infer_bf gives correct error messages", {
expect_error(
infer_bf(x = con,
y = exp,
n_x = 80,
ni_margin = 0.5),
str_c(
"Only 'x', 'y', and 'ni_margin' OR 'n_x', 'n_y', 'mean_x', 'mean_y', ",
"'sd_x', 'sd_y', and 'ni_margin' (or 'ci_margin' and 'ci_level' instead ",
"of 'sd_x' and 'sd_y') must be defined."
),
fixed = TRUE
)
expect_error(
infer_bf(x = con),
"All 'x', 'y', and 'ni_margin' must be defined.",
fixed = TRUE
)
expect_error(
infer_bf(n_x = 100,
n_y = 100,
mean_x = 10,
sd_x = 5),
str_c(
"All 'n_x', 'n_y', 'mean_x', 'mean_y', 'sd_x', 'sd_y', and ",
"'ni_margin' (or 'ci_margin' and 'ci_level' instead of 'sd_x' and ",
"'sd_y') must be defined."
),
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = c(exp[1:99], NA),
ni_margin = -0.5),
"'x' and 'y' must not contain missing values.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = c(exp[1:99], Inf),
ni_margin = -0.5),
"'x' and 'y' must not contain infinite values.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = rep(letters[1:10],
each = 10),
ni_margin = -0.5),
"'x' and 'y' must be numeric vectors.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = exp,
ni_margin = -0.5,
prior_scale = "x"),
"'prior_scale' must be a single numeric value.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = exp,
ni_margin = -0.5,
prior_scale = c(1, 2)),
"'prior_scale' must be a single numeric value.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = exp,
ni_margin = "0.5"),
"'ni_margin' must be a single positive numeric value.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = exp,
ni_margin = c(-0.5, 0.5)),
"'ni_margin' must be a single positive numeric value.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = exp,
ni_margin = -0.5),
"'ni_margin' must be a single positive numeric value.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = exp,
ni_margin = 0.5,
direction = 5),
"'direction' must be a single character value.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = exp,
ni_margin = 0.5,
direction = c("low", "high")),
"'direction' must be a single character value.",
fixed = TRUE
)
expect_error(
infer_bf(x = con,
y = exp,
ni_margin = 0.5,
direction = "abc"),
"'direction' must be one of 'low' or 'high'.",
fixed = TRUE
)
expect_error(
infer_bf(n_x = 100,
n_y = 100,
mean_x = 0,
mean_y = 2,
ci_margin = 5,
ci_level = 1.5,
ni_margin = 0.5),
"'ci_level' must be a single numeric value between 0 and 1.",
fixed = TRUE
)
expect_error(
infer_bf(n_x = 100,
n_y = 100,
mean_x = 0,
mean_y = 2,
ci_margin = 5,
ci_level = "high",
ni_margin = 0.5),
"'ci_level' must be a single numeric value between 0 and 1.",
fixed = TRUE
)
expect_error(
infer_bf(n_x = 100,
n_y = 100,
mean_x = 0,
mean_y = 2,
ci_margin = 5,
ci_level = c(0.3, 0.7),
ni_margin = 0.5),
"'ci_level' must be a single numeric value between 0 and 1.",
fixed = TRUE
)
})
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