# TEST n_ztest
#
# This code is testing the function "n_ztest" according to[1] .
# [1] M. Kieser: Fallzahlberechnung in der medizinischen Forschung [2018],
# Tabelle 4.2, p. 17-18.
context("Test n_z_test")
# I DATA TABLE
#
# CAVE: Note that the sample sizes are aranged in columns sorted in ascending
# order
pow_vector <- c(0.80, 0.90)
effect_vector <- c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.25, 1.5)
n_ztest.80 <- c(3140, 786, 350, 198, 126, 88, 66, 50, 40, 32, 22, 14)
n_ztest.90 <- c(4204, 1052, 468, 264, 170, 118, 86, 66, 52, 44, 28, 20)
table_42_z <- data.frame(
effect = effect_vector,
n.80 = n_ztest.80,
n.90 = n_ztest.90
)
# II TEST LOOP
for (i_effect in 1:length(effect_vector)) {
for (i_pow in 1:length(pow_vector)){
alpha <- .05
power <- pow_vector[i_pow]
effect <- effect_vector[i_effect]
sd <- 1
r <- 1
val_f <- n_ztest(
alpha = alpha,
power = power,
r = r,
effect = effect,
sd = sd
)$n
val_table <- table_42_z[i_effect, i_pow + 1]
test_that("Test n_z_test", {
expect_equal(
val_f, val_table,
info = sprintf(
"params: alpha=%.2f, power=%.2f, effect=%.2f, sd=%.2f, r = %.2f",
alpha, power, effect, sd, r
)
)
})
}
}
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