Nothing
test_that("toBinomialExact validates inputs", {
expect_error(toBinomialExact(list()), "must have class gsSurv")
x_bad <- gsSurv(
k = 2, test.type = 2, alpha = 0.025, beta = 0.1,
timing = 1, sfu = sfHSD, sfupar = -4,
lambdaC = 0.1, hr = 0.7, hr0 = 1, eta = 0.01,
gamma = 5, R = 6, T = 12, minfup = 6
)
expect_error(toBinomialExact(x_bad), "test.type must be 1 or 4")
x <- gsSurv(
k = 2, test.type = 4, alpha = 0.025, beta = 0.1,
timing = 1, sfu = sfHSD, sfupar = -4, sfl = sfLDOF, sflpar = 0,
lambdaC = 0.1, hr = 0.7, hr0 = 1, eta = 0.01,
gamma = 5, R = 6, T = 12, minfup = 6
)
expect_error(
toBinomialExact(x, observedEvents = c(1.5, 3)),
"must be a vector of increasing positive integers"
)
expect_error(
toBinomialExact(x, observedEvents = c(5, 4)),
"must be a vector of increasing positive integers"
)
expect_error(
toBinomialExact(x, observedEvents = 5),
"must have at least 2 values"
)
if (!is.null(x$maxn.IPlan) && is.finite(x$maxn.IPlan) && x$maxn.IPlan > 0) {
expect_error(
toBinomialExact(
x,
observedEvents = c(as.integer(x$maxn.IPlan), as.integer(x$maxn.IPlan) + 1)
),
"at most 1 value"
)
}
})
test_that("toBinomialExact returns gsBinomialExact objects", {
x <- gsSurv(
k = 2, test.type = 4, alpha = 0.025, beta = 0.1,
timing = 1, sfu = sfHSD, sfupar = -4, sfl = sfLDOF, sflpar = 0,
lambdaC = 0.1, hr = 0.7, hr0 = 1, eta = 0.01,
gamma = 5, R = 6, T = 12, minfup = 6
)
y <- toBinomialExact(x)
expect_s3_class(y, "gsBinomialExact")
expect_true(is.list(y$init_approx))
obs <- as.integer(round(x$n.I))
if (obs[2] <= obs[1]) obs[2] <- obs[1] + 1
y <- toBinomialExact(x, observedEvents = obs)
expect_s3_class(y, "gsBinomialExact")
})
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