Nothing
context("Check if points on ellipsoid lie on confidence region bounds")
test_that("check_point_location_succeeds", {
tico <- 3:10
b1 <- dip1$type == "R"
# Hotelling's T2 statistics
l_hs <- get_T2_two(m1 = as.matrix(dip1[b1, tico]),
m2 = as.matrix(dip1[!b1, tico]),
signif = 0.05)
# Get points by aid of the Method of Lagrange Multipliers (MLM) and by
# Newton-Raphson (nera) optimisation
tmp <- gep_by_nera(n_p = as.numeric(l_hs[["Parameters"]]["df1"]),
kk = as.numeric(l_hs[["Parameters"]]["K"]),
mean_diff = l_hs[["means"]][["mean.diff"]],
m_vc = l_hs[["S.pool"]],
ff_crit = as.numeric(l_hs[["Parameters"]]["F.crit"]),
y = rep(1, times = l_hs[["Parameters"]]["df1"] + 1),
max_trial = 100, tol = 1e-15)
# <-><-><-><->
res <- check_point_location(lpt = tmp, lhs = l_hs)
# <-><-><-><->
expect_equivalent(tmp$points.on.crb, NA)
expect_equivalent(res$points.on.crb, TRUE)
})
test_that("check_point_location_fails", {
tico <- 3:10
b1 <- dip1$type == "R"
# Hotelling's T2 statistics
l_hs <- get_T2_two(m1 = as.matrix(dip1[b1, tico]),
m2 = as.matrix(dip1[!b1, tico]),
signif = 0.05)
# Get points by aid of the Method of Lagrange Multipliers (MLM) and by
# Newton-Raphson (nera) optimisation
tmp <- gep_by_nera(n_p = as.numeric(l_hs[["Parameters"]]["df1"]),
kk = as.numeric(l_hs[["Parameters"]]["K"]),
mean_diff = l_hs[["means"]][["mean.diff"]],
m_vc = l_hs[["S.pool"]],
ff_crit = as.numeric(l_hs[["Parameters"]]["F.crit"]),
y = rep(1, times = l_hs[["Parameters"]]["df1"] + 1),
max_trial = 100, tol = 1e-15)
# Prepare non-standard lists
l_hs_mod <- l_hs[c(1:3)]
tmp_mod <- tmp[c(1:2, 4:5)]
# <-><-><-><->
expect_error(
check_point_location(lpt = "lpt", lhs = l_hs),
"lpt must be a list ")
expect_error(
check_point_location(lpt = tmp_mod, lhs = l_hs),
"lpt must be a list ")
expect_error(
check_point_location(lpt = tmp, lhs = "l_hs"),
"lhs must be a list ")
expect_error(
check_point_location(lpt = tmp, lhs = l_hs_mod),
"lhs must be a list ")
})
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