Nothing
test_that("Invalid `g`.", {
expect_error(el_eval(matrix(c(1, 1), ncol = 2)))
expect_error(el_eval(matrix(c(1, 1, 2, NA), ncol = 2)))
expect_error(el_eval(matrix(c(1, 1, 2, 2), ncol = 2)))
expect_error(el_eval(matrix(c(1, 1, 2, 2), ncol = 2), weights = c(1, 2)))
})
test_that("Invalid `control`.", {
expect_error(el_eval(women$height - 67, control = list(maxit = 200L)))
})
test_that("Convergence check.", {
x <- women$weight
grid <- seq(120, 160, length.out = 1000)
conv <- function(par) {
el_eval(x - par)$optim$convergence
}
expect_true(all(vapply(grid, conv, FUN.VALUE = logical(1))))
})
test_that("Probabilities add up to 1.", {
x <- women$height
w <- women$weight
fit <- el_eval(x - 60)
expect_equal(sum(exp(fit$logp)), 1, tolerance = 1e-07)
fit2 <- el_eval(x - 60, weights = w)
expect_equal(sum(exp(fit2$logp)), 1, tolerance = 1e-07)
})
test_that("Conversion between `logl` and `loglr`.", {
x <- women$height
n <- length(x)
w <- women$weight
fit <- el_eval(x - 60)
expect_equal(fit$logl + n * log(n), fit$loglr, tolerance = 1e-07)
wfit <- el_eval(x - 60, weights = w)
w <- weights(wfit)
expect_equal(wfit$logl + sum(w * (log(n) - log(w))), wfit$loglr,
tolerance = 1e-07
)
})
test_that("No missing values are returned.", {
x <- women$height
n <- length(x)
w <- women$weight
fit <- el_eval(x - 60)
expect_false(any(is.na(fit$logp)))
expect_false(any(is.nan(fit$logp)))
})
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