o <- optim(c(1, 1, 1),
normal_poisson_log_likelihood,
control = list(fnscale = -1, maxit = 1e6),
hessian = TRUE,
X = normal_poisson_x, N = normal_poisson_n)
b <- brix_simple(optimizer = optim,
#fn = normal_poisson_log_likelihood,
par = c(1, 1, 1),
control = list(fnscale = -1, maxit = 1e6),
hessian = TRUE,
fn = normal_poisson_log_likelihood,
X = normal_poisson_x, N = normal_poisson_n)
test_that("The log-likehood works", {
#
expect_true(class(normal_poisson_log_likelihood(c(1,2,3), X = normal_poisson_x, N = normal_poisson_n)) == "numeric")
expect_true(class(normal_poisson_log_likelihood(c(1,2,3), X = normal_poisson_x, N = normal_poisson_n, check = 2)) == "character")
expect_true(normal_poisson_log_likelihood(check = 3) == "normal-poisson")
})
test_that("brix_simple outputs correctly", {
#
expect_output(str(b$par), "Named num")
#
expect_true(class(b$hessian) == "matrix")
expect_true(class(b$X) == "data.frame")
expect_true(class(b$N) == "data.frame")
})
test_that("Estimates are reasonable", {
#
expect_equal(o$par[3],
0.1, tolerance = 0.1)
#
expect_equal(unname(b$par["lambda"]), # removes name first
o$par[3])
})
a <- augment(object = b)
a
plot(a)
plot(a, augment = FALSE)
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