Nothing
test_that("GEV", {
dist <- dist_gev(location = c(0, .5, 0), scale = c(1, 2, 3), shape = c(0, 0.1, 1.1))
euler <- 0.57721566490153286 # Euler's constant
# Mean
expect_equal(mean(dist), c(
euler,
0.5 + 2 * (gamma(0.9) - 1) / 0.1, # location + scale*(gamma(1 - shape) - 1)/shape
Inf # Since shape >= 1
), tolerance = 0.0001)
# Median
expect_equal(median(dist), c(
-log(log(2)),
0.5 + 2 * (log(2)^(-0.1) - 1) / 0.1, # location + scale*(log(2)^(-shape) - 1)/shape
3 * (log(2)^(-1.1) - 1) / 1.1 # location + scale*(log(2)^(-shape) - 1)/shape
), tolerance = 0.0001)
expect_equal(median(dist), quantile(dist, 0.5))
# Variance
expect_equal(distributional::variance(dist), c(
pi^2 / 6,
2^2 * (gamma(1 - 2 * 0.1) - gamma(1 - 0.1)^2) / 0.1^2, # scale^2 * (g2 - g1^2)/shape^2
Inf # since shape >= 0.5
), tolerance = 0.0001)
# Density
at <- (0:20) / 10
expect_equal(density(dist, at), list(
evd::dgev(at, loc = 0, scale = 1, shape = 0),
evd::dgev(at, loc = 0.5, scale = 2, shape = 0.1),
evd::dgev(at, loc = 0, scale = 3, shape = 1.1)
))
# CDF
expect_equal(distributional::cdf(dist, at), list(
evd::pgev(at, loc = 0, scale = 1, shape = 0),
evd::pgev(at, loc = 0.5, scale = 2, shape = 0.1),
evd::pgev(at, loc = 0, scale = 3, shape = 1.1)
))
# Quantiles
p <- (1:19) / 20
expect_equal(quantile(dist, p = p), list(
evd::qgev(p = p, loc = 0, scale = 1, shape = 0),
evd::qgev(p = p, loc = 0.5, scale = 2, shape = 0.1),
evd::qgev(p = p, loc = 0, scale = 3, shape = 1.1)
))
# Generate
set.seed(123)
rand_dist <- distributional::generate(dist, times = 1e6)
expect_equal(lapply(rand_dist[1:2], mean) |> unlist(),
mean(dist)[1:2],
tolerance = 0.01
)
expect_equal(lapply(rand_dist[1:2], var) |> unlist(),
distributional::variance(dist)[1:2],
tolerance = 0.01
)
expect_equal(lapply(rand_dist, median) |> unlist(),
median(dist),
tolerance = 0.01
)
})
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