test_that("dist_spec returns correct output for fixed lognormal distribution", {
result <- discretise(LogNormal(meanlog = 5, sdlog = 1, max = 19))
expect_equal(get_distribution(result), "nonparametric")
expect_equal(max(result), 19)
expect_equal(
as.vector(round(get_pmf(result), 2)),
c(0.00, 0.00, 0.00, 0.00, 0.01, 0.01, 0.02, 0.03,
0.03, 0.04, 0.05, 0.06, 0.07, 0.07, 0.08, 0.09,
0.10, 0.11, 0.11, 0.12)
)
})
test_that("dist_spec returns correct output for uncertain gamma distribution", {
result <- discretise(
Gamma(shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 19),
strict = FALSE
)
expect_equal(get_parameters(result)$shape$parameters$mean, 3)
expect_equal(get_parameters(result)$shape$parameters$sd, 0.5)
expect_equal(get_parameters(result)$rate$parameters$mean, 2)
expect_equal(get_parameters(result)$rate$parameters$sd, 0.5)
expect_equal(get_distribution(result), "gamma")
expect_equal(max(result), 19)
})
test_that("dist_spec returns correct output for gamma distribution parameterised with scale", {
result <- Gamma(shape = 3, scale = 2)
expect_equal(get_parameters(result)$shape, 3)
expect_equal(get_parameters(result)$rate, 0.5)
expect_equal(get_distribution(result), "gamma")
expect_true(is.infinite(max(result)))
})
test_that("dist_spec returns correct output for fixed distribution", {
result <- discretise(
fix_dist(LogNormal(meanlog = Normal(5, 3), sdlog = 1, max = 19))
)
expect_equal(get_distribution(result), "nonparametric")
expect_equal(max(result), 19)
expect_equal(
as.vector(round(get_pmf(result), 2)),
c(0.00, 0.00, 0.00, 0.00, 0.01, 0.01, 0.02, 0.03,
0.03, 0.04, 0.05, 0.06, 0.07, 0.07, 0.08, 0.09,
0.10, 0.11, 0.11, 0.12)
)
})
test_that("dist_spec returns error when mixed natural and unnatural parameters are specified", {
expect_error(
LogNormal(meanlog = 5, sd = 1, max = 20),
"Incompatible combination."
)
})
test_that("dist_spec returns error when the wrong number of parameters are given", {
expect_error(LogNormal(sd = 1, max = 20), "must be specified")
expect_error(Gamma(shape = 1, rate = 2, mean = 3), "must be specified")
})
test_that("c.dist_spec returns correct output for sum of two distributions", {
dist1 <- LogNormal(meanlog = 5, sdlog = 1, max = 19)
dist2 <- Gamma(shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 20)
result <- dist1 + dist2
expect_equal(get_parameters(result, 1)$meanlog, 5)
expect_equal(get_parameters(result, 1)$sdlog, 1)
expect_equal(get_parameters(get_parameters(result, 2)$shape)$mean, 3)
expect_equal(get_parameters(get_parameters(result, 2)$shape)$sd, 0.5)
expect_equal(get_parameters(get_parameters(result, 2)$rate)$mean, 2)
expect_equal(get_parameters(get_parameters(result, 2)$rate)$sd, 0.5)
expect_equal(length(result), 2)
})
test_that("collapse returns correct output for sum of two nonparametric distributions", {
dist1 <- NonParametric(c(0.1, 0.2, 0.3, 0.4))
dist2 <- NonParametric(c(0.1, 0.2, 0.3, 0.4))
result <- collapse(c(dist1, dist2))
expect_equal(get_distribution(result), "nonparametric")
expect_equal(max(result), 6)
expect_equal(ndist(result), 1)
expect_equal(
round(get_pmf(result), 2),
c(0.01, 0.04, 0.10, 0.20, 0.25, 0.24, 0.16)
)
})
test_that("`bound_dist` function can be applied to a convolution", {
# Create distributions
dist1 <- LogNormal(meanlog = 1.6, sdlog = 1, max = 19)
dist2 <- Gamma(mean = 3, sd = 2, max = 19)
# Compute combined distribution with default tolerance
combined <- collapse(discretise(c(dist1, dist2)))
# Compute combined distribution with larger tolerance
combined_tolerance <- bound_dist(combined, tolerance = 0.001)
combined_pmf <- get_pmf(combined)
combined_tolerance_pmf <- get_pmf(combined_tolerance)
# The length of the combined PMF should be greater with default tolerance
expect_true(length(combined_pmf) > length(combined_tolerance_pmf))
# Both should sum to 1
expect_equal(sum(combined_pmf), 1)
expect_equal(sum(combined_tolerance_pmf), 1)
# The first 5 entries should be within 0.01 of each other
expect_equal(
combined_pmf[1:5], combined_tolerance_pmf[1:5], tolerance = 0.01
)
expect_equal(mean(combined), mean(combined_tolerance), tolerance = 0.1)
})
test_that("summary functions return correct output for fixed lognormal distribution", {
dist <- discretise(LogNormal(mean = 3, sd = 1, max = 19))
expect_equal(mean(dist), 3.0, tolerance = 0.01)
expect_equal(EpiNow2:::sd(dist), 1.34, tolerance = 0.01)
expect_equal(max(dist), 19L)
})
test_that("summary functions return correct output for uncertain gamma distribution", {
dist <- Gamma(shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 19)
expect_equal(mean(dist, ignore_uncertainty = TRUE), 1.5)
expect_equal(max(dist), 19L)
})
test_that("mean returns correct output for sum of two distributions", {
dist1 <- LogNormal(meanlog = 1, sdlog = 1, max = 19)
dist2 <- Gamma(mean = 3, sd = 2, max = 19)
dist <- dist1 + dist2
expect_equal(mean(dist), c(4.48, 3), tolerance = 0.001)
expect_equal(EpiNow2:::sd(dist), c(5.87, 2), tolerance = 0.001)
## shortened due to tolerance level
expect_equal(max(dist), c(19L, 19L))
})
test_that("mean returns NA when applied to uncertain distributions", {
dist <- Gamma(shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 19)
expect_true(is.na(mean(dist)))
})
test_that("sd returns NA when applied to uncertain distributions", {
dist <- Gamma(shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 19)
expect_true(is.na(EpiNow2:::sd(dist)))
})
test_that("print.dist_spec correctly prints the parameters of the fixed lognormal", {
dist <- discretise(LogNormal(meanlog = 1.5, sdlog = 0.5, max = 19))
expect_output(print(dist),"- nonparametric distribution\\n PMF: \\[0\\.00068 0\\.027 0\\.11 0\\.18 0\\.19 0\\.16 0\\.11 0\\.078 0\\.052 0\\.035 0\\.023 0\\.015 0\\.0099 0\\.0065 0\\.0044 0\\.003 0\\.002 0\\.0014 0\\.00095 0\\.00066\\]" )
})
test_that("print.dist_spec correctly prints the parameters of the uncertain gamma", {
gamma <- Gamma(
shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 19
)
expect_output(print(gamma), "- gamma distribution \\(max: 19\\):\\n shape:\\n - normal distribution:\\n mean:\\n 3\\n sd:\\n 0\\.5\\n rate:\\n - normal distribution:\\n mean:\\n 2\\n sd:\\n 0\\.5")
})
test_that("print.dist_spec correctly prints the parameters of the uncertain lognormal", {
dist <- LogNormal(
meanlog = Normal(1.5, 0.1), sdlog = Normal(0.5, 0.1), max = 19
)
expect_output(print(dist), "- lognormal distribution \\(max: 19\\):\\n meanlog:\\n - normal distribution:\\n mean:\\n 1\\.5\\n sd:\\n 0\\.1\\n sdlog:\\n - normal distribution:\\n mean:\\n 0\\.5\\n sd:\\n 0\\.1")
})
test_that("print.dist_spec correctly prints the parameters of a combination of distributions", {
dist1 <- LogNormal(meanlog = 1.5, sdlog = 0.5, max = 19)
dist2 <- Gamma(shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 19)
combined <- dist1 + dist2
expect_output(print(combined), "Composite distribution:\\n- lognormal distribution \\(max: 19\\):\\n meanlog:\\n 1\\.5\\n sdlog:\\n 0\\.5\\n- gamma distribution \\(max: 19\\):\\n shape:\\n - normal distribution:\\n mean:\\n 3\\n sd:\\n 0\\.5\\n rate:\\n - normal distribution:\\n mean:\\n 2\\n sd:\\n 0\\.5")
})
test_that("plot.dist_spec returns a ggplot object", {
dist <- LogNormal(meanlog = 1.6, sdlog = 0.5, max = 19)
plot <- plot(dist)
expect_s3_class(plot, "ggplot")
})
test_that("plot.dist_spec correctly plots a single distribution", {
dist <- LogNormal(meanlog = 1.6, sdlog = 0.5, max = 19)
plot <- plot(dist)
expect_equal(length(plot$layers), 2)
expect_equal(length(plot$facet$params$facets), 1)
})
test_that("plot.dist_spec correctly plots multiple distributions", {
dist1 <- LogNormal(meanlog = 1.6, sdlog = 0.5, max = 19)
dist2 <- Gamma(shape = Normal(3, 5), rate = Normal(1, 2), max = 19)
combined <- dist1 + dist2
plot <- plot(combined)
expect_equal(length(plot$layers), 2)
expect_equal(length(plot$facet$params$facets), 1)
})
test_that("plot.dist_spec correctly plots a combination of fixed distributions", {
dist <- LogNormal(meanlog = 1.6, sdlog = 0.5, max = 19)
combined <- dist + dist
plot <- plot(combined)
expect_equal(length(plot$layers), 2)
expect_equal(length(plot$facet$params$facets), 1)
})
test_that("fix_dist works with composite delay distributions", {
dist1 <- LogNormal(meanlog = Normal(1, 0.1), sdlog = 1, max = 19)
dist2 <- Gamma(mean = 3, sd = 2, max = 19)
dist <- dist1 + dist2
expect_equal(ndist(collapse(discretise(fix_dist(dist)))), 1L)
})
test_that("composite delay distributions can be disassembled", {
dist1 <- LogNormal(meanlog = Normal(1, 0.1), sdlog = 1, max = 19)
dist2 <- Gamma(mean = 3, sd = 2, max = 19)
dist <- dist1 + dist2
expect_equal(EpiNow2:::extract_single_dist(dist, 1), dist1)
expect_equal(EpiNow2:::extract_single_dist(dist, 2), dist2)
})
test_that("constrained distributions are correctly identified", {
expect_false(is_constrained(Gamma(shape = 3, scale = 2)))
expect_true(is_constrained(Gamma(shape = 3, scale = 2, max = 10)))
expect_true(is_constrained(Gamma(shape = 3, scale = 2, tolerance = 0.1)))
expect_false(is_constrained(
Gamma(shape = 3, scale = 2) +
Gamma(shape = 3, scale = 2, max = 10)
))
expect_true(is_constrained(
Gamma(shape = 3, scale = 2, max = 10) +
Gamma(shape = 3, scale = 2, max = 10)
))
})
test_that("delay distributions can be specified in different ways", {
expect_equal(
unname(as.numeric(get_parameters(LogNormal(mean = 4, sd = 1)))),
c(1.4, 0.25),
tolerance = 0.1
)
expect_equal(
round(get_pmf(discretise(LogNormal(mean = 4, sd = 1, max = 10))), 2),
c(0.00, 0.00, 0.07, 0.27, 0.35, 0.21, 0.07, 0.02, 0.00, 0.00, 0.00)
)
expect_equal(
round(get_pmf(discretise(LogNormal(mean = 4, sd = 1, tolerance = 0.1))), 2),
c(0.00, 0.00, 0.08, 0.28, 0.36, 0.21, 0.07)
)
expect_equal(
unname(as.numeric(get_parameters(Gamma(mean = 4, sd = 1)))),
c(16, 4),
tolerance = 0.1
)
expect_equal(
round(get_pmf(discretise(Gamma(mean = 4, sd = 1, max = 7))), 2),
c(0.00, 0.00, 0.08, 0.26, 0.35, 0.22, 0.08, 0.02)
)
expect_equal(
round(get_pmf(discretise(Gamma(mean = 4, sd = 1, tolerance = 0.1))), 2),
c(0.00, 0.00, 0.08, 0.27, 0.35, 0.22, 0.08)
)
expect_equal(
unname(as.numeric(
get_parameters(get_parameters(
c(
Gamma(
shape = Normal(12, 3), rate = Normal(3, 0.5)
),
Gamma(
shape = Normal(16, 2), rate = Normal(4, 1)
)
), 2
)$shape)
)),
c(16, 2)
)
expect_equal(
unname(as.numeric(
get_parameters(get_parameters(
Gamma(
shape = Normal(16, 2), rate = Normal(4, 1)
)
)$rate)
)),
c(4, 1)
)
expect_equal(
unname(as.numeric(get_parameters(Normal(mean = 4, sd = 1)))), c(4, 1)
)
expect_equal(
round(get_pmf(discretise(Normal(mean = 4, sd = 1, max = 5))), 2),
c(0.00, 0.01, 0.09, 0.26, 0.38, 0.26)
)
expect_equal(
round(get_pmf(discretise(Normal(mean = 4, sd = 1, tolerance = 0.1))), 2),
c(0.00, 0.01, 0.08, 0.24, 0.35, 0.24, 0.08)
)
expect_equal(get_pmf(discretise(Fixed(value = 3))), c(0, 0, 0, 1))
expect_equal(get_parameters(Fixed(value = 3.5))$value, 3.5)
expect_equal(
get_pmf(NonParametric(c(0.1, 0.3, 0.2, 0.4))),
c(0.1, 0.3, 0.2, 0.4)
)
expect_equal(
round(get_pmf(NonParametric(c(0.1, 0.3, 0.2, 0.1, 0.1))), 2),
c(0.12, 0.37, 0.25, 0.12, 0.12)
)
expect_equal(
get_distribution(NonParametric(c(0.1, 0.3, 0.2, 0.1, 0.1))),
"nonparametric"
)})
test_that("get functions report errors", {
expect_error(get_parameters("test"), "only get parameters")
expect_error(get_distribution(Gamma(mean = 4, sd = 1), 2), "can't be greater")
expect_error(get_pmf(Gamma(mean = 4, sd = 1)), "parametric")
expect_error(
get_parameters(NonParametric(c(0.1, 0.3, 0.2, 0.1, 0.1))),
"nonparametric"
)
expect_error(get_parameters(c(
Gamma(mean = 4, sd = 1), Gamma(mean = 4, sd = 1)
)), "must be specified")
})
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