Nothing
test_that("get_dist can detect valid and invalid distributions", {
mean = 1
sd = 2
max = 100
expect_no_error(
def_dist(
dist = "gamma",
mean = mean,
mean_sd = NA,
sd = sd,
sd_sd = NA,
max = max
)
)
expect_error(
def_dist(
dist = "logamma",
mean = mean,
mean_sd = NA,
sd = sd,
sd_sd = NA,
max = max
)
)
})
test_that("gamma, norm, and lnorm distributions get specified correctly", {
mean = 1
sd = 2
max = 100
pars.sd <- list(
dist = "gamma",
mean = mean,
mean_sd = NA,
sd = sd,
sd_sd = NA,
max = max
)
pars.shape <- list(
dist = "gamma",
mean = mean,
mean_sd = NA,
shape = mean^2/sd^2,
sd_sd = NA,
max = max
)
pars.norm <- list(
dist = "norm",
mean = mean,
sd = sd,
max = max
)
pars.lnorm <- list(
dist = "lnorm",
meanlog = log(mean),
sdlog = log(sd),
max = max
)
expect_equal(
get_discrete_dist(
pars.sd
),
get_discrete_dist(
pars.shape
)
)
expect_no_error(
get_discrete_dist(
pars.norm
)
)
expect_no_error(
get_discrete_dist(
pars.lnorm
)
)
})
test_that("get_discrete_dist detects invalid distribution", {
mean = 1
sd = 2
max = 100
pars <- list(
dist = "gammanorm",
mean = mean,
sd = sd,
max = max
)
expect_error(
get_discrete_dist(
pars
)
)
})
test_that("sample_from_dist returns error when uniform distribution not defined,
and returns numeric vector when uniform distribution defined",
{
param.error = list(
dist = "gamma",
min = 2,
max = 200
)
param.correct = purrr::list_modify(param.error,
dist = "unif")
expect_error(
sample_from_dist(
n = 200,
params = param.error
)
)
expect_vector(
sample_from_dist(
n = 200,
params = param.correct
)
)
}
)
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