Nothing
context("mlpower")
## Data generation.
set.seed(313)
small_data <- extraDistr::rpower(100, 1, 1)
tiny_data <- extraDistr::rpower(10, 3, 7)
medium_data <- extraDistr::rpower(1000, 9, 11)
epsilon <- .Machine$double.eps^0.5
## Checks if the ML is correct.
mle1 <- suppressWarnings(nlm(function(p) {
-sum(extraDistr::dpower(small_data, max(small_data) + epsilon, p, log = TRUE))
}, p = 1))
mle2 <- suppressWarnings(nlm(function(p) {
-sum(extraDistr::dpower(tiny_data, max(tiny_data) + epsilon, p,
log = TRUE
))
}, p = 7))
mle3 <- suppressWarnings(nlm(function(p) {
-sum(extraDistr::dpower(medium_data, max(medium_data) + epsilon, p,
log = TRUE
))
}, p = 11))
expect_equal(mle1$estimate, as.numeric(mlpower(small_data))[2],
tolerance = 1e-5
)
expect_equal(mle2$estimate, as.numeric(mlpower(tiny_data))[2],
tolerance = 1e-5
)
expect_equal(mle3$estimate, as.numeric(mlpower(medium_data))[2],
tolerance = 1e-5
)
expect_equal(-mle1$minimum, attr(mlpower(small_data), "logLik"),
tolerance = 1e-5
)
expect_equal(-mle2$minimum, attr(mlpower(tiny_data), "logLik"),
tolerance = 1e-5
)
expect_equal(-mle3$minimum, attr(mlpower(medium_data), "logLik"),
tolerance = 1e-5
)
## Finds errors with na and data out of bounds.
expect_error(mlpower(c(tiny_data, NA)))
## Checks that na.rm works as intended.
expect_equal(
coef(mlpower(small_data)),
coef(mlpower(c(small_data, NA), na.rm = TRUE))
)
est <- mlpower(tiny_data)
## Check class.
expect_equal(attr(est, "model"), "PowerDist")
expect_equal(class(est), "univariateML")
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.