context("mlsged")
## Data generation.
set.seed(313)
small_data <- fGarch::rsged(100, 2, 3, 4)
tiny_data <- fGarch::rsged(100, 2, 3, 4)
## Finds errors with na and data out of bounds.
expect_error(mlsged(c(tiny_data, NA)))
## Checks that na.rm works as intended.
expect_equal(
coef(mlsged(small_data)),
coef(mlsged(c(small_data, NA), na.rm = TRUE))
)
## Is the log-likelihood correct?
est <- mlsged(small_data, na.rm = TRUE)
expect_equal(
sum(fGarch::dsged(small_data, est[1], est[2], est[3], est[4], log = TRUE)),
attr(est, "logLik")
)
## Are the fallback methods working correctly?
expect_equal(
log(dml(small_data, est)),
dml(small_data, est, log = TRUE)
)
expect_equal(
log(pml(small_data, est)),
pml(small_data, est, log.p = TRUE)
)
expect_equal(
1 - pml(small_data, est),
pml(small_data, est, lower.tail = FALSE)
)
expect_equal(
qml(1:3 / 4, est),
qml(log(1:3 / 4), est, log.p = TRUE)
)
expect_equal(
qml(1 - 1:3 / 4, est),
qml(1:3 / 4, est, lower.tail = FALSE)
)
## Check class.
expect_equal(attr(est, "model"), "Skew Generalized Error")
expect_equal(class(est), "univariateML")
## Check support.
expect_equal(class(attr(est, "support"))[[1]], "numeric")
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