Nothing
# Unit tests on the generic function
if (require(forecast) & require(testthat)) {
numObs <- 100L
context("Testing generic functions")
set.seed(2345)
# Seaosnal data is required for the stlm model and will throw a warning
inputSeries <- 10 + rnorm(numObs)
expect_warning(hm <- hybridModel(y = inputSeries))
test_that("Testing summary and print methods", {
# The generic methods should not throw an error
expect_error(summary(hm), NA)
expect_error(print(hm), NA)
expect_true(is.hybridModel(hm))
})
test_that("Testing generics is.hybridModel(), fitted(), residuals(), and accuracy()", {
inputSeries <- subset(USAccDeaths, end = 25)
# add some seasonality so there are roots to plot in the arima model
inputSeries <- 100 * (1:12) + USAccDeaths
exampleModel <- hybridModel(inputSeries)
expect_true(is.hybridModel(exampleModel))
expect_equal(length(fitted(exampleModel)),
length(residuals(exampleModel)))
expect_equal(length(fitted(exampleModel, individual = TRUE)),
length(residuals(exampleModel, individual = TRUE)))
expect_error(accuracy(exampleModel), NA)
expect_error(accuracy(exampleModel, individual = TRUE), NA)
expect_error(plot(exampleModel, type = "fit", ggplot = FALSE), NA)
expect_error(plot(exampleModel, type = "models", ggplot = FALSE), NA)
expect_error(plot(exampleModel, type = "fit", ggplot = TRUE), NA)
expect_error(plot(exampleModel, type = "models", ggplot = TRUE), NA)
})
test_that("Testing fitted and residual methods", {
# The generic methods should not throw an error
expect_error(fitted(hm), NA)
expect_error(residuals(hm), NA)
expect_error(fitted(hm, individual = TRUE), NA)
expect_error(residuals(hm, individual = TRUE), NA)
# There should be a fitted and residual for each observation in the input series
expect_true(length(fitted(hm)) == numObs)
expect_true(length(residuals(hm)) == numObs)
expect_true(length(residuals(hm, individual = TRUE)$nnetar) == numObs)
expect_true(length(fitted(hm, individual = TRUE)$tbats) == numObs)
# TSP attributes should match when the input series is not a ts object
expect_equal(tsp(residuals(hm)), tsp(fitted(hm)))
# Residuals should be smaller than actual or fitted values
hm <- hybridModel(wineind, model = "fs")
expect_true(all(residuals(hm) < fitted(hm), na.rm = TRUE))
expect_true(all(residuals(hm) < wineind, na.rm = TRUE))
# TSP attributes should match when the input series is a ts object
expect_equal(tsp(residuals(hm)), tsp(fitted(hm)))
expect_equal(tsp(wineind), tsp(fitted(hm)))
})
}
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