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
expect_warning(hm <- hybridModel(y = 10 + rnorm(numObs)))
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 is.hybridModel(), fitted.hybridModel(), residuals.hybridModel(), and accuracy.hybridModel()", {
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)
})
}
```

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