moar tests

Author: Nicholas Clark

tests/testthat/test-binomial.R

@@ -127,6 +127,8 @@ test_that("binomial() post-processing works", {
                        realisations = TRUE, trend_effects = TRUE))
   expect_no_error(plot(mod, type = 'smooths',
                        residuals = TRUE, trend_effects = TRUE))
+  expect_no_error(plot(mod, type = 're',
+                       trend_effects = TRUE))
   expect_no_error(plot(mod, type = 'pterms'))
 
   expect_true(inherits(SM(conditional_effects(mod)),
@@ -307,6 +309,7 @@ test_that("bernoulli() post-processing works", {
   expect_true(inherits(hindcast(mod), 'mvgam_forecast'))
   expect_true(inherits(hindcast(mod, type = 'expected'), 'mvgam_forecast'))
 
+  expect_no_error(plot(mod, type = 're'))
   expect_no_error(plot(mod, type = 'smooths'))
   expect_no_error(plot(mod, type = 'smooths',
                        realisations = TRUE))

tests/testthat/test-mvgam-methods.R

@@ -197,6 +197,49 @@ test_that("hindcast has reasonable outputs", {
                  which(mvgam:::mvgam_example2$obs_data$series == 'series_1')])
 })
 
+test_that("plot_mvgam_series reasonable outputs", {
+  simdat <- sim_mvgam()
+  expect_no_error(plot_mvgam_series(data = simdat$data_train,
+                                    newdata = simdat$data_test))
+  expect_no_error(plot_mvgam_series(data = simdat$data_train,
+                                    newdata = simdat$data_test,
+                                    series = 'all'))
+  expect_no_error(plot_mvgam_series(data = simdat$data_train,
+                                    newdata = simdat$data_test,
+                                    lines = FALSE))
+  expect_no_error(plot_mvgam_series(data = simdat$data_train,
+                                    newdata = simdat$data_test,
+                                    lines = FALSE,
+                                    series = 'all'))
+
+  # Should also work for list data
+  dat_train <- list()
+  for(i in 1:NCOL(simdat$data_train)){
+    dat_train[[i]] <- simdat$data_train[,i]
+  }
+  names(dat_train) <- colnames(simdat$data_train)
+
+  dat_test <- list()
+  for(i in 1:NCOL(simdat$data_test)){
+    dat_test[[i]] <- simdat$data_test[,i]
+  }
+  names(dat_test) <- colnames(simdat$data_test)
+
+  expect_no_error(plot_mvgam_series(data = dat_train))
+  expect_no_error(plot_mvgam_series(data = dat_train,
+                                    newdata = dat_test))
+  expect_no_error(plot_mvgam_series(data = dat_train,
+                                    newdata = dat_test,
+                                    series = 'all'))
+  expect_no_error(plot_mvgam_series(data = dat_train,
+                                    newdata = dat_test,
+                                    lines = FALSE))
+  expect_no_error(plot_mvgam_series(data = dat_train,
+                                    newdata = dat_test,
+                                    lines = FALSE,
+                                    series = 'all'))
+})
+
 test_that("forecast has reasonable outputs", {
   skip_on_cran()
   set.seed(1234)

tests/testthat/test-mvgam.R

@@ -188,7 +188,7 @@ test_that("outcome variable must be present in data", {
                'variable y not found in data')
 })
 
-test_that("time not requried in data if this is a no trend model", {
+test_that("time not required in data if this is a no trend model", {
   data = data.frame(out = rnorm(100),
                     temp = rnorm(100))
   mod <- mvgam(formula = out ~ dynamic(temp, rho = 20),