Test plotting functions with all saved models

tests/testthat/test-coev_plot_delta_theta.R

@@ -1,31 +1,51 @@
 test_that("coev_plot_delta_theta() produces expected errors and output", {
-  # load model
-  m <- readRDS(test_path("fixtures", "coevfit_example1.rds"))
-  m <- reload_fit(m, filename = "coevfit_example1-1.csv")
+  # load models
+  m1 <- readRDS(test_path("fixtures", "coevfit_example1.rds"))
+  m2 <- readRDS(test_path("fixtures", "coevfit_example2.rds"))
+  m3 <- readRDS(test_path("fixtures", "coevfit_example3.rds"))
+  m4 <- readRDS(test_path("fixtures", "coevfit_example4.rds"))
+  m5 <- readRDS(test_path("fixtures", "coevfit_example5.rds"))
+  m6 <- readRDS(test_path("fixtures", "coevfit_example6.rds"))
+  m1 <- reload_fit(m1, filename = "coevfit_example1-1.csv")
+  m2 <- reload_fit(m2, filename = "coevfit_example2-1.csv")
+  m3 <- reload_fit(m3, filename = "coevfit_example3-1.csv")
+  m4 <- reload_fit(m4, filename = "coevfit_example4-1.csv")
+  m5 <- reload_fit(m5, filename = "coevfit_example5-1.csv")
+  m6 <- reload_fit(m6, filename = "coevfit_example6-1.csv")
   # expect the following errors
   expect_error(
     coev_plot_delta_theta(object = "fail"),
     "Argument 'object' must be a fitted coevolutionary model of class coevfit."
   )
   expect_error(
-    coev_plot_delta_theta(object = m, variables = NA),
+    coev_plot_delta_theta(object = m1, variables = NA),
     "Argument 'variables' must be a character vector."
   )
   expect_error(
-    coev_plot_delta_theta(object = m, variables = "fail"),
+    coev_plot_delta_theta(object = m1, variables = "fail"),
     "Argument 'variables' must be of length > 1."
   )
   expect_error(
-    coev_plot_delta_theta(object = m, variables = c("x", "y", "fail")),
+    coev_plot_delta_theta(object = m1, variables = c("x", "y", "fail")),
     "Some variables in 'variables' are not included in the fitted model."
   )
   expect_error(
-    coev_plot_delta_theta(object = m, variables = c("x", "y", "y")),
+    coev_plot_delta_theta(object = m1, variables = c("x", "y", "y")),
     "Argument 'variables' contains duplicates."
   )
   # suppress warnings
   SW <- suppressWarnings
   # should run without error and produce ggplot object
-  expect_no_error(SW(coev_plot_delta_theta(m)))
-  expect_true(methods::is(SW(coev_plot_delta_theta(m)), "ggplot"))
+  expect_no_error(SW(coev_plot_delta_theta(m1)))
+  expect_no_error(SW(coev_plot_delta_theta(m2)))
+  expect_no_error(SW(coev_plot_delta_theta(m3)))
+  expect_no_error(SW(coev_plot_delta_theta(m4)))
+  expect_no_error(SW(coev_plot_delta_theta(m5)))
+  expect_no_error(SW(coev_plot_delta_theta(m6)))
+  expect_true(methods::is(SW(coev_plot_delta_theta(m1)), "ggplot"))
+  expect_true(methods::is(SW(coev_plot_delta_theta(m2)), "ggplot"))
+  expect_true(methods::is(SW(coev_plot_delta_theta(m3)), "ggplot"))
+  expect_true(methods::is(SW(coev_plot_delta_theta(m4)), "ggplot"))
+  expect_true(methods::is(SW(coev_plot_delta_theta(m5)), "ggplot"))
+  expect_true(methods::is(SW(coev_plot_delta_theta(m6)), "ggplot"))
 })

tests/testthat/test-coev_plot_flowfield.R

@@ -1,45 +1,62 @@
 test_that("coev_plot_flowfield() produces expected errors and output", {
-  # load model
-  m <- readRDS(test_path("fixtures", "coevfit_example1.rds"))
-  m <- reload_fit(m, filename = "coevfit_example1-1.csv")
+  # load models
+  m1 <- readRDS(test_path("fixtures", "coevfit_example1.rds"))
+  m2 <- readRDS(test_path("fixtures", "coevfit_example2.rds"))
+  m3 <- readRDS(test_path("fixtures", "coevfit_example3.rds"))
+  m4 <- readRDS(test_path("fixtures", "coevfit_example4.rds"))
+  m5 <- readRDS(test_path("fixtures", "coevfit_example5.rds"))
+  m6 <- readRDS(test_path("fixtures", "coevfit_example6.rds"))
+  m1 <- reload_fit(m1, filename = "coevfit_example1-1.csv")
+  m2 <- reload_fit(m2, filename = "coevfit_example2-1.csv")
+  m3 <- reload_fit(m3, filename = "coevfit_example3-1.csv")
+  m4 <- reload_fit(m4, filename = "coevfit_example4-1.csv")
+  m5 <- reload_fit(m5, filename = "coevfit_example5-1.csv")
+  m6 <- reload_fit(m6, filename = "coevfit_example6-1.csv")
   # expect the following errors
   expect_error(
     coev_plot_flowfield(object = "fail", var1 = "x", var2 = "y"),
     "Argument 'object' must be a fitted coevolutionary model of class coevfit."
   )
   expect_error(
-    coev_plot_flowfield(object = m, var1 = 0:1, var2 = "y"),
+    coev_plot_flowfield(object = m1, var1 = 0:1, var2 = "y"),
     "Argument 'var1' must be a character string of length one."
   )
   expect_error(
-    coev_plot_flowfield(object = m, var1 = "z", var2 = "y"),
+    coev_plot_flowfield(object = m1, var1 = "z", var2 = "y"),
     "Argument 'var1' must be a variable included in the fitted model."
   )
   expect_error(
-    coev_plot_flowfield(object = m, var1 = "x", var2 = 0:1),
+    coev_plot_flowfield(object = m1, var1 = "x", var2 = 0:1),
     "Argument 'var2' must be a character string of length one."
   )
   expect_error(
-    coev_plot_flowfield(object = m, var1 = "x", var2 = "z"),
+    coev_plot_flowfield(object = m1, var1 = "x", var2 = "z"),
     "Argument 'var2' must be a variable included in the fitted model."
   )
   expect_error(
-    coev_plot_flowfield(object = m, var1 = "x", var2 = "x"),
+    coev_plot_flowfield(object = m1, var1 = "x", var2 = "x"),
     "Argument 'var1' and 'var2' must refer to different variables."
   )
   expect_error(
     coev_plot_flowfield(
-      object = m, var1 = "x", var2 = "y", nullclines = "hello"
+      object = m1, var1 = "x", var2 = "y", nullclines = "hello"
       ),
     "Argument 'nullclines' must be logical."
   )
-  # suppress warnings
-  SW <- suppressWarnings
   # should run without error
-  expect_no_error(
-    SW(coev_plot_flowfield(m, var1 = "x", var2 = "y"))
-    )
-  expect_no_error(
-    SW(coev_plot_flowfield(m, var1 = "x", var2 = "y", nullclines = TRUE))
-    )
+  fun <- function(model, var1, var2, nullclines = FALSE) {
+    suppressWarnings(coev_plot_flowfield(model, var1, var2, nullclines))
+  }
+  expect_no_error(fun(m1, "x", "y"))
+  expect_no_error(fun(m2, "w", "x"))
+  expect_no_error(fun(m3, "w", "x"))
+  expect_no_error(fun(m4, "y", "z"))
+  expect_no_error(fun(m5, "w", "x"))
+  expect_no_error(fun(m6, "w", "x"))
+  expect_no_error(fun(m1, "x", "y", nullclines = TRUE))
+  expect_no_error(fun(m2, "w", "x", nullclines = TRUE))
+  expect_no_error(fun(m3, "w", "x", nullclines = TRUE))
+  expect_no_error(fun(m4, "y", "z", nullclines = TRUE))
+  expect_no_error(fun(m5, "w", "x", nullclines = TRUE))
+  expect_no_error(fun(m6, "w", "x", nullclines = TRUE))
 })

tests/testthat/test-coev_plot_predictive_check.R

@@ -1,44 +1,69 @@
 test_that("coev_plot_predictive_check() produces expected errors and output", {
-  # load model
-  m <- readRDS(test_path("fixtures", "coevfit_example1.rds"))
-  m <- reload_fit(m, filename = "coevfit_example1-1.csv")
+  # load models
+  m1 <- readRDS(test_path("fixtures", "coevfit_example1.rds"))
+  m2 <- readRDS(test_path("fixtures", "coevfit_example2.rds"))
+  m3 <- readRDS(test_path("fixtures", "coevfit_example3.rds"))
+  m4 <- readRDS(test_path("fixtures", "coevfit_example4.rds"))
+  m5 <- readRDS(test_path("fixtures", "coevfit_example5.rds"))
+  m6 <- readRDS(test_path("fixtures", "coevfit_example6.rds"))
+  m1 <- reload_fit(m1, filename = "coevfit_example1-1.csv")
+  m2 <- reload_fit(m2, filename = "coevfit_example2-1.csv")
+  m3 <- reload_fit(m3, filename = "coevfit_example3-1.csv")
+  m4 <- reload_fit(m4, filename = "coevfit_example4-1.csv")
+  m5 <- reload_fit(m5, filename = "coevfit_example5-1.csv")
+  m6 <- reload_fit(m6, filename = "coevfit_example6-1.csv")
   # expect the following errors
   expect_error(
     coev_plot_predictive_check(object = "fail"),
     "Argument 'object' must be a fitted coevolutionary model of class coevfit."
   )
   expect_error(
-    coev_plot_predictive_check(object = m, variables = 0),
+    coev_plot_predictive_check(object = m1, variables = 0),
     paste0(
       "Argument 'variables' must be a character string or a ",
       "vector of character strings."
     )
   )
   expect_error(
-    coev_plot_predictive_check(object = m, variables = "fail"),
+    coev_plot_predictive_check(object = m1, variables = "fail"),
     paste0(
       "Argument 'variables' contains variable names that are not ",
       "included in the fitted model."
     )
   )
   expect_error(
-    coev_plot_predictive_check(object = m, ndraws = "fail"),
+    coev_plot_predictive_check(object = m1, ndraws = "fail"),
     "Argument 'ndraws' must be a single integer."
   )
   expect_error(
-    coev_plot_predictive_check(object = m, ndraws = 0L),
+    coev_plot_predictive_check(object = m1, ndraws = 0L),
     "Argument 'ndraws' must be between 1 and the total number of draws."
   )
   expect_error(
     coev_plot_predictive_check(
-      object = m, ndraws = as.integer(nrow(m$fit$draws()) + 1)
+      object = m1, ndraws = as.integer(nrow(m1$fit$draws()) + 1)
       ),
     "Argument 'ndraws' must be between 1 and the total number of draws."
   )
   # suppress warnings
   SW <- suppressWarnings
   # should run without error and produce list of ggplot objects
-  expect_no_error(SW(coev_plot_predictive_check(m)))
-  expect_no_error(SW(coev_plot_predictive_check(m, variables = c("u","v"))))
-  expect_no_error(SW(coev_plot_predictive_check(m, ndraws = 1L)))
+  expect_no_error(SW(coev_plot_predictive_check(m1)))
+  expect_no_error(SW(coev_plot_predictive_check(m2)))
+  expect_no_error(SW(coev_plot_predictive_check(m3)))
+  expect_no_error(SW(coev_plot_predictive_check(m4)))
+  expect_no_error(SW(coev_plot_predictive_check(m5)))
+  expect_no_error(SW(coev_plot_predictive_check(m6)))
+  expect_no_error(SW(coev_plot_predictive_check(m1, variables = "x")))
+  expect_no_error(SW(coev_plot_predictive_check(m2, variables = "w")))
+  expect_no_error(SW(coev_plot_predictive_check(m3, variables = "w")))
+  expect_no_error(SW(coev_plot_predictive_check(m4, variables = "y")))
+  expect_no_error(SW(coev_plot_predictive_check(m5, variables = "w")))
+  expect_no_error(SW(coev_plot_predictive_check(m6, variables = "w")))
+  expect_no_error(SW(coev_plot_predictive_check(m1, ndraws = 1L)))
+  expect_no_error(SW(coev_plot_predictive_check(m2, ndraws = 1L)))
+  expect_no_error(SW(coev_plot_predictive_check(m3, ndraws = 1L)))
+  expect_no_error(SW(coev_plot_predictive_check(m4, ndraws = 1L)))
+  expect_no_error(SW(coev_plot_predictive_check(m5, ndraws = 1L)))
+  expect_no_error(SW(coev_plot_predictive_check(m6, ndraws = 1L)))
 })

tests/testthat/test-coev_plot_selection_gradient.R

@@ -1,51 +1,68 @@
 test_that("coev_plot_selection_gradient() produces expected errors and output", {
-  # load model
-  m <- readRDS(test_path("fixtures", "coevfit_example1.rds"))
-  m <- reload_fit(m, filename = "coevfit_example1-1.csv")
+  # load models
+  m1 <- readRDS(test_path("fixtures", "coevfit_example1.rds"))
+  m2 <- readRDS(test_path("fixtures", "coevfit_example2.rds"))
+  m3 <- readRDS(test_path("fixtures", "coevfit_example3.rds"))
+  m4 <- readRDS(test_path("fixtures", "coevfit_example4.rds"))
+  m5 <- readRDS(test_path("fixtures", "coevfit_example5.rds"))
+  m6 <- readRDS(test_path("fixtures", "coevfit_example6.rds"))
+  m1 <- reload_fit(m1, filename = "coevfit_example1-1.csv")
+  m2 <- reload_fit(m2, filename = "coevfit_example2-1.csv")
+  m3 <- reload_fit(m3, filename = "coevfit_example3-1.csv")
+  m4 <- reload_fit(m4, filename = "coevfit_example4-1.csv")
+  m5 <- reload_fit(m5, filename = "coevfit_example5-1.csv")
+  m6 <- reload_fit(m6, filename = "coevfit_example6-1.csv")
   # expect the following errors
   expect_error(
     coev_plot_selection_gradient(object = "fail", var1 = "x", var2 = "y"),
     "Argument 'object' must be a fitted coevolutionary model of class coevfit."
   )
   expect_error(
-    coev_plot_selection_gradient(object = m, var1 = 0:1, var2 = "y"),
+    coev_plot_selection_gradient(object = m1, var1 = 0:1, var2 = "y"),
     "Argument 'var1' must be a character string of length one."
   )
   expect_error(
-    coev_plot_selection_gradient(object = m, var1 = "z", var2 = "y"),
+    coev_plot_selection_gradient(object = m1, var1 = "z", var2 = "y"),
     "Argument 'var1' must be a variable included in the fitted model."
   )
   expect_error(
-    coev_plot_selection_gradient(object = m, var1 = "x", var2 = 0:1),
+    coev_plot_selection_gradient(object = m1, var1 = "x", var2 = 0:1),
     "Argument 'var2' must be a character string of length one."
   )
   expect_error(
-    coev_plot_selection_gradient(object = m, var1 = "x", var2 = "z"),
+    coev_plot_selection_gradient(object = m1, var1 = "x", var2 = "z"),
     "Argument 'var2' must be a variable included in the fitted model."
   )
   expect_error(
-    coev_plot_selection_gradient(object = m, var1 = "x", var2 = "x"),
+    coev_plot_selection_gradient(object = m1, var1 = "x", var2 = "x"),
     "Argument 'var1' and 'var2' must refer to different variables."
   )
   expect_error(
     coev_plot_selection_gradient(
-      object = m, var1 = "x", var2 = "y", contour = "hello"
+      object = m1, var1 = "x", var2 = "y", contour = "hello"
       ),
     "Argument 'contour' must be logical."
   )
-  # suppress warnings
-  SW <- suppressWarnings
   # should run without error and produce ggplot object
-  expect_no_error(
-    SW(coev_plot_selection_gradient(m, var1 = "x", var2 = "y"))
-    )
-  expect_no_error(
-    SW(coev_plot_selection_gradient(m, var1 = "x", var2 = "y", contour = TRUE))
-    )
-  expect_true(
-    methods::is(
-      SW(coev_plot_selection_gradient(m, var1 = "x", var2 = "y")),
-      "ggplot"
-      )
-    )
+  fun <- function(model, var1, var2, contour = FALSE) {
+    suppressWarnings(coev_plot_selection_gradient(model, var1, var2, contour))
+  }
+  expect_no_error(fun(m1, "x", "y"))
+  expect_no_error(fun(m2, "w", "x"))
+  expect_no_error(fun(m3, "w", "x"))
+  expect_no_error(fun(m4, "y", "z"))
+  expect_no_error(fun(m5, "w", "x"))
+  expect_no_error(fun(m6, "w", "x"))
+  expect_no_error(fun(m1, "x", "y", contour = TRUE))
+  expect_no_error(fun(m2, "w", "x", contour = TRUE))
+  expect_no_error(fun(m3, "w", "x", contour = TRUE))
+  expect_no_error(fun(m4, "y", "z", contour = TRUE))
+  expect_no_error(fun(m5, "w", "x", contour = TRUE))
+  expect_no_error(fun(m6, "w", "x", contour = TRUE))
+  expect_true(methods::is(fun(m1, "x", "y"), "ggplot"))
+  expect_true(methods::is(fun(m2, "w", "x"), "ggplot"))
+  expect_true(methods::is(fun(m3, "w", "x"), "ggplot"))
+  expect_true(methods::is(fun(m4, "y", "z"), "ggplot"))
+  expect_true(methods::is(fun(m5, "w", "x"), "ggplot"))
+  expect_true(methods::is(fun(m6, "w", "x"), "ggplot"))
 })