Add test coverage for mcmc_areas_ridges_data, mcmc_parcoord_data, mcmc_trace_data

NEWS.md

@@ -1,5 +1,6 @@
 # bayesplot (development version)
 
+* Added unit tests for `mcmc_areas_ridges_data()`, `mcmc_parcoord_data()`, and `mcmc_trace_data()`.
 * Added unit tests for `ppc_error_data()` and `ppc_loo_pit_data()` covering output structure, argument handling, and edge cases.
 * Added vignette sections demonstrating `*_data()` companion functions for building custom ggplot2 visualizations (#435)
 * Extract `drop_singleton_values()` helper in `mcmc_nuts_treedepth()` to remove duplicated filtering logic.

tests/testthat/test-mcmc-intervals.R

@@ -1,42 +1,5 @@
 source(test_path("data-for-mcmc-tests.R"))
 
-test_that("mcmc_intervals_data computes quantiles", {
-  xs <- melt_mcmc(merge_chains(prepare_mcmc_array(arr, pars = "beta[1]")))
-  d <- mcmc_intervals_data(arr, pars = "beta[1]",
-                           prob = .3, prob_outer = .5)
-
-  qs <- unlist(d[, c("ll", "l", "m", "h", "hh")])
-  by_hand <- quantile(xs$Value, c(.25, .35, .5, .65, .75))
-  expect_equal(qs, by_hand, ignore_attr = TRUE)
-
-  expect_equal(d$parameter, factor("beta[1]"))
-  expect_equal(d$outer_width, .5)
-  expect_equal(d$inner_width, .3)
-  expect_equal(d$point_est, "median")
-
-  d2 <- mcmc_areas_data(arr, pars = "beta[1]", prob = .3, prob_outer = .5)
-  sets <- split(d2, d2$interval)
-
-  expect_equal(range(sets$inner$x), c(d$l, d$h))
-  expect_equal(range(sets$outer$x), c(d$ll, d$hh))
-})
-
-test_that("mcmc_intervals_data computes point estimates", {
-  xs <- melt_mcmc(merge_chains(prepare_mcmc_array(arr, pars = "beta[2]")))
-  d <- mcmc_intervals_data(arr, pars = "beta[2]",
-                           prob = .3, prob_outer = .5, point_est = "mean")
-
-  expect_equal(d$m, mean(xs$Value), ignore_attr = TRUE)
-  expect_equal(d$parameter, factor("beta[2]"))
-  expect_equal(d$point_est, "mean")
-
-  d <- mcmc_intervals_data(arr, pars = "(Intercept)",
-                           prob = .3, prob_outer = .5,
-                           point_est = "none")
-  expect_true(!("m" %in% names(d)))
-  expect_equal(d$point_est, "none")
-})
-
 test_that("mcmc_intervals returns a ggplot object", {
   expect_gg(mcmc_intervals(arr, pars = "beta[1]", regex_pars = "x\\:"))
   expect_gg(mcmc_intervals(arr1chain, pars = "beta[1]", regex_pars = "Intercept"))
@@ -115,6 +78,45 @@ test_that("mcmc_intervals/areas with rhat", {
   }
 })
 
+# _data() tests ----------------------------------------------------------------
+
+test_that("mcmc_intervals_data computes quantiles", {
+  xs <- melt_mcmc(merge_chains(prepare_mcmc_array(arr, pars = "beta[1]")))
+  d <- mcmc_intervals_data(arr, pars = "beta[1]",
+                           prob = .3, prob_outer = .5)
+
+  qs <- unlist(d[, c("ll", "l", "m", "h", "hh")])
+  by_hand <- quantile(xs$Value, c(.25, .35, .5, .65, .75))
+  expect_equal(qs, by_hand, ignore_attr = TRUE)
+
+  expect_equal(d$parameter, factor("beta[1]"))
+  expect_equal(d$outer_width, .5)
+  expect_equal(d$inner_width, .3)
+  expect_equal(d$point_est, "median")
+
+  d2 <- mcmc_areas_data(arr, pars = "beta[1]", prob = .3, prob_outer = .5)
+  sets <- split(d2, d2$interval)
+
+  expect_equal(range(sets$inner$x), c(d$l, d$h))
+  expect_equal(range(sets$outer$x), c(d$ll, d$hh))
+})
+
+test_that("mcmc_intervals_data computes point estimates", {
+  xs <- melt_mcmc(merge_chains(prepare_mcmc_array(arr, pars = "beta[2]")))
+  d <- mcmc_intervals_data(arr, pars = "beta[2]",
+                           prob = .3, prob_outer = .5, point_est = "mean")
+
+  expect_equal(d$m, mean(xs$Value), ignore_attr = TRUE)
+  expect_equal(d$parameter, factor("beta[2]"))
+  expect_equal(d$point_est, "mean")
+
+  d <- mcmc_intervals_data(arr, pars = "(Intercept)",
+                           prob = .3, prob_outer = .5,
+                           point_est = "none")
+  expect_true(!("m" %in% names(d)))
+  expect_equal(d$point_est, "none")
+})
+
 test_that("mcmc_areas_data computes density", {
   areas_data <- mcmc_areas_data(arr, point_est = "none")
   areas_data <- areas_data[areas_data$interval_width == 1, ]
@@ -153,7 +155,7 @@ test_that("compute_column_density can use density options (#118)", {
   expect_error(mcmc_areas_data(arr, kernel = stop()))
 })
 
-test_that("probabilities outside of [0,1] cause an error", {
+test_that("mcmc_intervals_data errors for probabilities outside of [0,1]", {
   expect_error(mcmc_intervals_data(arr, prob = -0.1),
                "must be in \\[0,1\\]")
   expect_error(mcmc_intervals_data(arr, prob = 1.1),
@@ -164,14 +166,28 @@ test_that("probabilities outside of [0,1] cause an error", {
                "must be in \\[0,1\\]")
 })
 
-test_that("inconsistent probabilities raise warning (#138)", {
+test_that("mcmc_intervals_data warns for inconsistent probabilities (#138)", {
   expect_warning(
     mcmc_intervals_data(arr, prob = .9, prob_outer = .8),
     "`prob_outer` .* is less than `prob`"
   )
 })
 
 
+test_that("mcmc_areas_ridges_data returns correct structure", {
+  d <- mcmc_areas_ridges_data(arr, pars = c("beta[1]", "sigma"), prob = 0.5, prob_outer = 0.9)
+  expect_s3_class(d, "data.frame")
+  expect_named(
+    d,
+    c(
+      "parameter", "interval", "interval_width", "x", "density",
+      "scaled_density", "plotting_density"
+    )
+  )
+  expect_setequal(unique(d$interval), c("inner", "outer"))
+  expect_false("point" %in% d$interval)
+  expect_equal(unique(as.character(d$parameter)), c("beta[1]", "sigma"))
+})
 
 
 # Visual tests -----------------------------------------------------------------

tests/testthat/test-mcmc-scatter-and-parcoord.R

@@ -313,7 +313,6 @@ test_that("pairs_condition message if multiple args specified", {
 })
 
 
-
 # mcmc_parcoord -----------------------------------------------------------
 test_that("mcmc_parcoord returns a ggplot object", {
   expect_gg(mcmc_parcoord(arr, pars = c("(Intercept)", "sigma")))
@@ -351,7 +350,6 @@ test_that("mcmc_parcoord throws correct warnings and errors", {
   )
 })
 
-
 # parcoord_style_np -------------------------------------------------------
 test_that("parcoord_style_np returns correct structure", {
   style <- parcoord_style_np()
@@ -375,6 +373,42 @@ test_that("parcoord_style_np throws correct errors", {
   )
 })
 
+# mcmc_parcoord_data -------------------------------------------------
+
+test_that("mcmc_parcoord_data returns expected structure", {
+  d <- mcmc_parcoord_data(arr, pars = c("(Intercept)", "sigma"))
+  expect_s3_class(d, "data.frame")
+  expect_named(d, c("Draw", "Parameter", "Value", "Divergent"))
+
+  draws_by_parameter <- split(d$Draw, d$Parameter)
+  expected_draws <- seq_len(dim(arr)[1] * dim(arr)[2])
+  expect_equal(draws_by_parameter[[1]], expected_draws)
+  expect_equal(draws_by_parameter[[2]], expected_draws)
+})
+
+test_that("mcmc_parcoord_data sets Divergent to 0 when np is NULL", {
+  d <- mcmc_parcoord_data(arr, pars = c("(Intercept)", "sigma"))
+  expect_true(all(d$Divergent == 0))
+})
+
+test_that("mcmc_parcoord_data joins divergence information from np", {
+  fake_np <- data.frame(
+    Iteration = rep(seq_len(dim(arr)[1]), each = dim(arr)[2]),
+    Chain = rep(seq_len(dim(arr)[2]), times = dim(arr)[1]),
+    Parameter = factor("divergent__"),
+    Value = as.integer(rep(c(0, 1, 0, 1), times = dim(arr)[1]))
+  )
+  d <- mcmc_parcoord_data(arr, pars = c("(Intercept)", "sigma"), np = fake_np)
+
+  expect_false(anyNA(d$Divergent))
+  expect_equal(sum(d$Divergent == 1), 400)
+  expect_equal(sum(d$Divergent == 0), 400)
+})
+
+test_that("mcmc_parcoord_data errors with fewer than 2 parameters", {
+  expect_error(mcmc_parcoord_data(arr, pars = "sigma"), "at least two")
+})
+
 
 # Visual tests -----------------------------------------------------------------
 

tests/testthat/test-mcmc-traces.R

@@ -100,7 +100,44 @@ test_that("mcmc_trace 'np' argument works", {
                  "No divergences to plot.")
 })
 
+# mcmc_trace_data ----------------------------------------------------
+
+test_that("mcmc_trace_data returns plotting data with expected columns", {
+  d <- mcmc_trace_data(arr, pars = "beta[1]")
+  expect_s3_class(d, "tbl_df")
+  expect_named(
+    d,
+    c(
+      "parameter", "value", "value_rank", "iteration", "chain",
+      "n_chains", "n_iterations", "n_parameters", "highlight", "warmup"
+    )
+  )
+  expect_equal(nrow(d), dim(arr)[1] * dim(arr)[2])
+})
 
+test_that("mcmc_trace_data highlight argument works", {
+  d <- mcmc_trace_data(arr, pars = "beta[1]", highlight = 2)
+  expect_true(all(d$highlight[d$chain == 2]))
+  expect_true(all(!d$highlight[d$chain != 2]))
+})
+
+test_that("mcmc_trace_data warmup labeling works", {
+  d <- mcmc_trace_data(arr, pars = "beta[1]", n_warmup = 20)
+  expect_true(all(d$warmup[d$iteration <= 20]))
+  expect_true(all(!d$warmup[d$iteration > 20]))
+})
+
+test_that("mcmc_trace_data iter1 shifts iterations", {
+  d <- mcmc_trace_data(arr, pars = "beta[1]", iter1 = 100)
+  expect_true(min(d$iteration) == 101)
+})
+
+test_that("mcmc_trace_data computes value_rank within each parameter", {
+  d <- mcmc_trace_data(arr, pars = c("beta[1]", "beta[2]"))
+  observed_ranks <- split(d$value_rank, d$parameter)
+  expected_ranks <- lapply(split(d$value, d$parameter), rank, ties.method = "average")
+  expect_equal(observed_ranks, expected_ranks)
+})
 
 
 # Visual tests -----------------------------------------------------------------