Formatting probability of direction objects fails

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: bayestestR
 Title: Understand and Describe Bayesian Models and Posterior Distributions
-Version: 0.13.1.1
+Version: 0.13.1.2
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",

NAMESPACE

@@ -220,12 +220,14 @@ S3method(format,bayestestR_eti)
 S3method(format,bayestestR_hdi)
 S3method(format,bayestestR_si)
 S3method(format,describe_posterior)
+S3method(format,equivalence_test)
 S3method(format,map_estimate)
 S3method(format,p_direction)
 S3method(format,p_map)
 S3method(format,p_rope)
 S3method(format,p_significance)
 S3method(format,point_estimate)
+S3method(format,rope)
 S3method(hdi,BFBayesFactor)
 S3method(hdi,BGGM)
 S3method(hdi,MCMCglmm)

NEWS.md

@@ -4,6 +4,11 @@
 
 * Retrieving models from the environment was improved.
 
+## Bug fixes
+
+* Fixed issues in various `format()` methods, which did not work properly for
+  some few functions (like `p_direction()`).
+
 # bayestestR 0.13.1
 
 ## Changes

R/bayesfactor_parameters.R

@@ -136,13 +136,13 @@
 #'   bayesfactor_parameters(group_diff, prior = stan_model, verbose = FALSE)
 #'
 #'   # Or
-#'   group_diff_prior <- pairs(emmeans(unupdate(stan_model), ~group))
-#'   bayesfactor_parameters(group_diff, prior = group_diff_prior, verbose = FALSE)
+#'   # group_diff_prior <- pairs(emmeans(unupdate(stan_model), ~group))
+#'   # bayesfactor_parameters(group_diff, prior = group_diff_prior, verbose = FALSE)
 #' }
 #'
 #' # brms models
 #' # -----------
-#' if (require("brms")) {
+#' if (require("brms") && require("logspline")) {
 #'   contrasts(sleep$group) <- contr.equalprior_pairs # see vingette
 #'   my_custom_priors <-
 #'     set_prior("student_t(3, 0, 1)", class = "b") +

R/equivalence_test.R

@@ -118,13 +118,17 @@ equivalence_test.numeric <- function(x, range = "default", ci = 0.95, verbose =
   out <- as.data.frame(rope_data)
 
   if (all(ci < 1)) {
-    out$ROPE_Equivalence <- ifelse(out$ROPE_Percentage == 0, "Rejected",
-      ifelse(out$ROPE_Percentage == 1, "Accepted", "Undecided")
+    out$ROPE_Equivalence <- datawizard::recode_into(
+      out$ROPE_Percentage == 0 ~ "Rejected",
+      out$ROPE_Percentage == 1 ~ "Accepted",
+      default = "Undecided"
     )
   } else {
     # Related to guidelines for full rope (https://easystats.github.io/bayestestR/articles/4_Guidelines.html)
-    out$ROPE_Equivalence <- ifelse(out$ROPE_Percentage < 0.025, "Rejected",
-      ifelse(out$ROPE_Percentage > 0.975, "Accepted", "Undecided")
+    out$ROPE_Equivalence <- datawizard::recode_into(
+      out$ROPE_Percentage < 0.025 ~ "Rejected",
+      out$ROPE_Percentage > 0.975 ~ "Accepted",
+      default = "Undecided"
     )
   }
 

R/format.R

@@ -73,6 +73,11 @@ format.bayestestR_eti <- format.describe_posterior
 #' @export
 format.bayestestR_si <- format.describe_posterior
 
+#' @export
+format.equivalence_test <- format.describe_posterior
+
+#' @export
+format.rope <- format.describe_posterior
 
 
 

R/unupdate.R

@@ -102,15 +102,17 @@ unupdate.brmsfit_multiple <- function(model,
     insight::format_alert("Sampling priors, please wait...")
   }
 
-  utils::capture.output({model_prior <-
-    try(suppressMessages(suppressWarnings(
-      stats::update(
-        model,
-        sample_prior = "only",
-        newdata = newdata,
-        refresh = 0
-      )
-    )), silent = TRUE)})
+  utils::capture.output({
+    model_prior <-
+      try(suppressMessages(suppressWarnings(
+        stats::update(
+          model,
+          sample_prior = "only",
+          newdata = newdata,
+          refresh = 0
+        )
+      )), silent = TRUE)
+  })
 
   if (methods::is(model_prior, "try-error")) {
     if (grepl("proper priors", model_prior, fixed = TRUE)) {
@@ -143,7 +145,9 @@ unupdate.blavaan <- function(model, verbose = TRUE, ...) {
 
   cl$prisamp <- TRUE
   suppressMessages(suppressWarnings(
-    utils::capture.output({model_prior <- eval(cl)})
+    utils::capture.output({
+      model_prior <- eval(cl)
+    })
   ))
 
   model_prior

tests/testthat/test-check_prior.R

@@ -1,6 +1,8 @@
+skip_on_os(os = "mac")
+
 test_that("check_prior - stanreg", {
   skip_on_cran()
-  skip_on_os(os = "windows")
+  skip_on_os(os = c("windows", "mac"))
   skip_if_offline()
   skip_if_not_or_load_if_installed("rstanarm")
   skip_if_not_or_load_if_installed("BH")
@@ -10,12 +12,12 @@ test_that("check_prior - stanreg", {
   set.seed(333)
   model1 <- insight::download_model("stanreg_lm_1")
 
-  expect_equal(
+  expect_identical(
     check_prior(model1)$Prior_Quality,
     c("informative", "uninformative")
   )
 
-  expect_equal(
+  expect_identical(
     check_prior(model1, method = "lakeland")$Prior_Quality,
     c("informative", "informative")
   )
@@ -43,15 +45,15 @@ test_that("check_prior - brms (linux)", {
     )
   })
 
-  expect_warning(expect_equal(
+  expect_warning(expect_identical(
     check_prior(model2)$Prior_Quality,
     c(
       "uninformative", "informative", "informative", "uninformative",
       "uninformative", "not determinable", "not determinable", "not determinable"
     )
   ))
 
-  expect_warning(expect_equal(
+  expect_warning(expect_identical(
     check_prior(model2, method = "lakeland")$Prior_Quality,
     c(
       "informative", "informative", "informative", "informative",
@@ -88,7 +90,7 @@ test_that("check_prior - brms (linux)", {
   # ))
 
 
-  expect_warning(expect_equal(
+  expect_warning(expect_identical(
     check_prior(model2, method = "lakeland")$Prior_Quality,
     c(
       "informative", "informative", "informative", "informative",
@@ -99,7 +101,7 @@ test_that("check_prior - brms (linux)", {
 
 test_that("check_prior - brms (not linux or windows)", {
   skip_on_cran()
-  skip_on_os(os = c("linux", "windows"))
+  skip_on_os(os = c("linux", "windows", "mac"))
   skip_if_offline()
   skip_if_not_or_load_if_installed("rstanarm")
   skip_if_not_or_load_if_installed("BH")
@@ -117,15 +119,15 @@ test_that("check_prior - brms (not linux or windows)", {
     )
   })
 
-  expect_warning(expect_equal(
+  expect_warning(expect_identical(
     check_prior(model2)$Prior_Quality,
     c(
       "uninformative", "uninformative", "informative", "uninformative",
       "uninformative", "not determinable", "not determinable", "not determinable"
     )
   ))
 
-  expect_warning(expect_equal(
+  expect_warning(expect_identical(
     check_prior(model2, method = "lakeland")$Prior_Quality,
     c(
       "informative", "informative", "informative", "informative",

tests/testthat/test-format.R

@@ -0,0 +1,59 @@
+test_that("p_significance", {
+  set.seed(333)
+  x <- rnorm(100)
+  expect_equal(
+    format(point_estimate(x)),
+    data.frame(Median = "0.05", Mean = "-0.02", MAP = "0.13", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    format(ci(x)),
+    data.frame(`95% CI` = "[-1.93, 1.77]", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    format(p_rope(x)),
+    data.frame(ROPE = "[-0.10, 0.10]", `p (ROPE)` = "0.100", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    format(map_estimate(x)),
+    data.frame(x = "0.13", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    format(p_direction(x)),
+    data.frame(x = "0.51", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    format(p_map(x)),
+    data.frame(x = "0.97", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    format(p_significance(x)),
+    data.frame(x = "0.46", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    format(rope(x)),
+    data.frame(CI = "0.95", ROPE = "[-0.10, 0.10]", `% in ROPE` = "10.64%", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    format(equivalence_test(x)),
+    data.frame(
+      CI = "0.95", ROPE = "[-0.10, 0.10]", `% in ROPE` = "10.64%",
+      `Equivalence (ROPE)` = "Undecided", HDI_low = "-1.93", HDI_high = "1.77",
+      stringsAsFactors = FALSE
+    ),
+    ignore_attr = TRUE
+  )
+  skip_if_not_installed("logspline")
+  expect_equal(
+    format(bayesfactor_parameters(x, verbose = FALSE)),
+    data.frame(BF = "1.00", stringsAsFactors = FALSE),
+    ignore_attr = TRUE
+  )
+})

vignettes/bayes_factors.Rmd

@@ -920,14 +920,22 @@ posterior distribution, and estimate the HDI.
 
 In `bayestestR`, we can do this with the `weighted_posteriors()` function:
 
-```{r}
+```{r eval=FALSE}
 BMA_draws <- weighted_posteriors(mod, mod_carb, verbose = FALSE)
 
 BMA_hdi <- hdi(BMA_draws, ci = 0.95)
 BMA_hdi
 
 plot(BMA_hdi)
 ```
+```{r echo=FALSE}
+BMA_draws <- weighted_posteriors(mod, mod_carb, verbose = FALSE)
+
+BMA_hdi <- hdi(BMA_draws, ci = 0.95)
+BMA_hdi
+
+plot(BMA_hdi, data = BMA_draws)
+```
 
 We can see that across both models under consideration, the posterior of the
 `carb` effect is almost equally weighted between the alternative model and the