Revert "ROPE_Percentage -> ROPE_percentage"

This reverts commit 0eadf53.

Author: DominiqueMakowski

NEWS.md

@@ -2,7 +2,6 @@
 
 ## Breaking changes
 
-- `ROPE_Percentage` now becale `ROPE_percentage` 
 - plotting functions now require the installation of the `see` package
 - `estimate` argument name in `describe_posterior` and `point_estimate` changed to `centrality`
 - `hdi()`, `ci()`, `rope()` and `equivalence_test()` default `ci` to `0.89`

R/equivalence_test.R

@@ -59,7 +59,7 @@
 #'     \item \code{Parameter} The model parameter(s), if \code{x} is a model-object. If \code{x} is a vector, this column is missing.
 #'     \item \code{CI} The probability of the HDI.
 #'     \item \code{ROPE_low}, \code{ROPE_high} The limits of the ROPE. These values are identical for all parameters.
-#'     \item \code{ROPE_percentage} The proportion of the HDI that lies inside the ROPE.
+#'     \item \code{ROPE_Percentage} The proportion of the HDI that lies inside the ROPE.
 #'     \item \code{ROPE_Equivalence} The "test result", as character. Either "rejected", "accepted" or "undecided".
 #'     \item \code{HDI_low} , \code{HDI_high} The lower and upper HDI limits for the parameters.
 #'   }
@@ -124,13 +124,13 @@ equivalence_test.numeric <- function(x, range = "default", ci = .89, verbose = T
   out <- as.data.frame(rope_data)
 
   if (all(ci < 1)) {
-    out$ROPE_Equivalence <- ifelse(out$ROPE_percentage == 0, "rejected",
-      ifelse(out$ROPE_percentage == 100, "accepted", "undecided")
+    out$ROPE_Equivalence <- ifelse(out$ROPE_Percentage == 0, "rejected",
+      ifelse(out$ROPE_Percentage == 100, "accepted", "undecided")
     )
   } else {
     # Related to guidelines for full rope (https://easystats.github.io/bayestestR/articles/4_Guidelines.html)
-    out$ROPE_Equivalence <- ifelse(out$ROPE_percentage < 2.5, "rejected",
-      ifelse(out$ROPE_percentage > 97.5, "accepted", "undecided")
+    out$ROPE_Equivalence <- ifelse(out$ROPE_Percentage < 2.5, "rejected",
+      ifelse(out$ROPE_Percentage > 97.5, "accepted", "undecided")
     )
   }
 

R/p_rope.R

@@ -63,7 +63,7 @@ p_rope.numeric <- function(x, range = "default", precision = .1, ...) {
   rope_df <- rope(x, range, ci = seq(0, 1, by = precision / 100), verbose = FALSE)
   rope_df <- stats::na.omit(rope_df)
 
-  rope_values <- rope_df$ROPE_percentage
+  rope_values <- rope_df$ROPE_Percentage
 
   if (all(rope_values == min(rope_values))) {
     if (rope_values[1] == 0) {

R/print.equivalence_test.R

@@ -11,16 +11,16 @@ print.equivalence_test <- function(x, digits = 2, ...) {
   maxlen_low <- max(nchar(x$HDI_low))
   maxlen_high <- max(nchar(x$HDI_high))
 
-  x$ROPE_percentage <- sprintf("%.*f %%", digits, x$ROPE_percentage)
+  x$ROPE_Percentage <- sprintf("%.*f %%", digits, x$ROPE_Percentage)
   x$HDI <- sprintf("[%*s %*s]", maxlen_low, x$HDI_low, maxlen_high, x$HDI_high)
 
   ci <- unique(x$CI)
-  keep.columns <- c("CI", "Parameter", "ROPE_Equivalence", "ROPE_percentage", "HDI")
+  keep.columns <- c("CI", "Parameter", "ROPE_Equivalence", "ROPE_Percentage", "HDI")
 
   x <- x[, intersect(keep.columns, colnames(x))]
 
   colnames(x)[which(colnames(x) == "ROPE_Equivalence")] <- "H0"
-  colnames(x)[which(colnames(x) == "ROPE_percentage")] <- "inside ROPE"
+  colnames(x)[which(colnames(x) == "ROPE_Percentage")] <- "inside ROPE"
 
   # clean parameter names
   if ("Parameter" %in% colnames(x)) {

R/print.rope.R

@@ -15,7 +15,7 @@ print.rope <- function(x, digits = 2, ...) {
   # why we did this, so I'll comment a bit...
 
   # These are the base columns we want to print
-  cols <- c("Parameter", "ROPE_percentage", "Component", "Group")
+  cols <- c("Parameter", "ROPE_Percentage", "Component", "Group")
 
   # In case we have ropes for different CIs, we also want this information
   # So we first check if values in the CI column differ, and if so, we also
@@ -34,8 +34,8 @@ print.rope <- function(x, digits = 2, ...) {
   x <- subset(x, select = intersect(cols, colnames(x)))
 
   # This is just cosmetics, to have nicer column names and values
-  x$ROPE_percentage <- sprintf("%.*f %%", digits, x$ROPE_percentage)
-  colnames(x)[which(colnames(x) == "ROPE_percentage")] <- "inside ROPE"
+  x$ROPE_Percentage <- sprintf("%.*f %%", digits, x$ROPE_Percentage)
+  colnames(x)[which(colnames(x) == "ROPE_Percentage")] <- "inside ROPE"
 
   # In case we have multiple CI values, we create a subset for each CI value.
   # Else, parameter-rows would be mixed up with both CIs, which is a bit

R/rope.R

@@ -99,7 +99,7 @@ rope <- function(x, ...) {
 #' @method as.double rope
 #' @export
 as.double.rope <- function(x, ...) {
-  x$ROPE_percentage
+  x$ROPE_Percentage
 }
 
 
@@ -131,7 +131,7 @@ rope.numeric <- function(x, range = "default", ci = .89, verbose = TRUE, ...) {
 
   out <- do.call(rbind, rope_values)
   if (nrow(out) > 1) {
-    out$ROPE_percentage <- as.numeric(out$ROPE_percentage)
+    out$ROPE_Percentage <- as.numeric(out$ROPE_Percentage)
   }
 
   # Attributes
@@ -183,19 +183,19 @@ rope.BFBayesFactor <- function(x, range = "default", ci = .89, verbose = TRUE, .
   HDI_area <- .hdi_area <- hdi(x, ci, verbose)
 
   if (anyNA(HDI_area)) {
-    ROPE_percentage <- NA
+    rope_percentage <- NA
   } else {
     HDI_area <- x[x >= HDI_area$CI_low & x <= HDI_area$CI_high]
     area_within <- HDI_area[HDI_area >= min(range) & HDI_area <= max(range)]
-    ROPE_percentage <- length(area_within) / length(HDI_area) * 100
+    rope_percentage <- length(area_within) / length(HDI_area) * 100
   }
 
 
   rope <- data.frame(
     "CI" = ci * 100,
     "ROPE_low" = range[1],
     "ROPE_high" = range[2],
-    "ROPE_percentage" = ROPE_percentage
+    "ROPE_Percentage" = rope_percentage
   )
 
   attr(rope, "HDI_area") <- c(.hdi_area$CI_low, .hdi_area$CI_high)
@@ -230,7 +230,7 @@ rope.stanreg <- function(x, range = "default", ci = .89, effects = c("fixed", "r
       tmp <- .clean_up_tmp_stanreg(
         tmp,
         group = .x,
-        cols = c("CI", "ROPE_low", "ROPE_high", "ROPE_percentage", "Group"),
+        cols = c("CI", "ROPE_low", "ROPE_high", "ROPE_Percentage", "Group"),
         parms = names(parms)
       )
 
@@ -307,7 +307,7 @@ rope.brmsfit <- function(x, range = "default", ci = .89, effects = c("fixed", "r
         tmp,
         group = .x,
         component = .y,
-        cols = c("CI", "ROPE_low", "ROPE_high", "ROPE_percentage", "Component", "Group"),
+        cols = c("CI", "ROPE_low", "ROPE_high", "ROPE_Percentage", "Component", "Group"),
         parms = names(parms)
       )
 

man/equivalence_test.Rd

@@ -18,7 +18,7 @@ test_that("rope", {
   testthat::expect_equal(rope(rnorm(1000, mean = 0, sd = 3), ci = c(.1, .5, .9))$CI, c(10, 50, 90))
 
   x <- equivalence_test(rnorm_perfect(1000, 1, 1), ci = c(.50, .99))
-  testthat::expect_equal(x$ROPE_percentage[2], 4.94, tolerance = 0.01)
+  testthat::expect_equal(x$ROPE_Percentage[2], 4.94, tolerance = 0.01)
   testthat::expect_equal(x$ROPE_Equivalence[2], "undecided")
 
   testthat::expect_error(rope(rnorm_perfect(1000, 0, 1), range = c(0.0, 0.1, 0.2)))