Usability improvements for pd() and pd_to_p()

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: bayestestR
 Title: Understand and Describe Bayesian Models and Posterior Distributions
-Version: 0.14.0.2
+Version: 0.14.0.3
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",

NAMESPACE

@@ -26,6 +26,7 @@ S3method(as.numeric,map_estimate)
 S3method(as.numeric,p_direction)
 S3method(as.numeric,p_map)
 S3method(as.numeric,p_significance)
+S3method(as.vector,p_direction)
 S3method(bayesfactor_inclusion,BFBayesFactor)
 S3method(bayesfactor_inclusion,bayesfactor_models)
 S3method(bayesfactor_models,BFBayesFactor)
@@ -368,6 +369,8 @@ S3method(p_significance,stanfit)
 S3method(p_significance,stanreg)
 S3method(p_to_bf,default)
 S3method(p_to_bf,numeric)
+S3method(pd_to_p,data.frame)
+S3method(pd_to_p,numeric)
 S3method(plot,bayesfactor_models)
 S3method(plot,bayesfactor_parameters)
 S3method(plot,bayestestR_eti)

NEWS.md

@@ -1,5 +1,30 @@
 # bayestestR (development version)
 
+## Changes
+
+* Usability improvements for `p_direction()`:
+
+  - Results from `p_direction()` can directly be used in `pd_to_p()`.
+
+  - `p_direction()` gets an `as_p` argument, to directly convert pd-values into
+    frequentist p-values.
+
+  - `p_direction()` gets a `remove_na` argument, which defaults to `TRUE`, to
+    remove `NA` values from the input before calculating the pd-values.
+
+  - The `data.frame` method for `p_direction()` gets an `rvar_col` argument, to
+    specify the column that contains the `rvar` objects.
+
+  - Besides the existing `as.numeric()` method, `p_direction()` now also has an
+    `as.vector()` method.
+
+* `p_to_pd()` now also works with data frames returned by `p_direction()`. If
+  a data frame contains a `pd`, `p_direction` or `PD` column name, this is assumed
+  to be the pd-values, which are then converted to p-values.
+
+* `p_to_pd()` for data frame inputs gets a `as.numeric()` and `as.vector()`
+  method.
+
 ## Bug fixes
 
 * Fixed warning in CRAN check results.

R/bayesfactor_inclusion.R

@@ -163,7 +163,7 @@ bayesfactor_inclusion.bayesfactor_models <- function(models,
   attr(df.effect, "matched") <- match_models
   attr(df.effect, "priorOdds") <- prior_odds
 
-  return(df.effect)
+  df.effect
 }
 
 
@@ -185,8 +185,7 @@ bayesfactor_inclusion.BFBayesFactor <- function(models,
   eff_b <- strsplit(eff, ":", fixed = TRUE)
   effnames_b <- strsplit(effnames, ":", fixed = TRUE)
 
-  is_int <- vapply(effnames_b, function(x) length(x) > 1, TRUE)
-
+  is_int <- lengths(effnames_b) > 1
   temp <- logical(length(effnames))
 
   for (rr in seq_along(effnames)) {

R/bayesfactor_models.R

@@ -177,7 +177,7 @@ bayesfactor_models.default <- function(..., denominator = 1, verbose = TRUE) {
   cl$...$estimator <- cl$...$check_response <- NULL
 
 
-  names(mods) <- sapply(cl$`...`, insight::safe_deparse)
+  names(mods) <- sapply(cl[["..."]], insight::safe_deparse)
   names(denominator) <- insight::safe_deparse(cl$denominator)
 
   mods <- .cleanup_BF_models(mods, denominator, cl)
@@ -346,7 +346,7 @@ bayesfactor_models.stanreg <- function(..., denominator = 1, verbose = TRUE) {
   denominator <- list(denominator)
 
   cl <- match.call(expand.dots = FALSE)
-  names(mods) <- sapply(cl$`...`, insight::safe_deparse)
+  names(mods) <- sapply(cl[["..."]], insight::safe_deparse)
   names(denominator) <- insight::safe_deparse(cl$denominator)
 
   mods <- .cleanup_BF_models(mods, denominator, cl)
@@ -446,7 +446,7 @@ as.matrix.bayesfactor_models <- function(x, ...) {
 .cleanup_BF_models <- function(mods, denominator, cl) {
   if (length(mods) == 1 && inherits(mods[[1]], "list")) {
     mods <- mods[[1]]
-    mod_names <- .safe(sapply(cl$`...`[[1]][-1], insight::safe_deparse))
+    mod_names <- .safe(sapply(cl[["..."]][[1]][-1], insight::safe_deparse))
 
     if (!is.null(mod_names) && length(mod_names) == length(mods)) {
       names(mods) <- mod_names
@@ -562,10 +562,10 @@ as.matrix.bayesfactor_models <- function(x, ...) {
       is_wrapped <- grepl("(", m_txt, fixed = TRUE)
       m_txt[!is_wrapped] <- paste0("(", m_txt[!is_wrapped], ")")
 
-      return(m_txt)
+      m_txt
     },
     error = function(e) {
-      return(m_names)
+      m_names
     }
   )
 }

R/convert_pd_to_p.R

@@ -2,27 +2,33 @@
 #'
 #' Enables a conversion between Probability of Direction (pd) and p-value.
 #'
-#' @param pd A Probability of Direction (pd) value (between 0 and 1).
+#' @param pd A Probability of Direction (pd) value (between 0 and 1). Can also
+#' be a data frame with a column named `pd`, `p_direction`, or `PD`, as returned
+#' by [`p_direction()`]. In this case, the column is converted to p-values and
+#' the new data frame is returned.
 #' @param p A p-value.
 #' @param direction What type of p-value is requested or provided. Can be
 #'   `"two-sided"` (default, two tailed) or `"one-sided"` (one tailed).
 #' @param verbose Toggle off warnings.
 #' @param ... Arguments passed to or from other methods.
 #'
+#' @return A p-value or a data frame with a p-value column.
+#'
 #' @details
-#' Conversion is done using the following equation (see Makowski et al., 2019):
-#' \cr\cr
-#' When `direction = "two-sided"` -
-#' \cr\cr
+#' Conversion is done using the following equation (see _Makowski et al., 2019_):
+#'
+#' When `direction = "two-sided"`
+#'
 #' \deqn{p = 2 \times (1 - p_d)}{p = 2 * (1 - pd)}
-#' When `direction = "one-sided"` -
-#' \cr\cr
+#'
+#' When `direction = "one-sided"`
+#'
 #' \deqn{p = 1 - p_d}{p = 1 - pd}
-#' \cr\cr
+#'
 #' Note that this conversion is only valid when the lowest possible values of pd
 #' is 0.5 - i.e., when the posterior represents continuous parameter space (see
-#' [p_direction]). If any pd < 0.5 are detected, they are converted to a p of 1,
-#' and a warning is given.
+#' [`p_direction()`]). If any pd < 0.5 are detected, they are converted to a p
+#' of 1, and a warning is given.
 #'
 #' @references
 #' Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D. (2019).
@@ -34,7 +40,14 @@
 #' pd_to_p(pd = 0.95, direction = "one-sided")
 #'
 #' @export
-pd_to_p <- function(pd, direction = "two-sided", verbose = TRUE, ...) {
+pd_to_p <- function(pd, ...) {
+  UseMethod("pd_to_p")
+}
+
+
+#' @export
+#' @rdname pd_to_p
+pd_to_p.numeric <- function(pd, direction = "two-sided", verbose = TRUE, ...) {
   p <- 1 - pd
   if (.get_direction(direction) == 0) {
     p <- 2 * p
@@ -44,9 +57,9 @@ pd_to_p <- function(pd, direction = "two-sided", verbose = TRUE, ...) {
   if (any(less_than_0.5)) {
     if (verbose) {
       insight::format_warning(paste(
-        "pd values smaller than 0.5 detected.",
-        "pd-to-p conversion assumes a continious parameter space;",
-        "see help('p_direction') for more info."
+        "pd-values smaller than 0.5 detected, indicating inconsistent direction of the probability mass.",
+        "This usually happens when the parameters space is not continuous. Affected values are set to 1.",
+        "See help('p_direction') for more info."
       ))
     }
     p[less_than_0.5] <- 1
@@ -56,6 +69,22 @@ pd_to_p <- function(pd, direction = "two-sided", verbose = TRUE, ...) {
 }
 
 
+#' @export
+pd_to_p.data.frame <- function(pd, direction = "two-sided", verbose = TRUE, ...) {
+  # check if data frame has an appropriate column
+  pd_column <- intersect(c("pd", "p_direction", "PD"), colnames(pd))[1]
+  if (is.na(pd_column) || length(pd_column) == 0) {
+    insight::format_error("No column named `pd`, `p_direction`, or `PD` found.")
+  }
+
+  # add p-value column
+  pd$p <- pd_to_p(as.numeric(pd[[pd_column]]))
+  # remove pd-column
+  pd[[pd_column]] <- NULL
+  pd
+}
+
+
 #' @rdname pd_to_p
 #' @export
 p_to_pd <- function(p, direction = "two-sided", ...) {