add fun_avg to ppc_avg functions

DESCRIPTION

@@ -4,7 +4,7 @@ Title: Plotting for Bayesian Models
 Version: 1.12.0.9000
 Date: 2025-04-09
 Authors@R: c(person("Jonah", "Gabry", role = c("aut", "cre"), email = "jsg2201@columbia.edu"),
-             person("Tristan", "Mahr", role = "aut"),
+             person("Tristan", "Mahr", role = "aut", comment = c(ORCID = "0000-0002-8890-5116")),
              person("Paul-Christian", "Bürkner", role = "ctb"),
              person("Martin", "Modrák", role = "ctb"),
              person("Malcolm", "Barrett", role = "ctb"),
@@ -26,7 +26,7 @@ URL: https://mc-stan.org/bayesplot/
 BugReports: https://github.com/stan-dev/bayesplot/issues/
 SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
 Depends:
-    R (>= 3.1.0)
+    R (>= 4.1.0)
 Imports:
     dplyr (>= 0.8.0),
     ggplot2 (>= 3.4.0),

NEWS.md

@@ -2,6 +2,8 @@
 
 * Add possibility for left-truncation to `ppc_km_overlay()` and `ppc_km_overlay_grouped()` by @Sakuski
 * Added `ppc_loo_pit_ecdf()` by @TeemuSailynoja
+* PPC "avg" functions (`ppc_scatter_avg()`, `ppc_error_scatter_avg()`, etc.) gain a `stat` argument to set the averaging function. (Suggestion of #348, @kruschke).
+* `ppc_error_scatter_avg_vs_x(x = some_expression)` labels the *x* axis with `some_expression`.
 
 # bayesplot 1.12.0
 

R/bayesplot-helpers.R

@@ -469,3 +469,53 @@ grid_lines_y <- function(color = "gray50", size = 0.2) {
 overlay_function <- function(...) {
   stat_function(..., inherit.aes = FALSE)
 }
+
+
+
+# Resolve a function name and store the expression passed in by the user
+#' @noRd
+#' @param f a function-like thing: a string naming a function, a function
+#'   object, an anonymous function object, a formula-based lambda, and `NULL`.
+#' @param fallback character string providing a fallback function name
+#' @return the function named in `f` with an added `"tagged_expr"` attribute
+#' containing the expression to represent the function name and an
+#' `"is_anonymous_function"` attribute to flag if the expression is a call to
+#' `function()`.
+as_tagged_function <- function(f = NULL, fallback = "func") {
+  qf <- enquo(f)
+  f <- eval_tidy(qf)
+  if (!is.null(attr(f, "tagged_expr"))) return(f)
+
+  f_expr <- quo_get_expr(qf)
+  f_fn <- f
+
+  if (rlang::is_character(f)) {      # f = "mean"
+    # using sym() on the evaluated `f` means that a variable that names a
+    # function string `x <- "mean"; as_tagged_function(x)` will be lost
+    # but that seems okay
+    f_expr <- rlang::sym(f)
+    f_fn <- match.fun(f)
+  } else if (is_null(f)) {           # f = NULL
+    f_fn <- identity
+    f_expr <- rlang::sym(fallback)
+  } else if (is_callable(f)) {       # f = mean or f = function(x) mean(x)
+    f_expr <- f_expr                 # or f = ~mean(.x)
+    f_fn <- as_function(f)
+  }
+
+  # Setting attributes on primitive functions is deprecated, so wrap them
+  # and then tag
+  if (is_primitive(f_fn)) {
+    f_fn_old <- f_fn
+    f_factory <- function(f) { function(...) f(...) }
+    f_fn <- f_factory(f_fn_old)
+  }
+
+  attr(f_fn, "tagged_expr") <- f_expr
+  attr(f_fn, "is_anonymous_function") <- is_call(f_expr, name = "function") ||
+    is_formula(f_expr)
+  f_fn
+}
+
+
+

R/bayesplot-package.R

@@ -1,6 +1,5 @@
 #' **bayesplot**: Plotting for Bayesian Models
 #'
-#' @docType package
 #' @name bayesplot-package
 #' @aliases bayesplot
 #'
@@ -96,7 +95,7 @@
 #' ppd_hist(ypred[1:8, ])
 #' }
 #'
-NULL
+"_PACKAGE"
 
 
 # internal ----------------------------------------------------------------

R/ppc-errors.R

@@ -10,6 +10,10 @@
 #' @template args-group
 #' @template args-facet_args
 #' @param ... Currently unused.
+#' @param stat A function or a string naming a function for computing the
+#' posterior average. In both cases, the function should take a vector input and
+#' return a scalar statistic. The function name is displayed in the axis-label.
+#' Defaults to `"mean"`.
 #' @param size,alpha For scatterplots, arguments passed to
 #'   [ggplot2::geom_point()] to control the appearance of the points. For the
 #'   binned error plot, arguments controlling the size of the outline and
@@ -209,21 +213,26 @@ ppc_error_scatter_avg <-
   function(y,
            yrep,
            ...,
+           stat = "mean",
            size = 2.5,
            alpha = 0.8) {
     check_ignored_arguments(...)
 
     y <- validate_y(y)
     yrep <- validate_predictions(yrep, length(y))
     errors <- compute_errors(y, yrep)
+
+    stat <- as_tagged_function({{ stat }})
+
     ppc_scatter_avg(
       y = y,
       yrep = errors,
       size = size,
       alpha = alpha,
-      ref_line = FALSE
+      ref_line = FALSE,
+      stat = stat
     ) +
-      labs(x = error_avg_label(), y = y_label())
+      labs(x = error_avg_label(stat), y = y_label())
   }
 
 
@@ -234,13 +243,16 @@ ppc_error_scatter_avg_grouped <-
            yrep,
            group,
            ...,
+           stat = "mean",
            facet_args = list(),
            size = 2.5,
            alpha = 0.8) {
     check_ignored_arguments(...)
 
     y <- validate_y(y)
     yrep <- validate_predictions(yrep, length(y))
+    stat <- as_tagged_function({{ stat }})
+
     errors <- compute_errors(y, yrep)
     ppc_scatter_avg_grouped(
       y = y,
@@ -249,9 +261,10 @@ ppc_error_scatter_avg_grouped <-
       size = size,
       alpha = alpha,
       facet_args = facet_args,
-      ref_line = FALSE
+      ref_line = FALSE,
+      stat = stat
     ) +
-      labs(x = error_avg_label(), y = y_label())
+      labs(x = error_avg_label(stat), y = y_label())
   }
 
 
@@ -260,29 +273,37 @@ ppc_error_scatter_avg_grouped <-
 #' @param x A numeric vector the same length as `y` to use as the x-axis
 #'   variable.
 #'
-ppc_error_scatter_avg_vs_x <-
-  function(y,
-           yrep,
-           x,
-           ...,
-           size = 2.5,
-           alpha = 0.8) {
-    check_ignored_arguments(...)
+ppc_error_scatter_avg_vs_x <- function(
+    y,
+    yrep,
+    x,
+    ...,
+    stat = "mean",
+    size = 2.5,
+    alpha = 0.8
+) {
+  check_ignored_arguments(...)
 
-    y <- validate_y(y)
-    yrep <- validate_predictions(yrep, length(y))
-    x <- validate_x(x, y)
-    errors <- compute_errors(y, yrep)
-    ppc_scatter_avg(
-      y = x,
-      yrep = errors,
-      size = size,
-      alpha = alpha,
-      ref_line = FALSE
+  y <- validate_y(y)
+  yrep <- validate_predictions(yrep, length(y))
+  qx <- enquo(x)
+  x <- validate_x(x, y)
+  stat <- as_tagged_function({{ stat }})
+  errors <- compute_errors(y, yrep)
+  ppc_scatter_avg(
+    y = x,
+    yrep = errors,
+    size = size,
+    alpha = alpha,
+    ref_line = FALSE,
+    stat = stat
+  ) +
+    labs(
+      x = error_avg_label(stat),
+      y = as_label((qx))
     ) +
-      labs(x = error_avg_label(), y = expression(italic(x))) +
-      coord_flip()
-  }
+    coord_flip()
+}
 
 
 #' @rdname PPC-errors
@@ -414,8 +435,18 @@ error_hist_facets <-
 error_label <- function() {
   expression(italic(y) - italic(y)[rep])
 }
-error_avg_label <- function() {
-  expression(paste("Average ", italic(y) - italic(y)[rep]))
+
+error_avg_label <- function(stat = NULL) {
+  stat <- as_tagged_function({{ stat }}, fallback = "stat")
+  e <- attr(stat, "tagged_expr")
+  if (attr(stat, "is_anonymous_function")) {
+    e <- sym("stat")
+  }
+  de <- deparse1(e)
+  # dummy globals to pass R check for globals
+  italic <- sym("italic")
+  y <- sym("y")
+  expr(paste((!!de))*(italic(y) - italic(y)[rep]))
 }
 
 

R/ppc-scatterplots.R

@@ -11,6 +11,11 @@
 #' @template args-group
 #' @template args-facet_args
 #' @param ... Currently unused.
+#' @param stat A function or a string naming a function for computing the
+#'   posterior average. In both cases, the function should take a vector input
+#'   and return a scalar statistic. The function name is displayed in the
+#'   axis-label, and the underlying `$rep_label` for `ppc_scatter_avg_data()`
+#'   includes the function name. Defaults to `"mean"`.
 #' @param size,alpha Arguments passed to [ggplot2::geom_point()] to control the
 #'   appearance of the points.
 #' @param ref_line If `TRUE` (the default) a dashed line with intercept 0 and
@@ -31,10 +36,10 @@
 #'   }
 #'   \item{`ppc_scatter_avg()`}{
 #'    A single scatterplot of `y` against the average values of `yrep`, i.e.,
-#'    the points `(x,y) = (mean(yrep[, n]), y[n])`, where each `yrep[, n]` is
-#'    a vector of length equal to the number of posterior draws. Unlike
-#'    for `ppc_scatter()`, for `ppc_scatter_avg()` `yrep` should contain many
-#'    draws (rows).
+#'    the points `(x,y) = (average(yrep[, n]), y[n])`, where each `yrep[, n]` is
+#'    a vector of length equal to the number of posterior draws and `average()`
+#'    is summary statistic. Unlike for `ppc_scatter()`, for `ppc_scatter_avg()`
+#'    `yrep` should contain many draws (rows).
 #'   }
 #'   \item{`ppc_scatter_avg_grouped()`}{
 #'    The same as `ppc_scatter_avg()`, but a separate plot is generated for
@@ -59,6 +64,9 @@
 #' p1 + lims
 #' p2 + lims
 #'
+#' # "average" function is customizable
+#' ppc_scatter_avg(y, yrep, stat = "median", ref_line = FALSE)
+#'
 #' # for ppc_scatter_avg_grouped the default is to allow the facets
 #' # to have different x and y axes
 #' group <- example_group_data()
@@ -116,16 +124,19 @@ ppc_scatter_avg <-
   function(y,
            yrep,
            ...,
+           stat = "mean",
            size = 2.5,
            alpha = 0.8,
            ref_line = TRUE) {
     dots <- list(...)
+    stat <- as_tagged_function({{ stat }})
+
     if (!from_grouped(dots)) {
       check_ignored_arguments(...)
       dots$group <- NULL
     }
 
-    data <- ppc_scatter_avg_data(y, yrep, group = dots$group)
+    data <- ppc_scatter_avg_data(y, yrep, group = dots$group, stat = stat)
     if (is.null(dots$group) && nrow(yrep) == 1) {
       inform(
         "With only 1 row in 'yrep' ppc_scatter_avg is the same as ppc_scatter."
@@ -143,7 +154,7 @@ ppc_scatter_avg <-
       # ppd instead of ppc (see comment in ppc_scatter)
       scale_color_ppd() +
       scale_fill_ppd() +
-      labs(x = yrep_avg_label(), y = y_label()) +
+      labs(x = yrep_avg_label(stat), y = y_label()) +
       bayesplot_theme_get()
   }
 
@@ -155,6 +166,7 @@ ppc_scatter_avg_grouped <-
            yrep,
            group,
            ...,
+           stat = "mean",
            facet_args = list(),
            size = 2.5,
            alpha = 0.8,
@@ -184,16 +196,19 @@ ppc_scatter_data <- function(y, yrep) {
 
 #' @rdname PPC-scatterplots
 #' @export
-ppc_scatter_avg_data <- function(y, yrep, group = NULL) {
+ppc_scatter_avg_data <- function(y, yrep, group = NULL, stat = "mean") {
   y <- validate_y(y)
   yrep <- validate_predictions(yrep, length(y))
   if (!is.null(group)) {
     group <- validate_group(group, length(y))
   }
+  stat <- as_tagged_function({{ stat }})
 
-  data <- ppc_scatter_data(y = y, yrep = t(colMeans(yrep)))
+  data <- ppc_scatter_data(y = y, yrep = t(apply(yrep, 2, FUN = stat)))
   data$rep_id <- NA_integer_
-  levels(data$rep_label) <- "mean(italic(y)[rep]))"
+  levels(data$rep_label) <- yrep_avg_label(stat) |>
+    as.expression() |>
+    as.character()
 
   if (!is.null(group)) {
     data <- tibble::add_column(data,
@@ -206,7 +221,19 @@ ppc_scatter_avg_data <- function(y, yrep, group = NULL) {
 }
 
 # internal ----------------------------------------------------------------
-yrep_avg_label <- function() expression(paste("Average ", italic(y)[rep]))
+
+yrep_avg_label <- function(stat = NULL) {
+  stat <- as_tagged_function({{ stat }}, fallback = "stat")
+  e <- attr(stat, "tagged_expr")
+  if (attr(stat, "is_anonymous_function")) {
+    e <- sym("stat")
+  }
+  de <- deparse1(e)
+  # dummy globals to pass R check for globals
+  italic <- sym("italic")
+  y <- sym("y")
+  expr(paste((!!de))*(italic(y)[rep]))
+}
 
 scatter_aes <- function(...) {
   aes(x = .data$value, y = .data$y_obs, ...)