Comment out a dangling function.

R/predictive_plot_interaction.R

@@ -1,42 +1,42 @@
-#' Plot the posterior predictive pdf (interactions)
+#' #' Plot the posterior predictive pdf (interactions)
+#' #'
+#' #' Plot the posterior predictive pdf for each combination of 2 factors,
+#' #' fixing the others at the reference level.
+#' #' @param fit An object of class anova_bnp_model.
+#' #' @param d1 The 1st factor id.
+#' #' @param d2 The 2nd factor id.
+#' #' @return A ggplot2 plot.
+#' #' @importFrom dplyr mutate filter left_join
+#' #' @importFrom dplyr c_across across pull rowwise dense_rank
+#' #' @importFrom ggplot2 ggplot aes_string geom_line
+#' #' @importFrom rlang := .data
+#' #' @export
+#' predictive_plot_interaction <- function(fit, d1, d2) {
+#'   # Get the relevant groups
+#'   var1 <- paste0("x", d1)
+#'   var2 <- paste0("x", d2)
+#'   target_groups <-
+#'     group_codes(fit) |>
+#'     mutate(across(-c("group"), ~ dense_rank(.x))) |>
+#'     mutate(x0 = 1) |>
+#'     rowwise() |>
+#'     mutate(
+#'       touse = max(c_across(-c("group", var1, var2))),
+#'       touse = .data$touse == 1,
+#'       touse = .data$touse & (.data[[var1]] == 1 | .data[[var2]] != 1),
+#'       touse = .data$touse & (.data[[var1]] != 1 | .data[[var2]] == 1)
+#'     ) |>
+#'     filter(.data$touse) |>
+#'     pull("group")
 #'
-#' Plot the posterior predictive pdf for each combination of 2 factors,
-#' fixing the others at the reference level.
-#' @param fit An object of class anova_bnp_model.
-#' @param d1 The 1st factor id.
-#' @param d2 The 2nd factor id.
-#' @return A ggplot2 plot.
-#' @importFrom dplyr mutate filter left_join
-#' @importFrom dplyr c_across across pull rowwise dense_rank
-#' @importFrom ggplot2 ggplot aes_string geom_line
-#' @importFrom rlang := .data
-#' @export
-predictive_plot_interaction <- function(fit, d1, d2) {
-  # Get the relevant groups
-  var1 <- paste0("x", d1)
-  var2 <- paste0("x", d2)
-  target_groups <-
-    group_codes(fit) |>
-    mutate(across(-c("group"), ~ dense_rank(.x))) |>
-    mutate(x0 = 1) |>
-    rowwise() |>
-    mutate(
-      touse = max(c_across(-c("group", var1, var2))),
-      touse = .data$touse == 1,
-      touse = .data$touse & (.data[[var1]] == 1 | .data[[var2]] != 1),
-      touse = .data$touse & (.data[[var1]] != 1 | .data[[var2]] == 1)
-    ) |>
-    filter(.data$touse) |>
-    pull("group")
-
-  # Plot the posterior predictive pdf
-  group_codes(fit) |>
-    filter(.data$group %in% target_groups) |>
-    left_join(f_post(fit)) |>
-    mutate(
-      {{ var1 }} := factor(.data[[var1]]),
-      {{ var2 }} := factor(.data[[var2]])
-    ) |>
-    ggplot(aes_string(x = "y",y = "f", color = paste0(var1, ":", var2))) +
-    geom_line()
-}
+#'   # Plot the posterior predictive pdf
+#'   group_codes(fit) |>
+#'     filter(.data$group %in% target_groups) |>
+#'     left_join(f_post(fit)) |>
+#'     mutate(
+#'       {{ var1 }} := factor(.data[[var1]]),
+#'       {{ var2 }} := factor(.data[[var2]])
+#'     ) |>
+#'     ggplot(aes_string(x = "y",y = "f", color = paste0(var1, ":", var2))) +
+#'     geom_line()
+#' }