Errors and checking for bootstrap intervals

NAMESPACE

@@ -417,6 +417,7 @@ export(vfold_cv)
 import(rlang)
 import(vctrs)
 importFrom(cli,cli_abort)
+importFrom(cli,cli_text)
 importFrom(cli,cli_warn)
 importFrom(dplyr,"%>%")
 importFrom(dplyr,arrange)

R/bootci.R

@@ -4,47 +4,53 @@
 # helpers
 
 
-check_rset <- function(x, app = TRUE) {
-  if (!inherits(x, "bootstraps")) {
-    cli_abort("{.arg .data} should be an {.cls rset} object generated from {.fn bootstraps}.")
-  }
-
-  if (app) {
-    if (x %>% dplyr::filter(id == "Apparent") %>% nrow() != 1) {
-      cli_abort("Please set {.code apparent = TRUE} in {.fn bootstraps} function.")
-    }
+check_includes_apparent <- function(x, call = caller_env()) {
+  if (x %>% dplyr::filter(id == "Apparent") %>% nrow() != 1) {
+    cli_abort(c(
+      "The bootstrap resamples must include an apparent sample.",
+      i =  "Please set {.code apparent = TRUE} in the {.fn bootstraps} function."
+      ), 
+      call = call
+    )
   }
   invisible(NULL)
 }
 
 
-stat_fmt_err <- "{.arg statistics} should select a list column of tidy results."
-stat_nm_err <- paste(
-  "The tibble in {.arg statistics} should have columns for",
-  "'estimate' and 'term'."
+statistics_format_error <- cli::format_inline(
+  "{.arg statistics} should select a list column of tidy results."
 )
 std_exp <- c("std.error", "robust.se")
 
-check_tidy_names <- function(x, std_col) {
+check_statistics_names <- function(x, std_col, call = caller_env()) {
   # check for proper columns
   if (sum(colnames(x) == "estimate") != 1) {
-    cli_abort(stat_nm_err)
+    cli_abort(
+      "The tibble in {.arg statistics} must have a column for 'estimate'.",
+      call = call
+    )
   }
   if (sum(colnames(x) == "term") != 1) {
-    cli_abort(stat_nm_err)
+    cli_abort(
+       "The tibble in {.arg statistics} must have a column for 'term'.",
+       call = call
+    )
   }
   if (std_col) {
     std_candidates <- colnames(x) %in% std_exp
     if (sum(std_candidates) != 1) {
-      cli_abort("{.arg statistics} should select a single column for the standard error.")
+      cli_abort(
+        "{.arg statistics} should select a single column for the standard error.",
+        call = call
+      )
     }
   }
   invisible(TRUE)
 }
 
-check_tidy <- function(x, std_col = FALSE) {
+check_statistics <- function(x, std_col = FALSE, call = caller_env()) {
   if (!is.list(x)) {
-    rlang::abort(stat_fmt_err)
+    cli_abort(statistics_format_error, call = call)
   }
 
   # convert to data frame from list
@@ -57,10 +63,10 @@ check_tidy <- function(x, std_col = FALSE) {
   }
 
   if (inherits(x, "try-error")) {
-    cli_abort(stat_fmt_err)
+    cli_abort(statistics_format_error, call = call)
   }
 
-  check_tidy_names(x, std_col)
+  check_statistics_names(x, std_col, call = call)
 
   if (std_col) {
     std_candidates <- colnames(x) %in% std_exp
@@ -117,15 +123,15 @@ new_stats <- function(x, lo, hi) {
   tibble(.lower = min(res), .estimate = mean(x, na.rm = TRUE), .upper = max(res))
 }
 
-has_dots <- function(x) {
+check_has_dots <- function(x, call = caller_env()) {
   nms <- names(formals(x))
   if (!any(nms == "...")) {
-    cli_abort("{.arg .fn} must have an argument {.arg ...}.")
+    cli_abort("{.arg .fn} must have an argument {.arg ...}.", call = call)
   }
   invisible(NULL)
 }
 
-check_num_resamples <- function(x, B = 1000) {
+check_num_resamples <- function(x, B = 1000, call = caller_env()) {
   x <-
     x %>%
     dplyr::group_by(term) %>%
@@ -134,7 +140,10 @@ check_num_resamples <- function(x, B = 1000) {
 
   if (nrow(x) > 0) {
     terms <- x$term
-    cli_warn("Recommend at least {B} non-missing bootstrap resamples for {cli::qty(terms)} term{?s} {.code {terms}}.")
+    cli_warn(
+      "Recommend at least {B} non-missing bootstrap resamples for {cli::qty(terms)} term{?s} {.code {terms}}.",
+       call = call
+    )
   }
   invisible(NULL)
 }
@@ -145,11 +154,11 @@ check_num_resamples <- function(x, B = 1000) {
 
 pctl_single <- function(stats, alpha = 0.05) {
   if (all(is.na(stats))) {
-    cli_abort("All statistics have missing values.")
+    cli_abort("All statistics have missing values.", call = call2("int_pctl"))
   }
 
   if (!is.numeric(stats)) {
-    cli_abort("{.arg stats} must be a numeric vector.")
+    cli_abort("All statistics must be numeric.", call = call2("int_pctl"))
   }
 
   # stats is a numeric vector of values
@@ -283,19 +292,16 @@ int_pctl <- function(.data, ...) {
 #' @rdname int_pctl
 int_pctl.bootstraps <- function(.data, statistics, alpha = 0.05, ...) {
   check_dots_empty()
-  check_rset(.data, app = FALSE)
-  if (length(alpha) != 1 || !is.numeric(alpha)) {
-    cli_abort("{.arg alpha} must be a single numeric value.")
-  }
+  check_number_decimal(alpha, min = 0, max = 1)
 
   .data <- .data %>% dplyr::filter(id != "Apparent")
 
   column_name <- tidyselect::vars_select(names(.data), !!rlang::enquo(statistics))
   if (length(column_name) != 1) {
-    rlang::abort(stat_fmt_err)
+    cli_abort(statistics_format_error)
   }
   stats <- .data[[column_name]]
-  stats <- check_tidy(stats, std_col = FALSE)
+  stats <- check_statistics(stats, std_col = FALSE)
 
   check_num_resamples(stats, B = 1000)
 
@@ -319,7 +325,7 @@ t_single <- function(stats, std_err, is_orig, alpha = 0.05) {
   # which_orig is the index of stats and std_err that has the original result
 
   if (all(is.na(stats))) {
-    cli_abort("All statistics have missing values.")
+    cli_abort("All statistics have missing values.", call = call2("int_t"))
   }
 
   if (!is.logical(is_orig) || any(is.na(is_orig))) {
@@ -368,17 +374,15 @@ int_t <- function(.data, ...) {
 #' @export
 int_t.bootstraps <- function(.data, statistics, alpha = 0.05, ...) {
   check_dots_empty()
-  check_rset(.data)
-  if (length(alpha) != 1 || !is.numeric(alpha)) {
-    cli_abort("{.arg alpha} must be a single numeric value.")
-  }
+  check_includes_apparent(.data)
+  check_number_decimal(alpha, min = 0, max = 1)
 
   column_name <- tidyselect::vars_select(names(.data), !!enquo(statistics))
   if (length(column_name) != 1) {
-    cli_abort(stat_fmt_err)
+    cli_abort(statistics_format_error)
   }
   stats <- .data %>% dplyr::select(!!column_name, id)
-  stats <- check_tidy(stats, std_col = TRUE)
+  stats <- check_statistics(stats, std_col = TRUE)
 
   check_num_resamples(stats, B = 500)
 
@@ -394,11 +398,11 @@ int_t.bootstraps <- function(.data, statistics, alpha = 0.05, ...) {
 
 # ----------------------------------------------------------------
 
-bca_calc <- function(stats, orig_data, alpha = 0.05, .fn, ...) {
+bca_calc <- function(stats, orig_data, alpha = 0.05, .fn, ..., call = caller_env()) {
 
   # TODO check per term
   if (all(is.na(stats$estimate))) {
-    cli_abort("All statistics have missing values.")
+    cli_abort("All statistics have missing values.", call = call)
   }
 
   stat_groups_chr <- c("term", grep("^\\.", names(stats), value = TRUE))
@@ -414,9 +418,9 @@ bca_calc <- function(stats, orig_data, alpha = 0.05, .fn, ...) {
   # To test, we run on the first LOO data set and see if it is a vector or df
   loo_test <- try(rlang::exec(.fn, loo_rs$splits[[1]], ...), silent = TRUE)
   if (inherits(loo_test, "try-error")) {
-    cat("Running `.fn` on the LOO resamples produced an error:\n")
+    cli_text("Running {.fn .fn} on the LOO resamples produced an error:")
     print(loo_test)
-    cli_abort("{.arg .fn} failed.")
+    cli_abort("{.arg .fn} failed.", call = call)
   }
 
   loo_res <- furrr::future_map(loo_rs$splits, .fn, ...) %>% list_rbind()
@@ -477,19 +481,17 @@ int_bca <- function(.data, ...) {
 #' @rdname int_pctl
 #' @export
 int_bca.bootstraps <- function(.data, statistics, alpha = 0.05, .fn, ...) {
-  check_rset(.data)
-  if (length(alpha) != 1 || !is.numeric(alpha)) {
-    cli_abort("{.arg alpha} must be a single numeric value.")
-  }
+  check_includes_apparent(.data)
+  check_number_decimal(alpha, min = 0, max = 1)
 
-  has_dots(.fn)
+  check_has_dots(.fn)
 
   column_name <- tidyselect::vars_select(names(.data), !!enquo(statistics))
   if (length(column_name) != 1) {
-    cli_abort(stat_fmt_err)
+    cli_abort(statistics_format_error)
   }
   stats <- .data %>% dplyr::select(!!column_name, id, dplyr::starts_with("."))
-  stats <- check_tidy(stats)
+  stats <- check_statistics(stats)
 
   check_num_resamples(stats, B = 1000)
 

R/rsample-package.R

@@ -4,7 +4,7 @@
 ## usethis namespace: start
 #' @import rlang
 #' @importFrom lifecycle deprecated
-#' @importFrom cli cli_abort cli_warn
+#' @importFrom cli cli_abort cli_warn cli_text
 #' @importFrom utils globalVariables
 #' @importFrom purrr map map2 map_dbl pluck map_lgl list_rbind
 #' @importFrom tibble tibble is_tibble as_tibble obj_sum