update docs

Author: ‘topepo’

NAMESPACE

@@ -414,6 +414,7 @@ importFrom(stats,as.formula)
 importFrom(stats,binomial)
 importFrom(stats,coef)
 importFrom(stats,delete.response)
+importFrom(stats,median)
 importFrom(stats,model.frame)
 importFrom(stats,model.matrix)
 importFrom(stats,model.offset)

R/aaa_quantiles.R

@@ -45,28 +45,42 @@ new_quantile_pred <- function(values = list(), quantile_levels = double()) {
   )
 }
 
-
 #' Create a vector containing sets of quantiles
 #'
+#' [quantile_pred()] is a special vector class used to efficiently store
+#' predictions from a quantile regression model. It requires the same quantile
+#' levels for each row being predicted.
+#'
 #' @param values A matrix of values. Each column should correspond to one of
 #'   the quantile levels.
 #' @param quantile_levels A vector of probabilities corresponding to `values`.
+#' @param x An object produced by [quantile_pred()].
+#' @param .rows,.name_repair,rownames Arguments not used but required by the
+#' original S3 method.
+#' @param ... Not currently used.
 #'
 #' @export
-#' @return A vector of values associated with the quantile levels.
-#'
+#' @return
+#'   * [quantile_pred()] returns a vector of values associated with the
+#' quantile levels.
+#'   * [extract_quantile_levels()] returns a numeric vector of levels.
+#'   * [as_tibble()] returns a tibble with rows `".pred_quantile"`,
+#'   `".quantile_levels"`, and `".row"`.
+#'   * [as.matrix()] returns an unnamed matrix with rows as sames, columns as
+#'   quantile levels, and entries are predictions.
 #' @examples
-#' v <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+#' .pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
+#'
+#' unclass(.pred_quantile)
 #'
 #' # Access the underlying information
-#' attr(v, "quantile_levels")
-#' unclass(v)
+#' extract_quantile_levels(.pred_quantile)
 #'
-#' # tidy format
-#' as_tibble(v)
+#' # Matrix format
+#' as.matrix(.pred_quantile)
 #'
-#' # matrix format
-#' as.matrix(v)
+#' # Tidy format
+#' tibble::as_tibble(.pred_quantile)
 quantile_pred <- function(values, quantile_levels = double()) {
   check_quantile_pred_inputs(values, quantile_levels)
   quantile_levels <- vctrs::vec_cast(quantile_levels, double())
@@ -170,6 +184,16 @@ restructure_rq_pred <- function(x, object) {
 }
 
 #' @export
+#' @rdname quantile_pred
+extract_quantile_levels <- function(x) {
+  if ( !inherits(x, "quantile_pred") ) {
+    cli::cli_abort("{.arg x} should have class {.val quantile_pred}.")
+  }
+  attr(x, "quantile_levels")
+}
+
+#' @export
+#' @rdname quantile_pred
 as_tibble.quantile_pred <-
   function (x, ..., .rows = NULL, .name_repair = "minimal", rownames = NULL) {
     lvls <- attr(x, "quantile_levels")
@@ -183,16 +207,8 @@ as_tibble.quantile_pred <-
   }
 
 #' @export
+#' @rdname quantile_pred
 as.matrix.quantile_pred <- function(x, ...) {
   num_samp <- length(x)
   matrix(unlist(x), nrow = num_samp)
 }
-
-#' @export
-#' @rdname quantile_pred
-extract_quantile_levels <- function(x) {
-  if ( !inherits(x, "quantile_pred") ) {
-    cli::cli_abort("{.arg x} should have class {.val quantile_pred}.")
-  }
-  attr(x, "quantile_levels")
-}

R/parsnip-package.R

@@ -21,7 +21,7 @@
 #' @importFrom stats .checkMFClasses .getXlevels as.formula binomial coef
 #' @importFrom stats delete.response model.frame model.matrix model.offset
 #' @importFrom stats model.response model.weights na.omit na.pass predict qnorm
-#' @importFrom stats qt quantile setNames terms update
+#' @importFrom stats qt quantile setNames terms update median
 #' @importFrom tibble as_tibble is_tibble tibble
 #' @importFrom tidyr gather
 #' @importFrom utils capture.output getFromNamespace globalVariables head

man/quantile_pred.Rd

@@ -24,3 +24,16 @@ is_tf_ok <- function() {
   }
   res
 }
+
+# ------------------------------------------------------------------------------
+# for quantile regression tests
+
+data("Sacramento")
+
+Sacramento_small <-
+  Sacramento %>%
+  dplyr::mutate(price = log10(price)) %>%
+  dplyr::select(price, beds, baths, sqft, latitude, longitude)
+
+sac_train <- Sacramento_small[-(1:5), ]
+sac_test  <- Sacramento_small[  1:5 , ]

tests/testthat/helper-objects.R

@@ -1,14 +1,7 @@
 test_that('linear quantile regression via quantreg - single quantile', {
   skip_if_not_installed("quantreg")
 
-  data("Sacramento")
-
-  Sacramento_small <-
-    Sacramento %>%
-    dplyr::select(price, beds, baths, sqft, latitude, longitude)
-
-  sac_train <- Sacramento_small[-(1:5), ]
-  sac_test  <- Sacramento_small[  1:5 , ]
+  # data in `helper-objects.R`
 
   one_quant <-
     linear_reg() %>%
@@ -60,14 +53,7 @@ test_that('linear quantile regression via quantreg - single quantile', {
 test_that('linear quantile regression via quantreg - multiple quantiles', {
   skip_if_not_installed("quantreg")
 
-  data("Sacramento")
-
-  Sacramento_small <-
-    Sacramento %>%
-    dplyr::select(price, beds, baths, sqft, latitude, longitude)
-
-  sac_train <- Sacramento_small[-(1:5), ]
-  sac_test  <- Sacramento_small[  1:5 , ]
+  # data in `helper-objects.R`
 
   ten_quant <-
     linear_reg() %>%