Version 0.1.2

NAMESPACE

@@ -50,6 +50,7 @@ importFrom(stats,density)
 importFrom(stats,dunif)
 importFrom(stats,dweibull)
 importFrom(stats,optimize)
+importFrom(stats,quantile)
 importFrom(stats,runif)
 importFrom(utils,capture.output)
 importFrom(utils,head)

NEWS.md

@@ -30,8 +30,6 @@
 
 * The method `qqplot.accept_reject()` has been added, which constructs the QQ-Plot of an object of class `accept_reject` returned by the function `accept_reject()`;
 
-* The `qqplot.accept_reject()` function can be parallelized using the `parallel = TRUE` argument, allowing theoretical quantiles to be calculated in parallel. The cores argument controls the number of cores that will be used. The default value, `cores = NULL`, means that all processor cores will be used. If `parallel = FALSE`, the cores argument is ignored.
-
 * The `qqplot.accept_reject()` function utilizes the [**scattermore**](https://github.com/exaexa/scattermore) package if the point density is high, i.e., above 10 thousand observations;
 
 * The function `accept_reject()` now has the argument cores, which allows the user to control the number of cores that will be used if `parallel = TRUE`. The default, `cores = NULL`, means that all processor cores will be used. If `parallel = FALSE`, the cores argument is ignored;

R/print.r

@@ -1,8 +1,8 @@
 #' @title Print method for accept_reject objects
-#' @description Print method for accept_reject objects
-#' @param x An accept_reject object
-#' @param n_min Minimum number of observations to print
-#' @param ... Additional arguments
+#' @description Print method for accept_reject objects.
+#' @param x An accept_reject object.
+#' @param n_min Minimum number of observations to print.
+#' @param ... Additional arguments.
 #'
 #' @details
 #' The function [print.accept_reject()] is responsible for printing an object of

R/qqplot.R

@@ -1,25 +1,3 @@
-quantile_custom <- function(x, p) {
- f <- attr(x, "f")
- continuous <- attr(x, "continuous")
- xlim <- attr(x, "xlim")
-
- return(
- internal_quantile(
- continuous,
- f,
- p,
- xlim[1L],
- xlim[2L]
- )
- )
-}
-
-quantile_custom_vec <-
- Vectorize(
- quantile_custom,
- vectorize.args = "p"
- )
-
 #' QQ-Plot
 #' QQ-Plot between observed quantiles and theoretical quantiles.
 #' @param x Object of the class `accept_reject` returned by the function
@@ -49,24 +27,16 @@ qqplot <- function(x, ...) {
 #' @param color_line Color of the reference line (detault is `"#BB9FC9"`).
 #' @param size_points Size of the points (default is `1`).
 #' @param size_line Thickness of the reference line (default is `1`).
-#' @param parallel If `TRUE`, all cores will be used for internal calculations of
-#' theoretical quantiles. The default is `FALSE`. Use TRUE if you find the plot
-#' is taking too long.
-#' @param cores Number of cores to be used if `parallel = TRUE`. Defalut is `NULL`,
-#' which means all cores will be used.
-#' @param ... Additional arguments.
+#' @param ... Additional arguments for the `quantile()` function. For instance,
+#' it's possible to change the algorithm type for quantile calculation.
 #' @details
-#' Just like in the `accept_reject()` function, the `qqplot.accept_reject()`
-#' function uses parallelism using FORK, meaning it works on Unix-based
-#' operating systems (Linux and MacOS). What is parallelized are the internal
-#' calculations of theoretical quantiles of the true distribution. This
-#' parallelism will only be useful for excessively large samples. Additionally,
-#' for samples larger than ten thousand, the `geom_scattermost()` function from
-#' the [**scattermore**](https://CRAN.R-project.org/package=scattermore) library
+#' The function `qqplot.accept_reject()` for samples larger than ten thousand,
+#' the `geom_scattermost()` function from the
+#' [**scattermore**](https://CRAN.R-project.org/package=scattermore) library
 #' is used to plot the points, as it is more efficient than `geom_point()` from
 #' the [**ggplot2**](https://CRAN.R-project.org/package=ggplot2) library.
 #'
-#' @return An object of classes gg and ggplot with the QQ-Plot between the
+#' @return An object of classes `gg` and `ggplot` with the QQ-Plot between the
 #' observed quantiles generated by the return of the function `accept_reject()`
 #' and the theoretical quantiles of the true distribution.
 #' @examples
@@ -89,11 +59,16 @@ qqplot <- function(x, ...) {
 #' xlim = c(-4, 4)
 #' )
 #' qqplot(y)
+#'
+#' @seealso [accept_reject()], [plot.accept_reject()], [inspect()] and
+#' [qqplot()].
+#'
 #' @importFrom Rcpp evalCpp
 #' @importFrom ggplot2 ggplot geom_point geom_abline labs theme element_text
 #' coord_cartesian scale_x_continuous scale_y_continuous aes_string
 #' @importFrom scattermore geom_scattermore
 #' @importFrom parallel mclapply detectCores
+#' @importFrom stats quantile
 #' @export
 qqplot.accept_reject <-
  function(x,
@@ -102,27 +77,15 @@ qqplot.accept_reject <-
  color_line = "#BB9FC9",
  size_points = 1,
  size_line = 1,
- parallel = FALSE,
- cores = NULL,
  ...
  ) {
 
  continuous <- attr(x, "continuous")
  sample_quantiles <- sort(x)
- p <- (rank(sample_quantiles) - 0.375) / (length(sample_quantiles) + 0.25)
 
- theoretical_quantiles <-
- unlist(
- parallel::mclapply(
- X = p,
- FUN = function(i) quantile_custom_vec(x = x, i),
- mc.cores = ifelse(
- parallel,
- ifelse(is.null(cores), parallel::detectCores(), cores),
- 1L
- )
- )
- )
+ n <- length(x)
+ p <- seq(1L, n) / (n + 1L)
+ theoretical_quantiles <- quantile(x, probs = p, ...)
 
  df <- data.frame(Theoretical = theoretical_quantiles, Sample = sample_quantiles)
  xlim <- attr(x, "xlim")

docs/pkgdown.yml

@@ -1,8 +1,8 @@
-pandoc: 3.1.1
+pandoc: 3.1.11
 pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
  accept_reject: accept_reject.html
  inspect: inspect.html
-last_built: 2024-04-30T16:56Z
+last_built: 2024-05-02T11:05Z