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#' Continuous quantiles.
#'
#' @param quantiles conditional quantiles of y to calculate and display
#' @param formula formula relating y variables to x variables
#' @param method Quantile regression method to use. Currently only supports
#' \code{\link[quantreg]{rq}}.
#' @param na.rm If \code{FALSE} (the default), removes missing values with
#' a warning. If \code{TRUE} silently removes missing values.
#' @inheritParams stat_identity
#' @return a data.frame with additional columns:
#' \item{quantile}{quantile of distribution}
#' @export
#' @examples
#' \donttest{
#' msamp <- movies[sample(nrow(movies), 1000), ]
#' m <- ggplot(msamp, aes(y=rating, x=year)) + geom_point()
#' m + stat_quantile()
#' m + stat_quantile(quantiles = 0.5)
#' m + stat_quantile(quantiles = seq(0.1, 0.9, by=0.1))
#'
#' # Doesn't work. Not sure why.
#' # m + stat_quantile(method = rqss, formula = y ~ qss(x), quantiles = 0.5)
#'
#' # Add aesthetic mappings
#' m + stat_quantile(aes(weight=votes))
#'
#' # Change scale
#' m + stat_quantile(aes(colour = ..quantile..), quantiles = seq(0.05, 0.95, by=0.05))
#' m + stat_quantile(aes(colour = ..quantile..), quantiles = seq(0.05, 0.95, by=0.05)) +
#' scale_colour_gradient2(midpoint=0.5, low="green", mid="yellow", high="green")
#'
#' # Set aesthetics to fixed value
#' m + stat_quantile(colour="red", size=2, linetype=2)
#'
#' # Use qplot instead
#' qplot(year, rating, data=movies, geom="quantile")
#' }
stat_quantile <- function (mapping = NULL, data = NULL, geom = "quantile", position = "identity",
quantiles = c(0.25, 0.5, 0.75), formula = y ~ x, method = "rq",
na.rm = FALSE, ...) {
  StatQuantile$new(mapping = mapping, data = data, geom = geom,
  position = position, quantiles = quantiles, formula = formula,
  method = method, na.rm = na.rm, ...)
}

StatQuantile <- proto(Stat, {
  objname <- "quantile"

  default_geom <- function(.) GeomQuantile
  default_aes <- function(.) aes()
  required_aes <- c("x", "y")
  icon <- function(.) GeomQuantile$icon()

  calculate <- function(., data, scales, quantiles=c(0.25, 0.5, 0.75), formula=y ~ x, xseq = NULL, method="rq", na.rm = FALSE, ...) {
    try_require("quantreg")
    if (is.null(data$weight)) data$weight <- 1

    if (is.null(xseq)) xseq <- seq(min(data$x, na.rm=TRUE), max(data$x, na.rm=TRUE), length=100)

    data <- as.data.frame(data)
    data <- remove_missing(data, na.rm, c("x", "y"), name = "stat_quantile")
    
    method <- match.fun(method)
    model <- method(formula, data=data, tau=quantiles, weight=weight, ...)

    yhats <- stats::predict(model, data.frame(x=xseq), type="matrix")
    
    quantile <- rep(quantiles, each=length(xseq))
    data.frame(
      y = as.vector(yhats),
      x = xseq,
      quantile = quantile,
      group = paste(data$group[1], quantile, sep = "-")
    )
  }
  
})
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