# R/rkt_ecdf.R In ROCket: Simple and Fast ROC Curves

#### Documented in rkt_ecdf

```#' Empirical estimate of the CDF
#'
#' Calculate an empirical cumulative distribution function based on a sample \code{x} and optionally a vector \code{w} of weights.
#'
#' The weights vector \code{w} can contain the counts of each distinct value in \code{x}, this is the most natural use case.
#' In general the weights are describing the jumps of the final ecdf. Normalization is handled internally.
#'
#' If \code{x} contains duplicates, corresponding values in \code{w} will be summed up.
#' Only positive weights are allowed. Elements in \code{x} with non-positive weights will be ignored.
#'
#' @param x Numeric vector containing the sample. Alternatively, if \code{w} is supplied, distinct values within the sample. For S3 methods, a function of class \code{rkt_ecdf}.
#' @param w Optional. Numeric vector containing the weights of each value in \code{x}.
#' @param ... Further parameters.
#'
#' @return A function of class \code{rkt_ecdf}.
#' @export
#'
#' @examples
#' require(ROCket)
#'
#' plot(rkt_ecdf(rnorm(100)))
#' plot(rkt_ecdf(c(0, 1)))
#' plot(rkt_ecdf(c(0, 1), c(1, 10)))
rkt_ecdf <- function(x, w) {
if (missing(w)) {
df <- data.table(x)
df <- df[, .(w = .N), keyby = x]
} else if (is.numeric(w) && any(duplicated(x))) {
df <- data.table(x, w)
df <- df[, .(w = sum(w)), keyby = x]
} else if (is.numeric(w)) {
df <- data.table(x, w)
df <- df[order(x)]
} else {
stop("\"w\" needs to be numeric")
}

df <- df[!is.na(x) & w > 0]
total <- sum(df\$w)

stopifnot(total > 0)
df[, y := cumsum(w) / total]

out <- approxfun(df\$x, df\$y,
method = "constant",
yleft = 0,
yright = 1,
f = 0,
ties = "ordered")
class(out) <- c("rkt_ecdf", class(out))
attr(out, "singularities") <- df\$x

out
}

#' @export
#' @rdname rkt_ecdf
print.rkt_ecdf <- function(x, ...) {
cat(".:: ROCket ECDF Object \n")
cat("Class:", class(x), "\n")
}

#' @rdname rkt_ecdf
#' @export
mean.rkt_ecdf <- function(x, ...) {
weighted.mean(environment(x)\$x, get_jumps(x))
}

#' @rdname rkt_ecdf
#' @export
variance.rkt_ecdf <- function(x, ...) {
weighted.mean((environment(x)\$x - mean(x))^2, get_jumps(x))
}

#' @export
#' @rdname rkt_ecdf
plot.rkt_ecdf <- function(x, ...) {
inargs <- list(...)

outargs <- list(f = x,
ylim = c(0, 1),
xlab = expression(x),
ylab = expression(F[n](x)),
main = 'ECDF',
draw_area = FALSE,
h = c(0, 1))
outargs[names(inargs)] <- inargs

do.call(plot_function, outargs)

invisible()
}
```

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ROCket documentation built on Feb. 17, 2021, 5:07 p.m.