##### https://github.com/cran/bayestestR

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**40f7c88ddf855896018cf20ec8a7ac5fbd0ea2fb**authored by Dominique Makowski on**27 January 2020, 05:30 UTC**, committed by cran-robot on**27 January 2020, 05:30 UTC****1 parent**d8462ad

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**40f7c88ddf855896018cf20ec8a7ac5fbd0ea2fb**authored by**Dominique Makowski**on**27 January 2020, 05:30 UTC****version 0.5.1** Tip revision:

**40f7c88** overlap.R

```
#' Overlap Coefficient
#'
#' A method to calculate the overlap coefficient between two empirical distributions (that can be used as a measure of similarity between two samples).
#'
#' @param x Vector of x values.
#' @param y Vector of x values.
#' @param method_auc Area Under the Curve (AUC) estimation method. See \code{\link{area_under_curve}}.
#' @param method_density Density estimation method. See \code{\link{estimate_density}}.
#' @inheritParams estimate_density
#'
#' @examples
#' library(bayestestR)
#'
#' x <- distribution_normal(1000, 2, 0.5)
#' y <- distribution_normal(1000, 0, 1)
#'
#' overlap(x, y)
#' plot(overlap(x, y))
#' @importFrom stats approxfun
#' @export
overlap <- function(x, y, method_density = "kernel", method_auc = "trapezoid", precision = 2^10, extend = TRUE, extend_scale = 0.1, ...) {
# Generate densities
dx <- estimate_density(x, method = method_density, precision = precision, extend = extend, extend_scale = extend_scale, ...)
dy <- estimate_density(y, method = method_density, precision = precision, extend = extend, extend_scale = extend_scale, ...)
# Create density estimation functions
fx <- approxfun(dx$x, dx$y, method = "linear", rule = 2)
fy <- approxfun(dy$x, dy$y, method = "linear", rule = 2)
x_axis <- seq(min(c(dx$x, dy$x)), max(c(dx$x, dy$x)), length.out = precision)
data <- data.frame(x = x_axis, y1 = fx(x_axis), y2 = fy(x_axis))
# calculate intersection densities
data$intersection <- pmin(data$y1, data$y2)
data$exclusion <- pmax(data$y1, data$y2)
# integrate areas under curves
area_intersection <- area_under_curve(data$x, data$intersection, method = method_auc)
# area_exclusion <- area_under_curve(data$x, data$exclusion, method = method_auc)
# compute overlap coefficient
overlap <- area_intersection
attr(overlap, "data") <- data
class(overlap) <- c("overlap", class(overlap))
overlap
}
#' @export
print.overlap <- function(x, ...) {
insight::print_color("# Overlap\n\n", "blue")
cat(sprintf("%.2f", as.numeric(x)))
}
#' @importFrom graphics plot polygon
#' @export
plot.overlap <- function(x, ...) {
# Can be improved through see
data <- attributes(x)$data
plot(data$x, data$exclusion, type = "l")
polygon(data$x, data$intersection, col = "red")
}
```

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