# R/kmn_bin.R In mob: Monotonic Optimal Binning

#### Documented in kmn_bin

```#' Monotonic binning based on k-means clustering
#'
#' The function \code{kmn_bin} implements the monotonic binning based on
#' the k-means clustering
#'
#' @param x A numeric vector
#' @param y A numeric vector with 0/1 binary values
#'
#' @return A list of binning outcomes, including a numeric vector with cut
#'         points and a dataframe with binning summary
#'
#' @examples
#' data(hmeq)

kmn_bin <- function(x, y) {
x_ <- x[!is.na(x)]
y_ <- y[!is.na(x)]
n_ <- 2:max(2, min(50, length(unique(x_)) - 1))

set.seed(1)
c1 <- lapply(n_, function(n) kmeans(x_, centers = n, nstart = 10, iter.max = 500, algorithm = "MacQueen"))

c2 <- lapply(c1, function(c) sort(Reduce(c, lapply(split(x_, c\$cluster), max))))

p_ <- unique(append(lapply(c2, function(c) c[-length(c)]),
list(median(x_[y_ == 1]))))

l1 <- lapply(p_, function(p) list(cut = p, out = manual_bin(x_, y_, p)))

l2 <- lapply(l1[order(Reduce(c, lapply(l1, function(l) -length(l\$cut))))],
function(l) list(cut  = l\$cut,
minr = min(l\$out\$bads / l\$out\$freq),
maxr = max(l\$out\$bads / l\$out\$freq),
scor = round(cor(l\$out\$bin, l\$out\$bads / l\$out\$freq, method = "spearman"), 8)))

l3 <- l2[Reduce(c, lapply(l2, function(l) abs(l\$scor) == 1 & l\$minr > 0 & l\$maxr < 1))][[1]]

l4 <- l1[Reduce(c, lapply(l1, function(l) identical(l\$cut, l3\$cut)))][[1]]\$out

return(list(cut = l3\$cut, tbl = gen_woe(add_miss(l4, x, y), l3\$cut)))
}
```

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mob documentation built on July 31, 2021, 9:06 a.m.