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#' Quantile binning
#'
#' Bin continuous data using quantiles.
#'
#' @param data A \code{data.frame} or \code{tibble}.
#' @param response Response variable.
#' @param predictor Predictor variable.
#' @param bins Number of bins.
#' @param include_na logical; if \code{TRUE}, a separate bin is created for missing values.
#' @param x An object of class \code{rbin_quantiles}.
#' @param print_plot logical; if \code{TRUE}, prints the plot else returns a plot object.
#' @param ... further arguments passed to or from other methods.
#'
#' @return A \code{tibble}.
#'
#' @examples
#' bins <- rbin_quantiles(mbank, y, age, 10)
#' bins
#'
#' # plot
#' plot(bins)
#'
#' @export
#'
rbin_quantiles <- function(data = NULL, response = NULL, predictor = NULL, bins = 10, include_na = TRUE) UseMethod("rbin_quantiles")
#' @export
#'
rbin_quantiles <- function(data = NULL, response = NULL, predictor = NULL, bins = 10, include_na = TRUE) {
resp <- deparse(substitute(response))
pred <- deparse(substitute(predictor))
var_names <- names(data[, c(resp, pred)])
prep_data <- data[, c(resp, pred)]
if (include_na) {
bm <- prep_data
} else {
bm <- na.omit(prep_data)
}
colnames(bm) <- c("response", "predictor")
bm$bin <- NA
byd <- bm$predictor
l_freq <- ql_freq(byd, bins)
u_freq <- qu_freq(byd, bins)
for (i in seq_len(bins)) {
bm$bin[bm$predictor >= l_freq[i] & bm$predictor < u_freq[i]] <- i
}
k <- bin_create(bm)
sym_sign <- c(rep("<", (bins - 1)), ">=")
fbin2 <- f_bin(u_freq)
intervals <- create_intervals(sym_sign, fbin2)
if (include_na) {
na_present <- nrow(k) > bins
if (na_present) {
intervals <- rbind(intervals, cut_point = 'NA')
}
}
result <- list(bins = cbind(intervals, k),
method = "Quantile",
vars = var_names,
lower_cut = l_freq,
upper_cut = u_freq)
class(result) <- c("rbin_quantiles")
return(result)
}
#' @export
#'
print.rbin_quantiles <- function(x, ...) {
rbin_print(x)
cat("\n\n")
print(x$bins[c('cut_point', 'bin_count', 'good', 'bad', 'woe', 'iv', 'entropy')])
}
#' @rdname rbin_quantiles
#' @export
#'
plot.rbin_quantiles <- function(x, print_plot = TRUE, ...) {
p <- plot_bins(x)
if (print_plot) {
print(p)
}
return(p)
}
ql_freq <- function(byd, bins) {
cut_points <- cutpoints(byd, bins)
unname(append(min(byd, na.rm = TRUE), cut_points))
}
qu_freq <- function(byd, bins) {
cut_points <- cutpoints(byd, bins)
unname(c(cut_points, (max(byd, na.rm = TRUE) + 1)))
}
cutpoints <- function(byd, bins) {
bin_prob <- 1 / bins
bq <- stats::quantile(byd, seq(0, 1, bin_prob), na.rm = TRUE)
bin_len <- bins + 1
bq[c(-1, -bin_len)]
}
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