maximize: Aliases for individual desirability functions

Description

Aliases for individual desirability functions

Usage

maximize(x, low, high, scale = 1, missing = NA_real_, use_data = FALSE)
minimize(x, low, high, scale = 1, missing = NA_real_, use_data = FALSE)
target(
  x,
  low,
  target,
  high,
  scale_low = 1,
  scale_high = 1,
  missing = NA_real_,
  use_data = FALSE
)
constrain(x, low, high, missing = NA_real_, use_data = FALSE)
category(x, categories, missing = NA_real_)

Arguments

x: A vector of data to compute the desirability function
low, high, target: Single numeric values that define the active ranges of desirability.
scale, scale_low, scale_high: A single numeric value to rescale the desirability function (each should be great than 0.0). Values >1.0 make the desirability more difficult to satisfy while smaller values make it easier (see the examples below). scale_low and scale_high do the same for target functions with scale_low affecting the range below the target value and scale_high affecting values greater than target.
missing: A single numeric value on [0, 1] (or NA_real_) that defines how missing values in x are mapped to the desirability score.
use_data: Should the low, middle, and/or high values be derived from the data (x) using the minimum, maximum, or median (respectively)?
categories: A named vector of desirability values that match all possible categories to specific desirability values. Data that are not included in categories are given the value in missing.