Create a Step Distribution — stepdst • distplyr

A step distribution is one where the cdf and quantile function are step functions. This includes empirical distributions. stepdst() facilitates the creation of such a distribution by specifying the observations/breakpoints, along with their weights.

stepdst(y, data, weights = 1, ...)

is_stepdst(object)

is.stepdst(object)

Arguments

y	Outcomes to comprise the distribution. Should either evaluate to a vector, or be a name in the specified data.
data	Data frame, list, or environment containing the outcome name in `y`. If missing, `y` will be evaluated in the parent frame.
weights	Weights corresponding to the outcomes in `y`. Must not be negative, but need not sum to 1. If `data` is provided, the data will be searched for the name provided in this argument.
...	Additional arguments, currently not used.
object	Object to check

Value

A "stepdst" object, which is also a "dst" object. The cdf is a right-continuous step function, and the quantile function is a left-continuous step function.

Examples

require(graphics)
require(datasets)
marg <- stepdst(hp, data = mtcars)
plot(marg, "cdf", n = 1001)

K <- function(x) dnorm(x, sd = 25)
cond <- stepdst(hp, data = mtcars, weights = K(disp - 150))
plot(cond, "cdf", n = 1001, lty = 2, add = TRUE)