Build a p/d/q/r function with parameters fixed

Usage

pdqr_factory(..., dist, type, trans = NULL, trans_inv = NULL, transpars = NULL)

Arguments

...: parameter name-value pairs to be fixed in the returned function. Parameters not accepted by the target distribution function are ignored, so entire rows of a forecast data frame may be passed in.
dist: distribution root name, such as "norm" or "beta", or "distfromq" to interpolate a distribution through predictive quantiles.
type: one of "p", "d", "q" or "r".
trans: function of an outcome x and parameters, pre-composed with the p and d functions. Use this for distributions whose support must be rescaled, such as a beta distribution on [x_min, x_max].
trans_inv: inverse of trans, post-composed with the q and r functions.
transpars: parameters for trans and trans_inv that are not columns of df.

Value

A function of one argument: a cdf ("p"), density ("d"), quantile function ("q") or random generator ("r").

Examples

F <- pdqr_factory(mean = 5, sd = 2, dist = "norm", type = "p")
F(5)
#> [1] 0.5

# a distribution interpolated through predictive quantiles
Q <- pdqr_factory(
  ps = c(0.25, 0.5, 0.75), qs = c(4, 5, 6),
  dist = "distfromq", type = "q"
)
Q(0.5)
#> [1] 5