Specify the distribution of a predictor variable

Predictor variables can have any marginal distribution as long as a function is provided to sample from the distribution. Multivariate distributions are also supported: if the random generation function returns multiple columns, multiple random variables will be created, successively numbered.

Usage

predictor(dist, ...)

Arguments

dist: Name (as character vector) of the function to generate draws from this predictor's distribution.
...: Additional arguments to pass to dist when generating draws.

Value

A predictor_dist object, to be used in population() to specify a population distribution

Details

The random generation function must take an argument named n specifying the number of draws. For univariate distributions, it should return a vector of length n; for multivariate distributions, it should return an array or matrix with n rows and a column per variable.

Multivariate predictors are successively numbered. For instance, if predictor X is specified with

library(mvtnorm)
predictor(dist = "rmvnorm", mean = c(0, 1),
          sigma = matrix(c(1, 0.5, 0.5, 1), nrow = 2))

then the population predictors will be named X1 and X2, and will have covariance 0.5.

Examples

# Univariate normal distribution
predictor(dist = "rnorm", mean = 10, sd = 2.5)
#> rnorm(list(mean = 10, sd = 2.5))

# Multivariate normal distribution
library(mvtnorm)
predictor(dist = "rmvnorm", mean = c(0, 1, 7))
#> rmvnorm(list(mean = c(0, 1, 7)))