predictor {regressinator}

Specify the distribution of a predictor variable

Description

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.

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.

Value

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

Examples

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

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