betareg (version 3.2-1)

Beta01: Create a Zero- and/or One-Inflated Beta Distribution

Description

Class and methods for zero- and/or one-inflated beta distributions in regression specification using the workflow from the distributions3 package.

Usage

Beta01(mu, phi, p0 = 0, p1 = 0)

Value

A Beta01 distribution object.

Arguments

mu

numeric. The mean of the beta distribution (on the open unit interval).

phi

numeric. The precision parameter of the beta distribution.

p0

numeric. The probability for an observation of zero (often referred to as zero inflation).

p1

numeric. The probability for an observation of one (often referred to as one inflation).

Details

The zero- and/or one-inflated beta distribution is obtained by adding point masses at zero and/or one to a standard beta distribution.

Note that the support of the standard beta distribution is the open unit interval where values of exactly zero or one cannot occur. Thus, the inflation jargon is rather misleading as there is no probability that could be inflated. It is rather a hurdle or two-part (or three-part) model.

See Also

dbeta01, BetaR

Examples

 if(!requireNamespace("distributions3")) {
  if(interactive() || is.na(Sys.getenv("_R_CHECK_PACKAGE_NAME_", NA))) {
    stop("not all packages required for the example are installed")
  } else q() }

## package and random seed
library("distributions3")
set.seed(6020)

## three beta distributions
X <- Beta01(
  mu  = c(0.25, 0.50, 0.75),
  phi = c(1, 1, 2),
  p0 = c(0.1, 0, 0),
  p1 = c(0, 0, 0.3)
)
# \donttest{
X

## compute moments of the distribution
mean(X)
variance(X)

## support interval (minimum and maximum)
support(X)

## simulate random variables
random(X, 5)

## histograms of 1,000 simulated observations
x <- random(X, 1000)
hist(x[1, ])
hist(x[2, ])
hist(x[3, ])

## probability density function (PDF) and log-density (or log-likelihood)
x <- c(0.25, 0.5, 0.75)
pdf(X, x)
pdf(X, x, log = TRUE)
log_pdf(X, x)

## cumulative distribution function (CDF)
cdf(X, x)

## quantiles
quantile(X, 0.5)

## cdf() and quantile() are inverses
cdf(X, quantile(X, 0.5))
quantile(X, cdf(X, 1))

## point mass probabilities (if any) on boundary
cdf(X, 0, lower.tail = TRUE)
cdf(X, 1, lower.tail = FALSE)

## all methods above can either be applied elementwise or for
## all combinations of X and x, if length(X) = length(x),
## also the result can be assured to be a matrix via drop = FALSE
p <- c(0.05, 0.5, 0.95)
quantile(X, p, elementwise = FALSE)
quantile(X, p, elementwise = TRUE)
quantile(X, p, elementwise = TRUE, drop = FALSE)

## compare theoretical and empirical mean from 1,000 simulated observations
cbind(
  "theoretical" = mean(X),
  "empirical" = rowMeans(random(X, 1000))
)
# }