Random generation from the Beta-Binomial distribution

Generates random draws via the hierarchical representation: \(p \sim \text{Beta}(a, b)\), \(Y \mid p \sim \text{Binomial}(n, p)\).

Usage

rbetabinom(nn, n, mu, kappa)

Arguments

nn

Number of draws to generate (positive integer).

n

Integer vector of trial sizes (\(n \ge 0\)).

mu

Numeric vector of means, each in \([0, 1]\).

kappa

Numeric vector of concentrations, each \(> 0\).

Value

An integer vector of length nn.

Details

All parameter vectors are recycled to length nn. The generation is fully vectorised (no loop).

References

Ghosal, S., Ghosh, S., and Moores, M. (2020). “Hierarchical beta-binomial models for batch effects in cytometry data.” Journal of the Royal Statistical Society: Series A, 183(4), 1579–1601.

See also

Other distributions: compute_p0(), compute_ztbb_mean(), dbetabinom(), dhurdle_betabinom(), dztbetabinom(), hurdle_mean(), hurdle_variance(), pbetabinom(), rhurdle_betabinom(), rztbetabinom()

Examples

set.seed(42)
x <- rbetabinom(1000, n = 10, mu = 0.3, kappa = 5)
hist(x, breaks = -0.5:10.5)

mean(x)  # approximately 3
#> [1] 2.965

# Vectorised parameters
rbetabinom(4, n = c(5, 10), mu = c(0.2, 0.8), kappa = c(3, 20))
#> [1] 1 7 0 5