Random generation from the Zero-Truncated Beta-Binomial

Generates random draws from the zero-truncated Beta-Binomial via rejection sampling on the standard Beta-Binomial.

Usage

rztbetabinom(nn, n, mu, kappa)

Arguments

nn

Number of draws to generate (positive integer).

n

Integer vector of trial sizes (\(n \ge 1\)), recycled to length nn.

mu

Numeric vector of means in \((\varepsilon, 1 - \varepsilon)\), recycled to length nn.

kappa

Numeric vector of concentrations (\(> 0\)), recycled to length nn.

Value

An integer vector of length nn, each element in \(\{1, \ldots, n_i\}\).

Details

The function is vectorised over n, mu, and kappa using a parameter-grouping strategy. Unique parameter combinations are identified, and rejection sampling is performed once per group with smart batch sizing based on the analytical \(p_0\).

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(), rbetabinom(), rhurdle_betabinom()

Examples

set.seed(42)
x <- rztbetabinom(1000, n = 10, mu = 0.3, kappa = 5)
min(x)  # always >= 1
#> [1] 1
mean(x)
#> [1] 3.438

# Vectorised parameters
rztbetabinom(6, n = c(5, 10, 20), mu = c(0.2, 0.5, 0.8), kappa = c(3, 10, 50))
#> [1]  5  4 14  2  2 15