Random generation from the Hurdle Beta-Binomial

Generates random draws from the two-part hurdle model. First draws \(Z \sim \text{Bernoulli}(q)\), then for \(Z = 1\) draws \(Y \sim \text{ZT-BetaBin}(n, \mu, \kappa)\).

Usage

rhurdle_betabinom(nn, n, q, mu, kappa)

Arguments

nn

Number of draws to generate (positive integer).

n

Integer vector of trial sizes (\(n \ge 1\)).

q

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

mu

Numeric vector of intensity means in \((\varepsilon, 1 - \varepsilon)\).

kappa

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

Value

An integer vector of length nn.

Details

All parameter vectors (n, q, mu, kappa) are recycled to length nn. The function is fully vectorised.

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(), rztbetabinom()

Examples

set.seed(42)
x <- rhurdle_betabinom(1000, n = 10, q = 0.7, mu = 0.3, kappa = 5)
mean(x == 0)  # approximately 0.3
#> [1] 0.293
hist(x, breaks = -0.5:10.5)