rpbinom: Random Generation for Independent Multinomial Distributions

In multinomineq: Bayesian Inference for Multinomial Models with Inequality Constraints

Random Generation for Independent Multinomial Distributions

Description

Generates random draws from independent multinomial distributions (= product-multinomial pmultinom).

Usage

rpbinom(prob, n)

rpmultinom(prob, n, options, drop_fixed = TRUE)

Arguments

prob	vector with probabilities or a matrix with one probability vector per row. For rpbinom: probabilities of a success for each option. For rpmultinom: probabilities of all categories excluding the last category for each option (cf. drop_fixed). See also sampling_binom and sampling_multinom.
n	integer vector, specifying the number of trials for each binomial/multinomial distribution Note that this is the size argument in rmultinom, cf. Multinom.
options	number of observable categories/probabilities for each item type/multinomial distribution, e.g., c(3,2) for a ternary and binary item.
drop_fixed	whether the output matrix includes the last probability for each category (which is not a free parameter since probabilities must sum to one).

Value

a matrix with one vector of frequencies per row. For rpbinom, only the frequencies of 'successes' are returned, whereas for rpmultinom, the complete frequency vectors (which sum to n within each option) are returned.

Examples

# 3 binomials
rpbinom(prob = c(.2, .7, .9), n = c(10, 50, 30))

# 2 and 3 options:  [a1,a2,  b1,b2,b3]
rpmultinom(
  prob = c(a1 = .5, b1 = .3, b2 = .6),
  n = c(10, 20), options = c(2, 3)
)
# or:
rpmultinom(
  prob = c(a1 = .5, a2 = .5, b1 = .3, b2 = .6, b3 = .1),
  n = c(10, 20), options = c(2, 3),
  drop_fixed = FALSE
)

# matrix with one probability vector per row:
p <- rpdirichlet(
  n = 6, alpha = c(1, 1, 1, 1, 1),
  options = c(2, 3)
)
rpmultinom(prob = p, n = c(20, 50), options = c(2, 3))

multinomineq documentation built on Nov. 22, 2022, 5:09 p.m.