Multinomial Random Samples

Models the counts across two or more mutually exclusive categories from a fixed number of trials, in long format: one value per category per trial, with group identifying which rows belong to the same trial. All rows sharing a group must have the same size, and their prob values must sum to 1.

Usage

ran_multinom(size = 1, prob, group)

Arguments

size

A non-negative whole numeric vector of the number of trials.

prob

A numeric vector of the probability of the category. Must sum to 1 across the rows sharing the same group. NA in size or prob for any row of a trial makes the sample NA for every row of that trial, since a trial's categories are drawn jointly.

group

A vector identifying which rows belong to the same multinomial trial (whose x values sum to size and prob values sum to 1). Every group must have at least 2 rows and the same number of rows as the rest of the data (a fixed set of categories, as in multinomial logistic regression), and must not contain NA.

Value

An integer vector of the random samples, one per row of prob.

Details

Unlike the other ran_*() functions, ran_multinom() has no n argument: the number of samples is fully determined by length(prob) (equivalently length(group)), since a trial's categories can't be generated independently of one another.

References

Johnson, N.L., Kotz, S., and Balakrishnan, N. 1997. Discrete Multivariate Distributions. John Wiley and Sons, New York.

Gelman, A., Meng, X.-L., and Stern, H. 1996. Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica 6(4): 733-807.

See also

Other ran_dist: ran_bern(), ran_beta_binom(), ran_binom(), ran_gamma(), ran_gamma_pois(), ran_gamma_pois_zi(), ran_lnorm(), ran_neg_binom(), ran_norm(), ran_pois(), ran_pois_zi(), ran_skewlnorm(), ran_skewnorm(), ran_student()

Examples

ran_multinom(size = 10, prob = c(0.2, 0.3, 0.5), group = c(1, 1, 1))
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