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Negative Binomial Distribution, Using Multiple Draws

Source: R/distributions.R
dnbinom_rvec.Rd

Density, distribution function, quantile function and random generation for the negative binomial distribution, modified to work with rvecs.

Usage

dnbinom_rvec(x, size, prob, mu, log = FALSE)

pnbinom_rvec(q, size, prob, mu, lower.tail = TRUE, log.p = FALSE)

qnbinom_rvec(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE)

rnbinom_rvec(n, size, prob, mu, n_draw = NULL)

Arguments

x

Quantiles. Can be an rvec.

size

Number of trials. See dnbinom(). Can be an rvec.

prob

Probability of success in each trial. See dnbinom(). Can be an rvec.

mu

Mean value. See dnbinom(). Can be an rvec.

log, log.p

Whether to return results on a log scale. Default is FALSE. Cannot be an rvec.

q

Quantiles. Can be an rvec.

lower.tail

Whether to return \(P[X \le x]\), as opposed to \(P[X > x]\). Default is TRUE. Cannot be an rvec.

p

Probabilities. Can be an rvec.

n

The length of random vector being created. Cannot be an rvec.

n_draw

Number of random draws in the random vector being created. Cannot be an rvec.

Value

  • If any of the arguments are rvecs, or if a value for n_draw is supplied, then an rvec; otherwise an ordinary R vector.

  • Unlike base rbinom(), rnbinom_rvec() always returns doubles.

Details

Functions dnbinom_rvec(), pnbinom_rvec(), pnbinom_rvec() and rnbinom_rvec() work like base R functions dnbinom(), pnbinom(), qnbinom(), and rnbinom(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rnbinom_rvec() also returns an rvec if a value for n_draw is supplied.

dnbinom_rvec(), pnbinom_rvec(), pnbinom_rvec() and rnbinom_rvec() use tidyverse vector recycling rules:

  • Vectors of length 1 are recycled

  • All other vectors must have the same size

See also

Examples

x <- rvec(list(c(3, 5),
               c(0, 2)))
dnbinom_rvec(x, size = 6, prob = 0.2)
#> <rvec_dbl<2>[2]>
#> [1] 0.001835,0.005285  0.000064,0.0008602
pnbinom_rvec(x, size = 6, prob = 0.2)
#> <rvec_dbl<2>[2]>
#> [1] 0.003066,0.01165  0.000064,0.001231

rnbinom_rvec(n = 2,
             size = 2,
             mu = c(4, 8),
             n_draw = 1000)
#> <rvec_dbl<1000>[2]>
#> [1] 3 (0, 13) 7 (0, 25)