The Binomial Distribution, Using Multiple Draws

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

Usage

dbinom_rvec(x, size, prob, log = FALSE)

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

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

rbinom_rvec(n, size, prob, n_draw = NULL)

Arguments

x: Quantiles. Can be an rvec.
size: Number of trials. See stats::dbinom(). Can be an rvec.
prob: Probability of success in each trial. See stats::dbinom(). 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.
rbinom_rvec() always returns doubles (not integers).

Details

Functions dbinom_rvec(), pbinom_rvec(), pbinom_rvec() and rbinom_rvec() work like base R functions dbinom(), pbinom(), qbinom(), and rbinom(), except that they accept rvecs as inputs. If any input is an rvec, then the output will be too. Function rbinom_rvec() also returns an rvec if a value for n_draw is supplied.

dbinom_rvec(), pbinom_rvec(), pbinom_rvec() and rbinom_rvec() use tidyverse vector recycling rules:

Vectors of length 1 are recycled
All other vectors must have the same size

Warning

From version rvec version 0.7.4 onwards, rbinom_rvec() always returns doubles (not integers).

Examples

x <- rvec(list(c(3, 8),
               c(0, 2)))
dbinom_rvec(x, size = 8, prob = 0.3)
#> <rvec_dbl<2>[2]>
#> [1] 0.2541,0.00006561 0.05765,0.2965   
pbinom_rvec(x, size = 8, prob = 0.3)
#> <rvec_dbl<2>[2]>
#> [1] 0.8059,1       0.05765,0.5518

rbinom_rvec(n = 2,
            size = 10,
            prob = c(0.7, 0.3),
            n_draw = 1000)
#> <rvec_dbl<1000>[2]>
#> [1] 7 (4, 9) 3 (1, 6)