rint: Generate integer values within specified range

In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Generate integer values within specified range

Description

Efficiently generate positive and negative integer values with (default) or without replacement. This function is mainly a wrapper to the sample.int function (which itself is much more efficient integer sampler than the more general sample), however is intended to work with both positive and negative integer ranges since sample.int only returns positive integer values that must begin at 1L.

Usage

rint(n, min, max, replace = TRUE, prob = NULL)

Arguments

n	number of samples to draw
min	lower limit of the distribution. Must be finite
max	upper limit of the distribution. Must be finite
replace	should sampling be with replacement?
prob	a vector of probability weights for obtaining the elements of the vector being sampled

Author(s)

Phil Chalmers [e-mail]

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")}

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}

Examples

set.seed(1)

# sample 1000 integer values within 20 to 100
x <- rint(1000, min = 20, max = 100)
summary(x)

# sample 1000 integer values within 100 to 10 billion
x <- rint(1000, min = 100, max = 1e8)
summary(x)

# compare speed to sample()
system.time(x <- rint(1000, min = 100, max = 1e8))
system.time(x2 <- sample(100:1e8, 1000, replace = TRUE))

# sample 1000 integer values within -20 to 20
x <- rint(1000, min = -20, max = 20)
summary(x)

SimDesign documentation built on Sept. 11, 2024, 8 p.m.