Generate integer values within specified range
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.
rint(n, min, max, replace = TRUE, prob = NULL)
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 |
Phil Chalmers rphilip.chalmers@gmail.com
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.
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.
doi: 10.1080/10691898.2016.1246953
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)
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