Function generates data given a supplied random number generating function that are constructed to fall within a particular range. Sampled values outside this range are discarded and re-sampled until the desired criteria has been met.
Arguments
- n
number of observations to generate. This should be the first argument passed to
rfun- rfun
a function to generate random values. Function can return a numeric/integer vector or matrix, and additional arguments requred for this function are passed through the argument
...- range
a numeric vector of length two, where the first element indicates the lower bound and the second the upper bound. When values are generated outside these two bounds then data are redrawn until the bounded criteria is met. When the output of
rfunis a matrix then this input can be specified as a matrix with two rows, where each the first row corresponds to the lower bound and the second row the upper bound for each generated column in the output- ...
additional arguments to be passed to
rfun- redraws
the maximum number of redraws to take before terminating the iterative sequence. This is in place as a safety in case the
rangeis too small given the random number generator, causing too many consecutive rejections. Default is 100
Details
In simulations it is often useful to draw numbers from truncated distributions
rather than across the full theoretical range. For instance, sampling parameters
within the range [-4,4] from a normal distribution. The rtruncate
function has been designed to accept any sampling function, where the first
argument is the number of values to sample, and will draw values iteratively
until the number of values within the specified bound are obtained.
In situations where it is unlikely for the bounds to be located
(e.g., sampling from a standard normal distribution where all values are
within [-10,-6]) then the sampling scheme will throw an error if too many
re-sampling executions are required (default will stop if more that 100
calls to rfun are required).
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.
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
Author
Phil Chalmers rphilip.chalmers@gmail.com
Examples
# n = 1000 truncated normal vector between [-2,3]
vec <- rtruncate(1000, rnorm, c(-2,3))
summary(vec)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -1.96441 -0.59106 0.06295 0.05083 0.64803 2.94607
# truncated correlated multivariate normal between [-1,4]
mat <- rtruncate(1000, rmvnorm, c(-1,4),
sigma = matrix(c(2,1,1,1),2))
summary(mat)
#> V1 V2
#> Min. :-0.9951 Min. :-0.9982
#> 1st Qu.:-0.1587 1st Qu.:-0.1852
#> Median : 0.5094 Median : 0.3215
#> Mean : 0.6414 Mean : 0.4022
#> 3rd Qu.: 1.3206 3rd Qu.: 0.9156
#> Max. : 3.9608 Max. : 3.0498
# truncated correlated multivariate normal between [-1,4] for the
# first column and [0,3] for the second column
mat <- rtruncate(1000, rmvnorm, cbind(c(-1,4), c(0,3)),
sigma = matrix(c(2,1,1,1),2))
summary(mat)
#> V1 V2
#> Min. :-0.99785 Min. :0.002002
#> 1st Qu.: 0.07791 1st Qu.:0.316850
#> Median : 0.84390 Median :0.707062
#> Mean : 0.88020 Mean :0.792454
#> 3rd Qu.: 1.58988 3rd Qu.:1.147544
#> Max. : 3.89832 Max. :2.964880
# truncated chi-square with df = 4 between [2,6]
vec <- rtruncate(1000, rchisq, c(2,6), df = 4)
summary(vec)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 2.000 2.688 3.500 3.637 4.437 5.999