Simulate uniform variable from threefry generator

Care should be taken with this method not to encounter the birthday problem, described https://www.johndcook.com/blog/2016/01/29/random-number-generator-seed-mistakes/. Since the sitmo threefry, this currently generates one random deviate from the uniform distribution to seed the engine threefry and then run the code.

rxunif(min = 0, max = 1, n = 1L, ncores = 1L)

Arguments

min	lower and upper limits of the distribution. Must be finite.
max	lower and upper limits of the distribution. Must be finite.
n	number of observations. If length(n) > 1, the length is taken to be the number required.
ncores	Number of cores for the simulation rxnorm simulates using the threefry sitmo generator; rxnormV uses the vandercorput generator

Value

uniform random numbers

Details

Therefore, a simple call to the random number generated followed by a second call to random number generated may have identical seeds. As the number of random number generator calls are increased the probability that the birthday problem will increase.

The key to avoid this problem is to either run all simulations in the RxODE environment once (therefore one seed or series of seeds for the whole simulation), pre-generate all random variables used for the simulation, or seed the RxODE engine with rxSetSeed()

Also care should be made that the computer you will be running on can run the same number of cores as you are running so they can reproduce your results.

Examples

# \donttest{

## Use threefry engine

rxunif(min = 0, max = 4, n = 10) # with rxunif you have to explicitly state n
#>  [1] 2.134726240 0.007952281 1.279568680 2.254002313 0.841863684 0.325005806
#>  [7] 2.110872169 0.565347202 3.019588167 3.310655423
rxunif(min = 0, max = 4, n = 10, ncores = 2) # You can parallelize the simulation using openMP
#>  [1] 2.9263972 0.4667526 2.2519018 2.2168082 1.7568379 3.8306717 3.5771028
#>  [8] 3.0168567 3.7114864 2.1008062

rxunif()
#> [1] 0.8135653


## This example uses `rxunif` directly in the model

rx <- RxODE({
  a <- rxunif(0, 3)
})
#>  

et <- et(1, id = 1:2)

s <- rxSolve(rx, et)
# }