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.
Arguments
- rate
vector of rates.
- n
number of observations. If
length(n) > 1, the length is taken to be the number required.- ncores
Number of cores for the simulation
rxnormsimulates using the threefry sitmo generator;
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
rxode2 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 rxode2 engine with rxSetSeed()
Internally each ID is seeded with a unique number so that the results do not depend on the number of cores used.
Examples
# \donttest{
## Use threefry engine
rxexp(0.5, n = 10) # with rxexp you have to explicitly state n
#> [1] 0.01279471 3.94123844 2.98904444 0.24608033 2.03608165 0.99587818
#> [7] 2.02850661 0.04220234 0.19949474 3.34714168
rxexp(5, n = 10, ncores = 2) # You can parallelize the simulation using openMP
#> [1] 0.18639347 0.02426826 0.16596050 0.08868743 0.13760805 0.17785199
#> [7] 0.14670216 0.22780284 0.09303490 0.07668680
rxexp(1)
#> [1] 0.1882632
# }