This sets the seed for the RxODE parallel random number generation. If set, then whenever a seed is set for the threefry or vandercorput simulation engine, it will use this seed, increment for the number of seeds and continue with the sequence the next time the random number generator is called.
rxSetSeed(seed)| seed | An integer that represents the RxODE parallel and internal random number generator seed. When positive, use this seed for random number generation and increment and reseed any parallel or new engines that are being called. When negative, turn off the RxODE seed and generate a seed from the R's uniform random number generator. Best practice is to set this seed. |
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Nothing, called for its side effects
In contrast, when this is not called, the time that the vandercorput or threefry simulation engines are seeded it comes from a uniform random number generated from the standard R random seed. This may cause a duplicate seed based on the R seed state. This means that there could be correlations between simulations that do not exist This will avoid the birthday problem picking exactly the same seed using the seed state of the R random number generator. The more times the seed is called, the more likely this becomes.
JD Cook. (2016). Random number generator seed mistakes. https://tinyurl.com/m62v3kv9
Matthew Fidler
rxSetSeed(42)
# seed with generator 42
rxnorm()
#> [1] 0.2229005
# Use R's random number generator
rnorm(1)
#> [1] 1.793606
rxSetSeed(42)
# reproduces the same number
rxnorm()
#> [1] 0.2229005
# But R's random number is not the same
rnorm(1)
#> [1] 0.04863385
# If we reset this to use the R's seed
# (internally RxODE uses a uniform random number to span seeds)
# This can lead to duplicate sequences and seeds
rxSetSeed(-1)
# Now set seed works for both.
# This is not recommended, but illustrates the different types of
# seeds that can be generated.
set.seed(42)
rxnorm()
#> [1] -0.6306035
rnorm(1)
#> [1] -0.5646982
set.seed(42)
rxnorm()
#> [1] -0.6306035
rnorm(1)
#> [1] -0.5646982