Simulate F 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.

Usage

rxf(df1, df2, n = 1L, ncores = 1L)

Arguments

df1, df2

degrees of freedom. Inf is allowed.

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;

Value

f random deviates

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

## Use threefry engine

rxf(0.5, 0.5, n = 10) # with rxf you have to explicitly state n
#>  [1] 5.678346e-05 2.005725e-01 7.297065e+00 6.292906e-01 8.074573e-01
#>  [6] 1.768955e-03 8.977122e-03 1.198314e-05 1.309745e-01 2.286014e+02
rxf(5, 1, n = 10, ncores = 2) # You can parallelize the simulation using openMP
#>  [1]    1.5725060  750.8712711    0.4891759 1027.8595045  493.6965182
#>  [6] 2542.7594178    0.3760492    0.4362499   24.2308933    0.9622635

rxf(1, 3)
#> [1] 0.2139943