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

rxchisq(df, n = 1L, ncores = 1L)

Arguments

df

degrees of freedom (non-negative, but can be non-integer).

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 used to simulate with the vandercorput simulator, but since it didn't satisfy the normal properties it was changed to simple be an alias of rxnorm. It is no longer supported in rxode2({}) blocks

Value

chi squared 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

# \donttest{

## Use threefry engine

rxchisq(0.5, n = 10) # with rxchisq you have to explicitly state n
#>  [1] 8.310255e-01 5.056546e-04 1.283680e-02 1.156829e-01 1.791516e-01
#>  [6] 1.262242e+00 7.069398e-01 7.941145e-01 2.610348e-07 7.152674e+00
rxchisq(5, n = 10, ncores = 2) # You can parallelize the simulation using openMP
#>  [1] 2.285712 4.831273 3.575691 8.457173 6.136148 4.831445 3.387999 1.390385
#>  [9] 4.401743 4.226414

rxchisq(1)
#> [1] 0.4391989

## This example uses rxchisq directly in the model

rx <- function() {
model({
a <- rxchisq(2)
})
}

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

s <- rxSolve(rx, et)
#>
#>
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
# }