Seeding non-R RNG with numbers from R's RNG stream

Thank you for this. I'd like to be sure I understand the
intuition correctly. Is the following true from what you said?

I can just fix the seed at the C++ level and the results will still be
(pseudo) random because the initialization at the R level is (pseudo)
random.

On Thu, Jul 30, 2020 at 3:36 PM Duncan Murdoch <murdoch.duncan at gmail.com>
wrote:

I wouldn't trust the C++ generator to be as good if you seed it this way
as if you just seeded it once with your phone number (or any other fixed
value) and let it run, because it's probably never been tested to be
good when run this way.  Is it good enough for the way you plan to use
it?  Maybe.

Duncan Murdoch

On 30/07/2020 3:05 p.m., Tommy Jones wrote:

Hi,

I am constructing a function that does sampling in C++ using a non-R

RNG

stream for thread safety reasons. This C++ function is wrapped by an R
function, which is user facing. The R wrapper does some sampling itself

to

initialize some variables before passing them off to C++. So that my

users

do not have to manage two mechanisms to set random seeds, I've

constructed

a solution (shown below) that allows both RNGs to be seeded with

set.seed

and respond to the state of R's RNG stream.

I believe the below works. However, I am hoping to get feedback from

more

experienced useRs as to whether or not the below approach is unsafe in

ways

that may affect reproducibility, modify global variables in bad ways,

or

have other unintended consequences I have not anticipated.

Could I trouble one or more folks on this list to weigh in on the

safety

(or perceived wisdom) of using R's internal RNG stream to seed an RNG
external to R? Many thanks in advance.

This relates to a Stackoverflow question here:

https://stackoverflow.com/questions/63165955/is-there-a-best-practice-for-using-non-r-rngs-in-rcpp-code

Pseudocode of a trivial facsimile of my current approach is below.

--Tommy

sample_wrapper <- function() {
   # initialize a variable to pass to C++
   init_var <- runif(1)

   # get current state of RNG stream
   # first entry of .Random.seed is an integer representing the

algorithm used

   # second entry is current position in RNG stream
   # subsequent entries are pseudorandom numbers
   seed_pos <- .Random.seed[2]

   seed <- .Random.seed[seed_pos + 2]

   out <- sample_cpp(init_var = init_var, seed = seed)

   # move R's position in the RNG stream forward by 1 with a throw away

sample

   runif(1)

   # return the output
   out}

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