Pseudo-random sequence interfering with another pseudo-random generator

Question

I noticed that if another pseudo-random number generator is used when generating the pseudo-random sequence, the seed sequence is placed. My question is, is there anything to be done? Can you somehow provide the initial seed sequence? Let me show you an example:

A simple loop that prints a pseudo-random number taken from the normal distribution:

set.seed(145)
for (i in 1:10){
  print(rnorm(1,0,1))
}

Which gives the following conclusion:

[1] 0.6869129
[1] 1.066363
[1] 0.5367006
[1] 1.906029
[1] 1.06316
[1] 1.370344
[1] 0.5277918
[1] 0.4030967
[1] 1.167752
[1] 0.7926794

Next, we introduce a pseudo-random thread from a uniform distribution if the iterator is five.

set.seed(145)
for (i in 1:10){
  print(rnorm(1,0,1))
  if (i == 5){
    print(runif(1,0,1))
  }
}

Which gives the following conclusion (at the next output, the star marks a pseudo-random draw from the uniform distribution):

[1] 0.6869129
[1] 1.066363
[1] 0.5367006
[1] 1.906029
[1] 1.06316
[1] 0.9147102*
[1] -1.508828
[1] -0.03101992 
[1] -1.091504
[1] 0.2442405
[1] -0.6103299

, , set.seed(145), :

[1] 0.6869129
[1] 1.066363
[1] 0.5367006
[1] 1.906029
[1] 1.06316
[1] 0.9147102*
[1] 1.370344
[1] 0.5277918
[1] 0.4030967
[1] 1.167752
[1] 0.7926794

, .

EDIT:

Rui Barradas, , . rnorm for, if-statement, Rui. , , , - , , , , ( AR-1).

tt <- rnorm(500,0,1)*10 

test1 <- function(y, x0=1, n,qsigma = 3, alpha = 5, beta = 20, limit = 0.30){
  t <- length(y)
  gama <- (alpha + beta)/2
  x <- matrix(0,n,t)
  x[, 1] <- rep(x0,n)
  for(s in 2:t) {
    x[, s] <-pmax(alpha*(x[,s-1]<=gama) +beta*(x[,s-1]>gama)+rnorm(n,0,qsigma),1)
    if (s==250) {
      current <- .GlobalEnv$.Random.seed
      resamp <- sample(n, n, replace = TRUE)
      x[,s] <- x[resamp,s]
      .GlobalEnv$.Random.seed <- current
      }
  }
  list(x = x)
}

test3 <- function(y, x0=1, n,qsigma = 3, alpha = 5, beta = 20, limit = 0.30) {
  t <- length(y)
  gama <- (alpha + beta)/2
  x <- matrix(0,n,t)
  x[, 1] <- rep(x0,n)
  e_4 <- matrix(rnorm(n * (t), 0, qsigma),n, (t))

  for(s in 2:t) {
    x[, s] <-pmax(alpha*(x[,s-1]<=gama) +beta*(x[,s-1]>gama)+e_4[,(s-1)],1)
    if (s==250) {resamp <-sample(n, n, replace = TRUE)
      x[,s] <- x[resamp,s]
    }
  }
  list(x = x, pp = e_4)
}

set.seed(123)
dej11 <- test3(y = tt, n = 5000)$x
set.seed(123)
dej21 <- test1(y = tt, n = 5000)$x
all.equal(dej11,dej21)

, True , , 1,186448.

Answer 1

.Random.seed rng. help(".Random.seed"):

.Random.seed - , (RNG) R. , .

, .

set.seed(145)
for (i in 1:10){
  print(rnorm(1,0,1))
  if (i == 5){
    current <- .Random.seed
    print(runif(1,0,1))
    .Random.seed <- current
  }
}

, , Note.

, , , . .Random.seed .GlobalEnv. : .GlobalEnv$.Random.seed.

set.seed(145)

f <- function() {
    for (i in 1:10) {
        print(rnorm(1, 0, 1))
        if (i == 5) {
            current <- .GlobalEnv$.Random.seed
            print(runif(1, 0, 1))
            .GlobalEnv$.Random.seed <- current
        }
    }
}

f()
#[1] 0.6869129
#[1] 1.066363
#[1] 0.5367006
#[1] 1.906029
#[1] 1.06316
#[1] 0.9147102
#[1] 1.370344
#[1] 0.5277918
#[1] 0.4030967
#[1] 1.167752
#[1] 0.7926794

Answer 2

, , , , , . . , . , . , , ...

random_seed_fixed <- function(rfun, seed, buffer = 1000000, ...){
  set.seed(seed)
  values <- rfun(buffer, ...)
  next_index <- 1

  out <- function(n){
    new_index <- next_index + n
    # Give an error if we're going to exceed the bounds of our values
    stopifnot(new_index < buffer)

    id <- seq(next_index, new_index - 1, by = 1)
    next_index <<- new_index
    ans <- values[id]
    return(ans)
  }

  return(out)
}

, ...

> my_rnorm <- random_seed_fixed(rnorm, seed = 642, mean = 17, sd = 2.3)
> 
> my_rnorm(5)
[1] 18.53370 16.16721 15.43144 16.67967 18.27675
> my_rnorm(5)
[1] 19.26933 17.50994 18.90019 14.80153 18.18837
> 
> my_rnorm <- random_seed_fixed(rnorm, seed = 642, mean = 17, sd = 2.3)
> my_rnorm(5) # matches the previous first call of my_rnorm(5)
[1] 18.53370 16.16721 15.43144 16.67967 18.27675
> rnorm(1, 0, 1)
[1] 2.515765
> my_rnorm(5) # Still matches the previous second call of my_rnorm(5)
[1] 19.26933 17.50994 18.90019 14.80153 18.18837