R: Correctly Using the Break Statement-CodePudding

I am working with the R programming language.

I am trying to adapt the answer provided over here (Manual simulation of Markov Chain in R). The code below simulates some random numbers based on some user specified probabilities:

alpha <- c(0,1,0,0, 0)
mat <- matrix(c(1,0,0,0,0,0.2,0.2,0.5,0.05,0.05, 0.3, 0.1,0.1,0.2,0.3, 0.2, 0.2, 0.2, 0.2, 0.2, 0,0,0,0,1), nrow = 5, ncol = 5, byrow = TRUE) 

chainSim <- function(alpha, mat, n) {
  out <- numeric(n)
  out[1] <- sample(1:5, 1, prob = alpha)
  for(i in 2:n) 
    out[i] <- sample(1:5, 1, prob = mat[out[i - 1], ])
  out
}

When we run this function, we can see examples of these random numbers (here, we specify the function to generate 6 random numbers):

chainSim(alpha, mat, 6)
[1] 2 3 1 1 1 1

I want to adapt this code so that when the first "1" or "5" is encountered, the sequence stops. I tried to do this as follows (using the WHILE and BREAK commands):

alpha <- c(0,1,0,0, 0)
mat <- matrix(c(1,0,0,0,0,0.2,0.2,0.5,0.05,0.05, 0.3, 0.1,0.1,0.2,0.3, 0.2, 0.2, 0.2, 0.2, 0.2, 0,0,0,0,1), nrow = 5, ncol = 5, byrow = TRUE) 

chainSim <- function(alpha, mat, n) {
    out <- numeric(n)
    out[1] <- sample(1:5, 1, prob = alpha)
    for(i in 2:n) {
        repeat{
            out[i] <- sample(1:5, 1, prob = mat[out[i - 1], ])
            out
            if (1 %in% out[i] || 5 %in% out[i] ) break
        }
    }}

# simulate numbers until first 1 or 5 is encountered : does not work
chainSim(alpha, mat, n)

# repeat chainSim 100 times : does not work ("sim_final" will have likely have an uneven number of entries in each row)

sim <- replicate(chainSim(alpha, mat, n), n = 100)
sim_final = data.frame(t(sim))

But when I tried to do this, chainSim() does not produce any random numbers and "sim" produces 100 NULL's.

Can someone please show me how to fix this?

Thanks!

CodePudding user response：

There is no need for a repeat or while loop. The code below breaks after the first 1 or 5.

To return only the vector until that point, change the function's last instruction to out[out != 0]. But then the return vectors will be of different lengths and data.frame won't make any sense, the output of replicate should be kept a list.

chainSim <- function(alpha, mat, n) {
  out <- integer(n)
  out[1] <- sample(1:5, 1L, prob = alpha)
  for(i in 2:n) {
    if(out[i - 1L] %in% c(1, 5)) break
    out[i] <- sample(1:5, 1L, prob = mat[out[i - 1], ])
  }
  out
}

alpha <- c(0, 1, 0, 0, 0)
mat <- matrix(c(1, 0, 0, 0, 0,
                0.2, 0.2, 0.5, 0.05, 0.05,
                0.3, 0.1, 0.1, 0.2, 0.3, 
                0.2, 0.2, 0.2, 0.2, 0.2, 
                0, 0, 0, 0, 1), 
              nrow = 5, ncol = 5, byrow = TRUE)

set.seed(2022)
n <- 6L
# simulate numbers until first 1 or 5 is encountered
chainSim(alpha, mat, n)
#> [1] 2 1 0 0 0 0

sim <- replicate(chainSim(alpha, mat, n), n = 100)
sim_final <- data.frame(t(sim))
head(sim_final)
#>   X1 X2 X3 X4 X5 X6
#> 1  2  1  0  0  0  0
#> 2  2  1  0  0  0  0
#> 3  2  3  5  0  0  0
#> 4  2  3  1  0  0  0
#> 5  2  1  0  0  0  0
#> 6  2  3  4  4  4  1

^{Created on 2022-06-14 by the reprex package (v2.0.1)}