Double For loops in R

loops, r

Solution

Sorry for asking what is potentially a dumb question, but in what sense can R not do this? This works perfectly fine for me:

N <- 5
for (i in 0:(2*N)) {
   for (j in i:N) {
        print(paste(i,j,sep=","))
    }
}

Could it be that you just didn't put parentheses around the end of your sequence?

Edit: I see...you want to enforce that the sequence i:3 is always <= 3? I don't think that's possible with either a sequence or within the clause of a for statement. You can set a break within the loop, but that's no better than your current approach:

for (i in 1:6) {
    for (j in i:3) {
        if(j > 3) break()
        print(paste(i,j,sep=","))
    }
}

Here's another way to generate this sequence without a for loop:

x <- cbind(rep(c(1,2,3), 3),
    rep(c(1,2,3), each=3))

Or with expand.grid (as per Dirk's suggestion):

x <- expand.grid(x=1:3, y=1:3)

Then remove the unwanted cases:

x[x[,1] >= x[,2],]

Edit 2: This may not suit your needs, but I believe that Quantlib has a Libor Market Model implementation. I'm not sure if it's exposed in RQuantlib.

Problem

How do you properly write a double `for` loop in R? For example, in C I would do ``` int i, j; for (i = 1; i < 6; i++) { for (j=i; j <= 3; j++) { printf("%i,%i\n",i,j); } // Do more operations for i > 3... } ``` which would generate the (artificial) sequence: ``` 1,1 1,2 1,3 2,2 2,3 3,3 ``` In R you do not get the same behaviour when you write ``` for (i in 1:6) { for (j in i:3) { print(paste(i,j,sep=",")) } } ``` so I've been reduced to doing something like ``` for (i in 1:6) { j <- i while (j <= 3) { print(paste(i,j,sep=",")) j <- j+1 } } ``` Is there a better way? As Shane mentioned, maybe I should make this clear: I am particularly interested in the code-style matching the mathematics to make it easier for students to understand. It seems that students are the most comfortable with `for` loops. In particular, I want my students to simulate a LIBOR market model. The dynamics of the forward rate are to be simulated under the same probability measure. As such, for each time step and each forward rate, the appropriate drift correction \mu_i needs to be calculated and added.

Original source