How to skip an error in a loop

Question

I want to skip an error (if there is any) in a loop and continue the next iteration. I want to compute 100 inverse matrices of a 2 by 2 matrix with elements randomly sampled

Answer 1

This would put NULLs into inverses for the singular matrices:

inverses[[count]] <- tryCatch(solve(x), error=function(e) NULL)

If the first expression in a call to tryCatch raises an error, it executes and returns the value of the function supplied to its error argument. The function supplied to the error arg has to take the error itself as an argument (here I call it e), but you don't have to do anything with it.

You could then drop the NULL entries with inverses[! is.null(inverses)].

Alternatively, you could use the lower level try. The choice is really a matter of taste.

count <- 0
repeat {
    if (count == 100) break
    count <- count + 1
    x <- matrix(sample(0:2, 4, replace = T), 2, 2)
    x.inv <- try(solve(x), silent=TRUE)
    if ('try-error' %in% class(x.inv)) next
    else inverses[[count]] <- x.inv
}

If your expression generates an error, try returns an object with class try-error. It will print the message to screen if silent=FALSE. In this case, if x.inv has class try-error, we call next to stop the execution of the current iteration and move to the next one, otherwise we add x.inv to inverses.

Edit:

You could avoid using the repeat loop with replicate and lapply.

matrices <- replicate(100, matrix(sample(0:2, 4, replace=T), 2, 2), simplify=FALSE)
inverses <- lapply(matrices, function(mat) if (det(mat) != 0) solve(mat))

It's interesting to note that the second argument to replicate is treated as an expression, meaning it gets executed afresh for each replicate. This means you can use replicate to make a list of any number of random objects that are generated from the same expression.

Answer 2

try is just a way of telling R: "If you commit an error inside the following parentheses, then skip it and move on."

So if you're worried that x <- matrix(sample(0:2, 4, replace = T), 2, 2) might give you an error, then all you have to do is:

try(x <- matrix(sample(0:2, 4, replace = T), 2, 2))

However, keep in mind then that x will be undefined if you do this and it ends up not being able to compute the answer. That could cause a problem when you get to solve(x) - so you can either define x before try or just "try" the whole thing:

try(
      {
      x <- matrix(sample(0:2, 4, replace = T), 2, 2)
      inverses[[count]] <- solve(x)
      }
    )

Answer 3

Instead of using tryCatch you could simply calculate the determinant of the matrix with the function det. A matrix is singular if and only if the determinant is zero.

Hence, you could test whether the determinant is different from zero and calculate the inverse only if the test is positive:

set.seed(1)
count <- 1
inverses <- vector(mode = "list", 100)
repeat {
  x <- matrix(sample(0:2, 4, replace = T), 2, 2)
  # if (det(x)) inverses[[count]] <- solve(x)
  # a more robust replacement for the above line (see comment):
  if (is.finite(determinant(x)$modulus)) inverses[[count]] <- solve(x)
  count <- count + 1
  if (count > 100) break
}

---

Update:

It is, however, possible to avoid generating singular matrices. The determinant of a 2-by-2 matrix mat is definded as mat[1] * mat[4] - mat[3] * mat[2]. You could use this knowledge for sampling random numbers. Just do not sample numbers which will produce a singular matrix. This, of course, depends on the numbers sampled before.

set.seed(1)
count <- 1
inverses <- vector(mode = "list", 100)

set <- 0:2 # the set of numbers to sample from

repeat {

  # sample the first value
  x <- sample(set, 1)
  # if the first value is zero, the second and third one are not allowed to be zero.
  new_set <- ifelse(x == 0, setdiff(set, 0), set)
  # sample the second and third value
  x <- c(x, sample(new_set, 2, replace = T))
  # calculate which 4th number would result in a singular matrix
  not_allowed <- abs(-x[3] * x[2] / x[1])
  # remove this number from the set
  new_set <- setdiff(0:2, not_allowed)
  # sample the fourth value and build the matrix
  x <- matrix(c(x, sample(new_set, 1)), 2, 2)

  inverses[[count]] <- solve(x)
  count <- count + 1
  if (count > 100) break
}

This procedure is a guarantee that all generated matrices will have an inverse.

Answer 4

The documentation for try explains your problem pretty well. I suggest you go through it completely.

Edit: The documentation example looked pretty straightforward and very similar to the op's question. Thanks for the suggestion though. Here goes the answer following the example in the documentation page:

# `idx` is used as a dummy variable here just to illustrate that
# all 100 entries are indeed calculated. You can remove it.
set.seed(1)
mat_inv <- function(idx) {
    print(idx)
    x <- matrix(sample(0:2, 4, replace = T), nrow = 2)
    solve(x)
}
inverses <- lapply(1:100, function(idx) try(mat_inv(idx), TRUE))