Speed of for loops

Actually, why not use a closure to store previous value(s)?

In the simple case, which depends on x_i and y_{i-1}

gen.iter = function(x) {
    y = NA
    function(i) {
       y <<- if(is.na(y)) x[i] else y+x[i]
    }
}

y = sapply(1:10,gen.iter(x))

Obviously you can modify the function for the bookkeeping required to
manage whatever lag you need. I use this sometimes when I'm
implementing MCMC samplers of various kinds.


On 1/30/07, Herve Pages <hpages at fhcrc.org> wrote:

Tom McCallum wrote:

Hi Everyone,

I have a question about for loops.  If you have something like:

f <- function(x) {
      y <- rep(NA,10);
      for( i in 1:10 ) {
              if ( i > 3 ) {
                      if ( is.na(y[i-3]) == FALSE ) {
                              # some calculation F which depends on one or more of the previously
generated values in the series
                              y[i] = y[i-1]+x[i];
                      } else {
                              y[i] <- x[i];
                      }
              }
      }
      y
}

e.g.

f(c(1,2,3,4,5,6,7,8,9,10,11,12));

  [1] NA NA NA  4  5  6 13 21 30 40

is there a faster way to process this than with a 'for' loop?  I have
looked at lapply as well but I have read that lapply is no faster than a
for loop and for my particular application it is easier to use a for loop.
Also I have seen 'rle' which I think may help me but am not sure as I have
only just come across it, any ideas?

Hi Tom,

In the general case, you need a loop in order to propagate calculations
and their results across a vector.

In _your_ particular case however, it seems that all you are doing is a
cumulative sum on x (at least this is what's happening for i >= 6).
So you could do:

f2 <- function(x)
{
    offset <- 3
    start_propagate_at <- 6
    y_length <- 10
    init_range <- (offset+1):start_propagate_at
    y <- rep(NA, offset)
    y[init_range] <- x[init_range]
    y[start_propagate_at:y_length] <- cumsum(x[start_propagate_at:y_length])
    y
}

and it will return the same thing as your function 'f' (at least when 'x' doesn't
contain NAs) but it's not faster :-/

IMO, using sapply for propagating calculations across a vector is not appropriate
because:

  (1) It requires special care. For example, this:

        > x <- 1:10
        > sapply(2:length(x), function(i) {x[i] <- x[i-1]+x[i]})

      doesn't work because the 'x' symbol on the left side of the <- in the
      anonymous function doesn't refer to the 'x' symbol defined in the global
      environment. So you need to use tricks like this:

        > sapply(2:length(x),

                 function(i) {x[i] <- x[i-1]+x[i]; assign("x", x, envir=.GlobalEnv); x[i]})

  (2) Because of this kind of tricks, then it is _very_ slow (about 10 times
      slower or more than a 'for' loop).

Cheers,
H.

Many thanks

Tom

--
Byron Ellis (byron.ellis at gmail.com)
"Oook" -- The Librarian