It is surely an elegant way of doing things (although far from being
easy to parse visually) but is it really faster than a loop?
After all, the indexing problem is the same and sapply simply does the
same job as for in this case, plus "<<-" will _search_ through the
environment on every single step. Where is the gain?
Oleg
--
Dr Oleg Sklyar | EBI-EMBL, Cambridge CB10 1SD, UK | +44-1223-494466
Byron Ellis wrote:
> 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 <[EMAIL PROTECTED]> 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
>>>
>>>
>>>
>> ______________________________________________
>> R-devel@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>
>
>
______________________________________________
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
