Thank you all for your advice and tips. In the end, I think the for loop
is the easiest way forward due to other requirements but its good to know
that I haven't missed anything too obvious.
Tom
On Tue, 30 Jan 2007 23:42:27 -, Oleg Sklyar <[EMAIL PROTECTED]> wrote:
> It is surely an ele
Hi,
Byron Ellis wrote:
> IIRC a for loop has more per-iteration overhead that lapply, but the
> real answer is "it depends on what you're doing exactly." I've seen it
> be a faster, slower and equal approach.
gen.iter = function(y=NA) {
function(x) {
y <<- if(is.na(y)) x else x+y
}
}
sapply
IIRC a for loop has more per-iteration overhead that lapply, but the
real answer is "it depends on what you're doing exactly." I've seen it
be a faster, slower and equal approach.
On 1/30/07, Oleg Sklyar <[EMAIL PROTECTED]> wrote:
> It is surely an elegant way of doing things (although far from be
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
Actually, better yet:
gen.iter = function(y=NA) {
function(x) {
y <<- if(is.na(y)) x else x+y
}
}
sapply(x,gen.iter())
On 1/30/07, Byron Ellis <[EMAIL PROTECTED]> 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}
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 bo
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 ) {
>
On Tuesday 30 January 2007 15:46, Tamas K Papp wrote:
> On Tue, Jan 30, 2007 at 12:15:29PM +, Oleg Sklyar wrote:
> > magnitude using c-functions for "complex" vector indexing operations. If
> > you need instructions, I can send you a very nice "Step-by-step guide
> > for using C/C++ in R" which
On Tue, Jan 30, 2007 at 12:15:29PM +, Oleg Sklyar wrote:
> magnitude using c-functions for "complex" vector indexing operations. If
> you need instructions, I can send you a very nice "Step-by-step guide
> for using C/C++ in R" which goes beyond "Writing R Extensions" document.
Hi Oleg,
Ca
Tom,
*apply's generally speed up calculations dramatically. However, if and
only if you do a repetitive operation on a vector, list matrix which
does NOT require accessing other elements of that variable than the one
currently in the *apply index. This means in your case any of *apply
will not
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