Hi:

On Tue, May 25, 2010 at 11:43 PM, Alan Lue <alan.lue@gmail.com> wrote:

> Since `for' loops are slow in R, and since `apply' functions are
> faster, I was wondering whether there were a way to use an apply
> function—or to otherwise avoid using a loop—when iterating over a
> statement that updates its input.
>

That's not necessarily true. Loops that accumulate memory by copying
and recopying objects will slow things down, but apply functions execute
loops internally at the C level of code. If you loop efficiently in R, it
can be
as fast or faster than a given apply family function.


> For example, here's some such code:
>
> r.seq <- 2 * (1 / d$Dt[1] - 1)
> for (i in 2:nrow(d)) {
>  rf <- uniroot(bdt.deviation, interval=c(0, 1), D.T=d$Dt[i], r.prior=r.seq)
>  r.seq <- append(r.seq, rf$root)
> }
>
> The call to `uniroot()' both updates `r.seq' and reads it as input.
> We could save the output of each invocation of `uniroot()' and
> concatenate it later, but is there a better way to write this (i.e.,
> to execute more quickly) while updating `r.seq' in each iteration?
>

I would look into the Vectorize() function for this task.

HTH,
Dennis

>
> Alan
>
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