Is there a performance advantage to doing this, as opposed to growing
the vector within the loop? I suppose R could have to dynamically
reallocate memory at some point?
Alan
2010/5/30 Uwe Ligges <lig...@statistik.tu-dortmund.de>:
>
>
> On 26.05.2010 08:52, Alan Lue wrote:
>>
>> Come to think of it, we can't save the output of each invocation and
>> concatenate it later, since we need the output as input for the next
>> iteration.
>
>
> Yes, but you can do it a bit cleverer than before by initializing to the
> fill length as in:
>
> r.seq <- numeric(nrow(d))
> r.seq[1] <- 2 * (1 / d$Dt[1] - 1)
> for (i in 2:nrow(d)) {
> r.seq[i] <- uniroot(bdt.deviation, interval = c(0, 1),
> D.T = d$Dt[i], r.prior = r.seq[i-1])$root
> }
>
> Uwe Ligges
>
>
>
>> Alan
>>
>>
>> On Tue, May 25, 2010 at 11:43 PM, Alan Lue<alan....@gmail.com> wrote:
>>>
>>> Since `for' loops are slow in R, and since `apply' functions are
>>> faster, I was wondering whether there were a way to use an apply
>>> function—or to otherwise avoid using a loop—when iterating over a
>>> statement that updates its input.
>>>
>>> For example, here's some such code:
>>>
>>> r.seq<- 2 * (1 / d$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?
>>>
>>> Alan
>>>
>>
>>
>>
>
--
Alan Lue
Master of Financial Engineering
UCLA Anderson School of Management
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