[R] unavoidable loop? a better way??
Prof Brian Ripley
ripley at stats.ox.ac.uk
Sat Nov 13 09:00:49 CET 2004
Ah, that is now a recursive linear filter. In fact filter() would do both
your examples.
On Sat, 13 Nov 2004, James Muller wrote:
> Take 3:
>
> # p is a vector
> myfunc <- function (p) {
> x <- rep(0,length(p))
> x[1] <- p[1]
> for (i in c(2:length(p))) {
> x[i] <- 0.8*p[i] + 0.2*x[i-1] # note the x in the last term
> }
> return (x)
> }
>
> James
>
>
>
>
>
> On Sat, 13 Nov 2004 01:12:50 -0600, Deepayan Sarkar <deepayan at stat.wisc.edu>
> wrote:
>
>> On Saturday 13 November 2004 00:51, James Muller wrote:
>>> Hi all, I have the following problem, best expressed by my present
>>> solution:
>>>
>>> # p is a vector
>>> myfunc <- function (p) {
>>> x[1] <- p[1]
>>> for (i in c(2:length(p))) {
>>> x[i] <- 0.8*p[i] + 0.2*p[i-1]
>>> }
>>> return (x)
>>> }
>>
>> Does this work at all? I get
>>
>>> myfunc <- function (p) {
>> + x[1] <- p[1]
>> + for (i in c(2:length(p))) {
>> + x[i] <- 0.8*p[i] + 0.2*p[i-1]
>> + }
>> + return (x)
>> + }
>>>
>>> myfunc(1:10)
>> Error in myfunc(1:10) : Object "x" not found
>>
>>
>> Anyway, simple loops are almost always avoidable. e.g.,
>>
>> myfunc <- function (p) {
>> x <- p
>> x[-1] <- 0.8 * p[-1] + 0.2 * p[-length(p)]
>> x
>> }
>>
>> Deepayan
>>
>>>
>>> That is, I'm calculating a time-weighted average. Unfortunately the
>>> scale of the problem is big. length(p) in this case is such that each
>>> call takes about 6 seconds, and I have to call it about 2000 times
>>> (~3 hours). And, I'd like to do this each day. Thus, a more efficient
>>> method is desirable.
>>>
>>> Of course, this could be done faster by writing it in c, but I want
>>> to avoid doing that if there already exists something internal to do
>>> the operation quickly (because I've never programmed c for use in R).
>>>
>>> Can anybody offer a solution?
>>>
>>> I apologise if this is a naive question.
>>>
>>> James
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
>
>
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
More information about the R-help
mailing list