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 <[EMAIL PROTECTED]> 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
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