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 <[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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