Because I had too much time on my hands, here's a little function that
will do whatever you want over a window you specify. No, I haven't done
any time trials :-(
# my own boxcar tool, just because.
# use bfunc to specify what function to apply to the windowed
# region.
boxcar<-function(x, wi
if you want the speed, you can simply build an fts time series from
it, then apply the moving.sum function and throw away the dates.
this will probably be the fastest implementation of rolling applies
out there unless you do a cumsum difference function.
I got a sample timing of 2 seconds on 12m
On Tue, Dec 16, 2008 at 8:23 AM, Gabor Grothendieck
wrote:
> There seems to be something wrong:
>
>> slide(c(1, 1, 0, 1), 2)
> [1] 2 2
>
> but the output should be c(2, 1, 2)
That should be c(2, 1, 1)
>
> At any rate try this:
>
> library(zoo)
> 3 * rollmean(x, 3)
>
>
> On Mon, Dec 15, 2008 at 1
There seems to be something wrong:
> slide(c(1, 1, 0, 1), 2)
[1] 2 2
but the output should be c(2, 1, 2)
At any rate try this:
library(zoo)
3 * rollmean(x, 3)
On Mon, Dec 15, 2008 at 11:19 PM, Chris Oldmeadow
wrote:
> Hi all,
>
> I have a very large binary vector, I wish to calculate the num
For this particular proble (counting), doesn't cumsum solve it
effectively and efficiently?
vv <- cumsum(v)
vv[n:length(vv)] - vv[1:(length(vv)-n+1]
Of course, this doesn't work for the general case of an arbitrary
sliding window function.
-s
On 12/15/08, Chris Oldmeadow wrote:
>
:markle...@verizon.net]
>Envoyé : mardi 16 décembre 2008 10:25
>À : Veslot Jacques
>Cc : Chris Oldmeadow; r-help@r-project.org
>Objet : Re: [R] sliding window over a large vector
>
>Hi: Veslot: I'm too tired to even try to figure out why but I think
>that there is s
the function works for me
s<-rbinom(1000,1,0.5)
t<-slide(s,50)
just too slow.
Thanks.
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Hi Chris,
On Tuesday 16 December 2008, Chris Oldmeadow wrote:
> Hi all,
>
> I have a very large binary vector, I wish to calculate the number of
> 1's over sliding windows.
> [...snip...]
Your function does not seem to function very well, could you please offer a
self-contained, reproducible ex
Hi: Veslot: I'm too tired to even try to figure out why but I think
that there is something wrong with your sl function. see below for an
empirical
proof of that statement. OR maybe you're definition of sliding window
is different than rollapply's definition but rollapply's answer makes
more
> sl <- function(x,z) c(0,cumsum(diff(x)[1:(length(x)-z-1)])) +
> rep(sum(x[1:z]),length(x)-z)
> x <- rbinom(10, 1, 0.5)
> system.time(xx1 <- slide(x,12))
utilisateur système écoulé
36.860.45 37.32
> system.time(xx2 <- sl(x,12))
utilisateur système écoul
you can have a look at the rollapply() function in the zoo package, e.g.,
x <- rbinom(100, 1, 0.5)
z <- zoo(x)
rollapply(z, 3, sum)
I hope it helps.
Best,
Dimitris
Chris Oldmeadow wrote:
Hi all,
I have a very large binary vector, I wish to calculate the number of
1's over sliding windows
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