Suggest you try writing this in C.
On Tue, Sep 8, 2009 at 8:00 PM, R_help Help<rhelp...@gmail.com> wrote:
> Hi Gabor,
>
> I appreciate your suggestion. I'm wondering if there's any faster
> implementation that one could achieve. The dataset I have is about
> 400K rows and I have many of them. Just wondering if you have any
> thoughts. Thanks.
>
> Sakda
>
> On Tue, Sep 8, 2009 at 6:57 AM, R_help Help<rhelp...@gmail.com> wrote:
>> ---------- Forwarded message ----------
>> From: Gabor Grothendieck <ggrothendi...@gmail.com>
>> Date: Sun, Aug 30, 2009 at 11:08 PM
>> Subject: Re: [R-SIG-Finance] Aggregating irregular time series
>> To: R_help Help <rhelp...@gmail.com>
>> Cc: r-sig-fina...@stat.math.ethz.ch, r-help@r-project.org
>>
>>
>> Try this for the first question:
>>
>> neighborapply <- function(z, width, FUN) {
>> out <- z
>> ix <- seq_along(z)
>> jx <- findInterval(time(z) + width, time(z))
>> out[] <- mapply(function(i, j) FUN(c(0, z[seq(i+1, length =
>> j-i)])), ix, jx)
>> out
>> }
>>
>> # test - corrected :948 in last line
>>
>> library(zoo)
>> library(chron)
>>
>> Lines <- "Time x
>> 10:00:00.021 20
>> 10:00:00.224 20
>> 10:00:01.002 19
>> 10:00:02.948 20"
>>
>> z <- read.zoo(textConnection(Lines), header = TRUE, FUN = times)
>>
>> neighborapply(z, times("00:00:02"), sum)
>>
>> # and here is an alternative neighborapply
>> # using loops. The non-loop solution does
>> # have the disadvantage that it does as
>> # readily extend to other situations which
>> # is why we add this second solution to
>> # the first question.
>>
>> neighborapply <- function(z, width, FUN) {
>> out <- z
>> tt <- time(z)
>> for(i in seq_along(tt)) {
>> for(j in seq_along(tt)) {
>> if (tt[j] - tt[i] > width) break
>> }
>> if (j == length(tt) && tt[j] - tt[i] <= width) j <- j+1
>> out[i] <- FUN(c(0, z[seq(i+1, length = j-i-1)]))
>> }
>> out
>> }
>>
>> The second question can be answered along the lines
>> of the first by modifying neighborapply in the loop solution
>> appropriately.
>>
>> On Sun, Aug 30, 2009 at 9:38 PM, R_help Help<rhelp...@gmail.com> wrote:
>>> Hi,
>>>
>>> I have a couple of aggregation operations that I don't know how to
>>> accomplish. Let me give an example. I have the following irregular
>>> time series
>>>
>>> time x
>>> 10:00:00.021 20
>>> 10:00:00.224 20
>>> 10:00:01.002 19
>>> 10:00:02:948 20
>>>
>>> 1) For each entry time, I'd like to get sum of x for the next 2
>>> seconds (excluding itself). Using the above example, the output should
>>> be
>>>
>>> time sumx
>>> 10:00:00.021 39
>>> 10:00:00.224 19
>>> 10:00:01.442 20
>>> 10:00:02:948 0
>>>
>>> 2) For each i-th of x in the series, what's the first passage time to
>>> x[i]-1. I.e. the output should be
>>>
>>> time firstPassgeTime
>>> 10:00:00.021 0.981
>>> 10:00:00.224 0.778
>>> 10:00:01.442 NA
>>> 10:00:02:948 NA
>>>
>>> Is there any shortcut function that allows me to do the above? Thank you.
>>>
>>> adschai
>>>
>>> _______________________________________________
>>> r-sig-fina...@stat.math.ethz.ch mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
>>> -- Subscriber-posting only.
>>> -- If you want to post, subscribe first.
>>>
>>
>
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>
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