[R] Help with efficient double sum of max (X_i, Y_i) (X & Y vectors)
Thomas Lumley
tlumley at u.washington.edu
Fri Feb 2 15:05:38 CET 2007
On Thu, 1 Feb 2007, Ravi Varadhan wrote:
> Jeff,
>
> Here is something which is a little faster:
>
> sum1 <- sum(outer(x, x, FUN="pmax"))
> sum3 <- sum(outer(x, y, FUN="pmax"))
This is the sort of problem where profiling can be useful. My experience
with pmax() is that it is surprisingly slow, presumably because it handles
recycling and NAs
In the example I profiled (an MCMC calculation) it was measurably faster
to use
function(x,y) {i<- x<y; x[i]<-y[i]; x}
-thomas
>
> Best,
> Ravi.
>
> ----------------------------------------------------------------------------
> -------
>
> Ravi Varadhan, Ph.D.
>
> Assistant Professor, The Center on Aging and Health
>
> Division of Geriatric Medicine and Gerontology
>
> Johns Hopkins University
>
> Ph: (410) 502-2619
>
> Fax: (410) 614-9625
>
> Email: rvaradhan at jhmi.edu
>
> Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
>
>
>
> ----------------------------------------------------------------------------
> --------
>
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Jeffrey Racine
> Sent: Thursday, February 01, 2007 1:18 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] Help with efficient double sum of max (X_i, Y_i) (X & Y
> vectors)
>
> Greetings.
>
> For R gurus this may be a no brainer, but I could not find pointers to
> efficient computation of this beast in past help files.
>
> Background - I wish to implement a Cramer-von Mises type test statistic
> which involves double sums of max(X_i,Y_j) where X and Y are vectors of
> differing length.
>
> I am currently using ifelse pointwise in a vector, but have a nagging
> suspicion that there is a more efficient way to do this. Basically, I
> require three sums:
>
> sum1: \sum_i\sum_j max(X_i,X_j)
> sum2: \sum_i\sum_j max(Y_i,Y_j)
> sum3: \sum_i\sum_j max(X_i,Y_j)
>
> Here is my current implementation - any pointers to more efficient
> computation greatly appreciated.
>
> nx <- length(x)
> ny <- length(y)
>
> sum1 <- 0
> sum3 <- 0
>
> for(i in 1:nx) {
> sum1 <- sum1 + sum(ifelse(x[i]>x,x[i],x))
> sum3 <- sum3 + sum(ifelse(x[i]>y,x[i],y))
> }
>
> sum2 <- 0
> sum4 <- sum3 # symmetric and identical
>
> for(i in 1:ny) {
> sum2 <- sum2 + sum(ifelse(y[i]>y,y[i],y))
> }
>
> Thanks in advance for your help.
>
> -- Jeff
>
> --
> Professor J. S. Racine Phone: (905) 525 9140 x 23825
> Department of Economics FAX: (905) 521-8232
> McMaster University e-mail: racinej at mcmaster.ca
> 1280 Main St. W.,Hamilton, URL:
> http://www.economics.mcmaster.ca/racine/
> Ontario, Canada. L8S 4M4
>
> `The generation of random numbers is too important to be left to chance'
>
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>
> ______________________________________________
> 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
> and provide commented, minimal, self-contained, reproducible code.
>
Thomas Lumley Assoc. Professor, Biostatistics
tlumley at u.washington.edu University of Washington, Seattle
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