On Windows
> Tab
quantile kuantile quantile kuantile qsort
100 0.003 0.003 0.000 0.001 0.000
1000 0.000 0.000 0.002 0.000 0.000
10000 0.002 0.004 0.001 0.004 0.000
1e+05 0.008 0.006 0.016 0.010 0.018
1e+06 0.092 0.113 0.137 0.140 0.186
1e+07 0.920 0.685 1.386 0.992 2.274
so the comparison is far less favourable to kuantile.
I remain unconvinced that such small differences are worth the time spend
discussing them.
On Tue, 11 Apr 2006, roger koenker wrote:
> I've recently folded a new version of the Floyd-Rivest quantile
> algorithm
> for quantiles into my quantreg package. So it is easily available for
> comparative testing. On my G5 running last friday's R-devel, I get:
>
>
> Median Only 5 Quantiles
> n quantile kuantile quantile kuantile qsort
> 100 0.003 0.003 0.006 0.004 0.002
> 1000 0.002 0.002 0.002 0.002 0.002
> 10000 0.005 0.003 0.008 0.003 0.001
> 1e+05 0.022 0.010 0.035 0.012 0.017
> 1e+06 0.181 0.117 0.308 0.138 0.200
> 1e+07 1.853 0.762 3.180 1.003 2.287
>
>
> # Small timing experiment to compare kuantile and quantile
>
> require(quantreg)
> set.seed(1446)
>
> ns <- 10^(2:7)
> R <- 10
> T <- array(NA,c(R,length(ns),5))
> eps <- 20 * .Machine$double.eps
> for(j in 1:length(ns)){
> for(i in 1:R){
> y <- rnorm(ns[j])
> T[i,j,1] <- system.time(qy <- quantile(y,.5))[1]
> T[i,j,2] <- system.time(ky <- kuantile(y,.5))[1]
> stopifnot(abs(qy - ky) < eps)
> T[i,j,3] <- system.time(qy <- quantile(y))[1]
> T[i,j,4] <- system.time(ky <- kuantile(y))[1]
> stopifnot(abs(qy - ky) < eps)
> T[i,j,5] <- system.time(sort(y,method="quick"))[1]
> }
> }
>
> Tab <- apply(T,2:3,mean)
> dimnames(Tab) <- list(paste(ns),c(rep(c("quantile","kuantile"),
> 2),"qsort"))
>
> url: www.econ.uiuc.edu/~roger Roger Koenker
> email [EMAIL PROTECTED] Department of Economics
> vox: 217-333-4558 University of Illinois
> fax: 217-244-6678 Champaign, IL 61820
>
> ______________________________________________
> R-devel@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-devel
>
>
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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