Thanks Jim. After reinstall of new R version all mentioned packages work. I tested various functions which revealed that on my lognorm data there is no big difference in error of median or 10% quantile. I also found some function for quantile se computing in Hmisc package.
Petr > -----Original Message----- > From: Jim Lemon [mailto:j...@bitwrit.com.au] > Sent: Wednesday, October 31, 2012 9:56 AM > To: PIKAL Petr > Cc: r-help@r-project.org > Subject: Re: [R] standard error for quantile > > On 10/31/2012 12:46 AM, PIKAL Petr wrote: > > Dear all > > > > I have a question about quantiles standard error, partly practical > > partly theoretical. I know that > > > > x<-rlnorm(100000, log(200), log(2)) > > quantile(x, c(.10,.5,.99)) > > > > computes quantiles but I would like to know if there is any function > > to find standard error (or any dispersion measure) of these estimated > > values. > > > > And here is a theoretical one. I feel that when I compute median from > > given set of values it will have lower standard error then 0.1 > > quantile computed from the same set of values. > > > > Is it true? If yes can you point me to some reasoning? > > > Hi Petr, > Using a resampling method, it depends upon the distribution of the > values. If you have a "love-hate" distribution (bimodal and heavily > weighted toward extreme values), the median standard error can be > larger. Try this: > > x<-sample(-5:5,1000,TRUE, > prob=c(0.2,0.1,0.05,0.04,0.03,0.02,0.03,0.04,0.05,0.1,0.2)) > x<-ifelse(x<0,x+runif(1000),x-runif(1000)) > hist(x) > mcse.q(x, 0.1) > $est > [1] -3.481419 > > $se > [1] 0.06887319 > > mcse.q(x, 0.5) > $est > [1] 1.088475 > > $se > [1] 0.3440115 > > > mcse.q(x, 0.1) > $est > [1] -3.481419 > > $se > [1] 0.06887319 > > Jim ______________________________________________ R-help@r-project.org 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.
