Hello everybody, after searching around for quite some time, I haven't been able to find a package that provides a function to compute the Windorized mean and variance. Also I haven't found a function that computes the trimmed variance. Is there any such package around? thanks, Roberto
On Aug 26, 2009, at 8:58 PM, Roberto Perdisci wrote:> Hello everybody, > after searching around for quite some time, I haven't been able to > find a package that provides a function to compute the Windorized mean > and variance. Also I haven't found a function that computes the > trimmed variance. Is there any such package around?Perhaps it was a spledding problem? http://search.r-project.org/cgi-bin/namazu.cgi?query=winsorized&max=100&result=normal&sort=score&idxname=functions&idxname=Rhelp08&idxname=views&idxname=Rhelp02 If you can define what you mean by trimmed variance, I suspect that building a function would be straightforward.> > thanks, > Roberto > > ______________________________________________ > R-help at 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.David Winsemius, MD Heritage Laboratories West Hartford, CT
Roberto Perdisci wrote:> Hello everybody, > after searching around for quite some time, I haven't been able to > find a package that provides a function to compute the Windorized mean > and variance. Also I haven't found a function that computes the > trimmed variance. Is there any such package around? > >Hi Roberto, The Winsorized variance is similar to the trimmed variance, except that the extreme values are substituted rather than dropped. Define the quantiles within which you want to retain the original values and then substitute the values at the quantiles for all values more extreme in the respective sign direction. Like this: testdat<-rnorm(20) winsorVar<-function(x,probs=c(0.05,0.95)) { xq<-quantile(x,probs=probs) x[x < xq[1]]<-xq[1] x[x > xq[2]]<-xq[2] return(var(x)) } Jim