Hi It often depends on your attitude to limits for outlying observations. Boxplot has some identifying routine for selecting outlying points.
Any procedure usually requires somebody to choose which observation is outlying and why. You can use e.g. all values which are beyond some threshold based on sd but that holds only if distribution is normal. set.seed(1) x<-rnorm(x) ul <- mean(x) +3*sd(x) ll <- mean(x) -3*sd(x) beyond <- (x>ul) | ( x <ll) > x[beyond] [1] 3.810277 Regards Petr [EMAIL PROTECTED] [EMAIL PROTECTED] napsal dne 19.06.2007 11:29:17: > hello, > are there functions to detecte outlying observations in samples? > thanks. > > > > > > > > > ___________________________________________________________________________ > > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] 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. ______________________________________________ [email protected] 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.
