[EMAIL PROTECTED] napsal dne 19.06.2007 12:23:58: > 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)
Sorry, it shall be x <- rnorm(1000) > 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. ______________________________________________ [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.
