Hi Michael, I'm afraid this is one of those cases where the short answer is "No" and the long answer is, "No."
If you are working with a data set stored in a data frame, something like: sapply(mtcars, function(x) if (is.numeric(x)) range(x, na.rm = TRUE) else c(NA, NA)) should give you the range for all numeric variables---which is a simple check if any values fall outside the possible range (say you have an age variable with a -3 or 320). Beyond that, you can inspect data visually, but ultimately, you have to decide what an outlier is and justify it. Cheers, Josh On Fri, Dec 30, 2011 at 9:03 AM, Michael <comtech....@gmail.com> wrote: > Happy holidays all! > > I know it's very subjective to determine whether some data is outlier or > not... > > But are there reasonally good and realistic methods of identifying outliers > in R? > > Thanks a lot! > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. -- Joshua Wiley Ph.D. Student, Health Psychology Programmer Analyst II, Statistical Consulting Group University of California, Los Angeles https://joshuawiley.com/ ______________________________________________ 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.
