Hi Jahan, What data are you going to use for analyses? The original data or the log transformed? It does not make sense to evaluate your transformed data for analysis based on the original untransformed data (unless you are planning on using the untransformed for the analyses).
There is a several good fortunes on outliers: library(fortunes) fortune("just be an outlier") Cheers, Josh On Tue, Nov 30, 2010 at 12:15 PM, Jahan <jahan.mohiud...@gmail.com> wrote: > I have a statistical question. > The data sets I am working with are right-skewed so I have been > plotting the log transformations of my data. I am using a Grubbs Test > to detect outliers in the data, but I get different outcomes depending > on whether I run the test on the original data or the log(data). Here > is one of the problematic sets: > > fgf2p50=c(1.563,2.161,2.529,2.726,2.442,5.047) > stripchart(fgf2p50,vertical=TRUE) > #This next step requires you have the 'outliers' package > library(outliers) > grubbs.test(fgf2p50) > #the output says p<0.05 so 5.047 is an outlier > #Next, I run the test on the log(data) > log10=c(0.194,0.335,0.403,0.436,0.388,0.703) > grubbs.test(log10) > #output is that p>0.05 so we reject that there is an outlier. > > The question is, which outlier test do I accept? > > ______________________________________________ > 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 University of California, Los Angeles http://www.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.