Felipe Carrillo <mazatlanmexico <at> yahoo.com> writes: > > Hello all: > Is there a way to detect in R if a dataset is normally distributed or skewed without graphically seeing it? > The reason I want to be able to do this is because I have developed and application with Visual Basic where > Word,Access and Excel "talk" to each other and I want to integrate R to this application to estimate > confidence intervals on fish sizes (mm). I basically want to automate the process from Excel by detecting > if my data has a normal distribution then use t.test, but if my data is skewed then use wilcox.test. > Something like the pseudo code below: > > fishlength <- c(35,32,37,39,42,45,37,36,35,34,40,42,41,50) > if fishlength= "normally distributed" then > t.test(fishlength) > else > wilcox.text(fishlength) > > I hope this isn't very confussing > > Felipe D. Carrillo > Supervisory Fishery Biologist > Department of the Interior > US Fish & Wildlife Service > California, USA
There's a whole package (nortest) devoted to tests of normality, BUT: I would suggest that your procedure is not a good idea. It's often hard to detect non-normality, and "fail to reject" shouldn't mean "accept". If you're concerned about non-normality, you should probably just use the Wilcoxon test all the time (it has about 95% of the power of the t-test if the data are normal: http://en.wikipedia.org/wiki/Mann-Whitney_U ), or use robust statistics (e.g. rlm in the MASS package). Ben Bolker ______________________________________________ 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.
