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From | "Nick Cox" <n.j.cox@durham.ac.uk> |
To | <statalist@hsphsun2.harvard.edu> |
Subject | st: RE: non parametric anova equivalent for factorial experiments? |
Date | Thu, 4 Mar 2010 14:07:27 -0000 |
I'd translate the problem into an equivalent regression-type model. I think practitioners vary in how seriously to take these preliminary tests and that in practice ANOVA often works well despite mild deviations from ideal conditions. The point need not be an article of faith. Guessing that the problem is a highish outlier, I'd explore sensitivity to the outlier by glm ... glm ... , link(power 0.5) glm ... , link(log) where ... indicates the appropriate response and predictors. That would require use of factor variables (11) or -xi- (<= 10). I'd then monitor how the questionable data points move around on residual vs fitted plots and observed vs fitted plots. -modeldiag- from SJ 4(4) contains plotting commands that work after -glm-. Alona will find examples of -glm-s being applied in our joint paper: Cox, N.J. J. Warburton, A. Armstrong and V.J. Holliday. 2008. Fitting concentration and load rating curves with generalised linear models. Earth Surface Processes and Landforms 33: 25-39 (doi: 10.1002/esp.1523) A roughly equivalent but in my view inferior approach is to try transformations of the response directly. Nick n.j.cox@durham.ac.uk -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Armstrong, Alona Sent: 04 March 2010 10:01 To: statalist@hsphsun2.harvard.edu Subject: st: non parametric anova equivalent for factorial experiments? Hi, I have undertaken two factorial experiments. One has 3 factors each with 2 levels and the other has two factors, one with 2 levels and the other with 3. The data are balanced with 3 replicates. I have tested for homogeneity of variances by Levenes and Hartleys Maximum F ratio and the data fail. Much of this is caused by one factor producing more variable data for one level compared with the other but there are also some outliers (but I would prefer not to exclude them as this is representative of what I am studying). So, I have been looking around for some non-parametric alternatives to ANOVA that can handle a factorial design but with limited success (I have tried a rank-anova approach but the same problems persist and there seems to be some debate about the validity of that approach). Does anyone have any suggestions and know of any existing Stata code? Any thoughts, recommendations or pointers would be greatly appreciated. Regards, Alona Dr Alona Armstrong PDRA Lancaster Environment Centre Lancaster University Lancaster LA1 4YQ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/