Dear All, When I run rlm to obtain robust standard errors, my output does not include p-values. Is there any reason p-values should not be used in this case? Is there an argument I could use in rlm so that the output does include p-values? Thanks in advance, Celso [[alternative HTML version deleted]]
On Wed, 5 Jul 2006, Celso Barros wrote:> Dear All, > > When I run rlm to obtain robust standard errors, my output does not include > p-values. Is there any reason p-values should not be used in this case? Is > there an argument I could use in rlm so that the output does > include p-values?First you would have to derive a reliable theory for the tests you want p-values for. The t ratios are approximately normally distributed, but to quote realistic p-values you would need much more accurate distribtution theory. For summary.lm we can use Student's t distribution and say the results are exact only under normality. There is no analogue for summary.rlm. Note that S does not quote p-values for summmary.glm for similar reasons: R does but I do not consider it to be a good idea.> > Thanks in advance, > > Celso > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >-- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
[Oops! Written 6 hours ago, the following was accidentally not sent.]>>>>> "Celso" == Celso Barros <celso.barros at gmail.com> >>>>> on Wed, 5 Jul 2006 04:09:17 -0300 writes:Celso> When I run rlm to obtain robust standard errors, my output does not include Celso> p-values. Is there any reason p-values should not be used in this case? yes (see also below). Celso> Is there an argument I could use in rlm so that the output does Celso> include p-values? no. What are the reasons? How to properly do hypothesis testing in the context of robust regression has partly been an open research problem. Whereas or has been solved in Elvezio Ronchetti's PhD thesis (1982) by tau-tests, see chapter 7 of Hampel, Rousseeuw, Ronchetti, Stahel (1986), these are not (directly) related to standard errors, and t-tests with some degrees of freedom. Hence they are not so intuitively explainable, and not entirely trivial to implement. Probably this is one of reasons, why they (tau-tests) haven't been programmed for MASS (the book and the R package). Recent research, namely, Croux, C., Dhaene, G. and Hoorelbeke, D. (2003) _Robust standard errors for robust estimators_, Discussion Papers Series 03.16, K.U. Leuven, CES. has been made use of by Matias Salibian-Barrera's roblm() function now available as lmrob() from package 'robustbase'. There, mod <- lmrob(........); summary( mod ) does provide you with P-values. But we still recommend *not* to ``believe in the P-values'' blindly, but rather base your data analysis on serious analysis of residuals and other model checking.