I'm sorry, maybe the question was bad posed. Ista has well described my problem.
Thanks Massimo >----Messaggio originale---- >Da: iz...@psych.rochester.edu >Data: 28/07/2011 17.52 >A: "David Winsemius"<dwinsem...@comcast.net> >Cc: "m.fen...@libero.it"<m.fen...@libero.it>, <r-help@r-project.org> >Ogg: Re: [R] Problem with anova.lmRob() "robust" package > >I found the question really confusing as well, but see below. > >On Thu, Jul 28, 2011 at 11:42 AM, David Winsemius ><dwinsem...@comcast.net> wrote: >> >> On Jul 28, 2011, at 9:13 AM, m.fen...@libero.it wrote: >> >>> >>> Dear R users, >>> I'd like to known your opinion about a problem with anova.lmRob() of >>> "Robust" package that occurs when I run a lmRob() regression on my dataset. >>> I check my univariate model by single object anova as anova(lmRob(y~x)). >>> If I compare my model with the null model (y~1), I must obtain the same >>> results, >>> but not for my data. >>> Is it possible? >>> >>> My example: >>> >>> x<-c(rep(0,8),rep(1,8),rep(2,7)) >>> >>> y<-c(1,0.6,-0.8,0.7,1.6,-0.2,-1.2,-3.8,-1.8,-2.6,-1.7,-2.1,-0.3,-1.4,1.4, -0.3,-0.3,0.5,0.4,-0.9,-1.6,0.4,0.4) >>> library(robust) >>> lmR<-lmRob(y~factor(x)) >>> anova(lmR) >>> lmR0<-lmRob(y~1) >>> anova(lmR,lmR0) >>> >>> If I run the code omitting the factor() (then treating "x" as continuous), >>> the results are the same.. >>> >> >> I do not get the same results with that code. And the code does not appear >> to track your description, since the second model does not have an "x" term >> in it. Even when I create the model that it sounded as though you would have >> written, namely lmR0 <- lmRob(y ~ x), it is clearly _not_ the same result. >> >>> coef(lmR) >> (Intercept) factor(x)1 factor(x)2 >> 0.2428571 -1.3432007 -0.4000000 >>> coef(lmR0) >> (Intercept) x >> -0.509524217 0.005820355 >>> >>> What is the explanation of these different results? >> >> Since you didn't post your results and since your complaint was that they >> are "the same", it's hard to know what you are talking about. > >I think the question is why > >lm1 <- lm(y ~ factor(x)) >lm0 <- lm(y ~ 1) >anova(lm1) >anova(lm0, lm1) > >gives the same result, but > >lmR<-lmRob(y~factor(x)) >lmR0<-lmRob(y~1) >anova(lmR) >anova(lmR,lmR0) > >does not. > >I don't know the answer, but I think it is an interesting question. > >Best, >Ista > >> >> -- >> David Winsemius, MD >> West Hartford, CT >> >> ______________________________________________ >> 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. >> > > > >-- >Ista Zahn >Graduate student >University of Rochester >Department of Clinical and Social Psychology >http://yourpsyche.org > ______________________________________________ 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.
