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.. What is the explanation of these different results? Thanks in advance! Massimo Massimo FenatiDVM - Specialized in Animal Health and HusbandryVia Mameli 5 - 35040 Boara Pisani (PD)Italye-mail: m.fen...@libero.it [[alternative HTML version deleted]] ______________________________________________ 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.