For quantile regression you can again just use summary(rq(...)).
url: www.econ.uiuc.edu/~roger Roger Koenker email rkoen...@uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Urbana, IL 61801 On May 8, 2013, at 10:51 AM, Anton Kochepasov wrote: > Hi everyone, > > A few years ago there was a discussion about a robust regression confidence > interval (https://stat.ethz.ch/pipermail/r-sig-robust/2008/000217.html) and I > would like to resort your courtesy again. > > I'm trying to compare a few regression models for my data. For linear > regression everything is quite understandable, but robust and quantile > regressions are not so obvious. I could not find almost anything about > calculating confidence interval for these regression models unless I looked > for something wrong. > > My code in R looks as follows: > # Robust linear modeling > library(MASS) > library(robustbase) > library(robust) > set.seed(343); > x <- rnorm(1000) > y <- x + 2*rnorm(1000) > > lm1<-lm(y~x); rlm1<-rlm(y~x); rlm2 <- lmRob(y~x); rlm3 <- lmrob(y~x) > cbind(summary(lm1)$coeff, confint(lm1)) > cbind(summary(rlm1)$coeff, confint(rlm1)) > cbind(summary(rlm2)$coeff, confint(rlm2)) > cbind(summary(rlm3)$coeff, confint(rlm3)) > > And produces the following result: >> cbind(summary(lm1)$coeff, confint(lm1)) > Estimate Std. Error t value Pr(>|t|) 2.5 % 97.5 > % > (Intercept) -0.06973191 0.06408983 -1.088034 2.768429e-01 -0.1954982 > 0.05603438 > x 0.97647196 0.06619635 14.751145 1.071805e-44 0.8465720 > 1.10637196 >> cbind(summary(rlm1)$coeff, confint(rlm1)) > Value Std. Error t value 2.5 % 97.5 % > (Intercept) -0.06131788 0.06714405 -0.9132288 NA NA > x 0.96016596 0.06935096 13.8450275 NA NA >> cbind(summary(rlm2)$coeff, confint(rlm2)) > Error in UseMethod("vcov") : > no applicable method for 'vcov' applied to an object of class "lmRob" >> cbind(summary(rlm3)$coeff, confint(rlm3)) > Estimate Std. Error t value Pr(>|t|) 2.5 % 97.5 > % > (Intercept) -0.0568964 0.06608987 -0.8608945 3.895029e-01 -0.1865874 > 0.07279464 > x 0.9612520 0.06821558 14.0913850 2.921913e-41 0.8273896 > 1.09511448 > > It's easy to spot that linear model works OK and only one robust regression > gives a sensible result. Another observation is that lmrob(), which produces > some actual confidence interval, calculates it in the same manner as lm(), > with using 1.96 as the student coefficient. > > Could you share your opinion if it is a correct way to produce a confidence > interval for the robust regression model (same as for the linear regression)? > May the same method be used for the quantile regression model? If not, what > should I use? > > Thank you in advance, > Anton > _______________________________________________ > R-SIG-Robust@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-robust _______________________________________________ R-SIG-Robust@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-robust
