Hi, I am using robust regression, i.e. model.robust<-ltsreg(MXD~ORR,data=DATA). My question:- is there any way to determine the Robust Multiple R-Squared (as returned in the summary output in splus)? I found an equivalent model in the rrcov package which included R-square, residuals etc in it's list of components, but when I used this package the only results returned were equivalent to the LTS reg in the MASS package, which obviously indicates that the summary method does not work for this class of models.
If required: ##The output for the LTS reg (MASS) using print and summary Call: lqs.formula(formula = MXD ~ ORR, data = DATA, method = "lts") Coefficients: (Intercept) ORR 7.578e+08 2.533e+01 Scale estimates 1.333e+09 1.303e+09 Length Class Mode crit 1 -none- numeric sing 1 -none- character coefficients 2 -none- numeric bestone 2 -none- numeric fitted.values 4899 -none- numeric residuals 4899 -none- numeric scale 2 -none- numeric terms 3 terms call call 4 -none- call xlevels 0 -none- list model 2 data.frame list ## The output for the LTS reg (rrcov) using print and summary Coefficients: Intercept ORR 1.178e+09 2.387e+01 Scale estimate 1.722e+09 Length Class Mode best 2451 -none- numeric raw.coefficients 2 -none- numeric alpha 1 -none- numeric quan 1 -none- numeric raw.scale 1 -none- numeric raw.resid 4899 -none- numeric coefficients 2 -none- numeric scale 1 -none- numeric resid 4899 -none- numeric lts.wt 4899 -none- numeric crit 1 -none- numeric rsquared 1 -none- numeric residuals 4899 -none- numeric intercept 1 -none- logical method 1 -none- character RD 4899 -none- numeric X 9798 -none- numeric Y 4899 -none- numeric fitted.values 4899 -none- numeric ## The output for the LTS reg (SPLUS) using print and summary ****{What I am wanting to achieve in R}**** > model.robust<-ltsreg(MXD~ORR,data=DATA,na.action=na.exclude) > print(model.robust) Method: Least Trimmed Squares Robust Regression. Call: ltsreg(formula = MXD ~ ORR, data = DATA, na.action = na.exclude) Coefficients: Intercept ORR 1.465502e+009 2.325200e+001 Scale estimate of residuals: 1639000000 Total number of observations: 4899 Number of observations that determine the LTS estimate: 4409 > summary(model.robust) Method: [1] "Least Trimmed Squares Robust Regression." Call: ltsreg(formula = MXD ~ ORR, data = DATA, na.action = na.exclude) Coefficients: Intercept ORR 1.465502e+009 2.325200e+001 Scale estimate of residuals: 1639000000 Robust Multiple R-Squared: 0.4733 Total number of observations: 4899 Number of observations that determine the LTS estimate: 4409 Residuals: Min. 1st Qu. Median 3rd Qu. Max. -228135629879 -1032103953 -231375637 1234533512 55539148696 Weights: 0 1 588 4311 Thanks very much for any help you can offer. Kylie-Anne Richards ______________________________________________ R-help@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