[R] Robust Regression - LTS
Kylie-Anne Richards
kar at itga.com.au
Thu Sep 1 10:25:14 CEST 2005
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
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