[RsR] results of robust regression vs. OLS regression

[D. Alain]

Dear R-List, 

due to outliers in my data I wanted to carry out a robust regression. 
According to APA standards, reporting (usually OLS) regression results should include
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1. unstandardized beta coefficients
2. standardized beta coefficients
3. SE
4. t values
5. r squared
6. r squared adjusted
7. F (df.num/df.den)

Now I tried the robust version using lmrob (package="robustbase")

lmrob.fit<-lmrob(y~x1+x2+x3,data=mydat)

I got 
1. unstandardized beta coef
3. SE
4. t values

What about? 
2. standardized beta coef
5. r squared
6. r squared adjusted
7. F (df.num/df.den)

I
 have read in an R-threat 
(http://tolstoy.newcastle.edu.au/R/e5/help/08/11/7271.html) that R2 is 
only valid in the context of least-square methods. 

(1) Is there an 
equivalent I could
 report for non-least-square methods? 
(2) Why does lmrob-output not 
include standardized beta coefs and F statistic? How can I compute both 
of them?

Then I realized that ltsReg (package="robustbase") does 
actually report almost everything I would need, but I could not find 
"standardized beta coefficients".
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(3) Does anyone know how I could compute standardized beta coefs using the output of ltsReg(fit)?

Though, the authors of the package "strongly recommend 
using lmrob() instead of ltsReg". 

(4) Is this due to inefficiency or are the
 coefs biased or what is the reason for that recommendation? 

Finally I found lmRob (package="robust") which 
does report at least a multiple R2, but which is apparently biased and 
needs correction as I found in a threat of Renaud & Victoria-Feser� https://stat.ethz.ch/pipermail/r-sig-robust/2010/000290.html, where the authors recommend to correct R2 for bias (Renaud, O. & 
Victoria-Feser, M.-P. (2010). A robust coefficient of determination for 
regression. Journal of Statistical Planning and Inference, 140, 
1852-1862. http://dx.doi.org/10.1016/j.jspi.2010.01.008).
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(5) Does that 
mean, that "multiple r squared" can be reported even though it is not a 
least square method, but should be corrected for bias before? 
(6) What does 
that mean for the rest of lmRob-output (e.g. t-values)? Should they be corrected too?

I must confess that I am somewhat confused and I would be very thankful for any clarification in this matter. 
Thank you in advance and sorry for my question if it reveals some serious lack of knowledge on my side.

Best wishes.

Alain

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