Dear R-List, 

due to outliers in my data I wanted to carry out a robust regression. 
According to APA standards, reporting OLS regression results should include
 
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. Is there no equivalent I could 
report for non-least-square methods? Then 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" 
(does anyone know how I could compute these coefs?)
Though, the authors of the package "strongly recommend using lmrob() instead of 
ltsReg". Is this due to inefficiency or are the coefs biased? 

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). 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? Then, what does that mean for the rest of 
lmRob-output (e.g. t-values)? 

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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