How does "rlm" in R decide its "w" weights? Following user603's advice, I opened a new question thread for this question.
For your reference, the original question is here:
Pointers to understand "rlm" in R better?

My question is:
How does "rlm" decide its "w" for each IRLS iteration then? 
Also, is my understanding below correct?
The input argument "w" is used for the initial values of the rlm IRLS weighting and the output value "w" is the converged "w".
The "weights" input argument is actually what I want to apply.
And the real/actual weights are the product of "weights"(I supplied) and the converged output "w" (an output).
Thanks to all. 
 A: Several things:


*

*Use the lmrob() implementation in the robustbase package. Avoid the MASS implementations and its strange default behaviors. Then, the lmrob() function does indeed not accept weights to interfer with the robust fitting procedure (which, in my understanding, is the sane behavior) so you have do the weighting "outside" of it:
library(robustbase)
x<-matrix(rnorm(100*2),100,2)
y<-x%*%(c(3,-1))+rnorm(100)-3
mod<-lmrob(y~x)
myinitiweights<-runif(100)
myinitiweights<-myinitiweights/sum(myinitiweights)
myfinalweights<-myinitiweights*mod$weights
myfinalfitwols<-lm(y~x,w=myfinalweights)


*For more info on IRLS and robust regression, have a look at this report(1) (page 16 onwards)
(1): Robust Regression, C. Stuart, April, 2011 report. 
EDIT:
should you want to have your myinitiweights have an influence on the IRLS 
algorithm then you have to add in the weights before the call to lmrob():
yw<-y*sqrt(myinitiweights)
xw<-cbind(1,x)*sqrt(myinitiweights)
wm<-lmrob(yw~xw-1)
coef(wm)

but read the report i linked to on IRLS and ask yourself if this is really the best way to achieve what you are trying to achieve.
