Could you please point me to tutorial/notes that can help me understand "rlm" better?
Here is an example: summary(rlm(stack.loss ~ ., stackloss, weights=myweights))
My questions are:
a. How does the iterative procedure work?
b. What's the relation between the weights I supplied above "myweights" and the final used weights?
My understanding is that the final weights used are actually the Huber loss weights? So are myweights not used at all? Ultimately, I want to use myweights... I have a feeling that actually I should put "myweights" into the "w" argument not the "weights" argument...
c. Could you please give an example showing whether the final used weights are actually the Huber weights or my weights?
I am also confused about the manual:
The input arguments:
wt.method are the weights case weights (giving the relative importance of case, so a weight of 2 means there are two of these) or the inverse of the variances, so a weight of two means this error is half as variable?
w (optional) initial down-weighting for each case.
init (optional) initial values for the coefficients OR a method to find initial values OR the result of a fit with a coef component. Known methods are "ls" (the default) for an initial least-squares fit using weights w*weights, and "lts" for an unweighted least-trimmed squares fit with 200 samples.
The returned values:
w the weights used in the IWLS process
wresid a working residual, weighted for "inv.var" weights only.
Anybody please shed some light?
Thank you!
rlm()
), then you're optimizing the function seen in this answer - stats.stackexchange.com/questions/29563/…. $\endgroup$ – Macro Jul 17 '12 at 14:44rlm
function in theMASS
package for R? (that is what it looks like, but there could easily be other functions with that name). If that is the case then the whole MASS package is a suport package to go along with a book (who's intials happen to be 'MASS'). So that book would seem to be the obvious place to start when looking for tutorials or notes to help with understanding the function and the science behind it. $\endgroup$ – Greg Snow Jul 17 '12 at 20:50