R help - Jul 2012 - How does "rlm" in R decide its "w" weights for each IRLS iteration?

Hi all,

I am also confused about the manual:

           a.  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.



b. The returned values:

w the weights used in the IWLS process

wresid a working residual, weighted for "inv.var" weights only.



How to use these input arguments?

Anybody please shed some light?

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 - if I
have
my own set of weights.
And the real/actual weights are the product of "weights"(I supplied)
and
the converged output "w" (an output).


If my understanding above is correct, how does "rlm" decide its
"w" for
each IRLS iteration then?

Any pointers/tutorials/notes to the calculation of these "w"'s in
each IRLS
iteration?

Thanks to all.

	[[alternative HTML version deleted]]

> -----Original Message-----
> Subject: [RsR] How does "rlm" in R decide its "w"
weights for
> each IRLS iteration?
> I am also confused about the manual:
> 
>            a.  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?
When you give rlm weights (called 'weights', not 'w' on input,
though you can abbreviate to 'w'), you need to tell it which of these
two possibilities you used.
If you gave it case numbers, say wt.method="case"; if you gave it
inverse variance weights, say wt.method="inv.var".
The default is "inv.var".

> The input argument "w" is used for the initial values of the 
> rlm IRLS weighting and the output value "w" is the converged
"w".
There is no input argument 'w' for rlm (see above). 
The output w are a  calculated using the psi function, so between 0 and 1.
The effective weights for the final estimate would then be something like
w*weights, using the full name of the input argument (and if I haven't
forgotten a square root somewhere). At least, that would work for a simple
location estimate (eg rlm(x~1)).
> If my understanding above is correct, how does "rlm" decide 
> its "w" for each IRLS iteration then?
It uses the given psi functions to calculate the iterative weights based on the
scaled residuals.
> Any pointers/tutorials/notes to the calculation of these 
> "w"'s in each IRLS iteration?
Read the cited references for a detailed guide. Or, of course, MASS - the
package is, after all, intended to support the book, not replace it.



S Ellison

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