>>>>> Stromberg, Arnold <astr...@uky.edu>
>>>>> on Mon, 16 Feb 2015 20:14:06 +0000 writes:
> Jonathan,
> Seems straightforward theoretically, let's see if anyone has implemented
them in R.
> Arny
> Arnold J. Stromberg
> Professor and Chair, Department of Statistics, University of Kentucky
[ ........... ]
Well, the predict() method for lmrob() fits (package
'robustbase') has built in.
I wonder why nobody has seen that and mentioned it here.
In the mean time, Jonathan has also asked on R-help
and got some advice there...
and now has found predict.lmrob "in some way" and asked me (as
'robustbase' maintainer) about it.
I'm taking the liberty of answering here -- so others are also
helped in the future, *and* this thread is somewhat decently
closed within the R-SIG-robust list :
>>>>> Burns, Jonathan (NONUS) <jonathan.bur...@gdit.com>
>>>>> on Thu, 19 Feb 2015 21:37:26 +0000 writes:
[..........]
> I am interested in creating prediction intervals for the robust
regression models. I tried to use the function predict.lmrob(); however, R
gave me an error - could not find function "predict.lmrob". I thought perhaps
this was because I was using an older version of the package. I updated the
package and I still get the error. I am using R version 3.1.0.
> I also got the same error with the function print.lmrob, plot.lmrob() and
anova.lmrob(). Lmrob() itself works fine.
> This is the result that I get when I list the functions in robustbase:
>> ls("package:robustbase")
> [1] "adjbox" "adjboxStats" "adjOutlyingness"
> [4] "aircraft" "airmay" "alcohol"
......................
......................
> [103] "vaso" "wagnerGrowth" "wgt.himedian"
> [106] "wood"
print(), predict() etc are all generic functions,
their lmrob S3 methods *are* called print.lmrob(), predict.lmrob(), etc,
but they are *hidden* and you do not see them normally.
Rather you should use print(..), predict(...), etc.
If you really need to see them you can use
getAnywhere("predict.lmrob")
etc.
{This is all general R knowledge - somewhat intermediate level -
about using S3 methods in R packages and namespaces}
Note that you've also asked about this on the R-help mailing list on
Feb 11, and you got two answers, the second one by Prof Brian
Ripley explained to you that matters *are* actually more complicated:
If you use robustness for a good reason, it seems a bit
optimistic to assume that a future observation has normal errors,
(rather than a mixture of normal + "outlier") and so the
standard assumptions about prediction intervals would be doubtful.
But I agree (with you I assume) that sometimes you *want* to
make this somewhat optimistic assumption.... and for that case,
everything is ready for you on a silver plate :
Why did you not just read the help page for predict.lmrob ?
Even though the object is hidden -- because you should call predict() --
it still has nice help page {well, that can be improved, and I
will for the next version of robustbase}, and that help
*does* answer your question on how to compute prediction intervals:
Andreas Ruckstuhl, the author of the function does provide them
(under the optimistic assumption), in the exact same way as the
predict() method for lm {called "predict.lm"} does.
==> Just use predict( <fitted lmrob object>, interval = "prediction")
(or a variant where you specify new data, weights, etc).
Best regards,
Martin
> -----Original Message-----
> From: R-SIG-Robust [mailto:r-sig-robust-boun...@r-project.org] On Behalf
Of Jonathan Burns
> Sent: Wednesday, February 11, 2015 12:42 PM
> To: r-sig-robust@r-project.org
> Subject: [RsR] Prediction Intervals for Robust Regression
> I have created robust regression models using least trimmed squares and
MM- regression (using robustbase).
> I am now looking to create prediction intervals for the predicted
results. While I have seen some discussion in the literature about confidence
intervals on the estimates for robust regression, I haven’t had much success on
prediction intervals for the results. I was wondering anyone would be able to
provide some direction on how to create these prediction intervals in the
robust regression setting.
> Thanks,
> Jonathan Burns
> Sr. Statistician
> General Dynamics Information Technology
> Medicare & Medicaid Solutions
> One West Pennsylvania Avenue
> Baltimore, MD 21204
> jonathan.bur...@gdit.com
> _______________________________________________
> R-SIG-Robust@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-robust
> _______________________________________________
> R-SIG-Robust@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-robust
_______________________________________________
R-SIG-Robust@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-robust
