>From ?rq.fit.pfn I see:
Details:
Preprocessing algorithm to reduce the effective sample size for QR
problems with (plausibly) iid samples. The preprocessing relies
on subsampling of the original data, so situations in which the
observations are not plausibly iid, are likely to cause problems.
The tolerance eps may be relaxed somewhat.
And from 1 in the Quantreg FAQ that Roger indicated there is:
>From ?rq.fit.fn:
eps: tolerance parameter for convergence. In cases of multiple
optimal solutions there may be some discrepancy between
solutions produced by method '"fn"' and method '"br"'. This
is due to the fact that '"fn"' tends to converge to a point
near the centroid of the solution set, while '"br"' stops at
a vertex of the set.
So it sounds like the pfn version of quantile regression estimates might
differ because it is intended for independent identically distributed data
(think homogeneous), it involves subsampling of the data set, and
convergence criteria are slightly different for fn and pfn than for the br
(standard Barrodale and Roberts simplex linear program) algorithm. All
conditions that could lead to different estimates. My recommendation for
the sample sizes you are considering is to stick with the Barrodale and
Roberts algorithm as it is the best understood, most reliable procedure.
Brian
Brian S. Cade, PhD
U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8818
email: ca...@usgs.gov <brian_c...@usgs.gov>
tel: 970 226-9326
On Wed, Oct 14, 2015 at 3:03 PM, T.Riedle <tr...@kent.ac.uk> wrote:
> The fn and br methods return the same results but the results provided by
> pfn differ. I do not find an explanation for this observation in the papers
> on quantile regression. Therefore my question.
>
> -----Original Message-----
> From: Roger Koenker [mailto:rkoen...@illinois.edu]
> Sent: 14 October 2015 22:33
> To: T.Riedle
> Cc: r-help@r-project.org
> Subject: Re: [R] algorithmic method quantile regression
>
> Did you read item 1 in the quantreg FAQ()?
>
>
> url: www.econ.uiuc.edu/~roger Roger Koenker
> email rkoen...@uiuc.edu Department of Economics
> vox: 217-333-4558 University of Illinois
> fax: 217-244-6678 Urbana, IL 61801
>
> > On Oct 14, 2015, at 2:56 PM, T.Riedle <tr...@kent.ac.uk> wrote:
> >
> > Greetings R Community,
> > I am trying to run a quantile regression using the quantreg package. My
> regression includes 7 independent variables with approx. 800 daily
> observations each. Thus, I think that the Barrodale and Roberts algorithm
> should do the trick. However, the Frisch-Newton after preprocessing returns
> different results and more significant coefficients than the br method.
> Which algorithmic method should I use now? Do the results mean that the
> Frisch-Newton after preprocessing dominates the br method?
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
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> > and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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