quantreg: rq.fit.pfn – R documentation

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rq.fit.pfn

Preprocessing Algorithm for Quantile Regression

Description

A preprocessing algorithm for the Frisch Newton algorithm for quantile regression. This is one possible method for rq().

Usage

rq.fit.pfn(x, y, tau=0.5, Mm.factor=0.8, max.bad.fixups=3, eps=1e-06)

Arguments

`x`	design matrix usually supplied via rq()
`y`	response vector usually supplied via rq()
`tau`	quantile of interest
`Mm.factor`	constant to determine sub sample size m
`max.bad.fixups`	number of allowed mispredicted signs of residuals
`eps`	convergence tolerance

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.

Value

Returns an object of type rq

Author(s)

Roger Koenker <rkoenker@uiuc.edu>

References

Portnoy and Koenker, Statistical Science, (1997) 279-300

Roger Koenker [cre, aut], Stephen Portnoy [ctb] (Contributions to Censored QR code), Pin Tian Ng [ctb] (Contributions to Sparse QR code), Blaise Melly [ctb] (Contributions to preprocessing code), Achim Zeileis [ctb] (Contributions to dynrq code essentially identical to his dynlm code), Philip Grosjean [ctb] (Contributions to nlrq code), Cleve Moler [ctb] (author of several linpack routines), Yousef Saad [ctb] (author of sparskit2), Victor Chernozhukov [ctb] (contributions to extreme value inference code), Ivan Fernandez-Val [ctb] (contributions to extreme value inference code), Brian D Ripley [trl, ctb] (Initial (2001) R port from S (to my everlasting shame -- how could I have been so slow to adopt R!) and for numerous other suggestions and useful advice)

Initial release