# rq.fit.pfn

From quantreg v5.54
by Roger Koenker

##### Preprocessing Algorithm for Quantile Regression

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

- Keywords
- regression

##### Usage

`rq.fit.pfn(x, y, tau=0.5, Mm.factor=0.8, max.bad.fixup=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.fixup
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

##### References

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

##### See Also

*Documentation reproduced from package quantreg, version 5.54, License: GPL (>= 2)*

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