https://github.com/cran/quantreg
Tip revision: d37f852036a87c4097be1667f8d54418fc665972 authored by Unknown author on 08 August 1977, 00:00:00 UTC
version 2.0-2
version 2.0-2
Tip revision: d37f852
Readme
quantreg -- Quantile Regression (Version 2.0-2)
This is a submission to compute regression quantiles and some related
rank statistics. It is a revision of a prior submission to incorporate
some recent develoopments in rank based inference which employ
regression quantile ideas. The submission consists of:
rq.r--A ratfor subroutine
and several S functions:
rq--which directly calls rq.o
trq--which computes analogues of the trimmed mean for regression
trq.print--which prints ls.print style output for trq
qrq--which is called by trq
ranks--which computes a vector of rank statistics from rq() output
rrs.test--which computes tests of linear hypotheses
rq.omega--estimates a scale parameter for the covariance matrix
dn--computes bandwidth for sparsity estimate
These eight functions all reside in a file called rq.s which could be read
by S with the source() command. Help files for the S functions are also
provided in the directory doc and could be moved to the appropriate .Data/.Help
directory.
The files were packaged as a unix shar archive. A Makefile generated by
the S CHAPTER command has been included to automate this process. It has
been tested on Sun hardware, but may need editing. With luck you might
try
make install
make load
make clean
If this works smoothly then invoking Splus in this directory will
automatically load the new rq function and you can proceed from there.
This software has been submitted to Statlib and may be freely
used and redistributed for non-commercial purposes. No guarantees
are offered or implied. Comments, bug reports, etc are welcome
and should be sent to roger@ysidro.econ.uiuc.edu or to
Roger Koenker
Department of Economics
University of Illinois
Champaign, Illinois, 61820
Acknowledgements: Thanks to all those who have contributed comments
on this software following its initial submission in 1991, particularly
to Gib Bassett, Steve Portnoy, Jana Jureckova, and Cornelius Gutenbrunner.
Special thanks to Pin Ng (U. of Houston) for help in preparing the new
version.
Quantreg -- Quantile Regression (Version 2.0)
This is a submission to compute regression quantiles and some related
rank statistics. It is a revision of a prior submission to incorporate
some recent develoopments in rank based inference which employ
regression quantile ideas. The submission consists of:
rq.r--A ratfor subroutine
and several S functions:
rq--which directly calls rq.o
trq--which computes analogues of the trimmed mean for regression
trq.print--which prints ls.print style output for trq
qrq--which is called by trq
ranks--which computes a vector of rank statistics from rq() output
rrs.test--which computes tests of linear hypotheses
rq.omega--estimates a scale parameter for the covariance matrix
dn--computes bandwidth for sparsity estimate
These eight functions all reside in a file called rq.s which could be read
by S with the source() command. Help files for the S functions are also
provided in the directory doc and could be moved to the appropriate .Data/.Help
directory.
The files were packaged as a unix shar archive. A Makefile generated by
the S CHAPTER command has been included to automate this process. It has
been tested on Sun hardware, but may need editing. With luck you might
try
make install
make load
make clean
If this works smoothly then invoking Splus in this directory will
automatically load the new rq function and you can proceed from there.
This software has been submitted to Statlib and may be freely
used and redistributed for non-commercial purposes. No guarantees
are offered or implied. Comments, bug reports, etc are welcome
and should be sent to roger@ysidro.econ.uiuc.edu or to
Roger Koenker
Department of Economics
University of Illinois
Champaign, Illinois, 61820
Acknowledgements: Thanks to all those who have contributed comments
on this software following its initial submission in 1991, particularly
to Gib Bassett, Steve Portnoy, Jana Jureckova, and Cornelius Gutenbrunner.
Special thanks to Pin Ng (U. of Houston) for help in preparing the new
version.
Quantreg -- Quantile Regression (Version 2.0)
This is a submission to compute regression quantiles and some related
rank statistics. It is a revision of a prior submission to incorporate
some recent develoopments in rank based inference which employ
regression quantile ideas. The submission consists of:
rq.r--A ratfor subroutine
and several S functions:
rq--which directly calls rq.o
trq--which computes analogues of the trimmed mean for regression
trq.print--which prints ls.print style output for trq
qrq--which is called by trq
ranks--which computes a vector of rank statistics from rq() output
rrs.test--which computes tests of linear hypotheses
rq.omega--estimates a scale parameter for the covariance matrix
dn--computes bandwidth for sparsity estimate
These eight functions all reside in a file called rq.s which could be read
by S with the source() command. Help files for the S functions are also
provided in the directory doc and could be moved to the appropriate .Data/.Help
directory.
The files were packaged as a unix shar archive. A Makefile generated by
the S CHAPTER command has been included to automate this process. It has
been tested on Sun hardware, but may need editing. With luck you might
try
make install
make load
make clean
If this works smoothly then invoking Splus in this directory will
automatically load the new rq function and you can proceed from there.
This software has been submitted to Statlib and may be freely
used and redistributed for non-commercial purposes. No guarantees
are offered or implied. Comments, bug reports, etc are welcome
and should be sent to roger@ysidro.econ.uiuc.edu or to
Roger Koenker
Department of Economics
University of Illinois
Champaign, Illinois, 61820
Acknowledgements: Thanks to all those who have contributed comments
on this software following its initial submission in 1991, particularly
to Gib Bassett, Steve Portnoy, Jana Jureckova, and Cornelius Gutenbrunner.
Special thanks to Pin Ng (U. of Houston) for help in preparing the new
version.