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%\VignetteIndexEntry{Quantile Regression}
%\VignetteDepends{quantreg,MASS,SparseM,tripack}
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\title{Quantile Regression in R: A Vignette}
\thanks{Version:  \today}
\author{Roger Koenker}

\begin{document}
\bibliographystyle{plainnat}
\begin{abstract}
Quantile regression is an evolving body of statistical
methods for estimating and drawing inferences about conditional
quantile functions.  An implementation of these methods in the
\R language is available in the package \Rpackage{quantreg}.
This vignette offers a brief tutorial introduction to
the package.  \R and the package \Rpackage{quantreg} are open-source
\texttt{http://cran.r-project.org}.
\end{abstract}

\maketitle

\markboth{\sc Quantile Regression in R}{\sc Roger Koenker}

<<label=R options,echo=FALSE>>=
options(width = 60)
options(SweaveHooks = list(fig = function() par(mar=c(3,3,1,0.5),mgp = c(2,1,0))))
@

\section{Introduction}

Beran's (2003) \nocite{Beran.03} provocative definition
of statistics as the study of algorithms for data analysis'' elevates
computational considerations to the  forefront of the field.  It is
apparent that the evolutionary success of statistical methods is to a significant degree
determined by considerations of computational convenience.
As a result,
design and dissemination of statistical software has become an integral
part of statistical research.
Algorithms are no longer the exclusive purview of
the numerical analyst, or the proto-industrial software firm;
they are an essential part of the artisanal research process.
Fortunately, modern computing has also transformed the software development
process and greatly facilitated collaborative research;
the massive collective international effort represented by the \R project
exceeds the most idealistic Marxist imagination.

Algorithms have been a crucial part of the research challenge of quantile regression
methods from their inception in the 18th century.  \citet*{Stigler.84}  describes an
amusing episode in 1760 in which the itinerant Croatian Jesuit Rudjer Boscovich sought
computational advice in London  regarding his nascent method for  median regression.
Ironically, a fully satisfactory answer to Boscovich's
questions only emerged with dawn of modern computing.  The discovery of the simplex method
and subsequent developments in linear programming have made quantile regression methods
competitive with traditional least squares methods in terms of their
computational effort.  These computational developments have also
played a critical role in encouraging a deeper appreciation of the

Since the early 1980's I have been developing software for quantile regression:
initially for the S language of Chambers and Becker (1984), later for its
commercial manifestation Splus, and since 1999 for the splendid open source
dialect \R, initiated by \citet*{IG} and sustained by the \citet*{Rcore}.
Although there is now some functionality for quantile regression in most of the
major commercial statistical packages, I have a natural predilection
for the \R
environment and the software that I have developed for \R.  In what follows, I
have tried to provide a brief tutorial introduction to this environment for
quantile regression.

\section{What is a vignette? }

This document was written in the Sweave format of \citet*{Leisch}.
Sweave is an implementation designed for \R of the literate programming style
advocated by \citet*{Knuth.LP}.  The format permits a natural interplay between code
written in {\R}, the output of that code, and commentary on the code.
Sweave documents are preprocessed by \R to produce a \LaTeX~
document that may then be processed by conventional methods.  Many
\R packages now have Sweave vignettes describing their basic
functionality.  Examples of vignettes can be found for many of the
\R packages including this one for the \texttt{quantreg} packages in
the source distribution directory \texttt{inst/doc}.

\section{Getting Started}

I will not attempt to provide another  introduction to \R.  There are already
several excellent resources intended to accomplish this task.  The
books of \citet*{Dalgaard.02} and \citet*{VR} are particularly recommended.
The CRAN website link to contributed documentation
also offers excellent  introductions in several languages.

\R is a open source software project and can be freely downloaded from the CRAN
website along with its associated documentation.  For unix-based operating systems
it is usual to download and build \R from source, but binary versions are available
for most computing platforms and can be easily installed.  Once \R is running the
installation of additional packages is quite straightward.
To install the quantile regression package from \R one simply types,

\vspace{2mm}
\noindent
\texttt{> install.packages("quantreg")}

\vspace{2mm}
\noindent
Provided that your machine has a proper internet connection and you
have write permission in the appropriate system directories,
the installation of the package should proceed automatically.
Once the \texttt{quantreg} package is installed, it needs
to be made accessible to the current \R session by the command,
<<results = hide>>=
library(quantreg)
@
of specialized \texttt{packages} for statistical  analysis.
As we proceed a variety of other packages will be called upon.

precisely what you are
looking for, and would simply  like to check the details
of a particular command you can, for example, try:
<<eval=FALSE>>=
help(package="quantreg")
help(rq)
@
The former command gives a brief summary of the available commands in the package, and
the latter requests more detailed information about a specific command.
An convenient shorthand for the latter command is to type simply
\texttt{?rq}.
More generally one can initiate a web-browser help session with the command,

\vspace{2mm}
\noindent
\texttt{> help.start()}

\vspace{2mm}
\noindent
and navigate as desired.  The browser approach is better adapted to exploratory
inquiries, while the command line approach is better suited to confirmatory ones.

A valuable feature of \R help files is that the examples used to illustrate commands
are executable, so they can be pasted into an \R session, or run as a group with
a command like,

\vspace{2mm}
\noindent
\texttt{> example(rq)}

\vspace{2mm}
\noindent
The examples for the basic \texttt{rq} command include an analysis of the
Brownlee stackloss data:  first the median regression, then the first quantile
regression is computed, then the full quantile regression process.  A curious
feature of this often analysed data set, but one that is very
difficult to find without quantile regresion fitting,  is the fact the 8 of the 21
points fall exactly on a hyperplane in 4-space.

The second example in the \texttt{rq} helpfile computes
a weighted univariate median using randomly generated data.  The original
\citet*{engel.1857} data on the relationship between food expenditure and household
income is considered in the third example.  The data is plotted and then six
fitted quantile regression lines are superimposed on the scatterplot.
The final example illustrates the imposition of inequality constraints on the quantile
regression coefficients using a simulated data set.

Let's consider the median regression results for the Engel example in somewhat more
detail.  Executing,
<<>>=
data(engel)
fit1 <- rq(foodexp ~ income, tau = .5, data = engel)
@
assigns the output of the median regression computation to the object \texttt{fit1}.
In the command \texttt{rq()} there are also
many options.  The first argument is a formula'' that specifies the model that
is desired.  In this case we wanted to fit a simple bivariate linear model
so the formula is just \texttt{y $\sim$ x}, if we had two covariates we could say,
e.g. \texttt{y $\sim$ x+z}.  Factor variables, that is variables
taking only a few discrete values, are treated specially by the formula
processing and result in a group of indicator (dummy) variables.

If we would like to see a concise summary of the result we can simply type,
<<>>=
fit1
@
By convention for all the \R linear model fitting routines,
we see only the estimated coefficients and some
information about the model being estimated.  To obtain a more
detailed evaluation of the fitted model, we can use,
<<>>=
summary(fit1)
@
The resulting table gives the estimated intercept and slope in the first column
and confidence intervals for these parameters in the second and third columns.
By default, these confidence intervals are computed by the rank inversion method
described in \citet*{K.05}, Section 3.4.5.
To extract the residuals or the coefficients of the
fitted relationship we can write,
<<>>=
r1 <- resid(fit1)
c1 <- coef(fit1)
@
They can then be easily used in subsequent calculations.

\section{Object Orientation}

A brief digression on the role of object orientation in \R is perhaps
worthwhile at this juncture.  Expressions in \R manipulate objects,
objects may be data in the form of vectors, matrices  or higher order
arrays, but objects may also be functions, or more complex collections
of objects.  Objects have a class and this clsss identifier helps to
recognize their special features and enables functions to act on them
appropriately.
Thus, for example, the function \texttt{summary} when operating on an
object of class \texttt{rq} as produced by the function \texttt{rq}
can act quite differently on the object than it would if the object
were of another class, say \texttt{lm}  indicating that it was the
product of least squares fitting.  Summary of a data structure like
a matrix or data.frame would have yet another intent and outcome.  In
the earlier dialects of \S and \R methods for various classes were
distinguished by appending the class name to the method separated by
a .''.  Thus, the function \texttt{summary.rq} would summarize an \texttt{rq}
object, and \texttt{summary.lm} would summarize an \texttt{lm}
object.  In either case the main objective of the summary is
to produce some inferential  evidence to accompany the point estimates
of parameters.  Likewise, plotting of various classes of \R objects
can be carried out by the expression \texttt{plot(x)} with the
expectation that the plot command will recognize the class of the
object \texttt{x} and proceed accordingly.  More recently,
\citet*{C.98} has introduced an elegant elaboration of the
class, method-dispatch framework for \S and \R.

Assignment of objects
is usually accomplished by the operator \texttt{<-}, and once assigned
these new objects are available for the duration of the \R session, or
until they are explicitly removed from the session.
\R is an open source language so {\it all} of the source files describing
the functionality of the language are ultimately accessible to the
individual user, and users are free to modify and extend the
functionality of the language in any way they see fit.  To accomplish
this one needs to be able to find functions and modify them.  This
takes us somewhat beyond the intended tutorial scope of this vignette,
however suffice it to say that most of the functions of the
\texttt{quantreg} package you will find used below, can be viewed
by simple typing the name of the function perhaps concatenated with
a class name.

\section{Formal Inference}

There are several alternative methods of  conducting inference about quantile
regression coefficients.  As an alternative to the rank-inversion
confidence intervals, one can obtain a more conventional looking
table of coefficients, standard errors, t-statistics, and p-values
using the \texttt{summary} function:
<<>>=
summary(fit1,se = "nid")
@
The standard errors reported in this table are computed as described in Section 3.2.3
for the quantile regression sandwich formula,
and using the Hall-Sheather bandwidth rule.  To obtain
the Powell kernel version of the covariance matrix estimate, one specifies the option
\texttt{se="ker"} in the \texttt{summary} command.  It is also possible to control the
bandwidths employed with the \texttt{bandwidth} option.
Another option available in \texttt{summary.rq} is
to compute bootstrapped standard errors.  This is accomplished by
specifying the option \texttt{se="boot"}.  There are currently three
flavors of the bootstrap available:  the standard $xy$-pair bootstrap,
the \citet*{PWY.94} version, and the Markov chain marginal bootstrap
of \citet*{HeHu} and \citet*{KHM}.  There is also the ability
to specify  $m$ out $n$ versions of the bootstrap in which the sample
size of the bootstrap samples is different from (typically smaller
than) the original sample size.  This subsampling'' approach has a
number of advantages, not the least of which is that it can be
considerably faster than the full $n$ out of $n$ version.
By default \texttt{summary}
also produces components estimating the full covariance matrix of the estimated
parameters and its constituent pieces.  For further details,
see the documentation for \texttt{summary.rq}.  In the case of the
bootstrap methods the full matrix of bootstrap replications is also
available.

There are several options to the basic fitting routine \texttt{rq}.  An important option
that controls the choice of the algorithm used in the fitting is \texttt{method}.
The default is \texttt{method = "br"} which invokes a variant of the
\citet*{BR.74} simplex algorithm
described in \citet*{KO.87}.  For problems with more than a few thousand observations it
is worthwhile considering \texttt{method = "fn"}  which invokes the Frisch-Newton algorithm
described in \citet*{PK.1997}.   Rather than traversing around the
exterior of the constraint set like the simplex method, the interior
point approach embodied in the Frisch-Newton algorithm burrows from
within the constraint set toward the exterior.  Instead of taking
steepest descent steps at each intersection of exterior edges, it
takes Newton steps based on a log-barrier Lagrangian form of the
objective function.  Special forms of Frisch-Newton are available for
problems that include linear inequality constraints and for problems
with sparse design matrices.
For extremely large problems with plausibly exchangeable
observations  \texttt{method = "pfn"} implements a version of
the Frisch-Newton algorithm with
a preprocessing step that can further speed things up considerably.

In problems of moderate size where the default simplex option is quite practical,
the parametric programming approach to finding the rank inversion
confidence intervals can be rather slow.  In such cases it may be
advantageous to try one of the other inference methods based on
estimation of the asymptotic covariance matrix, or to consider
the bootstrap.  Both approaches are described in more detail below.

To provide a somewhat more elaborate visualization of the Engel  example consider
an example that superimposes several estimated conditional quantile
functions on the Engel data  scatterplot.
In the resulting figure the median regression line appears as a solid (blue)
line, and the least squares line as a dashed (red) line.  The other quantile
regression lines appear in grey.  Note that the plotting of the  fitted
lines is easily accomplished by the convention that the command \texttt{abline}
looks for a pair of coefficients, which if found are treated as the slope and
intercept of the plotted line.  There are many options that can be used to
further fine tune the plot.  Looping over the quantiles is also conveniently
handled by {\R}'s \texttt{for} syntax.

\begin{figure}
\begin{center}
<<label=engelplot,fig=TRUE>>=
library(quantreg)
data(engel)
attach(engel)
plot(income,foodexp,cex=.25,type="n",xlab="Household Income", ylab="Food Expenditure")
points(income,foodexp,cex=.5,col="blue")
abline(rq(foodexp~income,tau=.5),col="blue")
taus <- c(.05,.1,.25,.75,.90,.95)
for( i in 1:length(taus)){
abline(rq(foodexp~income,tau=taus[i]),col="gray")
}
@
\end{center}
\caption{Scatterplot and Quantile Regression Fit of the Engel Food
Expenditure Data:  The plot shows a scatterplot of the Engel data
on food expenditure vs household income for a sample of 235 19th
century working class Belgian households.  Superimposed on the
plot are the $\{ .05 , .1, .25, .75, .90, .95\}$ quantile regression
lines in gray, the median fit in solid black, and the least squares
estimate of the conditional mean function as the dashed (red) line.}
\end{figure}

Often it is useful to compute quantile regressions on a discrete set
of $\tau$'s; this can be accomplished by specifying  \texttt{tau}
as a vector in \texttt{rq}:
<<label=engelcoef>>=
xx <- income - mean(income)
fit1 <- summary(rq(foodexp~xx,tau=2:98/100))
fit2 <- summary(rq(foodexp~xx,tau=c(.05, .25, .5, .75, .95)))
@
The results can be summarized as a plot.
<<label=engelcoefplot, results = hide>>=
pdf("engelcoef.pdf",width=6.5,height=3.5)
plot(fit1,mfrow = c(1,2))
dev.off()
@
or by producing a latex-formatted table.
<<label=engeltable>>=
latex(fit2, caption="Engel's Law", transpose=TRUE)
@

The \texttt{pdf} command preceding the plot tells \R that
instructions for the plotting should be written in encapsulated
pdf format and placed in  the file
\texttt{engelcoef.pdf}.  Such files are then conveniently
included in \LaTeX~  documents, for example.  The \texttt{dev.off()}
command closes the current pdf device and concludes the figure.
The horizontal lines in the coefficient plots represent the least
squares fit and its associated confidence interval.

In the one-sample
setting we know that integrating the quantile function over the
entire domain [0,1] yields the mean of the (sample) distribution,
$\mu = \int_{-\infty}^\infty x dF(x) = \int_0^1 F^{-1} (t)dt.$
Similarly, in the coefficient plots we may expect to see that integrating
individual coefficients yields roughly mean
effect as estimated by the associated least squares coefficient.
heterogeneous situations.  For the Engel data, note that the least
squares intercept is significantly above any of the fitted quantile
regression curves in our initial scatter plot.  The least squares
fit is strongly affected by the two outlying observations with
relatively low food expenditure; their attraction tilts the fitted
line so its intercept drawn upward.  In fact, the intercept for the
Engel model is difficult to interpret since it asks us to consider
food expenditure for households with zero income.  Centering the
covariate observations so they have mean zero, as we have done prior to
computing \texttt{fit1} for the coefficient plot restores a reasonable
interpretation of the intercept parameter.   After centering the
least squares estimate of the intercept is a prediction of mean
food expenditure for a household with mean income, and the quantile
regression intercept, $\hat \alpha (\tau)$ is a prediction  of the
$\tau$th quantile of food expenditure for households with mean income.
In the terminology of Tukey, the intercept'' has become a centercept.''

\begin{figure}
\begin{center}
\resizebox{\textwidth}{!}
{\includegraphics{engelcoef.pdf}}
\end{center}
\caption{Engel Coefficient Plots:    the slope and intercept of the
estimated linear quantile regression linear for the Engel food
expenditure data are plotted as a function of $\tau$.  Note that
the household income variable has been centered at its mean value
for this plot, so the intercept is really a centercept and  estimates
the quantile function of food expenditure conditional on mean income.}
\end{figure}

\input{fit2.tex}

The \texttt{latex} command produces a \LaTeX~ formatted table that can be easily
included in  documents.  In many instances the plotted form of
the results will provide a more economical and informative display.
It should again be stressed that since the quantile regression functions
and indeed all of \R is open source, users can always modify the available
functions to achieve special effects required for a particular
application.  When such modifications appear to be of general
applicability, it is desirable to communicate them to the package
author, so they could be shared with the larger community.

If we want to see {\it all} the distinct quantile regression solutions
for a particular model application we can specify a tau outside the range [0,1], e.g.
<<label=rqProcess>>=
z <- rq(foodexp~income,tau=-1)
@
This form of the function carries out the parametric programming steps
required to find the entire sample path of the quantile regression
process.  The returned object is of class \texttt{rq.process}
and has several components:
the primal solution in \texttt{z\$sol}, and the dual solution in \texttt{ z\$dsol}.   In interactive mode typing the name of an \R object
causes the program to print the object in some reasonably intelligible
manner determined by the print method designated for the object's class.
so there is a special \texttt{plot} method for objects of class
\texttt{rq.process}.

Estimating the conditional quantile functions of
\texttt{y} at a specific values of \texttt{x} is also quite easy.
In the following code we plot the estimated empirical quantile
functions  of food expenditure for households that are at the
10th percentile of the sample income distribution,
and the 90th percentile.
In the right panel we plot corresponding density
estimates for the two groups.  The density estimates employ the
adaptive kernel method proposed by \citet*{Silverman} and implemented
in the \texttt{quantreg} function  \texttt{akj}.  This function
is particularly convenient since it permits unequal mass to be
associated with the observations such as those produced by the quantile
regression process.
\begin{figure}
\begin{center}
<<label=eqfs,fig=TRUE,height = 3 >>=
x.poor <- quantile(income,.1) #Poor is defined as at the .1 quantile of the sample distn
x.rich <- quantile(income,.9) #Rich is defined as at the .9 quantile of the sample distn
ps <- z$sol[1,] qs.poor <- c(c(1,x.poor)%*%z$sol[4:5,])
qs.rich <- c(c(1,x.rich)%*%z$sol[4:5,]) #now plot the two quantile functions to compare par(mfrow = c(1,2)) plot(c(ps,ps),c(qs.poor,qs.rich), type="n", xlab = expression(tau), ylab = "quantile") plot(stepfun(ps,c(qs.poor[1],qs.poor)), do.points=FALSE, add=TRUE) plot(stepfun(ps,c(qs.poor[1],qs.rich)), do.points=FALSE, add=TRUE, col.hor = "gray", col.vert = "gray") ## now plot associated conditional density estimates ## weights from ps (process) ps.wts <- (c(0,diff(ps)) + c(diff(ps),0)) / 2 ap <- akj(qs.poor, z=qs.poor, p = ps.wts) ar <- akj(qs.rich, z=qs.rich, p = ps.wts) plot(c(qs.poor,qs.rich),c(ap$dens,ar$dens),type="n", xlab= "Food Expenditure", ylab= "Density") lines(qs.rich, ar$dens, col="gray")
lines(qs.poor, ap$dens, col="black") legend("topright", c("poor","rich"), lty = c(1,1), col=c("black","gray")) @ \end{center} \caption{Estimated conditional quantile and density functions for food expenditure based on the Engel data: Two estimates are presented one for relatively poor households with income of \Sexpr{format(round(x.poor,2))} Belgian francs, and the other for relatively affluent households with \Sexpr{format(round(x.rich,2))} Belgian francs.} \end{figure} Thus far we have only considered Engel functions that are linear in form, and the scatterplot as well as the formal testing has revealed a strong tendency for the dispersion of food expenditure to increase with household income. This is a particularly common form of heteroscedasticity. If one looks more carefully at the fitting, one sees interesting departures from symmetry that would not be likely to be revealed by the typical textbook testing for heteroscedasticity. One common remedy for symptoms like these would be to reformulate the model in log linear terms. It is interesting to compare what happens after the log transformation with what we have already seen. \begin{figure} \begin{center} <<label=engellogplot,fig=TRUE>>= plot(income,foodexp,log="xy",xlab="Household Income", ylab="Food Expenditure") taus <- c(.05,.1,.25,.75,.90,.95) abline(rq(log10(foodexp)~log10(income),tau=.5),col="blue") abline(lm(log10(foodexp)~log10(income)),lty = 3,col="red") for( i in 1:length(taus)){ abline(rq(log10(foodexp)~log10(income),tau=taus[i]),col="gray") } @ \end{center} \caption{Quantile regression estimates for a log-linear version of the Engel food expenditure model.} \end{figure} Note that the flag \texttt{log="xy"} produces a plot with log-log axes, and for convenience of axis labeling these logarithms are base 10, so the subsequent fitting is also specified as base 10 logs for plotting purposes, even though base 10 logarithms are {\it unnatural} and would never be used in reporting numerical results. This looks much more like a classical iid error regression model, although again some departure from symmetry is visible. An interesting exercise would be to conduct some formal testing for departures from the iid assumption of the type already considered above. \section{More on Testing} Now let's consider some other forms of formal testing. A natural first question is: do the estimated quantile regression relationships conform to the location shift hypothesis that assumes that all of the conditional quantile functions have the same slope parameters. To begin, suppose we just estimate the quartile fits for the Engel data and look at the default output: <<>>= fit1 <- rq(foodexp~income,tau=.25) fit2 <- rq(foodexp~income,tau=.50) fit3 <- rq(foodexp~income,tau=.75) @ Recall that \texttt{rq} just produces coefficient estimates and \texttt{summary} is needed to evaluate the precision of the estimates. This is fine for judging whether covariates are significant at particular quantiles but suppose that we wanted to test that the slopes were the same at the three quartiles? This is done with the \texttt{anova} command as follows: <<>>= anova(fit1, fit2, fit3) @ This is an example of a general class of tests proposed in \citet*{KB.82b}. It may be instructive to look at the code for the command \texttt{anova.rq} to see how this test is carried out. The Wald approach is used and the asymptotic covariance matrix is estimated using the approach of \citet*{HK.92}. It also illustrates a general syntax for testing in \R adapted to the present situation. If you have estimated two models with different covariate specifications, but the same$\tau$then \texttt{anova(f0,f1)} tests whether the more restricted model is preferred. Note that this requires that the models with fits say \texttt{f0} and \texttt{f1} are nested. The procedure \texttt{anova.rq} attempts to check whether the fitted models are nested, but this is not foolproof. These tests can be carried out either as Wald tests based on the estimated joint asymptotic covariance matrix of the coefficients, or using the rank test approach described in \citet*{GJKP.94}. A variety of other options are described in the documentation of the function \texttt{anova.rq}. \section{Inference on the Quantile Regression Process} In least squares estimation of linear models it is implicitly assumed that we are able to model the effects of the covariates as a pure location shift, or somewhat more generally as a location and scale shift of the response distribution. In the simplest case of a single binary treatment effect this amounts to assuming that the treatment and control distributions differ by a location shift, or a location-scale shift. Tests of these hypotheses in the two sample model can be conducted using the conventional two-sample Kolmogorov Smirnov statistic, but the appearance of unknown nuisance parameters greatly complicates the limiting distribution theory. Similar problems persist in the extension of these tests to the general quantile regression setting. Using an approach introduced by \citet*{khma.81}, \citet*{KX.02} consider general forms of such tests. The tests can be viewed as a generalization of the simple tests of equality of slopes across quantiles described in the previous section. In this section we briefly describe how to implement these tests in \R. The application considered is the quantile autoregression (QAR) model for weekly U.S. gasoline prices considered in \citet*{KX.06}. The data consists of 699 weekly observations running from August 1990 to February, 2004. The model considered is a QAR(4) model utilizing four lags. We are interested in testing whether the classical location-shift AR(4) is a plausible alternative to the QAR(4) specification, that is whether the four QAR lag coefficients are constant with respect to$tau$. To carry out the test we can either compute the test using the full quantile regression process or on a moderately fine grid of taus: <<label=gastest>>= source("gasprice.R") x <- gasprice n <- length(x) p <- 5 # lag length X <- cbind(x[(p-1):(n-1)], x[(p-2):(n-2)], x[(p-3):(n-3)], x[(p-4):(n-4)]) y <- x[p:n] T1 <- KhmaladzeTest(y ~ X,taus = -1, nullH="location") T2 <- KhmaladzeTest(y ~ X,taus = 10:290/300, nullH="location",se="ker") @ When \texttt{taus} contains elements outside of the the interval$(0,1)$then the process is standardized by a simple form of the covariance matrix that assumes iid error. In the second version of the test Powell's kernel form of the sandwich formula estimate is used, see \texttt{summary.rq}. The function \texttt{KhmaladzeTest} computes both a joint test that {\it all} the covariate effects satisfy the null hypothesis, and a coefficient by coefficient version of the test. In this example the former component, \texttt{T1\$Tn}
is \Sexpr{format(round(T1$Tn,2))}. This test has a 1 percent critical value of 5.56, so the test weakly rejects the null. For the Powell form the standardization the corresponding test statistic is more decisive, taking the value \Sexpr{format(round(T2$Tn,2))}.

Tests of the location-scale shift form of the null hypothesis
can be easily  done by making the appropriate change in the
\texttt{nullH} argument of the function.

\section{Nonlinear Quantile Regression}

Quantile regression models with response functions that are nonlinear
in parameters can be estimated with the function \texttt{nlrq}.  For
such models the specification of the model formula is somewhat more
esoteric than for ordinary linear models, but follows the conventions
of the \R command \texttt{nls} for nonlinear least squares estimation.

To illustrate the use of \texttt{nlrq} consider the problem of
estimating the quantile functions of the Frank copula model introduced
in \citet*{K.05}, Section 8.4.  We begin by setting some parameters
and generating data from the Frank model:

<<label = Frank>>=
n <- 200
df <- 8
delta <- 8
set.seed(4003)
x <- sort(rt(n,df))
u <- runif(n)
v <- -log(1-(1-exp(-delta))/(1+exp(-delta*pt(x,df))*((1/u)-1)))/delta
y <- qt(v,df)
@
We plot the observations, superimpose three conditional quantile
functions, and then estimate the same three quantile functions and
plot their estimated curves as the dashed curves.
\begin{figure}
\begin{center}
<<label = Frankplot, results = hide, height = 4, eval = TRUE, fig=TRUE>>=
plot(x,y,col="blue",cex = .25)
us <- c(.25,.5,.75)
for(i in 1:length(us)){
u <- us[i]
v <- -log(1-(1-exp(-delta))/
(1+exp(-delta*pt(x,df))*((1/u)-1)))/delta
lines(x,qt(v,df))
}
Dat <- NULL
Dat$x <- x Dat$y <- y
deltas <- matrix(0,3,length(us))
FrankModel <- function(x,delta,mu,sigma,df,tau){
z <- qt(-log(1-(1-exp(-delta))/
(1+exp(-delta*pt(x,df))*((1/tau)-1)))/delta,df)
mu + sigma*z
}
for(i in 1:length(us)){
tau = us[i]
fit <- nlrq(y~FrankModel(x,delta,mu,sigma,df=8,tau=tau),
data=Dat,tau= tau, start=list(delta=5,
mu = 0, sigma = 1),trace=TRUE)
lines(x, predict(fit, newdata=x), lty=2, col="green")
deltas[i,] <- coef(fit)
}
@
\end{center}
\caption{Nonlinear Conditional Quantile Estimation of the Frank Copula
Model:  The solid curves are the true conditional quantile functions
and the corresponding estimated curves are indicated by the dashed
curves.}
\end{figure}

\section{Nonparametric Quantile Regression}

Nonparametric quantile regression is initially
most easily considered within a locally polynomial framework.
Locally linear fitting is carried out by the following function:
<<label=lprq>>=
"lprq" <-
function(x, y, h, m=50 , tau=.5)
{
xx <- seq(min(x),max(x),length=m)
fv <- xx
dv <- xx
for(i in 1:length(xx)) {
z <- x - xx[i]
wx <- dnorm(z/h)
r <- rq(y~z, weights=wx, tau=tau, ci=FALSE)
fv[i] <- r$coef[1.] dv[i] <- r$coef[2.]
}
list(xx = xx, fv = fv, dv = dv)
}
@
If you study the function a bit you will see that it is simply a matter
of computing a quantile regression fit at each of $m$ equally spaced $x$-values
distributed over the support of the observed $x$ points.  The function value estimates
are returned as \texttt{fv} and the first derivative estimates at the $m$ points
are returned as \texttt{dv}.  As usual you can specify $\tau$, but now you also
need to specify a bandwidth \texttt{h}.

Let's begin by exploring the effect of the \texttt{h} argument for
fitting the motorcycle data.
\begin{figure}
\begin{center}
<<label=mcycle1,fig=TRUE>>=
library(MASS)
data(mcycle)
attach(mcycle)
plot(times,accel,xlab = "milliseconds", ylab = "acceleration")
hs <- c(1,2,3,4)
for(i in hs){
h = hs[i]
fit <- lprq(times,accel,h=h,tau=.5)
lines(fit$xx,fit$fv,lty=i)
}
legend(45,-70,c("h=1","h=2","h=3","h=4"),lty=1:length(hs))
@
\end{center}
\caption{Estimation of a locally linear median regression
model for the motorcycle data:  four distinct bandwidths}
\end{figure}

Fitting derivatives, of course,  requires somewhat larger bandwidth
and larger sample size to achieve the same precision as function fitting.
It is a straightforward exercise to adapt the function \texttt{lprq}
so that it does locally quadratic rather than locally linear fitting.

Another simple, yet quite general, strategy for nonparametric quantile regression
uses regression splines.  The function \texttt{bs()} in the
package \texttt{splines} gives a very flexible way to construct B-spline basis
expansions.  For example you can fit a new motorcycle model like this:

\begin{figure}
\begin{center}
<<label = regspline, fig=TRUE>>=
library(splines)
plot(times,accel,xlab = "milliseconds", ylab = "acceleration",type="n")
points(times,accel,cex = .75)
X <- model.matrix(accel ~ bs(times, df=15))
for(tau in 1:3/4){
fit <- rq(accel ~ bs(times, df=15), tau=tau, data=mcycle)
accel.fit <- X %*% fit$coef lines(times,accel.fit) } @ \end{center} \caption{B-spline estimates of the three conditional quartile functions of the motorcycle data} \end{figure} The fitted conditional quantile functions do reasonably well except in the region beyond 50 milliseconds where the data is so sparse that all the quantile curve want to coalesce. This procedure fits a piecewise cubic polynomial with 15 knots (breakpoints in the third derivative) arranged at quantiles of the$x$'s. (You can also explicitly specify the knot sequence and the order of the spline using the optional arguments to \texttt{bs}.) In this instance we have estimated three quartile curves for the B-spline model; a salient feature of this example is the quite dramatic variable in variability over the time scale. There is essentially no variability for the first few milliseconds, but quite substantial variability after the crash. One advantage of the B-spline approach is that it is very easy to add a partially linear model component. If there were another covariate, say z, it could be added as a parametric component using the usual \texttt{formula} syntax, \vspace{2mm} \noindent \texttt{> fit <- rq(y~bs(x,df=5)+z,tau=.33)} \vspace{2mm} \noindent Another appealing approach to nonparametric smoothing involves penalty methods. \citet*{KNP.94} describe total variation penalty methods for fitting univariate functions; \citet*{KM.04}, extend to approach to bivariate function estimation. Again, partially linear models are easily adapted, and there are also easy ways to impose monotonicity and convexity on the fitted functions. In some applications it is desirable to consider models that involve nonparametric fitting with several covariates. A tractable approach that has been explored by many authors is to build additive models. This approach is available in \R for least squares fitting of smoothing spline components in the \texttt{mgcv} and \texttt{gss} packages. A prototype package for quantile regression models of this type is available using the \texttt{rqss} function of the \texttt{quantreg} package. The function \texttt{rqss} offers a formula interface to nonparametric quantile regression fitting with total variation roughness penalties. Consider the running speed of mammals example from Chapter 7. The objective is to estimate a model for the upper envelope of the scatterplot, a model that would reflect best evolutionary practice in mammalian ambulatory efficiency. In contrast to the least squares analysis of \citet*{Chappell.89} where they are omitted, no special allowance is made for the specials'' indicated by the plotting character \texttt{s} or hoppers'' indicated by \texttt{h}. The data is plotted on a (natural) log scale, the model is fit using$\lambda = 1$as the penalty parameter, and the fitted curve is plotted using a special plotting function that understands the structure of the objects returned from \texttt{rqss}. The estimated turning point of the piecewise linear fitted function occurs at a weight of about 40 Kg. <<label = nprq, results = hide>>= data(Mammals) attach(Mammals) @ \begin{figure} \begin{center} <<label = mammals, fig=TRUE>>= x <- log(weight) y <- log(speed) plot(x,y, xlab="Weight in log(Kg)", ylab="Speed in log(Km/hour)",type="n") points(x[hoppers],y[hoppers],pch = "h", col="red") points(x[specials],y[specials],pch = "s", col="blue") others <- (!hoppers & !specials) points(x[others],y[others], col="black",cex = .75) fit <- rqss(y ~ qss(x, lambda = 1),tau = .9) plot(fit) @ \end{center} \caption{Maximal Running Speed of Terrestial Mammals: The figure illustrates the relationship between adult body mass and maximal running speed for 107 species of terrestial mammals. The piecewise linear curve is an estimate of the .90 conditional quantile function estimated subject to a constraint on the total variation of the function gradient.} \end{figure} Bivariate nonparametric fitting using the triogram methods described in Chapter 7 can be handled in a similar manner. If we consider the Cobar mining data from \citet*{gree:silv.94} \begin{figure} \begin{center} <<label = cobar, fig = TRUE >>= data(CobarOre) fit <- rqss(z ~ qss(cbind(x,y), lambda = .01, ndum=100),data = CobarOre) plot(fit, axes = FALSE, xlab = "", ylab = "") rm(list=ls()) @ \end{center} \caption{Contour plot of a triogram fit of the Cobar mining data.} \end{figure} The \texttt{qss} term in this case case requires both$x$and$y$components. In addition one needs to specify a smoothing parameter$\lambda\$, and  the parameter \texttt{ndum} may be used to specify
the number of artificial vertices introduced into the fitting
procedure in addition to the actual observations.  These artificial
vertices contribute to the penalty term, but not to the fidelity.

By default the fit is rendered as a contour plot, but there are also
two forms of perspective plots.  A conventional \R \texttt{persp} plot
can be obtained by passing option \texttt{render = "persp"} to the
plot command.  More adventurous \R~gonauts are encouraged to explore
the option \texttt{render = "rgl"}, which produces a perspective
plot in the dynamic graphics interface to the open GL library provided
by the package \texttt{rgl}.  Of course this package must be
installed.  A demonstration of how to create animations of
\texttt{rqss} triogram output using \texttt{rgl} is available by
running the command \texttt{demo(cobar)}.

Another advantage of the penalty approach embodied in \texttt{rqss}
is that it is straightforward to impose additional qualitative
constraints on the fitted functions.  Univariate functions can be
constrained to be monotone and/or convex or concave.  Bivariate
functions can be constrained to be either convex or concave.
This functionality is implemented by simply imposing nonnegativity
constraints on certain linear combinations of model parameters
and illustrates one of many possible applications for such constraints.
An interesting open research problem involves formal inference on such
constraints.

\section{Conclusion}

A few of the capabilities of the \R \texttt{quantreg}
package have been described.  Inevitably, new applications will
demand new features and reveal unforseen bugs.  In either case I hope
that users will send me their comments and suggestions.
This document will be periodically updated and the current version
will be made available in the \R distribution of \texttt{quantreg}.
See the \R command \texttt{vignette()} for details on how to find
and view vignettes from within \R.

\bibliography{abbrev,book}
\end{document}


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