{smcl}
{* 11oct2006}{...}
{hline}
help for {hi:fractileplot}
{hline}
{title:Smoothing with respect to distribution function predictors}
{p 8 12 2}
{cmd:fractileplot}
{it:yvar xvarlist}
{ifin}
[{cmd:,}
{cmd:a(}{it:#}{cmd:)}
{cmd:combine(}{it:combine_options}{cmd:)}
{cmdab:cyc:les(}{it:#}{cmd:)}
{cmdab:dr:aw(}{it:numlist}{cmd:)}
{cmdab:gen:erate(}{it:stub}{cmd:)}
{cmd:nograph}
{cmd:locpoly}[{cmd:(}{it:locpoly_options}{cmd:)}]
{cmd:log}
{cmd:lowess(}{it:lowess_options}{cmd:)}
{cmdab:om:it(}{it:numlist}{cmd:)}
{cmdab:p:redict(}{it:newvar}{cmd:)}
{cmdab:nopt:s}
{cmd:replace}
{cmdab:sc:atter(}{it:scatter_options}{cmd:)}
{it:line_options}]
{title:Description}
{p 4 4 2}
{cmd:fractileplot} computes smooths of {it:yvar} on all predictors in
{it:xvarlist} simultaneously; that is, each smooth is adjusted for the others.
Each predictor x_j is treated on the scale of its distribution function,
F(x_j), estimated for a sample of n as (rank of x_j - a) / (n - 2a + 1). a
defaults to 0.5. By default smoothing is with {helpb lowess}. Optionally
smoothing may be with {helpb locpoly}, so long as that has been installed.
Fitted values may be saved in new variables with names
beginning with {it:stub}, as specified in the {cmd:generate()} option.
{p 4 4 2}By default, for each {it:xvar} in {it:xvarlist} adjusted
values of {it:yvar} and the smooth for F({it:xvar}) are plotted against
F({it:xvar}). See {hi:Remarks} for more details.
{title:Options}
{p 4 8 2}{cmd:a(}{it:#}{cmd:)} specifies a in the formula for F. The default
is a = 0.5, giving (i - 0.5) / n. Other choices include a = 0, giving i / (n +
1), and a = 1/3, giving (i - 1/3) / (n + 1/3). More discussion on this
is available at {browse "http://www.stata.com/support/faqs/stat/pcrank.html"}.
{p 4 8 2} {cmd:combine(}{it:combine_options}{cmd:)} specifies any of the
options allowed by the {helpb graph combine} command. Useful examples are
{cmd:combine(ycommon)} and {cmd:combine(saving(}{it:graphname}{cmd:))}.
{p 4 8 2} {cmd:cycles(}{it:#}{cmd:)} sets the number of cycles. The default is
{cmd:cycles(3)}.
{p 4 8 2} {cmd:draw(}{it:numlist}{cmd:)} specifies that smooths for a subset of
the variables in {it:xvarlist} be plotted. The elements of {it:numlist} are
indexes determined by the order of the variables in {it:xvarlist}. For
example, {cmd:fractileplot y x1 x2 x3, draw(2 3)} would plot smooths only for
F({cmd:x2}) and F({cmd:x3}). By default results for all variables in
{it:varlist} are plotted. {cmd:draw()} takes precedence over {cmd:omit()} in
the sense that results for variables included (by index) in {it:numlist} are
plotted, even if they are excluded by {cmd:omit()}. See also {cmd:omit()}.
{p 4 8 2} {cmd:generate(}{it:stub}{cmd:)} specifies that fitted values for each
member of {it:xvarlist} be saved in new variables with names beginning with
{it:stub}.
{p 4 8 2}{cmd:nograph} suppresses the graph.
{p 4 8 2}{cmd:locpoly} specifies that {helpb locpoly} (Gutierrez, Linhart and
Pitblado 2003, 2005a, 2005b) should be used
for smoothing. This should be installed beforehand. {cmd:locpoly} may
be specified with options. Key are
{p 8 8 2}
{cmdab:d:egree(}{it:#}{cmd:)} specifies the degree of the polynomial to be
used in the smoothing. Zero is the default, meaning local mean smoothing.
{p 8 8 2}
{cmdab:w:idth(}{it:#}{cmd:)} specifies the halfwidth of the kernel, the
width of the smoothing window around each point. If {cmd:width()} is not
specified, then the "default" width is used; see {hi:[R] kdensity}. This
default is entirely inappropriate for local polynomial smoothing.
Choose your own.
{p 8 8 2}
{cmdab:ep:anechnikov}, {cmdab:bi:weight}, {cmdab:cos:ine}, {cmdab:gau:ssian},
{cmdab:par:zen}, {cmdab:rec:tangle}, and {cmdab:tri:angle} specify the kernel.
By default, {cmd:epanechnikov}, meaning the Epanechnikov
kernel, is used.
{p 4 8 2}{cmd:log} displays the squared correlation coefficient between the
overall fitted values and {it:yvar} at each cycle for monitoring convergence.
This option is provided mainly for pedagogic interest.
{p 4 8 2}{cmd:lowess(}{it:lowess_options}{cmd:)} control the operation
of {helpb lowess} in generating smooths. Key are
{p 8 8 2}{cmdab:m:ean} specifies running-mean smoothing; the default is
running-line least-squares smoothing.
{p 8 8 2}{cmdab:now:eight} prevents the use of Cleveland's tricube weighting
function; the default is to use the weighting function.
{p 8 8 2}{cmdab:bw:idth(}{it:#}{cmd:)} specifies the bandwidth.
Centred subsets of {cmd:bwidth()} * n
observations are used for calculating smoothed values for each point
in the data except for end points, where smaller, uncentred subsets
are used. The greater the {cmd:bwidth()}, the greater the smoothing. The
default is 0.8.
{p 4 8 2}{cmd:omit(}{it:numlist}{cmd:)} specifies that smooths for a subset of
the variables in {it:xvarlist} not be plotted. The elements of {it:numlist} are
indexes determined by the order of the variables in {it:varlist}. For example,
{cmd:fractileplot y x1 x2 x3, omit(3)} would plot smooths only for F({cmd:x1}) and
F({cmd:x2}). By default results for no variables in {it:varlist} are omitted.
{cmd:draw()} takes precedence over {cmd:omit()}. See also {cmd:draw()}.
{p 4 8 2}{cmd:predict(}{it:newvar}{cmd:)} specifies that the predicted values
be saved in new variable {it:newvar}.
{p 4 8 2}{cmd:nopts} suppresses the points in the plots. Only the lines
representing the smooths are drawn.
{p 4 8 2}{cmd:replace} allows variables specified by any of the
{cmd:generate()} and {cmd:predict()} options to be replaced if they already
exist.
{p 4 8 2}{cmd:scatter(}{it:scatter_options}{cmd:)} specifies any of the options
allowed by the {helpb scatter} command. These should be specified to control
the rendering of the data points. The default includes {cmd:msymbol(oh)}, or
{cmd:msymbol(p)} with over 299 observations.
{p 4 8 2}{it:line_options} are any of the options allowed with {helpb line}.
These should be specified to control the rendering of the smoothed lines or the
overall graph.
{title:Remarks}
{p 4 4 2}Smoothing with respect to distribution functions has various
elementary attractions. An F scale provides a common scale for variables with
different level and spread and even different units. Subject to the occurrence
of ties, values are equally spaced on the F scale and so in good condition for
smoothing. This can be especially useful when predictors are highly skewed. F
is invariant under strictly increasing transformations, so that for example
F(log x) is identical to F(x) so long as x > 0. This can be useful when it is
not clear whether predictors should be transformed.
{p 4 4 2}Sen (2005) gives a useful recent account of kernel smoothing of
responses with respect to distribution functions of predictors. The canonical
reference is Mahalanobis (1960), which introduced the term "fractile graphical
analysis". Mahalanobis plotted means of one variable for bins defined by
selected fractiles of the other variable. Binning and averaging now appear
arbitrary and awkward, and some kind of kernel-based smoothing is more
appealing. The approach in {cmd:fractileplot} is based on methodology for
generalised additive models (Hastie and Tibshirani 1990).
{p 4 4 2}Terminology is problematic here. Terms such as "fractile graph" (Sen
2005) and "fractile plot" (Nordhaus 2006) persist in recent literature for
modern versions of Mahalanobis' plots, even though neither ordinate nor
abscissa in the resulting graphs is a fractile. The term "fractile" was
introduced to the English literature by Hald (1952) with the sense of
"quantile", but it has never supplanted "quantile" and is often misunderstood
to mean fraction or cumulative probability or plotting position (e.g. Nordhaus
2006). Hald used "fractile diagram" for normal probability plots {c -} in Stata
terms, his examples are equivalent to {cmd:qnorm} with axes reversed {c -}
and this usage also continues in recent literature (e.g. Bl{c ae}sild and
Granfeldt 2003). In the absence of an obvious alternative, customary
terminology is used here under protest.
{p 4 4 2}An R-square (squared correlation coefficient) is provided as a
goodness of fit indicator. However, this R-square can typically be increased simply
by just smoothing less, which is often likely to be unhelpful. As the resulting
predictions come closer to interpolating the data, R-square will approach 1,
but scientific usefulness and the possibility of insight will usually diminish.
{p 4 4 2}Note that you do not need the machinery here to do this for just
one predictor. The following is a basic recipe:
{p 8 8 2}{cmd:. gen touse = (}{it:y}{cmd: < .) & (}{it:x}{cmd: < .)}{p_end}
{p 8 8 2}{cmd:. egen abscissa = rank(}{it:x}{cmd:) if touse}{p_end}
{p 8 8 2}{cmd:. count if touse}{p_end}
{p 8 8 2}{cmd:. replace abscissa = (abscissa - 0.5) / r(N)}{p_end}
{p 8 8 2}{cmd:. lowess }{it:y}{cmd: abscissa, xti("fraction of data")}
{p 4 4 2}
Suppose that there are p >= 1 predictors. {cmd:fractileplot} estimates the
smooths f_1,...,f_p by using a backfitting algorithm and a lowess or local
polynomial smoother S[y|F(x_j)] for each predictor, as follows:
{p 4 8 2}
1. Initialize: alpha = mean({it:yvar}), f_1,...,f_p
estimated by multiple linear regression.
{p 4 8 2}
2. Cycle: j = 1,...,p, 1,...,p, ...
{p 8 8 2}
f_j = S[y - alpha - sum_{i != j} f_i|F(x_j)]
{p 4 8 2}
3. Continue for {cmd:cycles()} rounds.
{p 4 4 2}
No convergence criterion is applied. In practice, three cycles are
usually more than sufficient to get results adequate for exploratory work.
{p 4 4 2}
The smooths are adjusted so that the mean of each equals the mean of {it:yvar}.
{p 4 4 2}
The points in the plots provided by {cmd:fractileplot}
depict y - sum_{i != j} f_i|F(x_j), i.e., the partial residuals plus alpha.
{title:Examples}
{p 4 8 2}
{cmd:. fractileplot mpg weight displ length}
{p 4 8 2}
{cmd:. fractileplot mpg weight displ length, lowess(mean)}
{p 4 8 2}
{cmd:. fractileplot mpg weight displ length, locpoly(degree(1) biweight width(0.4))}
{p 4 8 2}
{cmd:. fractileplot mpg weight displ length, generate(S) nograph}
{p 4 8 2}
{cmd:. fractileplot mpg weight displ length, omit(2) combine(saving(graph1))}
{p 4 4 2}For comparison, bivariate smooths may be compared like this:
{p 4 8 2}{cmd:. foreach v in weight displ length {c -(}}{p_end}
{p 4 8 2}{cmd:. {space 8}fractileplot mpg `v', combine(saving(fl_`v'))}{p_end}
{p 4 8 2}{cmd:. {c )-}}{p_end}
{p 4 8 2}{cmd:. graph combine "fl_weight" "fl_displ" "fl_length"}
{title:Author}
{p 4 4 2}Nicholas J. Cox{break}
Durham University{break}
n.j.cox@durham.ac.uk
{title:Acknowledgements}
{p 4 4 2}The main features of the implementation here depend on the work of
Patrick Royston, as reported by Royston and Cox (2005).
{title:References}
{p 4 8 2}Bl{c ae}sild, P. and Granfeldt, J. 2003.
{it:Statistics with applications in geology and biology.}
Boca Raton, FL: Chapman & Hall/CRC.
{p 4 8 2}
Gutierrez, R.G., Linhart, J.M. and Pitblado, J.S. 2003.
From the help desk: Local polynomial regression and Stata plugins.
{it:Stata Journal} 3(4): 412{c -}419. Software Updates: 2005a. 5(1): 139
and 2005b. 5(2): 285.
{p 4 8 2}
Hald, A. 1952.
{it:Statistical theory with engineering applications.}
New York: John Wiley.
{p 4 8 2}
Hastie, T. and Tibshirani, R. 1990.
{it:Generalized additive models.}
London: Chapman and Hall.
{p 4 8 2}
Mahalanobis, P.C. 1960. A method of fractile graphical analysis.
{it:Econometrica} 28: 325{c -}351. Reprinted 1961.
{it:Sankhya} Series A 23: 41{c -}64.
{p 4 8 2}
Nordhaus, W.D. 2006. Geography and macroeconomics: new data and new
findings. {it:Proceedings, National Academy of Sciences} 103(10): 3510{c -}3517.
{p 4 8 2}
Royston, P. and Cox, N.J. 2005.
A multivariable scatterplot smoother.
{it:Stata Journal} 5(3): 405{c -}412.
{p 4 8 2}
Sen, B. 2005. Estimation and comparison of fractile graphs using kernel
smoothing techniques. {it:Sankhya} 67: 305{c -}334.
{browse "http://sankhya.isical.ac.in/search/67_2/2005014.pdf"}
{p 4 4 2}Note: {it:Sankhya} should carry a bar accent on
its final "a".
{title:Also see}
{p 4 13 2}Online: {helpb lowess}, {helpb locpoly} (if installed){p_end}