This is extremely helpful.
Thanks Nick
Quoting Nick Cox <[email protected]>:
> Min did specify a need only to work
> with two variables, in which case
> official Stata alone should work fine.
>
> sysuse auto, clear
> gen touse = (mpg < .) & (weight < .)
> egen fractile = rank(weight) if touse
> count if touse
> replace fractile = (fractile - 0.5) / r(N)
> label var fractile "fraction of data"
> lowess mpg fractile
>
> or
>
> locpoly mpg fractile
>
> For "mpg" substitute y variable and for
> "weight" substitute x variable.
>
> My program was more ambitious and allows
> several predictors simultaneously.
>
> Nick
> [email protected]
>
> Nick Cox
>
> > I think I understand this, if for "kernel density
> > function" one can substitute "kernel-like smoothing".
> >
> > The idea seems to go back to Prasanta C. Mahalanobis circa
> > 1960. There is a nice example at
> >
> > http://www.pnas.org/cgi/reprint/0509842103v1.pdf
> >
> > in which the term "kernel density function" is also
> > apparently abused, even though the author is a member of the
> > U.S. National Academy of Sciences.
> >
> > There are lots of slightly different recipes here.
> > The easiest way of getting the smooths in Stata
> > is probably through -lowess-. (-locpoly- (for which
> > use -search- or -findit-) is not much more difficult.)
> >
> > For fractiles, I wire in (rank - 0.5) / #ranks, but
> > see e.g. http://www.stata.com/support/faqs/stat/pcrank.html
> > for excruciatingly pedantic discussion of alternatives.
> >
> > Here is a fudge-kludge for experimentation:
> >
> > --------------------------------------- fractileplot.ado
> > *! NJC 1.0.0 4 Oct 2006
> > program fractileplot
> > version 8
> > syntax varlist(numeric) [if] [in] [, lowess(str asis) *]
> >
> > marksample touse
> > qui count if `touse'
> > if r(N) == 0 error 2000
> >
> > local n = r(N)
> > tokenize `varlist'
> > local y "`1'"
> > local Y : variable label `y'
> > if `"`Y'"' == "" local Y "`y'"
> > mac shift
> > local x "`*'"
> > local menu "solid dash dot dash_dot shortdash"
> > local menu "`menu' shortdash_dot longdash longdash_dot"
> > tokenize `menu'
> >
> > local j = 1
> > qui foreach v of local x {
> > tempvar f
> > egen `f' = rank(`v') if `touse'
> > replace `f' = (`f' - 0.5) / `n'
> > local J = mod(`j',9)
> > local call "`call' lowess `y' `f', lp(``J'')
> > `lowess' ||"
> > local V : variable label `v'
> > if `"`V'"' == "" local V "`v'"
> > local order `order' `j' `"`V'"'
> > local ++j
> > }
> >
> > twoway `call', ///
> > legend(order(`order')) ytitle(`"`Y'"') ///
> > xtitle(fraction of data) `options'
> > end
> > ----------------------------------------- cut here
> >
> >
> > sysuse auto, clear
> >
> > after which
> >
> > fractileplot mpg weight length displacement, yla(, ang(h))
> >
> > works quite (vlw: American sense, not British) nicely.
> >
> > fractileplot mpg weight length displacement, lowess(bw(0.2))
> > yla(, ang(h))
> >
> > shows how you can tune the smoothing, in this case to ill effect.
> >
> > The syntax is regression-like: the first variable is the response;
> > others are predictors, and each relationship is smoothed and
> > shown in relation not to values of predictor, but to cumulative
> > probabilities. Thus different bivariate relationships can be
> > shown on the same scale.
>
> Min
>
> > > I want a fractile plot over two variables. I would like to
> > > see my primary
> > > variable on Y-axis and fractile on the X-axis, and a line in
> > > the graph to fit
> > > a kernel density function line representing the bivariate
> > > relationship between
> > > Y and my other variable.
> > > Clear enough?
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
