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Re: st: smoothing quantiles


From   László Sándor <sandorl@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: smoothing quantiles
Date   Sat, 2 Apr 2011 09:28:13 -0400

Thank you, Nick, this is very useful.

It turns out that I myself would have been more interested in
something like density estimation (well, more like CDFs), but instead
of 3D plots of density (I know that this is possible), using "level
sets" or contour curves in 2D, which basically plotting the quantiles
of yvar in terms of values (not quantiles) of xvar.

The code you just sent does not seem to do that. But maybe this is
only how we can plot the results of hdquantile? I should play with it
more. In any case, you say that CDFs (quantiles) are harder to smooth
than densities? This makes sense. But for descriptive purposes I would
still try to do that in a dataset rich (large) enough.

Am I wrong on this? If not, do you know of an easyish workaround?

Thank you again!

Laszlo

On Sat, Apr 2, 2011 at 3:13 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> One way of seeing what -hdquantile- does is, naturally, graphical.
>
> sysuse auto
> hdquantile mpg, gen(hdq_mpg)
> qplot *mpg , c(. l) ms(Oh none)
>
> where -qplot- is from SJ.
>
> It seems curious to me that quantile smoothing is far less practised
> than density estimation.
>
> Nick
>
> On Fri, Apr 1, 2011 at 5:50 PM, Nick cox <njcoxstata@gmail.com> wrote:
>
>> I implemented one method in -hdquantile- from SSC.
>
>> On 1 Apr 2011, at 17:43, László Sándor <sandorl@gmail.com> wrote:
>>
>>> Hi all,
>>>
>>> I would like to plot smooth predicted quantiles with Stata 11.2 on
>>> Unix (console).
>>>
>>> My goal is to have something like what lowess is for the mean for the
>>> yvar. Preferably, it would be nice to be able to plot multiple
>>> quantiles in the same plot (e.g., all 3 quartiles). This would be a
>>> useful way to summarize the distribution of a yvar across the values
>>> of an xvar.
>>>
>>> I'd rather not reinvent the wheel on this one --- I hope there is
>>> something like this already out there (or built in), only I could not
>>> find it.
>>>
>
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