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Re: st: Get fitted values after locpoly (kernel regression)
From
Austin Nichols <[email protected]>
To
[email protected]
Subject
Re: st: Get fitted values after locpoly (kernel regression)
Date
Sun, 18 Sep 2011 07:34:35 -0400
Tania Treibich <[email protected]>:
See http://www.stata.com/help.cgi?lpoly
webuse motorcycle
lpoly accel time, degree(3) kernel(epan2) generate(hat) at(time) nograph
On Sun, Sep 18, 2011 at 7:03 AM, Tania Treibich
<[email protected]> wrote:
> Dear Partho,
>
> thank you for your interest!
>
> What I want to do is to fit the model Yi=m(Xi)+ei without making any
> assumption on the functional form of m().
> The output generated by the kernel regression is a graph as well as a
> series of coordinates (new variables created in the database which I
> called xker and yker). These coordinates are the points used for the
> smoothing. Indeed, xker defines the smoothing grid : at each x0 in the
> grid, a weighted regression is performed and associated with a value
> y0. Then all these points are joined.
>
> What I want to retrieve instead is for each value of X, the associated
> value Yhat through the transformation m().
>
> I am now using locpoly for this regression but I saw I can also use
> kernreg2, but I didn't find any help files.
>
> Regards,
>
> Tania
>
>
> 2011/9/18 Partho Sarkar <[email protected]>:
>> Hello Tania
>>
>> I was puzzled by this post at first, as I knew of locpoly as a R
>> package, rather than a Stata command. But a quick search via findit
>> showed the user-written locpoly (a good find for me, as I have an
>> interest in kernel regression too!) But after looking through the
>> program help file and some examples, I am still puzzled by your
>> question. What exactly do you mean by "fitted values"? Are the
>> generated yker values in your example not exactly what one usually
>> calls by that name, or am I missing something?
>>
>> Regards
>> Partho
>>
>> On Sat, Sep 17, 2011 at 1:18 AM, Tania Treibich
>> <[email protected]> wrote:
>>> Dear Statalist,
>>>
>>> I have done a kernel regression using the command locpoly :
>>> locpoly inv_rate l_kap_L, degree(3) width(1.5) n(20) generate (xker
>>> yker) noscatter
>>>
>>> I want to retrieve the fitted values, is that possible?
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