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Re: st: conditional merging
Robert Picard <email@example.com>
Re: st: conditional merging
Wed, 7 Nov 2012 12:28:43 -0800
Here is one way to find the most recent inspection
condition for each home sale.
*----------- begin example -------------
input home_id inspection_year condition
50121 2002 4
50121 2006 4
50121 2011 3
50681 2004 2
50681 2010 3
51040 2006 2
51040 2010 2
51040 2011 3
qui save "`condition'"
input home_id sale_year
append using "`condition'"
gen year = cond(mi(sale_year), inspection_year, sale_year)
sort home_id year inspection_year
gen cond_sale = condition
by home_id: replace cond_sale = condition[_n-1] if mi(cond_sale)
list, noobs sepby(home_id)
keep if !mi(sale_year)
keep home_id sale_year cond_sale
*----------- end example -------------
On Wed, Nov 7, 2012 at 11:11 AM, Nick Cox <firstname.lastname@example.org> wrote:
> I am not planning to implement weights. The point about
> nearest-neighbour as I define it is that unknown points get
> interpolated with the value of the nearest neighbour with a known
> value. I've got to think about ways of handling cases in which two
> neighbours tie for nearest.
> On Wed, Nov 7, 2012 at 7:03 PM, Ben Hoen <email@example.com> wrote:
>> I see. I like the nearest neighbor approach in that one could calculate
>> separately a weight of the "interpolation" such that as one interpolated
>> values "further" (in time) away from the "known" values their weight would
>> Thanks for those insights. As always, very interesting & helpful.
>> I will see if anyone comes forward with a merge idea.
>> Ben Hoen
>> Office: 845-758-1896
>> Cell: 718-812-7589
>> -----Original Message-----
>> From: firstname.lastname@example.org
>> [mailto:email@example.com] On Behalf Of Nick Cox
>> Sent: Wednesday, November 07, 2012 1:25 PM
>> To: firstname.lastname@example.org
>> Subject: Re: st: conditional merging
>> I will split this into two:
>> 0. Interpolation. Carry-forward is crude but has the advantage that
>> only legitimate values that occur can be carried forward.
>> I decided this morning to write a nearest-neighbour interpolation
>> program, which would have the same characteristic, except that the
>> nearest neighbour could be later as well as before.
>> The program would just be an analogue of -ipolate- and therefore not
>> assume spacing in time, but would assume position in one dimension
>> (not two).
>> 1. Merging. I am not a merge-master. There should be others on this
>> list who merge day in, day out and can give better advice.
>> On Wed, Nov 7, 2012 at 3:37 PM, Ben Hoen <email@example.com> wrote:
>>> Thanks Nick.
>>> I am not sure there is a standard way that these "condition" values trend
>>> over time across the whole dataset, and therefore interpolating them might
>>> not be appropriate. Moreover, for each home, there might not be many data
>>> points. Finally, the values that are allowable for condition are discreet
>>> (non-continuous), and therefore would complicate a linear, cubic, cubic
>>> spline process (though, of course that could be dealt with by using
>>> ). Would the interpolation allow me to take into account all of these
>>> For, in part, this reason, I was hoping to find some way to execute a
>>> "conditional merge" (again, my words). Additionally, the process of
>>> learning how one might do it with this "condition" data, could be applied
>>> extracting other characteristic data that are also only present
>>> across time (e.g., size of the home) but that also might periodically
>>> (e.g., the home might be added to).
>>> Is there a way to use if/then statements in a merge process?
>> Nick Cox
>>> Carry forward can be as little as one line of code: see
>>> FAQ . . . . . . . . . . . . . . . . . . . . . . . Replacing missing
>>> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J.
>>> 2/03 How can I replace missing values with previous or
>>> following nonmissing values?
>>> I don't see that this is an imputation problem at all. It calls for
>>> interpolation. Indeed, have you considered some kind of interpolation,
>>> say linear, cubic, cubic spline?
>>> On Tue, Nov 6, 2012 at 7:33 PM, Ben Hoen <firstname.lastname@example.org> wrote:
>>>> I have two files sales.dta and condition.dta. sales.dta has two
>>>> (home_id saleyear), and condition.dta has three variables (home_id
>>>> inspection_year condition). The variable inspection_year can take the
>>>> of 2000-2011 for any home but for many homes only some years are present
>>>> many years the home was not inspected. Therefore a sample of the data
>>>> look like:
>>>> home_id inspection_year condition
>>>> 50121 2002 4
>>>> 50121 2006 4
>>>> 50121 2011 3
>>>> 50681 2004 2
>>>> 50681 2010 3
>>>> 51040 2006 2
>>>> 51040 2010 2
>>>> 51040 2011 3
>>>> I would like to populate the sales.dta file with the condition of the
>>>> in the inspection_year that is the closest to, but not following the
>>>> So, for example, the following dataset would result
>>>> home_id sale_year condition
>>>> 50121 2007 4
>>>> 50121 2011 3
>>>> 50681 2008 2
>>>> 51040 2003 .
>>>> 51040 2010 3
>>>> I know I am not the first person to have this problem, but could not find
>>>> threads on this. Maybe I am using the wrong search terms. Any help
>>>> be greatly appreciated.
>>>> (As I wrote this I realized one not as elegant work-around would be to
>>>> fill-in missing data for each missing year in the condition.dta file,
>>>> potentially using the user-written "carryforward" or even imputing the
>>>> using, e.g., mi impute, and then matching home_id sale_year to home_id
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