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Re: st: How to get rid of outliers
From
Xixi Lin <[email protected]>
To
statalist <[email protected]>
Subject
Re: st: How to get rid of outliers
Date
Fri, 25 Oct 2013 10:08:23 -0400
Sergiy,
WOW! Thank you so much for the detailed explanations. I will
reconsider how to deal with the outliers.
Best,
Xixi Lin
On Thu, Oct 24, 2013 at 4:50 PM, Sergiy Radyakin <[email protected]> wrote:
> Xixi,
> Richard has already answered this and correctly explained the reason
> (you must use drop if in this particular case, which was not important
> in the original situation). However I am more concerned about a
> different point. This is not a mechanical extension of the case of one
> variable to the case of three variables. Note that you are eliminating
> the whole person, if any of his characteristics are extreme. But by
> doing this you are distorting the distribution of the other
> characteristics! From the practical point of view, you will have your
> resulting sample different depending e.g. on the order of how the
> variables Size, Volume, etc listed in the list. Which is never a good
> thing. Especially if you have a qualitative conclusion, which reverses
> itself when you change the order of these variables. Let's hope this
> is not your case. But given this new information that you have shown,
> I urge you to follow Nick's advice, and carefully reconsider, why you
> want to eliminate anyone from your sample.
>
> Also I noticed you renamed r and l to y and z. Not a good idea.
> Originally letters were picked to denote "left" and "right". It is way
> easier to read the code that way. Mentioning inrange(x,y,z) gives the
> impression that x,y,z are of equal 'rank', while in fact they are not
> (first is a variable, the other two are constants).
>
> In any case here is an example with looping:
> do http://radyakin.org/statalist/2013102401/remove_outliers2.do
>
> (due to low number of observations and all observations coming from
> the same year 1988, I took collgrad as the stratification var)
>
> Best, Sergiy Radyakin
>
>
>
> On Thu, Oct 24, 2013 at 5:23 PM, Richard Williams
> <[email protected]> wrote:
>> At 03:08 PM 10/24/2013, Xixi Lin wrote:
>>>
>>> Sergiy,
>>>
>>> One more question to bother you, I tried the single variable and it
>>> works. Then I tried to do it by period. The code seems to be not
>>> successful.
>>>
>>> Here is my code:
>>> forvalues i=1/496{
>>> foreach x in Return_lead1 Momentum Size Volume MB {
>>> qui centile `x' if Period==`i', c(0.5 99.5)
>>> local y=r(c_1)
>>> local z=r(c_2)
>>> keep if inrange(`x',`y',`z')& Period==`i'
>>> }
>>> }
>>
>>
>> Everything in periods 2 through 496 is going to get dropped after the first
>> time through the loop, because they won't meet the requirement that Period
>> == 1. Not sure, but this might work:
>>
>> drop if !inrange(`x',`y',`z')& Period==`i'
>>
>>
>>
>>> Do you know what is wrong with my code? Thank you.
>>>
>>> Best,
>>> Xixi Lin
>>>
>>> On Thu, Oct 24, 2013 at 3:47 PM, Sergiy Radyakin <[email protected]>
>>> wrote:
>>> > Xixi,
>>> >
>>> > statalist FAQ in 3.1 suggests to "Explain what doesn't work".
>>> >
>>> > The code I posted removes the persons from NLSW88 dataset shipped with
>>> > Stata that report very low or very high wages, compared to the other
>>> > people in this dataset (2.5% of low earners and 2.5% of high earners).
>>> > It also plots the distribution graph, to give you an idea of what it
>>> > is going to do (keep only people between the two red lines, remove the
>>> > persons in the tails).
>>> >
>>> > The code is here:
>>> > do http://radyakin.org/statalist/2013102401/remove_outliers.do
>>> >
>>> > The picture is here:
>>> > http://radyakin.org/statalist/2013102401/wage_cut.png
>>> >
>>> > The program drops 112 persons, which is roughly .0498 of the sample.
>>> > (you can only drop a _whole_ person, so that is not exactly 0.05).
>>> >
>>> > Now, what "seems to be not working" mean?
>>> >
>>> > Best, Sergiy Radyakin
>>> >
>>> >
>>> >
>>> > On Thu, Oct 24, 2013 at 2:54 PM, Xixi Lin <[email protected]> wrote:
>>> >> Hi Sergiy,
>>> >>
>>> >> I tried your code, but it seems to be not working.
>>> >>
>>> >> Best,
>>> >> Xixi Lin
>>> >>
>>> >> On Thu, Oct 24, 2013 at 11:55 AM, Sergiy Radyakin
>>> >> <[email protected]> wrote:
>>> >>> Xixi, listen to Nick's advice. But if you still want to drop them,
>>> >>> here is how:
>>> >>>
>>> >>> sysuse nlsw88
>>> >>> centile wage, c(2.5 97.5)
>>> >>> local l=r(c_1)
>>> >>> local r=r(c_2)
>>> >>> kdensity wage, xline(`l') xline(`r')
>>> >>> keep if inrange(wage, `l', `r')
>>> >>>
>>> >>> Best, Sergiy Radyakin
>>> >>>
>>> >>>
>>> >>> On Thu, Oct 24, 2013 at 10:45 AM, Nick Cox <[email protected]>
>>> >>> wrote:
>>> >>>> If the question is simple
>>> >>>>
>>> >>>> How to get rid of outliers?
>>> >>>>
>>> >>>> then there is a good simple long answer
>>> >>>>
>>> >>>> Don't (usually).
>>> >>>>
>>> >>>> and a good simple short answer
>>> >>>>
>>> >>>> Don't.
>>> >>>>
>>> >>>> There are of course even longer answers in many places. The thread
>>> >>>> starting at
>>> >>>>
>>> >>>> http://www.stata.com/statalist/archive/2007-06/msg00185.html
>>> >>>>
>>> >>>> throws a variety of lights on outliers and immodesty leads me to
>>> >>>> recommend
>>> >>>>
>>> >>>> http://www.stata.com/statalist/archive/2007-06/msg00239.html
>>> >>>>
>>> >>>> as particularly long-winded, and respect leads me to nominate Richard
>>> >>>> Goldstein's concise remark
>>> >>>>
>>> >>>> http://www.stata.com/statalist/archive/2007-06/msg00240.html
>>> >>>>
>>> >>>> as most penetrating of all. But the whole thread is worth looking
>>> >>>> through
>>> >>>>
>>> >>>> One rather long footnote to the thread is provided by
>>> >>>>
>>> >>>> SJ-13-3 st0313 . . . . . . . . . . . . . . Speaking Stata: Trimming
>>> >>>> to taste
>>> >>>> (help trimmean, trimplot if installed) . . . . . . . . . .
>>> >>>> N. J. Cox
>>> >>>> Q3/13 SJ 13(3):640--666
>>> >>>> tutorial review of trimmed means, emphasizing the scope for
>>> >>>> trimming to varying degrees in describing and exploring data
>>> >>>>
>>> >>>> but the best Stata incantation of all is likely to be -glm-.
>>> >>>>
>>> >>>> More generally, modify your model so that outliers are accommodated.
>>> >>>>
>>> >>>> Don't modify your data because they are awkward to analyse.
>>> >>>>
>>> >>>> Nick
>>> >>>> [email protected]
>>> >>>>
>>> >>>>
>>> >>>> On 24 October 2013 15:31, Xixi Lin <[email protected]> wrote:
>>> >>>>> Hi All,
>>> >>>>>
>>> >>>>> I know it seems to be a very simple question. But I still wanna ask
>>> >>>>> how to keep 99%(95%) of the data? Is it just chop off 2 standard
>>> >>>>> deviations? How to code it then?
>>> >>>>>
>>> >>>>> Thanks a lot.
>>> >>>>>
>>> >>>>> Best,
>>> >>>>> Xixi Lin
>>> >>>>> *
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>>
>>
>> -------------------------------------------
>> Richard Williams, Notre Dame Dept of Sociology
>> OFFICE: (574)631-6668, (574)631-6463
>> HOME: (574)289-5227
>> EMAIL: [email protected]
>> WWW: http://www.nd.edu/~rwilliam
>>
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