thank you.
Shuaizhang
--- Austin Nichols <[email protected]> wrote:
> Shuaizhang Feng--
> Ignoring the bad idea that imputing in this way
> might be, and assuming
> that the "predicted percentiles" are in the set
> {1/100, ... 99/100},
> you might try this:
>
> clear
> input y x
> 200 .
> 100 .
> . 0.5
> . 0.1
> 234 .
> end
> gen yi=y
> gen oldobs=_n
> gsort -yi, mf
> gen newobs=_n
> su newobs if mi(yi)
> forv i=1/`r(max)' {
> _pctile y, nq(100)
> qui replace yi=r(r`=int(x[`i']*100)') in `i'
> }
> sort oldobs
> drop oldobs newobs
> li
>
> Note also that _pctile allows weights.
>
> On 3/19/07, Shuaizhang Feng <[email protected]>
> wrote:
> > The real situation I am facing is: I have a
> variable y
> > which has certain values missing, I would like to
> > impute the missing values based on a predicted
> > percentil. So the x variable contains a
> percentile
> > that I will use to forecast the y variables for
> those
> > missing.
> >
> > Example:
> > y x
> > 200 .
> > 100 .
> > . 0.5
> > . 0.1
> > 234 .
> >
> > How can I assign a value for those missing y(s)
> based
> > on the perceniles from x?
> >
> > In this example, I guess the y value corresponding
> to
> > x=0.5 is 200 and for x=0.1 is 100. Note I am
> using
> > the distribution of y only for those observed.
> *
> * 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/
>
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