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help for locpr
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Semi-parametrically estimate probability/proportion as a function of one regres
> sor

Syntax

      locpr [varlist] [, stub(string) logit combine other options ]

    options               Description
    -------------------------------------------------------------------------
      stub(string)        save graphed predictions to new variables with
                            names beginning with string
      plot(string)        add string to each graph command (useful for adding
                            plots)
      logit               estimate a logit model and graph results
      combine             combine logit and semi-parametric estimates in one
                            graph
      rarea(string)       pass options string to rarea command graphing CI
      loptions(string)    pass options string to logit prediction graph
      coptions(string)    pass options string to combined graph
      nquantiles(integer) specifies how many points at which to estimate the
                            local regression (default is 99)}
      levels              estimate the local regression at each distinct
                            value of the regressor}
    -------------------------------------------------------------------------

Description

locpr semi-parametrically estimates a probability or proportion as a function
of one other variable and graphs the result.  Specifically, it estimates a
local linear regression using lpoly and approximates the endpoints of the
confidence interval via a logit transformation. The estimates are computed at a
number of quantiles (99 percentiles by default) of the regressor (or at each
value of the regressor within the range of those quantiles, if there are fewer
distinct values than the number of quantiles, unless the levels option requests
estimation at every distinct value of the regressor) and graphed. The logit
option offers a direct comparison to parametric logistic regression.  Other
twoway options may be specified that apply to the graph of local regression
estimates.

Examples
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        sysuse nlsw88, clear
        locpr never_married hours, l c
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        webuse nhanes2, clear
        g bmi=weight/height^2*10000
        lpoly highbp bmi [aw=finalwgt], nosc ci name(lpoly)
        locpr highbp bmi [pw=finalwgt], l c name(hibp)
        locpr diabetes bmi [pw=finalwgt], l c name(diab)
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        webuse psidextract, clear
        locpr ms lwage, l c name(lwage)
        locpr ms wks, l c name(wks)
        locpr ms ed, l c name(ed)
        *why graphs exclude bottom and top 1% of X by default:
        *(also add a rug plot and vertical lines at 1%ile and 99%ile)
        g l="|"
        g o=-.1
        _pctile wks, nq(100)
        loc p1=r(r1)
        loc p99=r(r99)
        locpr union wks, yla(0(.5)1) xli(`p1') xli(`p99') plot(|| scatter o
            wks, ms(none) mlabel(l) mlabp(0))
        locpr union wks, levels yla(0(.5)1) xli(`p1') xli(`p99') plot(||
            scatter o wks, ms(none) mlabel(l) mlabp(0))


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Author

    Austin Nichols
    Urban Institute
    Washington, DC, USA
    austinnichols@gmail.com

Also see

 On-line: logit, probit, glm, lpoly, lowess, mlowess (on SSC), transint (on
          SSC)