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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Constrained Lowess |

Date |
Fri, 2 May 2008 11:47:34 +0100 |

Quite how to get useful results from smoothing a binary response is not clear to me. If the data were proportions on (0,1) or even [0,1] I would suggest some kind of transformation approach. -lowess, logit- is presumably intended to help. Otherwise consider something like an angular or folded root transformation, applying -lowess- and then transforming back. But for binary data any transformation just maps two distinct values to two other distinct values and so cannot help, so far as I can see. In the case of unemployment data, presumably you are dealing with individuals? If they are aggregate data for lots of individuals I would collapse by age to get proportion of unemployed, and then smooth if necessary. It sounds as if you want something quite different, however. Also, as you regard -age- as categorical I probably don't understand what you are trying to do. Nick n.j.cox@durham.ac.uk Sergiy Radyakin I am plotting a smoothed graph (-lowess-) of a binary variable (e.g. unemployed) by categorical (e.g. age). However the smoothed values are not necessarily in the [0;1] range, where unemployment must be by definition. I can save the smoothed values into a new variable with the option -generate(newvar)- and then truncate the negatives and values larger than one, but I believe smoothing must look differently if I could tell -lowess- to look for such a constrained value in the first place. As it follows from the description of -lowess- it doesn't have such a feature. Is there any user-written command or simple algorithm for this purpose? * * 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/

**Follow-Ups**:**Re: st: RE: Constrained Lowess***From:*"Sergiy Radyakin" <serjradyakin@gmail.com>

**References**:**st: Constrained Lowess***From:*"Sergiy Radyakin" <serjradyakin@gmail.com>

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