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From |
Martin Trombetta <martintrombetta@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Fitting a linear regression where coefficients are bounded proportions |

Date |
Thu, 12 Dec 2013 11:49:47 -0200 |

other software, but since I regularly work in Stata, I would be happy to find a way to do it there Maarten: I do not just want them to be bounded to the (0,1) interval, I want them to be bounded to the (a,b) interval, where 0<a<b<1 and I can choose a and b arbitrarily Nick: sounds interesting, maybe I will plot a few things like that and send them later. Thanks everybody for your attention so far 2013/12/12 Nick Cox <njcoxstata@gmail.com>: > This is, I know, not what you are asking but > > y as a linear function of nine predictors > > each coefficient being in the same interval > > the coefficients summing to 1 > > sounds rather close to > > y is the average of the predictors > > as your coefficients must average 1/9 by your own rules. > > This is all apart from some intercept (which you can always subtract > out, at least approximately). So, if I were reviewing/hearing about > your work I would ask for a graph of > > y vs average of predictors > > as giving an easy but possibly informative idea of your data. It might > also be a supplementary graph to throw light on your fitted > hyperplane, especially if the eventual fit is puzzling or problematic > in any detail. > Nick > njcoxstata@gmail.com > > > On 12 December 2013 09:49, Maarten Buis <maartenlbuis@gmail.com> wrote: >> On Wed, Dec 11, 2013 at 8:00 PM, Martin Trombetta wrote: >>> Thanks Maarten, I had read this post before and, even though it was >>> useful at first, I think the methods suggested there do not quite help >>> with my particular problem. Please notice that I wish to include both >>> an equality constraint and several inequality constraints in the same >>> problem, I do not see how to use the methods from this post. >> >> Example 6 of <http://www.stata.com/support/faqs/statistics/linear-regression-with-interval-constraints/> >> does exactly what you want: it incorporates both the inequality >> constraint that all proportions must be between 0 and 1 _and_ the >> constraint that they must add up to 1. >> >> -- Maarten >> >> --------------------------------- >> Maarten L. Buis >> WZB >> Reichpietschufer 50 >> 10785 Berlin >> Germany >> >> http://www.maartenbuis.nl >> --------------------------------- >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> * http://www.ats.ucla.edu/stat/stata/ > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ -- Martin Trombetta * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Fitting a linear regression where coefficients are bounded proportions***From:*Nick Cox <njcoxstata@gmail.com>

**References**:**st: Fitting a linear regression where coefficients are bounded proportions***From:*Martin Trombetta <martintrombetta@gmail.com>

**Re: st: Fitting a linear regression where coefficients are bounded proportions***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Fitting a linear regression where coefficients are bounded proportions***From:*Martin Trombetta <martintrombetta@gmail.com>

**Re: st: Fitting a linear regression where coefficients are bounded proportions***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Fitting a linear regression where coefficients are bounded proportions***From:*Nick Cox <njcoxstata@gmail.com>

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