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From |
Nick Cox <njcoxstata@gmail.com> |

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

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

Date |
Thu, 12 Dec 2013 14:54:15 +0000 |

<> Should be (a,b). Nick njcoxstata@gmail.com On 12 December 2013 14:53, Nick Cox <njcoxstata@gmail.com> wrote: > Your interval [a,b] still calls for a reparameterisation using logit > ideas, just in a generalized form, ln((x - a)/(b - x)) at a quick > guess. I am assuming that b and a are specified in advance and the > same for each predictor. > Nick > njcoxstata@gmail.com > > > On 12 December 2013 13:49, Martin Trombetta <martintrombetta@gmail.com> wrote: >> 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/ * * 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/

**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>

**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:*Nick Cox <njcoxstata@gmail.com>

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