Re: [R] Constrained regression

From: Mike Cheung <mikewlcheung_at_gmail.com>
Date: Mon, 03 Mar 2008 23:29:23 +0800

Dear Carlos,

One approach is to use structural equation modeling (SEM). Some SEM packages, such as LISREL, Mplus and Mx, allow inequality and nonlinear constraints. Phantom variables (Rindskopf, 1984) may be used to impose inequality constraints. Your model is basically: y = b0 + b1*b1*x1 + b2*b2*x2 +...+ bp*bp*xp + e 1 = b1*b1 + b2*b2 +...+ bp*bp

Alternatively, you can set some condition bounds on the parameter estimates. Then you only have to impose the second constraint.

Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49, 37-47.

Regards,
Mike

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 Mike W.L. Cheung               Phone: (65) 6516-3702
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On Mon, Mar 3, 2008 at 11:52 AM, Carlos Alzola <calzola_at_cox.net> wrote:

> Dear list members,
>
> I am trying to get information on how to fit a linear regression with
> constrained parameters. Specifically, I have 8 predictors , their
> coeffiecients should all be non-negative and add up to 1. I understand it is
> a quadratic programming problem but I have no experience in the subject. I
> searched the archives but the results were inconclusive.
>
> Could someone provide suggestions and references to the literature, please?
>
> Thank you very much.

>
> Carlos
>
> Carlos Alzola
> calzola_at_cox.net
> (703) 242-6747
>
>
> [[alternative HTML version deleted]]
>
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______________________________________________ R-help_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Received on Mon 03 Mar 2008 - 15:33:25 GMT

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