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 -- --------------------------------------------------------------------- Mike W.L. Cheung Phone: (65) 6516-3702 Department of Psychology Fax: (65) 6773-1843 National University of Singapore http://courses.nus.edu.sg/course/psycwlm/internet/ --------------------------------------------------------------------- On Mon, Mar 3, 2008 at 11:52 AM, Carlos Alzola <[EMAIL PROTECTED]> 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 > [EMAIL PROTECTED] > (703) 242-6747 > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@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. > ______________________________________________ R-help@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.
