Thank you Wensui and Professor Ripley for your responses. Prof. Ripley, your assumptions regarding the context in which I'm using 'WLS' and 'regression' are correct. The function solve.QP in the quadprog package sounds like a great way to go. Thank you, and I will try this method.
Westley A. Ritz Analyst 215-641-2243 [EMAIL PROTECTED] TRC www.trchome.com -----Original Message----- From: Prof Brian Ripley [mailto:[EMAIL PROTECTED] Sent: Wednesday, September 26, 2007 1:13 PM To: Wensui Liu Cc: Westley Ritz; r-help@r-project.org Subject: Re: [R] Constraining Predicted Values to be Greater Than 0 You seem to be assuming that 'regression' has to do with 'gaussian assumption'. However, I presume WLS stands for 'weighted least squares', and 'regression' is historically associated with fitting linear models by least squares. I don't see why even in the model-based framework you assert that Westley cannot impose any constraints he wants on the *means*: the positivity constraint is on the means and not on the observations. E.g. in chemistry it is reasonable to assume that concentrations are non-negative, but indirectly measured values need not be. Note though that it is more usual to require that all predictions (at new points as well as data points) would be non-negative, which typically does reduce to constraints on the coefficients. As to how to do this, a WLS problem with inequality constraints on the fitted values is a linearly-constrained quadratic programme. So one avenue is to use solve.QP in package quadprog. If you have a large problem you can make use of the necessary redundancy of the constraints: e.g. if the predictions at the convex hull of the data points are non-negative, they all are. On Wed, 26 Sep 2007, Wensui Liu wrote: > if your regression under gaussian assumption, then you can't > constraint your predicted to be positive. > I don't know much about your dep in the model. but given more > appropriate distribution assumption, the constraint is doable. One > possibility that I can think of is poisson. > > On 9/25/07, Westley Ritz <[EMAIL PROTECTED]> wrote: >> I have a WLS regression with 1 dependent variable and 3 independent >> variables. I wish to constrain the predicted values (the fitted >> values) so that they are greater than zero (i.e. they are positive). >> I do not know how to impose this constraint in R. Please respond if >> you have any suggestions. >> >> There are some previous postings about constraining the coefficients, >> but this won't accomplish what I am trying to do. The coefficients can >> be negative, just as long as the predicted values are positive. >> >> Thank you in advance for your time. >> >> Westley A. Ritz >> Analyst >> 215-641-2243 >> [EMAIL PROTECTED] >> >> TRC >> www.trchome.com >> >> ______________________________________________ >> 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. >> >> > > > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ 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.
