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.
