Dear Ariel Thank you for your detailed explanations and the example. Indeed, it should be rather straightforward to implement observation-specific weights in systemfit (i.e. in the estimation of systems of linear or non-linear equations). As you indicated that many people are looking for this feature, I wonder if I should start a crowd-funding campaign...
Best regards, Arne On 16 August 2013 18:23, Ariel <ariel.muld...@oregonstate.edu> wrote: > Arne Henningsen-3 wrote >>> Is it possible >>> to run SUR with weights using systemfit? I mean weighted seemingly >> unrelated >>> regression (weighted SUR) >> >> Currently, systemfit cannot estimate (SUR) models with >> observation-specific >> weights :-( >> >>> or weighted nonlinear unrelated regression (weighted NSUR). >> >> We are still not yet finished with implementing nonlinear models in >> systemfit (see http://www.systemfit.org/) :-( > > I recently had a student come to me with a very similar (okay, identical) > problem as the OP. I had to learn PROC MODEL, anyway, so I thought I’d poke > around in R while I was at it. I have nothing to add about any problems > with or the lack of maturity of the estimation procedure for nlsystemfit(), > but I do have some ideas about observation-level weights. > > It took me awhile to make the leap from the fairly straightforward linear > weighted least squares (for example, see Weisberg's Applied Linear > Regression textbook equation 5.8) to understanding how weighting worked in > nonlinear least squares. The R help forum certainly came in handy: > https://stat.ethz.ch/pipermail/r-help/2004-November/060424.html. I can add > weights into a nonlinear regression by simply multiplying both the response > and the nonlinear function by the square root of the desired weights. > Here’s a toy example, where I compare a model fit using the “weights” > argument in nls() with a model where I put the weights in “by hand” : > > DNase1 = subset(DNase, Run == 1) > fit2 = nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)), > data = DNase1, > start = list(Asym = 3, xmid = 0, scal = 1), weights = > rep(1:8, each = 2)) > summary(fit2) > > # Take the square root of the weights for fitting “by hand” > sw = sqrt(rep(1:8, each = 2) ) > fit3 = nls(sw*density ~ sw*(Asym/(1 + exp((xmid - log(conc))/scal))), > DNase1, > start = list(Asym = 3, xmid = 0, scal = 1) ) > summary(fit3) > > # The predicted values for fit3 need to be divided by the weights > # but the residuals are weighted residuals > predict(fit2) > predict(fit3)/sw > > It seems like this weighted approach could be easily extended to the model > formulas for a system of nonlinear equations (it would be similar for linear > equations) to be fit with systemfit. > >> Parresol (2001) in his paper >> titled Additivity of nonlinear biomass >> equations has run weighted NSUR using PROC MODEL (SAS institute Inc.1993). >> I was wondering if r can do that. > > It turned out I had to use this weighting approach in PROC MODEL, as well, > when each equation in the system had a different set of weights. The > estimates I get when fitting the Parresol example mentioned by the OP using > nlsystemfit and PROC MODEL are within spitting distance of each other, so I > feel like I am at least making the same mistakes in both software packages. > > I'm wondering if my logic is sound or if I'm missing some complication that > occurs when working with systems of equations. I’ve seen several folks > looking to fit weighted systems of equations in R with systemfit, and this > approach might get them what they need. > > Ariel > > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Weighted-SUR-NSUR-tp4670602p4673973.html > Sent from the R help mailing list archive at Nabble.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. -- Arne Henningsen http://www.arne-henningsen.name ______________________________________________ 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.
