R2.3, WinXP
Dear all, I am using the following functions: f1 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(x))/exp(log(Phi4))) f2 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(r)-log(x))/exp(log(Phi4))) subject to the residual weighting Var(e[i]) = sigma^2 * abs( E(y) )^(2*Delta) Here is my question, in steps: 1. Function f1 is separately fitted to two different datasets corresponding to two different dose response curves. These fits are unweighted. 2. Function f2 is fitted to the pooled data such that the two dose response curves are assumed to differ _only_ in log(r). This fit is also unweighted. 3. The residuals from #2 are used to estimate an appropriate sigma^2 and Delta to use in weighting. 4. The functions described in #1 and #2 are refitted, but this time weighted using the information gathered in #3. 5. How many degrees of freedom should be allocated to the weighted residual sums of squares? (There are three such SSE's, one for each individual model, and one for the overall joint model) Much thanks in advance, Greg ______________________________________________ R-help@stat.math.ethz.ch 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.