On 8/22/07, Greg Tarpinian <[EMAIL PROTECTED]> wrote: > 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) Which R function(s) are you using to fit these models? Did you try a call to anova with multiple arguments? (Or should we consider your email address of "sasprog474", mention of an out-of-date version of R and lack of R code to be more than a coincidence?) ______________________________________________ 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.
