Hi all; I'm trying to fit a reparameterization of the assymptotic regression model as that shown in Ratkowsky (1990) page 96.
Y~y1+(((y2-y1)*(1-((y2-y3)/(y3-y1))^(2*(X-x1)/(x2-x1))))/(1-((y2-y3)/(y3-y1))^2)) where y1,y2,y3 are expected-values for X=x1, X=x2, and X=average(x1,x2), respectively. I tried first with Statistica v7 by LS and Gauss-Newton algorithm without success (no convergence: predictors are redundant....). Then I tried with the option CUSTOM LOSS FUNCTION and several algorithms like Quasi-Newton, Simplex, Hookes-Jeeves, among others. In all these cases the model converged to some values for the parameters in it. My question is (after searching the help pages) : Is there such a thing implemented in R or can it be easily implemented? In other words, is it possible to define which loss function to use and the algorithm to find the parameters estimates? Thanks Christian ______________________________________________ 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
