[R] custom loss function + nonlinear models

From: Christian Mora <crmora_nc_at_yahoo.com>
Date: Tue 05 Apr 2005 - 06:59:48 EST


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



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