[RsR] Preliminary release of functions for robust nonlinear regression

[Eduardo Conceicao]

Hi all,

I am making available some basic code for robust nonlinear regression. 
Currently, there are available three functions for the tau-, CM-, and MTL-
estimates with a rule for the adaptive choice of coverage.

Please, keep in mind that the code is in a very initial stage. However, if you find it 
useful I want to use it, I would be delighted. License is GPL (>= 2).

I would very much appreciate comments regarding in particular (a) issues of 
statistical methodology and (b) ways of improving the code.
You can download file nonlinrobreg_0.0.1.zip from:
https://www.dropbox.com/s/1oiqiansbgzed4j/nonlinrobreg_0.0.1.zip


= A few details =
The implementation follows loosely the code of nlrob() from package 
robustbase. The approach for computing the estimates is the direct solution of 
the respective optimization problem using the Differential Evolution (DE) 
heuristic (coupled with an algorithm for dealing with constraints for the CM-
estimates).

The code uses packages PolynomF, robustbase, and ggplot2.

The methods for predict(), coef(), resid(), etc. are those developed for nlrob(). 
However, summary() is not compatible.


= Contents =
deopt.R: implementation of the jDE variant of the DE algorithm
test-funs.R: a few benchmark problems for unconstrained and constrained 
optimization
nlrobreg.R:
    nlrob.tau() (tau-estimator),
    nlrob.CM() (CM-estimator),
    nlrob.mtl() (MTL-estimator with adaptive coverage)
example_nlrobreg: example (based on the example for nlrob() from package 
robustbase)


= Running the example =
library(PolinomF)
library(robustbase)
library(ggplot2)

source(deopt.R)
source(nlrobreg.R)
#run the code from the example file

Eduardo
----------------------------------------
Eduardo L. T. Conceicao             econceicao using kanguru.pt
CIEPQPF
Department of Chemical Engineering
University of Coimbra, Portugal