-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch [SMTP:r-help-bounces at stat.math.ethz.ch] On Behalf Of Vermeiren, Hans [VRCBE]
Sent: Sunday, November 13, 2005 7:48 PM
To: 'r-help at stat.math.ethz.ch'
Subject: [R] Robust Non-linear Regression
Hi,
I'm trying to use Robust non-linear regression to fit dose response curves.
Maybe I didnt look good enough, but I dind't find robust methods for NON
linear regression implemented in R. A method that looked good to me but is
unfortunately not (yet) implemented in R is described in
http://www.graphpad.com/articles/RobustNonlinearRegression_files/frame.htm
<http://www.graphpad.com/articles/RobustNonlinearRegression_files/frame.htm>
in short: instead of using the premise that the residuals are gaussian they
propose a Lorentzian distribution,
in stead of minimizing the squared residus SUM (Y-Yhat)^2, the objective
function is now
SUM log(1+(Y-Yhat)^2/ RobustSD)
where RobustSD is the 68th percentile of the absolute value of the residues
my question is: is there a smart and elegant way to change to objective
function from squared Distance to log(1+D^2/Rsd^2) ?