> -----Original Message----- > From: [EMAIL PROTECTED] [SMTP:[EMAIL PROTECTED] On Behalf Of Vermeiren, Hans > [VRCBE] > Sent: Sunday, November 13, 2005 7:48 PM > To: 'r-help@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) ? > ----------- I do not know about in-built robustness options in R but I have found that Dave Fournier's robust likelihood for nonlinear regression in ADMB does a pretty good job in detecting and counter-acting the influence of outliers (in my applications this has been used to counter-act the effect of reading errors in determination of the age of fish based on rings in bones). It relies on a likelihood function based on a mixture of a normal and another distribution with fatter tails. You can find the documentation in the ADMB manual at the ADMB website: http://otter-rsch.com/admodel.htm Ruben
______________________________________________ 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
