Message-ID: <9AC105024CEA64458BF66D1DE13CA50D070FB3BF@tibbemeexs1.eu.jnj.com>
Date: 2005-11-15T12:56:14Z
From: Vermeiren, Hans [VRCBE]
Subject: Robust Non-linear Regression
thank you all for the valuable suggestions
rnls() is indeed what I was looking for
I've to apologize to Roger Koenker for not mentioning that I did try
quantile regression (saw his answer in a previous post with a similar
question, yes i did my homework) however, least medians regression gave not
always satisfying results, I now understand that this is in fact due to
variability in the concentrations (x-axis) (thanks to Martin Maechlers
remark), my example dataset was in that sense a bit unfortunate
regards
Hans Vermeiren
-----Original Message-----
From: Martin Maechler [mailto:maechler at stat.math.ethz.ch]
Sent: Monday, November 14, 2005 12:41 PM
To: Vermeiren, Hans [VRCBE]
Cc: 'r-help at stat.math.ethz.ch'; R-SIG-robust at stat.math.ethz.ch
Subject: Re: [R] Robust Non-linear Regression
Package 'sfsmisc' has had a function 'rnls()' for a while
which does robust non-linear regression via M-estimation.
Since you have only outliers in 'y' and none in 'x',
you could use the 'nlrq' (nonlinear regression quantiles)
package that Roger Koenker mentioned.