On Wed, 26 Mar 2008, glenn andrews wrote: > I am using the non-linear least squares routine in "R" -- nls. I have a > dataset where the nls routine outputs tight confidence intervals on the > 2 parameters I am solving for.
nls() does not ouptut confidence intervals, so what precisely did you do? I would recommend using confint(). BTW, as in most things in R, nls() is 'a' non-linear least squares routine: there are others in other packages. > As a check on my results, I used the Python SciPy leastsq module on the > same data set and it yields the same answer as "R" for the > coefficients. However, what was somewhat surprising was the the > condition number of the covariance matrix reported by the SciPy leastsq > program = 379. > > Is it possible to have what appear to be tight confidence intervals that > are reported by nls, while in reality they mean nothing because of the > ill-conditioned covariance matrix? The covariance matrix is not relevant to profile-based confidence intervals, and its condition number is scale-dependent whereas the estimation process is very much less so. This is really off-topic here (it is about misunderstandings about least-squares estimation), so please take it up with your statistical advisor. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
