On 5 January 2015 at 21:08, Ben Bolker <bbol...@gmail.com> wrote: > Roger Coppock <rcoppock <at> cox.net> writes: > >> >> When will "R" implement the "se.fit" option to the >> predict.nls() function? Is there some schedule? >> > > I think this is unlikely to happen, ever (sorry). The exact method > for finding confidence intervals on nonlinear fits would be > to compute likelihood profiles for each prediction, which would > be rather tedious.
I understand profile likelihoods for parameters, but what do you mean by a profile likelihood for yet unobserved observations, i.e. predictions? > > Another reasonable approach would be to use bootstrapping (see > linked r-help thread below). > > An approximate approach would be to use the delta method. > > The nlstools package might be useful. Alternatively the propagate package: it provides a function predictNLS that computes uncertainty measures for nls predictions using (first and second order) Taylor approximations as well as simulation methods. I think the appropriateness of a simple (first order) Taylor/Delta method depends on the application. I can think of two important aspects: (1) if the model function is close to linear, you might be ok. (2) if you are interested in a prediction-type (rather than confidence) interval and the residual spread dominates the uncertainty, any inaccuracies in the model function uncertainty (where you apply the approximation) is swamped by the residual spread anyway. In a recent application on shelf life estimation that I worked on, both of these aspects were applicable and a simple approximation was fine. Cheers, Rune ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
