Generally nlsr package has better reliability in getting parameter estimates because it tries to use automatic derivatives rather than a rather poor numerical estimate, and also uses a Levenberg-Marquardt stabilization of the linearized model. However, nls() can sometimes be a bit more flexible.
JN On 2020-04-05 3:20 p.m., Jeff Newmiller wrote: > err... stats::nls... > > On April 5, 2020 12:14:15 PM PDT, Jeff Newmiller <jdnew...@dcn.davis.ca.us> > wrote: >> stats::nlm? >> >> On April 5, 2020 11:53:10 AM PDT, Bernard Comcast >> <mcgarvey.bern...@comcast.net> wrote: >>> Any recommendations on an R package to fit data to a nonlinear model >>> Y=f(x) with a single x and y variable? >>> >>> I want to be able to generate parameter uncertainty estimates and >>> prediction uncertainties if possible. >>> >>> Bernard >>> Sent from my iPhone so please excuse the spelling!" >>> ______________________________________________ >>> 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. > ______________________________________________ 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.
