Use nlme. Here is an example, fitting the Michaelis-Menten model to some enzyme data:
library(nlme) data(Puromycin) fit.nls <- nlsList(rate ~ SSmicmen(conc, Vm, K)|state, data=Puromycin) summary(fit.nls) # Use the output to calculate starting values for the nlme fit. fit.nlme <- nlme(rate ~ SSmicmen(conc, Vm, K), fixed=Vm+K~state, groups=~state, start=c(212, -52, 0.06,- 0.01), data=Puromycin) summary(fit.nlme) You get explicit tests between the states for each of the two model parameters. This example is from Pinheiro and Bates. Thankyou Jose and Doug! I use it in a course that I teach. Cheers, Simon. Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au Policies: 1. I will NOT analyse your data for you. 2. Your deadline is your problem. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. -----Original Message----- From: [EMAIL PROTECTED] on behalf of Joerg van den Hoff Sent: Fri 28/03/2008 8:10 PM To: Frank Scherr Cc: r-help@r-project.org Subject: Re: [R] Compare parameter estimates of a nlsList object On Wed, Mar 26, 2008 at 05:27:22PM +1300, Frank Scherr wrote: > Hello together, > > Is there a tool to test the statistical differences between parameter > estimates of a nlsList fit? > > I fitted degradation data using the nlsList method and want to find out > whether derived rate constants are significantly different from each other at > the grouping factors soil and temperature. > here is a physicist's (not a mathematician's) answer: from each nls-fit you get an estimate of the std. error of the parameter estimate. so you have,e.g., (a1 +/- del_a1) from fit 1 and (a2 +/- del_a2) -- where a1 and a2 are actually the same parameter in the model -- from fit 2. since you thus have actual estimated errors, I'd simply ask "what is the error estimate of the difference", i.e. a1 - a2 and, assuming independent underlying data, compute this by gaussian error propagation (i.e. assuming normal distributions of the parameter estimates). here, the variances (squares of ths std. errors) add up: del_[a1-a2]^2 = del_a1^2 + del_a2^2 if (a1-a2) +/- del_[a-a2] (or rather 2-3 times that error) is compatible with zero, a and a2 do not differ significantly, else they do. HTH joerg ______________________________________________ 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. [[alternative HTML version deleted]] ______________________________________________ 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.
