Greetings, I am having some troubles with the nls() function in R V 2.14.2. I am doing some modelling where I want to predict the mass of leaf litter on the forest floor (X) as a function of time since fire (t). Fortunately, I have a differential equation that I can fit to the data which is acceptable on theoretical grounds. It is: X(t) = (L/k)[1-exp(-kt)], where L is the litter fall rate (t/ha/yr) and k is the decomposition rate (/yr). I have two problems:
(1) I have experimental error in both X and t. Is there a way to take this into account with nls? (2) Is there a way to constrain the parameter estimates from nls? For example, for a data snippet: X = 4.6 4.1 4.7 11.0 t = 1.5 4.5 7.0 8.0 After I run nls I get: L = 0.873 k = -0.059 The estimate for L is ok, but k (by definition) should be greater than 0. Is there a way around this? Many thanks, Nic Surawski. -- View this message in context: http://r.789695.n4.nabble.com/Non-linear-least-squares-tp4524812p4524812.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
