Dear R experts! I have the y(x) data (length(y) == length(x) == 10,000) and I want to fit it with function exp(-a1 * x) + exp(-a2 * x) a1, a2 -- are parameters to be estimated. What the "right" way to do it? Can I transform data to something so that linear model could be used? Or, I must use a non-linear model, definitely? Thank you very much. -- WBR, Timur. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
fitting simple y(x) data
2 messages · Timur Elzhov, Ben Bolker
This is definitely nonlinear. nls() is what you need.
On Sat, 2 Nov 2002, Timur Elzhov wrote:
Dear R experts! I have the y(x) data (length(y) == length(x) == 10,000) and I want to fit it with function exp(-a1 * x) + exp(-a2 * x) a1, a2 -- are parameters to be estimated. What the "right" way to do it? Can I transform data to something so that linear model could be used? Or, I must use a non-linear model, definitely? Thank you very much. -- WBR, Timur. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
318 Carr Hall bolker at zoo.ufl.edu Zoology Department, University of Florida http://www.zoo.ufl.edu/bolker Box 118525 (ph) 352-392-5697 Gainesville, FL 32611-8525 (fax) 352-392-3704 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._