If you can solve the system and the parameters are identifiable from the y component, then I would think that nls() can do it. However, beware unidentifiablity: If x stays constant at its equilibrium value of (er...) d1*c/(m+d1), then even knowing x won't allow you to tease out c, d1, and m; and not knowing x you can't separate m from x in the mx term in dy/dt. So it likely depends on the exact experiment, especially the initial conditions of the system whether you can estimate things or not.
-pd > On 29 Aug 2018, at 15:19 , Fanny Gallais <[email protected]> wrote: > > Dear R users, > > > > I am working on a simple mathematical model made of two ordinary > differential equations: > > > > dx/dt=-mx-d1(x-c) > > dy/dt=mx-d2y > > > > I would like to fit this model to my data and estimate the corresponding > parameters. The thing is I only have observed data for y, x is unknown. Is > it possible to do this in R? > > > > Thank you for your help > > Fanny > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] 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. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: [email protected] Priv: [email protected] ______________________________________________ [email protected] 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.
