[R-sig-dyn-mod] Fitting ODE and DDE models with FME using missing data
I agree completely with Johannes. Observational gaps are no technical problem for FME, because it is possible to define an own cost function, or use a "long format" for the data. Examples are found in the package help files and vignettes or a poster: https://desolve.r-forge.r-project.org/user2014/examples/FME/ But, the data should describe all features of your process to ensure identifiability. This is a fundamental scientific (or mathematical) question and not a technical problem of any package. Thomas
On 14.03.2023 at 20:09 Johannes Ranke wrote:
Hi, In principle, this is possible, you just define the cost function based only on the observed variables. But you may run into a lack of identifiability of the parameters determining the unobserved variable(s), which may result in non- convergence, or very wide confidence intervals for these parameters. Kind regards, Johannes Am Dienstag, 14. M?rz 2023, 19:59:55 CET schrieb Nicola Gambaro:
Dear all, I have to estimate some unknown parameters of an ODE/DDE model. However, we only have access to data of some but not all state variables. Also, some state variable data may have observational gaps in between the time series. Is there a way to at least approximately fit the model with FME? If not, could anyone point me to alternative packages or relevant algorithms? Many thanks, Nicola