Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME

By Karline Soetaert, Thomas Petzoldt. 2010

907 citations recorded by the source corpus.

Mathematical simulation models are commonly applied to analyze experimental or environmental data and eventually to acquire predictive capabilities. Typically these models depend on poorly defined, unmeasurable parameters that need to be given a value. Fitting a model to data, so-called inverse modelling, is often the sole way of finding reasonable values for these parameters. There are many challenges involved in inverse model applications, e.g., the existence of non-identifiable parameters, the estimation of parameter uncertainties and the quantification of the implications of these uncertainties on model predictions. The R package FME is a modeling package designed to confront a mathematical model with data. It includes algorithms for sensitivity and Monte Carlo analysis, parameter identifiability, model fitting and provides a Markov-chain based method to estimate parameter confidence intervals. Although its main focus is on mathematical systems that consist of differential equations, FME can deal with other types of models. In this paper, FME is applied to a model describing the dynamics of the HIV virus.

Subfields: Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/769885f54e1485e6a2ca863323d3e1d2061cd128

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In Statistical and Nonlinear Physics

1. Karline Soetaert
2. Thomas Petzoldt

Citations in the corpus

• Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective (2015, methodology): “Different software packages for implementation of these methods exist (Vézina and Platt 1988; Reichert 1994; Lunn et al. 2000; Soetaert and Petzoldt 2010; Van Oevelen et al. 2010; Doherty 2015).”
• Solving Differential Equations in R: Package deSolve (2010, methodology): “…modelling in R , simecol , emerged (Petzoldt and Rinke 2007); packages for inverse modelling, ( FME ) and reactive transport modelling ( ReacTran ) (Soetaert and Petzoldt 2010; Soetaert and Meysman 2009) were created, while a framework for more general continuous dynamic modeling, Rdynamic (Setzer…”
• Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, background): “The most relevant packages regarding ABC are abc (Csillery et al. 2012), EasyABC (Jabot et al. 2013), pomp (King et al. 2013), FME (Soetaert & Petzoldt 2010) and MCMChybridGP (Fielding 2011).”