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xsample(): An R Function for Sampling Linear Inverse Problems

Author

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  • Van den Meersche, Karel
  • Soetaert, Karline
  • Van Oevelen, Dick

Abstract

An R function is implemented that uses Markov chain Monte Carlo (MCMC) algorithms to uniformly sample the feasible region of constrained linear problems. Two existing hit-and-run sampling algorithms are implemented, together with a new algorithm where an MCMC step reflects on the inequality constraints. The new algorithm is more robust compared to the hit-and-run methods, at a small cost of increased calculation time.

Suggested Citation

  • Van den Meersche, Karel & Soetaert, Karline & Van Oevelen, Dick, 2009. "xsample(): An R Function for Sampling Linear Inverse Problems," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(c01).
    • Handle: RePEc:jss:jstsof:v:030:c01
    DOI: http://hdl.handle.net/10.18637/jss.v030.c01
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    1. Chaalali, Aurélie & Beaugrand, Grégory & Raybaud, Virginie & Lassalle, Géraldine & Saint-Béat, Blanche & Le Loc’h, François & Bopp, Laurent & Tecchio, Samuele & Safi, Georges & Chifflet, Marina & Lobr, 2016. "From species distributions to ecosystem structure and function: A methodological perspective," Ecological Modelling, Elsevier, vol. 334(C), pages 78-90.
    2. van der Heijden, L.H. & Niquil, N. & Haraldsson, M. & Asmus, R.M. & Pacella, S.R. & Graeve, M. & Rzeznik-Orignac, J. & Asmus, H. & Saint-Béat, B. & Lebreton, B., 2020. "Quantitative food web modeling unravels the importance of the microphytobenthos-meiofauna pathway for a high trophic transfer by meiofauna in soft-bottom intertidal food webs," Ecological Modelling, Elsevier, vol. 430(C).
    3. Mukashov, A., 2023. "Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 187-202.
    4. Hosack, Geoffrey R. & Eldridge, Peter M., 2009. "Do microbial processes regulate the stability of a coral atoll's enclosed pelagic ecosystem?," Ecological Modelling, Elsevier, vol. 220(20), pages 2665-2682.
    5. Bourhis, Yoann & Poggi, Sylvain & Mammeri, Youcef & Le Cointe, Ronan & Cortesero, Anne-Marie & Parisey, Nicolas, 2017. "Foraging as the landscape grip for population dynamics—A mechanistic model applied to crop protection," Ecological Modelling, Elsevier, vol. 354(C), pages 26-36.
    6. Saint-Béat, B. & Vézina, A.F. & Asmus, R. & Asmus, H. & Niquil, N., 2013. "The mean function provides robustness to linear inverse modelling flow estimation in food webs: A comparison of functions derived from statistics and ecological theories," Ecological Modelling, Elsevier, vol. 258(C), pages 53-64.
    7. Small, Gaston E. & Sterner, Robert W. & Finlay, Jacques C., 2014. "An Ecological Network Analysis of nitrogen cycling in the Laurentian Great Lakes," Ecological Modelling, Elsevier, vol. 293(C), pages 150-160.
    8. Pacella, Stephen R. & Lebreton, Benoit & Richard, Pierre & Phillips, Donald & DeWitt, Theodore H. & Niquil, Nathalie, 2013. "Incorporation of diet information derived from Bayesian stable isotope mixing models into mass-balanced marine ecosystem models: A case study from the Marennes-Oléron Estuary, France," Ecological Modelling, Elsevier, vol. 267(C), pages 127-137.
    9. Nogues, Quentin & Baulaz, Yoann & Clavel, Joanne & Araignous, Emma & Bourdaud, Pierre & Ben Rais Lasram, Frida & Dauvin, Jean-Claude & Girardin, Valérie & Halouani, Ghassen & Le Loc'h, François & Lo, 2023. "The usefulness of food web models in the ecosystem services framework: Quantifying, mapping, and linking services supply," Ecosystem Services, Elsevier, vol. 63(C).
    10. Shirin Fallahi & Hans J Skaug & Guttorm Alendal, 2020. "A comparison of Monte Carlo sampling methods for metabolic network models," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-24, July.

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