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Modeling Time Series of Animal Behavior by Means of a Latent‐State Model with Feedback

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  • Walter Zucchini
  • David Raubenheimer
  • Iain L. MacDonald

Abstract

Summary We describe a family of models developed for time series of animal feeding behavior. The models incorporate both an unobserved state, which can be interpreted as the motivational state of the animal, and a mechanism for feedback to this state from the observed behavior. We discuss methods for evaluating and maximizing the likelihood of an observed series of behaviors, and thereby estimating parameters, and for inferring the most likely sequence of underlying states. We indicate several extensions of the models, including the incorporation of random effects. We apply these methods in an analysis of the feeding behavior of the caterpillar Helicoverpa armigera, and thereby demonstrate the potential of this family of models as a tool in the investigation of behavior.

Suggested Citation

  • Walter Zucchini & David Raubenheimer & Iain L. MacDonald, 2008. "Modeling Time Series of Animal Behavior by Means of a Latent‐State Model with Feedback," Biometrics, The International Biometric Society, vol. 64(3), pages 807-815, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:807-815
    DOI: 10.1111/j.1541-0420.2007.00939.x
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    References listed on IDEAS

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    1. Altman, Rachel MacKay, 2007. "Mixed Hidden Markov Models: An Extension of the Hidden Markov Model to the Longitudinal Data Setting," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 201-210, March.
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    Cited by:

    1. Roland Langrock & Thomas Kneib & Alexander Sohn & Stacy L. DeRuiter, 2015. "Nonparametric inference in hidden Markov models using P-splines," Biometrics, The International Biometric Society, vol. 71(2), pages 520-528, June.
    2. Pirotta, Enrico & New, Leslie & Harwood, John & Lusseau, David, 2014. "Activities, motivations and disturbance: An agent-based model of bottlenose dolphin behavioral dynamics and interactions with tourism in Doubtful Sound, New Zealand," Ecological Modelling, Elsevier, vol. 282(C), pages 44-58.
    3. Roland Langrock & Timo Adam & Vianey Leos‐Barajas & Sina Mews & David L. Miller & Yannis P. Papastamatiou, 2018. "Spline‐based nonparametric inference in general state‐switching models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 179-200, August.
    4. Iain L. MacDonald, 2014. "Numerical Maximisation of Likelihood: A Neglected Alternative to EM?," International Statistical Review, International Statistical Institute, vol. 82(2), pages 296-308, August.

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