Hierarchical models in ecology: confidence intervals, hypothesis testing, and model selection using data cloning
書誌事項
- 公開日
- 2009-02
- 権利情報
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- http://onlinelibrary.wiley.com/termsAndConditions#vor
- DOI
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- 10.1890/08-0967.1
- 公開者
- Wiley
この論文をさがす
説明
<jats:p>Hierarchical statistical models are increasingly being used to describe complex ecological processes. The data cloning (DC) method is a new general technique that uses Markov chain Monte Carlo (MCMC) algorithms to compute maximum likelihood (ML) estimates along with their asymptotic variance estimates for hierarchical models. Despite its generality, the method has two inferential limitations. First, it only provides Wald‐type confidence intervals, known to be inaccurate in small samples. Second, it only yields ML parameter estimates, but not the maximized likelihood values used for profile likelihood intervals, likelihood ratio hypothesis tests, and information‐theoretic model selection. Here we describe how to overcome these inferential limitations with a computationally efficient method for calculating likelihood ratios via data cloning. The ability to calculate likelihood ratios allows one to do hypothesis tests, construct accurate confidence intervals and undertake information‐based model selection with hierarchical models in a frequentist context. To demonstrate the use of these tools with complex ecological models, we reanalyze part of Gause's classic<jats:italic>Paramecium</jats:italic>data with state–space population models containing both environmental noise and sampling error. The analysis results include improved confidence intervals for parameters, a hypothesis test of laboratory replication, and a comparison of the Beverton‐Holt and the Ricker growth forms based on a model selection index.</jats:p>
収録刊行物
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- Ecology
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Ecology 90 (2), 356-362, 2009-02
Wiley
