Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway.
G3 (Bethesda). 2013 Aug 7;3(8):1241-51. doi: 10.1534/g3.113.006700.
Animal models are generalized linear mixed models used in evolutionary biology and animal breeding to identify the genetic part of traits. Integrated Nested Laplace Approximation (INLA) is a methodology for making fast, nonsampling-based Bayesian inference for hierarchical Gaussian Markov models. In this article, we demonstrate that the INLA methodology can be used for many versions of Bayesian animal models. We analyze animal models for both synthetic case studies and house sparrow (Passer domesticus) population case studies with Gaussian, binomial, and Poisson likelihoods using INLA. Inference results are compared with results using Markov Chain Monte Carlo methods. For model choice we use difference in deviance information criteria (DIC). We suggest and show how to evaluate differences in DIC by comparing them with sampling results from simulation studies. We also introduce an R package, AnimalINLA, for easy and fast inference for Bayesian Animal models using INLA.
动物模型是进化生物学和动物育种中使用的广义线性混合模型，用于识别特征的遗传部分。集成嵌套拉普拉斯逼近（INLA）是一种用于对分层高斯马尔可夫模型进行快速、非抽样贝叶斯推断的方法。在本文中，我们证明了 INLA 方法可用于许多版本的贝叶斯动物模型。我们使用 INLA 对具有高斯、二项式和泊松似然的合成案例研究和家麻雀（Passer domesticus）种群案例研究进行了动物模型分析。我们将推断结果与使用马尔可夫链蒙特卡罗方法的结果进行了比较。对于模型选择，我们使用了偏差信息准则（DIC）的差异。我们建议并展示了如何通过与模拟研究的抽样结果进行比较来评估 DIC 的差异。我们还引入了一个 R 包 AnimalINLA，用于使用 INLA 对贝叶斯动物模型进行简单快速的推断。
