title: Animal models and integrated nested Laplace approximations.
abstract: 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.
authors.affiliation: Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway. anna.m.holand@ntnu.no
authors.forename: Anna Marie
authors.initials: AM
authors.lastname: Holand
authors.forename: Ingelin
authors.initials: I
authors.lastname: Steinsland
authors.forename: Sara
authors.initials: S
authors.lastname: Martino
authors.forename: Henrik
authors.initials: H
authors.lastname: Jensen
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D008390:Markov Chains; D023421:Models, Animal; D009010:Monte Carlo Method; D016011:Normal Distribution; D046370:Sparrows / Q000502:physiology
keywords: AnimalINLA; additive genetic models; approximate Bayesian inference; heritability; quantitative genetics