dalmatian:一个通过JAGS和nimble在R中拟合双重层次线性模型的包

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
S. Bonner, Hanjoe Kim, D. Westneat, A. Mutzel, Jonathan Wright, Matthew R. Schofield
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引用次数: 3

摘要

传统的回归模型,包括广义线性混合模型,侧重于理解影响响应变量均值的确定性因素。许多生物学研究试图理解表型或生态反应变量的变异或分散的非确定性模式。我们描述了一个新的R包,dalmatian,它提供了拟合双层次广义线性模型的方法,该模型包含固定和随机的均值和方差预测因子。通过JAGS或nimble实现的马尔可夫链蒙特卡罗采样来拟合模型,并且该软件包提供了简单的功能来监控采样器和总结结果。我们将这些功能应用于通过繁殖斑蝇(Ficedula hypoleuca)来传递食物的数据。我们的目的是,这个包使从业者更容易实现这些模型,而不必学习复杂的马尔可夫链蒙特卡洛方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble
Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many biological studies seek to understand non-deterministic patterns in the variance or dispersion of a phenotypic or ecological response variable. We describe a new R package, dalmatian, that provides methods for fitting double hierarchical generalized linear models incorporating fixed and random predictors of both the mean and variance. Models are fit via Markov chain Monte Carlo sampling implemented in either JAGS or nimble and the package provides simple functions for monitoring the sampler and summarizing the results. We illustrate these functions through an application to data on food delivery by breeding pied flycatchers (Ficedula hypoleuca). Our intent is that this package makes it easier for practitioners to implement these models without having to learn the intricacies of Markov chain Monte Carlo methods.
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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