Lortie Christopher J, Filazzola Alex
Department of Biology York University Toronto ON Canada.
The National Center for Ecological Analysis and Synthesis UCSB Santa Barbara CA USA.
Ecol Evol. 2020 Sep 14;10(20):10916-10921. doi: 10.1002/ece3.6747. eCollection 2020 Oct.
There is extensive choice in R to support meta-analyses.Two packages in this ecosystem include meta and metafor and provide an excellent opportunity to apply a structured checklist previously developed for contrasts between R packages relevant to challenges in ecology and evolution.Meta is a direct, intuitive choice for rapid implementation of general meta-analytical statistics. Metafor is a comprehensive package best suited for relatively more complex models.Both packages provide estimates of heterogeneity, excellent visualization tools, and functions to explore publication bias.The package metafor has a steeper learning curve but greater rewards. Reference to the learning curve and capacities of the statistical software Stata provided a benchmark outside the R ecosystem and confirmed the consistency in statistics.The usefulness of meta-analyses is not just in the synthesis of the research but in the process of doing the scientific synthesis. Reporting of contrasts and checks for robust statistics is an important contribution to more transparent and reproducible scientific syntheses.
在R语言中有大量支持元分析的选择。这个生态系统中的两个包包括meta和metafor,它们为应用先前为与生态和进化挑战相关的R包之间的对比而开发的结构化清单提供了绝佳机会。Meta是快速实施一般元分析统计的直接、直观选择。Metafor是一个全面的包,最适合相对更复杂的模型。这两个包都提供异质性估计、出色的可视化工具以及探索发表偏倚的函数。包metafor的学习曲线更陡,但回报更大。参考统计软件Stata的学习曲线和能力提供了R生态系统之外的一个基准,并证实了统计数据的一致性。元分析的有用性不仅在于研究的综合,还在于进行科学综合的过程。对比的报告和稳健统计的检查对更透明和可重复的科学综合做出了重要贡献。
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