稳健中介分析:R包

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
A. Alfons, N. Ateş, P. Groenen
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引用次数: 6

摘要

调解分析是在社会、行为和医学科学中使用最广泛的统计技术之一。中介模型允许研究自变量如何通过一个或多个中介变量间接影响因变量,这些中介变量被称为中介。分析通常通过一系列线性回归进行,在这种情况下,间接影响可以计算为这些回归系数的乘积。间接效应的统计显著性通常通过基于普通最小二乘估计的自举检验来评估。然而,该测试对异常值或偏离正态假设的其他偏差很敏感,这对调解机制理论的实证检验构成了严重威胁。R包实现了一个健壮的中介分析过程,该过程基于健壮回归估计器的快速健壮的自举方法,即使在数据偏离通常的正态性假设时也会产生可靠的结果。包中还包括用于中介分析的各种其他程序。此外,robmed引入了一个新的公式接口,该接口允许使用单个公式指定中介模型,并提供用于诊断的各种图或结果的可视化表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Mediation Analysis: The R Package robmed
Mediation analysis is one of the most widely used statistical techniques in the social, behavioral, and medical sciences. Mediation models allow to study how an independent variable affects a dependent variable indirectly through one or more intervening variables, which are called mediators. The analysis is often carried out via a series of linear regressions, in which case the indirect effects can be computed as products of coefficients from those regressions. Statistical significance of the indirect effects is typically assessed via a bootstrap test based on ordinary least-squares estimates. However, this test is sensitive to outliers or other deviations from normality assumptions, which poses a serious threat to empirical testing of theory about mediation mechanisms. The R package robmed implements a robust procedure for mediation analysis based on the fast-and-robust bootstrap methodology for robust regression estimators, which yields reliable results even when the data deviate from the usual normality assumptions. Various other procedures for mediation analysis are included in package robmed as well. Moreover, robmed introduces a new formula interface that allows to specify mediation models with a single formula, and provides various plots for diagnostics or visual representation of the results.
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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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