hdpGLM: An R Package to Estimate Heterogeneous Effects in Generalized Linear Models Using Hierarchical Dirichlet Process

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Diogo Ferrari
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引用次数: 0

Abstract

The existence of latent clusters with different responses to a treatment is a major concern in scientific research, as latent effect heterogeneity often emerges due to latent or unobserved features - e.g., genetic characteristics, personality traits, or hidden motivations - of the subjects. Conventional random- and fixed-effects methods cannot be applied to that heterogeneity if the group markers associated with that heterogeneity are latent or unobserved. Alternative methods that combine regression models and clustering procedures using Dirichlet process are available, but these methods are complex to implement, especially for non-linear regression models with discrete or binary outcomes. This article discusses the R package hdpGLM as a means of implementing a novel hierarchical Dirichlet process approach to estimate mixtures of generalized linear models outlined in Ferrari (2020). The methods implemented make it easy for researchers to investigate heterogeneity in the effect of treatment or background variables and identify clusters of subjects with differential effects. This package provides several features for out-of-the-box estimation and to generate numerical summaries and visualizations of the results. A comparison with other similar R packages is provided.
用层次Dirichlet过程估计广义线性模型中的异质效应的R包
对治疗有不同反应的潜在集群的存在是科学研究中的一个主要问题,因为潜在效应的异质性往往是由于潜在的或未观察到的特征(例如,受试者的遗传特征、人格特征或隐藏的动机)而出现的。如果与该异质性相关的群体标记是潜在的或未观察到的,则传统的随机效应和固定效应方法不能应用于该异质性。使用Dirichlet过程结合回归模型和聚类过程的替代方法是可用的,但这些方法实现起来很复杂,特别是对于具有离散或二元结果的非线性回归模型。本文讨论了R包hdpGLM作为实现一种新的分层狄利克雷过程方法的手段,该方法用于估计法拉利(2020)中概述的广义线性模型的混合物。所采用的方法使研究人员易于调查治疗效果或背景变量的异质性,并识别具有差异效应的受试者群。这个包为开箱即用的估计提供了几个特性,并生成数值摘要和结果的可视化。提供了与其他类似R包的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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