{"title":"FRESA.CAD::ILAA: Estimating the exploratory residualization transform","authors":"José Gerardo Tamez-Peña","doi":"10.1016/j.softx.2024.101926","DOIUrl":null,"url":null,"abstract":"<div><div>Multicollinearity among observed variables may have a large impact on statistical modeling and the discovery of associations between the observed variables and clinical outcomes. A viable method to address the multicollinearity is to find a suitable linear transform that mitigates the degree of collinearity. The Iterative Linear Association Analysis (ILAA) method was developed to explore the association among observed variables and to return a suitable linear transformation matrix based on variable residualization that effectively mitigates the degree of multicollinearity via controlling the maximum correlation measure present in the transformed dataset. This paper presents the software implementation of the ILAA method as an R function inside the FRESA.CAD 3.4.7 R package, hence providing researchers with a simple tool to explore tabular data in a new interpretable latent space.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101926"},"PeriodicalIF":2.4000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711024002966","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Multicollinearity among observed variables may have a large impact on statistical modeling and the discovery of associations between the observed variables and clinical outcomes. A viable method to address the multicollinearity is to find a suitable linear transform that mitigates the degree of collinearity. The Iterative Linear Association Analysis (ILAA) method was developed to explore the association among observed variables and to return a suitable linear transformation matrix based on variable residualization that effectively mitigates the degree of multicollinearity via controlling the maximum correlation measure present in the transformed dataset. This paper presents the software implementation of the ILAA method as an R function inside the FRESA.CAD 3.4.7 R package, hence providing researchers with a simple tool to explore tabular data in a new interpretable latent space.
观测变量之间的多重共线性可能会对统计建模和发现观测变量与临床结果之间的关联产生很大影响。解决多重共线性的可行方法是找到一种合适的线性变换,以减轻共线性的程度。迭代线性关联分析(ILAA)方法的开发旨在探索观察变量之间的关联,并在变量残差化的基础上返回一个合适的线性变换矩阵,通过控制变换后数据集中存在的最大关联度,有效减轻多重共线性的程度。本文介绍了 ILAA 方法的软件实现,它是 FRESA.CAD 3.4.7 R 软件包中的一个 R 函数,从而为研究人员提供了一个在新的可解释潜空间中探索表格数据的简单工具。
期刊介绍:
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.