Elmabrok Masaoud, Abulwahid Sirtiyah, Mohammed Abdulqadir, Asem Mohamed, Abdeulmajid Khapoli, Kamal Alshaybani
{"title":"Relative importance analysis of correlated predictors in aquatic veterinary science: Application to an abalone dataset.","authors":"Elmabrok Masaoud, Abulwahid Sirtiyah, Mohammed Abdulqadir, Asem Mohamed, Abdeulmajid Khapoli, Kamal Alshaybani","doi":"10.5455/OVJ.2026.v16.i5.55","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Relative importance refers to the process of quantifying how much each predictor in a linear regression model contributes to explaining the outcome variation. In veterinary science, linear regression is widely applied as an analytical tool, allowing researchers to examine relationships between predictors and outcomes of interest in animal populations.</p><p><strong>Aim: </strong>This study aims to provide an introduction to statistical approaches for quantifying the relative importance of correlated predictors. This study focuses exclusively on statistical procedures implemented in R software that are applicable to linear regression within veterinary science. Additionally, the study demonstrates relative importance analysis for correlated predictors using the abalone dataset as an example.</p><p><strong>Methods: </strong>This study evaluates several statistical approaches for assessing predictor importance, including Shapley value-based decomposition, Genizi method, relative weights analysis, and dominance analysis.</p><p><strong>Results: </strong>The shell weight of abalone was consistently ranked as the most influential predictor in predicting the ring count, which serves as a proxy for abalone age, across all methods. The Proportional Marginal Variance Decomposition (PMVD) metric showed overlapping importance among predictors, while the Genizi metric provided clear separation between all predictors. The Genizi metric and relative weight method consistently produced narrow confidence intervals (CIs) across all estimates, while the PMVD produced wider bootstrap intervals, Lindeman, Merenda, and Gold (LMG) method showed moderate variability, indicating a slight difference in estimates across both metrics.</p><p><strong>Conclusion: </strong>Shell weight is the most dominant predictor of ring count, serving as a proxy for the age of abalone. Both the relative weights and the Genizi method performed well, resulting in narrow CIs. Dominance analysis provided deeper hierarchical insights into predictor importance but requires greater computational resources, particularly for complex models.</p><p><strong>Keywords: </strong>Abalone dataset, Correlated predictors, Dominance analysis, Relative weights analysis, Shapley value.</p>","PeriodicalId":19531,"journal":{"name":"Open Veterinary Journal","volume":"16 5","pages":"3144-3154"},"PeriodicalIF":1.6000,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13314151/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Veterinary Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5455/OVJ.2026.v16.i5.55","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/5/31 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"VETERINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Relative importance refers to the process of quantifying how much each predictor in a linear regression model contributes to explaining the outcome variation. In veterinary science, linear regression is widely applied as an analytical tool, allowing researchers to examine relationships between predictors and outcomes of interest in animal populations.
Aim: This study aims to provide an introduction to statistical approaches for quantifying the relative importance of correlated predictors. This study focuses exclusively on statistical procedures implemented in R software that are applicable to linear regression within veterinary science. Additionally, the study demonstrates relative importance analysis for correlated predictors using the abalone dataset as an example.
Methods: This study evaluates several statistical approaches for assessing predictor importance, including Shapley value-based decomposition, Genizi method, relative weights analysis, and dominance analysis.
Results: The shell weight of abalone was consistently ranked as the most influential predictor in predicting the ring count, which serves as a proxy for abalone age, across all methods. The Proportional Marginal Variance Decomposition (PMVD) metric showed overlapping importance among predictors, while the Genizi metric provided clear separation between all predictors. The Genizi metric and relative weight method consistently produced narrow confidence intervals (CIs) across all estimates, while the PMVD produced wider bootstrap intervals, Lindeman, Merenda, and Gold (LMG) method showed moderate variability, indicating a slight difference in estimates across both metrics.
Conclusion: Shell weight is the most dominant predictor of ring count, serving as a proxy for the age of abalone. Both the relative weights and the Genizi method performed well, resulting in narrow CIs. Dominance analysis provided deeper hierarchical insights into predictor importance but requires greater computational resources, particularly for complex models.
背景：相对重要性是指量化线性回归模型中每个预测因子对解释结果变化的贡献程度的过程。在兽医科学中，线性回归作为一种分析工具被广泛应用，使研究人员能够检查动物种群中预测因子和结果之间的关系。目的：本研究旨在介绍量化相关预测因子相对重要性的统计方法。本研究专门关注在R软件中实现的统计程序，这些程序适用于兽医科学中的线性回归。此外，本研究还以鲍鱼数据为例，对相关预测因子进行了相对重要性分析。方法：本研究评估了几种评估预测因子重要性的统计方法，包括基于Shapley值的分解、Genizi方法、相对权重分析和优势分析。结果：在所有方法中，鲍鱼的壳重一直被列为预测环数最具影响力的预测因子，而环数是鲍鱼年龄的代理。比例边际方差分解（PMVD）指标显示了预测因子之间的重叠重要性，而Genizi指标在所有预测因子之间提供了明确的分离。Genizi度量和相对权重法在所有估计中始终产生较窄的置信区间（ci），而PMVD产生较宽的引导区间，Lindeman, Merenda, and Gold （LMG）方法显示中等变异性，表明两种度量之间的估计略有差异。结论：壳重是环数最主要的预测因子，可作为鲍鱼年龄的代表。相对权值法和Genizi法均表现较好，结果ci较窄。优势分析提供了对预测因子重要性的更深层次的见解，但需要更多的计算资源，特别是对于复杂的模型。关键词：鲍鱼数据集，相关预测因子，优势度分析，相对权重分析，Shapley值
期刊介绍：
Open Veterinary Journal is a peer-reviewed international open access online and printed journal that publishes high-quality original research articles. reviews, short communications and case reports dedicated to all aspects of veterinary sciences and its related subjects. Research areas include the following: Infectious diseases of zoonotic/food-borne importance, applied biochemistry, parasitology, endocrinology, microbiology, immunology, pathology, pharmacology, physiology, epidemiology, molecular biology, immunogenetics, surgery, ophthalmology, dermatology, oncology and animal reproduction. All papers are peer-reviewed. Moreover, with the presence of well-qualified group of international referees, the process of publication will be done meticulously and to the highest standards.
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