Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package

doi:10.1111/2041-210X.13800

	Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package
	Lai, Jiangshan 1; Zou, Yi 2; Zhang, Jinlong 3; Peres-Neto, Pedro R.
	2022
发表期刊	METHODS IN ECOLOGY AND EVOLUTION
ISSN	2041-210X
卷号	13 期号:4 页码:782-788
摘要	Canonical analysis, a generalization of multiple regression to multiple-response variables, is widely used in ecology. Because these models often involve many parameters (one slope per response per predictor), they pose challenges to model interpretation. Among these challenges, we lack quantitative frameworks for estimating the overall importance of single predictors in multi-response regression models. Here we demonstrate that commonality analysis and hierarchical partitioning, widely used for both estimating predictor importance and improving the interpretation of single-response regression models, are related and complementary frameworks that can be expanded for the analysis of multiple-response models. In this application, we (a) demonstrate the mathematical links between commonality analysis, variation and hierarchical partitioning; (b) generalize these frameworks to allow the analysis of any number of predictor variables or groups of predictor variables as in the case of variation partitioning; and (c) introduce and demonstrate the implementation of these generalized frameworks in the R package rdacca.hp.
关键词	averaging over orderings CCA commonality analysis constrained ordination db-RDA explained variation RDA relative importance
学科领域	Ecology
DOI	10.1111/2041-210X.13800
收录类别	SCI
语种	英语
WOS关键词	RELATIVE IMPORTANCE ; DOMINANCE ANALYSIS ; PREDICTORS
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED) ; Social Science Citation Index (SSCI)
WOS记录号	WOS:000777994600003
出版者	WILEY
文献子类	Article
出版地	HOBOKEN
EISSN	2041-2096
资助机构	National Science and Technology Basic Resources Survey Program of China [2019FY100204] ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19050404] ; Canada Research Chair (CRC) program
作者邮箱	lai@ibcas.ac.cn
作品OA属性	Green Submitted
引用统计	被引频次：344[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/28666
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Xian Jiaotong Liverpool Univ, Dept Hlth & Environm Sci, Suzhou, Peoples R China 4.Kadoorie Farm & Bot Garden, Flora Conservat Dept, Hong Kong, Peoples R China 5.Peres-Neto, Pedro R.] Concordia Univ, Dept Biol, Montreal, PQ, Canada 6.Peres-Neto, Pedro R.] Concordia Univ, Canada Res Chair Biodivers & Spatial Ecol, Montreal, PQ, Canada
推荐引用方式 GB/T 7714	Lai, Jiangshan,Zou, Yi,Zhang, Jinlong,et al. Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package[J]. METHODS IN ECOLOGY AND EVOLUTION,2022,13(4):782-788.
APA	Lai, Jiangshan,Zou, Yi,Zhang, Jinlong,&Peres-Neto, Pedro R..(2022).Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.METHODS IN ECOLOGY AND EVOLUTION,13(4),782-788.
MLA	Lai, Jiangshan,et al."Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package".METHODS IN ECOLOGY AND EVOLUTION 13.4(2022):782-788.

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