Cited by 109Advanced Statistical Methods and ModelsStatistical Methods and ApplicationsData Analysis with R

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

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Olea, Pedro P., Mateo-Tomás, Patricia, de Frutos, Ángel·2010·PLoS ONE

Abstract

BACKGROUND: Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software "hier.part package" has been developed for running HP in R software. Its authors highlight a "minor rounding error" for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. METHODOLOGY/PRINCIPAL FINDINGS: Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. CONCLUSIONS/SIGNIFICANCE: HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given.

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  • Olea et al. have shown that for more than nine explanatory variables, the order in which variables are entered in an HP analysis can affect the amount of independent variance explained by a given predictor.

    “Olea et al. (150) have shown that for more than nine explanatory variables, the order in which variables are entered in an HP analysis can affect the amount of independent variance explained by a given predictor.”

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  1. [27]Cramp S Simmons KEL 1980 The Birds of the Western Paleartic. Vol II Oxford University press, Oxford
  2. [32]Crawley MJ 2007 The R Book Chichester, England John Wiley and Sons, Ltd

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Authors
Olea, P. P., Mateo-Tomás, P., & de Frutos, Á.
Affiliations
IE University (ES)
Corresponding author
Olea, Pedro P.
Venue
PLoS ONE
Published
2010-07-21
Volume
5
Issue
7
Pages
e11698
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2089525213
Cited by
109
References
34
Citations by year
2026: 3 · 2025: 5 · 2024: 6 · 2023: 11 · 2022: 10 · 2021: 10 · 2020: 6 · 2019: 4 · 2018: 11 · 2017: 5 · 2016: 6 · 2015: 9 · 2014: 6 · 2013: 4 · 2012: 7
FWCI
4.36
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
Keywords
hierarchical partitioning, multicollinearity, explained variance, conservation prioritization, environmental management, ecological inference, numerical simulation
MeSH terms
Conservation of Natural Resources, Environmental Monitoring, Models, Theoretical, Software, Bias

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