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

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-doma...

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Vydané v:PloS one Ročník 5; číslo 7; s. e11698
Hlavní autori: Olea, Pedro P., Mateo-Tomás, Patricia, de Frutos, Ángel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 21.07.2010
Public Library of Science (PLoS)
Predmet:
Bias
Bibliographic data bases
Bibliometrics
Biodiversity
Biology
Computer programs
Computer simulation
Conservation
Conservation of Natural Resources - methods
Decision making
Ecological monitoring
Ecology
Ecology/Behavioral Ecology
Ecology/Community Ecology and Biodiversity
Ecology/Conservation and Restoration Ecology
Ecology/Global Change Ecology
Ecology/Population Ecology
Ecology/Spatial and Landscape Ecology
Endangered & extinct species
Environmental management
Environmental Monitoring - methods
Environmental science
Generalized linear models
Hierarchies
Independent variables
Information theory
Models, Theoretical
Multiple regression analysis
Numerical analysis
Numerical simulations
Partitioning
Principal components analysis
Ranking
Regression analysis
Robustness (mathematics)
Rounding
Software
Statistical analysis
Trends
Variables
Variance
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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. 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. 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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Conceived and designed the experiments: PPO. Performed the experiments: PPO. Analyzed the data: PPO PMT DF. Contributed reagents/materials/analysis tools: PPO PMT DF. Wrote the paper: PPO PMT.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0011698