1991
DOI: 10.2307/2684366
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Hierarchical Partitioning

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Cited by 626 publications
(697 citation statements)
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“…Therefore, we performed a hierarchical partitioning (HP) analysis to evaluate the independent contribution of each predictor to population indices. Hierarchical partitioning averages the differences in the R 2 values among all combinations of models with and without a given covariate, and returns the independent contribution of each variable to the variance of the dependent variable (Chevan andSutherland 1991, Mac Nally andWalsh 2004). However, this procedure does not produce regression coefficients, nor does it identify a set of models that explains the observed variation in the dependent variables.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, we performed a hierarchical partitioning (HP) analysis to evaluate the independent contribution of each predictor to population indices. Hierarchical partitioning averages the differences in the R 2 values among all combinations of models with and without a given covariate, and returns the independent contribution of each variable to the variance of the dependent variable (Chevan andSutherland 1991, Mac Nally andWalsh 2004). However, this procedure does not produce regression coefficients, nor does it identify a set of models that explains the observed variation in the dependent variables.…”
Section: Methodsmentioning
confidence: 99%
“…In all analyses, vegetation structures (shrub cover, tree density) were treated as continuous variables. We tested for pairwise differences among treatment classes using general linear hypothesis tests (Searle 1971) in the 'MULTCOMP' package (Hothorn et al 2007) (Chevan & Sutherland 1991). We used generalized linear mixed models (GLMM) with binomial error structure (Broströ m 2007) to test the hypothesis that warbler settlement/absence depended on our treatments over repeated visits.…”
Section: Methodsmentioning
confidence: 99%
“…The SE values allow us to determine the precision of the estimated model and variables; therefore, if the SE value is two times greater than the estimated parameter, then we can conclude that this parameter is not a good estimator of the response variable (Anderson, 2008). In order to evaluate the relative importance of each variable, we established the weight of each explanatory variable using the function to calculate relative importance metrics for linear models (calc.relimp) with R^2 contribution averaged over orderings among regressors (Chevan and Sutherland, 1991). The statistical analyses were done using the R statistic program (R Development Core Team R, 2013).…”
Section: Methodsmentioning
confidence: 99%