Aug 26, 2009

Performance of several variable-selection methods applied to real ecological data

Ecology Letters
Paul A Murtaugh

Abstract

I evaluated the predictive ability of statistical models obtained by applying seven methods of variable selection to 12 ecological and environmental data sets. Cross-validation, involving repeated splits of each data set into training and validation subsets, was used to obtain honest estimates of predictive ability that could be fairly compared among methods. There was surprisingly little difference in predictive ability among five methods based on multiple linear regression. Stepwise methods performed similarly to exhaustive algorithms for subset selection, and the choice of criterion for comparing models (Akaike's information criterion, Schwarz's Bayesian information criterion or F statistics) had little effect on predictive ability. For most of the data sets, two methods based on regression trees yielded models with substantially lower predictive ability. I argue that there is no 'best' method of variable selection and that any of the regression-based approaches discussed here is capable of yielding useful predictive models.

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  • Citations64

References

Mentioned in this Paper

Bionomics
Biodiversity
Regression Analysis
Vitelliform Macular Dystrophy
BEST1 gene
Oculocerebral Hypopigmentation Syndrome Type Preus
Linear Regression Body Surface Area Formula for Infants and Children

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