relaimpo: Relative importance of regressors in linear models. relaimpo provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.

References in zbMATH (referenced in 26 articles , 1 standard article )

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  1. Cammarota, Camillo; Pinto, Alessandro: Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance (2021)
  2. Watson, David S.; Wright, Marvin N.: Testing conditional independence in supervised learning algorithms (2021)
  3. Alam, M. Jahangir: Capital misallocation: cyclicality and sources (2020)
  4. Bao, Li; Cheung, William; Unger, Stephan: Hedging housing price risks: some empirical evidence from the US (2020)
  5. Crager, Michael R.: Extensions of the absolute standardized hazard ratio and connections with measures of explained variation and variable importance (2020)
  6. Hu, Xingwei: A theory of dichotomous valuation with applications to variable selection (2020)
  7. Qian, George; Mahdi, Adam: Sensitivity analysis methods in the biomedical sciences (2020)
  8. Lu, Yonggang; Westfall, Peter: Simple and flexible Bayesian inferences for standardized regression coefficients (2019)
  9. Colini-Baldeschi, Riccardo; Scarsini, Marco; Vaccari, Stefano: Variance allocation and Shapley value (2018)
  10. Marković, Dušan: Appraisal of science and economic factors on total number of granted patents (2018)
  11. Ye, Chenglong; Yang, Yi; Yang, Yuhong: Sparsity oriented importance learning for high-dimensional linear regression (2018)
  12. Owen, Art B.; Prieur, Clémentine: On Shapley value for measuring importance of dependent inputs (2017)
  13. Teisseyre, Paweł; Kłopotek, Robert A.; Mielniczuk, Jan: Random subspace method for high-dimensional regression with the \textttRpackage \textttregRSM (2016)
  14. Lipovetsky, Stan; Conklin, W. Michael: Predictor relative importance and matching regression parameters (2015)
  15. Mielniczuk, Jan; Teisseyre, Paweł: Using random subspace method for prediction and variable importance assessment in linear regression (2014)
  16. Ortmann, Karl Michael: A cooperative value in a multiplicative model (2013)
  17. Huettner, Frank; Sunder, Marco: Axiomatic arguments for decomposing goodness of fit according to Shapley and Owen values (2012)
  18. Zahran, Sammy; Long, Michael A.; Berry, Kenneth J.: Measures of predictor sensitivity for order-insensitive partitioning of multiple correlation (2012)
  19. Pintér, Miklós: Regression games (2011)
  20. Waller, Niels G.: The geometry of enhancement in multiple regression (2011)

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