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 21 articles , 1 standard article )

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  1. Alam, M. Jahangir: Capital misallocation: cyclicality and sources (2020)
  2. Crager, Michael R.: Extensions of the absolute standardized hazard ratio and connections with measures of explained variation and variable importance (2020)
  3. Qian, George; Mahdi, Adam: Sensitivity analysis methods in the biomedical sciences (2020)
  4. Colini-Baldeschi, Riccardo; Scarsini, Marco; Vaccari, Stefano: Variance allocation and Shapley value (2018)
  5. Marković, Dušan: Appraisal of science and economic factors on total number of granted patents (2018)
  6. Ye, Chenglong; Yang, Yi; Yang, Yuhong: Sparsity oriented importance learning for high-dimensional linear regression (2018)
  7. Owen, Art B.; Prieur, Clémentine: On Shapley value for measuring importance of dependent inputs (2017)
  8. Teisseyre, Paweł; Kłopotek, Robert A.; Mielniczuk, Jan: Random subspace method for high-dimensional regression with the \textttRpackage \textttregRSM (2016)
  9. Lipovetsky, Stan; Conklin, W. Michael: Predictor relative importance and matching regression parameters (2015)
  10. Mielniczuk, Jan; Teisseyre, Paweł: Using random subspace method for prediction and variable importance assessment in linear regression (2014)
  11. Ortmann, Karl Michael: A cooperative value in a multiplicative model (2013)
  12. Huettner, Frank; Sunder, Marco: Axiomatic arguments for decomposing goodness of fit according to Shapley and Owen values (2012)
  13. Zahran, Sammy; Long, Michael A.; Berry, Kenneth J.: Measures of predictor sensitivity for order-insensitive partitioning of multiple correlation (2012)
  14. Pintér, Miklós: Regression games (2011)
  15. Waller, Niels G.: The geometry of enhancement in multiple regression (2011)
  16. Bornhorst, Fabian; Ichino, Andrea; Kirchkamp, Oliver; Schlag, Karl H.; Winter, Eyal: Similarities and differences when building trust: the role of cultures (2010)
  17. Grömping, Ulrike; Landau, Sabine: Do not adjust coefficients in Shapley value regression (2010)
  18. Ulrike Grömping: Inference with Linear Equality and Inequality Constraints Using R: The Package ic.infer (2010) not zbMATH
  19. Retzer, J. J.; Soofi, E. S.; Soyer, R.: Information importance of predictors: concept, measures, Bayesian inference, and applications (2009)
  20. Waller, Niels G.: Fungible weights in multiple regression (2008)

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