R package penalized: Efficient approximate k-fold and leave-one-out cross-validation for ridge regression. In model building and model evaluation, cross-validation is a frequently used resampling method. Unfortunately, this method can be quite time consuming. In this article, we discuss an approximation method that is much faster and can be used in generalized linear models and Cox’ proportional hazards model with a ridge penalty term. Our approximation method is based on a Taylor expansion around the estimate of the full model. In this way, all cross-validated estimates are approximated without refitting the model. The tuning parameter can now be chosen based on these approximations and can be optimized in less time. The method is most accurate when approximating leave-one-out cross-validation results for large data sets which is originally the most computationally demanding situation. In order to demonstrate the method’s performance, it will be applied to several microarray data sets. An R package penalized, which implements the method, is available on CRAN. (Source:

References in zbMATH (referenced in 24 articles )

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  1. Khan, Zardad; Gul, Asma; Perperoglou, Aris; Miftahuddin, Miftahuddin; Mahmoud, Osama; Adler, Werner; Lausen, Berthold: Ensemble of optimal trees, random forest and random projection ensemble classification (2020)
  2. Pan, Yuqing; Mai, Qing; Zhang, Xin: Covariate-adjusted tensor classification in high dimensions (2019)
  3. Imdad Ullah Muhammad, Aslam Muhammad: lmridge: A Comprehensive R Package for Ridge Regression (2018) not zbMATH
  4. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  5. Hoff, Peter D.: Lasso, fractional norm and structured sparse estimation using a Hadamard product parametrization (2017)
  6. Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon: Regularized Ordinal Regression and the ordinalNet R Package (2017) arXiv
  7. Moore, Dirk F.: Applied survival analysis using R (2016)
  8. Tutz, Gerhard; Koch, Dominik: Improved nearest neighbor classifiers by weighting and selection of predictors (2016)
  9. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  10. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
  11. Zucknick, Manuela; Saadati, Maral; Benner, Axel: Nonidentical twins: comparison of frequentist and Bayesian Lasso for Cox models (2015)
  12. Chaturvedi, Nimisha; De Menezes, Renée X.; Goeman, Jelle J.: Fused Lasso algorithm for (\mathrmCox^\prime) proportional hazards and binomial logit models with application to copy number profiles (2014)
  13. Martin Sill; Thomas Hielscher; Natalia Becker; Manuela Zucknick: c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models (2014) not zbMATH
  14. Stephan Ritter; Nicholas Jewell; Alan Hubbard: R Package multiPIM: A Causal Inference Approach to Variable Importance Analysis (2014) not zbMATH
  15. Stephen Reid; Rob Tibshirani: Regularization Paths for Conditional Logistic Regression: The clogitL1 Package (2014) not zbMATH
  16. Meijer, Rosa J.; Goeman, Jelle J.: Efficient approximate (k)-fold and leave-one-out cross-validation for ridge regression (2013)
  17. Nagarajan, Radhakrishnan; Scutari, Marco; Lèbre, Sophie: Bayesian networks in R. With applications in systems biology (2013)
  18. Scutari, Marco; Mackay, Ian; Balding, David: Improving the efficiency of genomic selection (2013)
  19. Yang, Yi; Zou, Hui: A cocktail algorithm for solving the elastic net penalized Cox’s regression in high dimensions (2013)
  20. Noah Simon; Jerome Friedman; Trevor Hastie; Rob Tibshirani: Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent (2011) not zbMATH

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