glmnet v3.0

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Lasso and Elastic-Net Regularized Generalized Linear Models

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.

Lasso and Elastic-Net Regularized Generalized Linear Models

We provide extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression (gaussian), multi-task gaussian, logistic and multinomial regression models (grouped or not), Poisson regression and the Cox model. The algorithm uses cyclical coordinate descent in a path-wise fashion. Details may be found in Friedman, Hastie, and Tibshirani (2010), Simon et al. (2011), Tibshirani et al. (2012), Simon, Friedman, and Hastie (2013).

Version 3.0 is a major release with several new features, including:

• Relaxed fitting to allow models in the path to be refit without regularization. CV will select from these, or from specified mixtures of the relaxed fit and the regular fit;
• Progress bar to monitor computation;
• Assessment functions for displaying performance of models on test data. These include all the measures available via cv.glmnet, as well as confusion matrices and ROC plots for classification models;
• print methods for CV output;
• Functions for building the x input matrix for glmnet that allow for one-hot-encoding of factor variables, appropriate treatment of missing values, and an option to create a sparse matrix if appropriate.
• A function for fitting unpenalized a single version of any of the GLMs of glmnet.

References

Friedman, Jerome, Trevor Hastie, and Rob Tibshirani. 2010. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, Articles 33 (1): 1–22. https://doi.org/10.18637/jss.v033.i01.
Simon, Noah, Jerome Friedman, and Trevor Hastie. 2013. “A Blockwise Descent Algorithm for Group-Penalized Multiresponse and Multinomial Regression.”
Simon, Noah, Jerome Friedman, Trevor Hastie, and Rob Tibshirani. 2011. “Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent.” Journal of Statistical Software, Articles 39 (5): 1–13. https://doi.org/10.18637/jss.v039.i05.
Tibshirani, Robert, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, and Ryan J. Tibshirani. 2012. “Strong Rules for Discarding Predictors in Lasso-Type Problems.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74 (2): 245–66. https://doi.org/10.1111/j.1467-9868.2011.01004.x.

Functions in glmnet

 Name Description coxgrad compute gradient for cox model print.cv.glmnet print a cross-validated glmnet object plot.glmnet plot coefficients from a "glmnet" object coxnet.deviance compute deviance for cox model output predict.cv.glmnet make predictions from a "cv.glmnet" object. na.replace Replace the missing entries in a matrix columnwise with the entries in a supplied vector plot.cv.glmnet plot the cross-validation curve produced by cv.glmnet glmnet.measures Display the names of the measures used in CV for different "glmnet" families makeX convert a data frame to a data matrix with one-hot encoding glmnet.control internal glmnet parameters glmnet fit a GLM with lasso or elasticnet regularization print.glmnet print a glmnet object rmult Generate multinomial samples from a probability matrix cv.glmnet Cross-validation for glmnet deviance.glmnet Extract the deviance from a glmnet object assess.glmnet assess performace of a 'glmnet' object using test data. Cindex compute C index for a Cox model glmnet-internal Internal glmnet functions glmnet-package Elastic net model paths for some generalized linear models beta_CVX Simulated data for the glmnet vignette bigGlm fit a glm with all the options in glmnet coef.glmnet Extract coefficients from a glmnet object No Results!