glmnet v4.1-1

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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, Cox model, multiple-response Gaussian, and the grouped multinomial regression. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family. This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. 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.

Version 4.0 is a major release that allows for any GLM family, besides the built-in families.

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 get_start Get null deviance, starting mu and lambda max glmnet-internal Internal glmnet functions stratifySurv Add strata to a Surv object Cindex compute C index for a Cox model assess.glmnet assess performance of a 'glmnet' object using test data. predict.glmnetfit Get predictions from a glmnetfit fit object obj_function Elastic net objective function value pen_function Elastic net penalty value print.cv.glmnet print a cross-validated glmnet object dev_function Elastic net deviance value fid Helper function for Cox deviance and gradient rmult Generate multinomial samples from a probability matrix elnet.fit Solve weighted least squares (WLS) problem for a single lambda value print.glmnet print a glmnet object bigGlm fit a glm with all the options in glmnet survfit.coxnet Compute a survival curve from a coxnet object glmnet.control internal glmnet parameters mycoxpred Helper function to amend ... for new data in survfit.coxnet beta_CVX Simulated data for the glmnet vignette deviance.glmnet Extract the deviance from a glmnet object response.coxnet Make response for coxnet na.replace Replace the missing entries in a matrix columnwise with the entries in a supplied vector coxgrad Compute gradient for Cox model makeX convert a data frame to a data matrix with one-hot encoding glmnet-package Elastic net model paths for some generalized linear models survfit.cv.glmnet Compute a survival curve from a cv.glmnet object cox.path Fit a Cox regression model with elastic net regularization for a path of lambda values cox.fit Fit a Cox regression model with elastic net regularization for a single value of lambda use.cox.path Check if glmnet should call cox.path mycoxph Helper function to fit coxph model for survfit.coxnet glmnet fit a GLM with lasso or elasticnet regularization predict.cv.glmnet make predictions from a "cv.glmnet" object. coef.glmnet Extract coefficients from a glmnet object cox_obj_function Elastic net objective function value for Cox regression model glmnet.fit Fit a GLM with elastic net regularization for a single value of lambda weighted_mean_sd Helper function to compute weighted mean and standard deviation cv.glmnet Cross-validation for glmnet coxnet.deviance Compute deviance for Cox model glmnet.path Fit a GLM with elastic net regularization for a path of lambda values get_cox_lambda_max Get lambda max for Cox regression model get_eta Helper function to get etas (linear predictions) 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 plot.glmnet plot coefficients from a "glmnet" object No Results!