Man pages for glmnet
Lasso and Elastic-Net Regularized Generalized Linear Models
| assess.glmnet | assess performance of a 'glmnet' object using test data. |
| beta_CVX | Simulated data for the glmnet vignette |
| bigGlm | fit a glm with all the options in 'glmnet' |
| BinomialExample | Synthetic dataset with binary response |
| Cindex | compute C index for a Cox model |
| CoxExample | Synthetic dataset with right-censored survival response |
| cox.fit | Fit a Cox regression model with elastic net regularization... |
| coxgrad | Compute gradient for Cox model |
| coxnet.deviance | Compute deviance for Cox model |
| cox_obj_function | Elastic net objective function value for Cox regression model |
| cox.path | Fit a Cox regression model with elastic net regularization... |
| cv.glmnet | Cross-validation for glmnet |
| dev_function | Elastic net deviance value |
| deviance.glmnet | Extract the deviance from a glmnet object |
| elnet.fit | Solve weighted least squares (WLS) problem for a single... |
| fid | Helper function for Cox deviance and gradient |
| get_cox_lambda_max | Get lambda max for Cox regression model |
| get_eta | Helper function to get etas (linear predictions) |
| get_start | Get null deviance, starting mu and lambda max |
| glmnet | fit a GLM with lasso or elasticnet regularization |
| glmnet.control | internal glmnet parameters |
| glmnet.fit | Fit a GLM with elastic net regularization for a single value... |
| glmnet-internal | Internal glmnet functions |
| glmnet.measures | Display the names of the measures used in CV for different... |
| glmnet-package | Elastic net model paths for some generalized linear models |
| glmnet.path | Fit a GLM with elastic net regularization for a path of... |
| makeX | convert a data frame to a data matrix with one-hot encoding |
| MultiGaussianExample | Synthetic dataset with multiple Gaussian responses |
| MultinomialExample | Synthetic dataset with multinomial response |
| mycoxph | Helper function to fit coxph model for survfit.coxnet |
| mycoxpred | Helper function to amend ... for new data in survfit.coxnet |
| na.replace | Replace the missing entries in a matrix columnwise with the... |
| obj_function | Elastic net objective function value |
| pen_function | Elastic net penalty value |
| plot.cv.glmnet | plot the cross-validation curve produced by cv.glmnet |
| plot.glmnet | plot coefficients from a "glmnet" object |
| PoissonExample | Synthetic dataset with count response |
| predict.cv.glmnet | make predictions from a "cv.glmnet" object. |
| predict.glmnet | Extract coefficients from a glmnet object |
| predict.glmnetfit | Get predictions from a 'glmnetfit' fit object |
| print.cv.glmnet | print a cross-validated glmnet object |
| print.glmnet | print a glmnet object |
| QuickStartExample | Synthetic dataset with Gaussian response |
| response.coxnet | Make response for coxnet |
| rmult | Generate multinomial samples from a probability matrix |
| SparseExample | Synthetic dataset with sparse design matrix |
| stratifySurv | Add strata to a Surv object |
| survfit.coxnet | Compute a survival curve from a coxnet object |
| survfit.cv.glmnet | Compute a survival curve from a cv.glmnet object |
| use.cox.path | Check if glmnet should call cox.path |
| weighted_mean_sd | Helper function to compute weighted mean and standard... |
