Lasso and Elastic-Net Regularized Generalized Linear Models
plot the cross-validation curve produced by cv.glmnet
plot coefficients from a "glmnet" object
make predictions from a "cv.glmnet" object.
Extract coefficients from a glmnet object
Compute a survival curve from a coxnet object
Get predictions from a glmnetfit fit object
print a cross-validated glmnet object
print a glmnet object
assess performance of a 'glmnet' object using test data.
Simulated data for the glmnet vignette
Make response for coxnet
fit a glm with all the options in glmnet
compute C index for a Cox model
Fit a Cox regression model with elastic net regularization for a singl...
Fit a Cox regression model with elastic net regularization for a path ...
Elastic net objective function value for Cox regression model
Compute gradient for Cox model
Generate multinomial samples from a probability matrix
Compute deviance for Cox model
Cross-validation for glmnet
Elastic net deviance value
Extract the deviance from a glmnet object
Solve weighted least squares (WLS) problem for a single lambda value
Helper function for Cox deviance and gradient
Get lambda max for Cox regression model
Helper function to get etas (linear predictions)
Get null deviance, starting mu and lambda max
Internal glmnet functions
Elastic net model paths for some generalized linear models
internal glmnet parameters
Fit a GLM with elastic net regularization for a single value of lambda
Display the names of the measures used in CV for different "glmnet" fa...
Fit a GLM with elastic net regularization for a path of lambda values
fit a GLM with lasso or elasticnet regularization
Add strata to a Surv object
convert a data frame to a data matrix with one-hot encoding
Helper function to fit coxph model for survfit.coxnet
Helper function to amend ... for new data in survfit.coxnet
Replace the missing entries in a matrix columnwise with the entries in...
Elastic net objective function value
Elastic net penalty value
Compute a survival curve from a cv.glmnet object
Check if glmnet should call cox.path
Helper function to compute weighted mean and standard deviation
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; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). 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 cited.