Package index • glmnet

All functions

BinomialExample: Synthetic dataset with binary response
Cindex(): compute C index for a Cox model
CoxExample: Synthetic dataset with right-censored survival response
MultiGaussianExample: Synthetic dataset with multiple Gaussian responses
MultinomialExample: Synthetic dataset with multinomial response
PoissonExample: Synthetic dataset with count response
QuickStartExample: Synthetic dataset with Gaussian response
SparseExample: Synthetic dataset with sparse design matrix
assess.glmnet() confusion.glmnet() roc.glmnet(): assess performance of a 'glmnet' object using test data.
beta_CVX x y: Simulated data for the glmnet vignette
bigGlm(): fit a glm with all the options in glmnet
cox.fit(): Fit a Cox regression model with elastic net regularization for a single value of lambda
cox.path(): Fit a Cox regression model with elastic net regularization for a path of lambda values
cox_obj_function(): Elastic net objective function value for Cox regression model
coxgrad(): Compute gradient for Cox model
coxnet.deviance(): Compute deviance for Cox model
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 lambda value
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-package: Elastic net model paths for some generalized linear models
glmnet() relax.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 of lambda
glmnet.measures(): Display the names of the measures used in CV for different "glmnet" families
glmnet.path(): Fit a GLM with elastic net regularization for a path of lambda values
makeX(): convert a data frame to a data matrix with one-hot encoding
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 entries in a supplied vector
obj_function(): Elastic net objective function value
pen_function(): Elastic net penalty value
plot(<cv.glmnet>) plot(<cv.relaxed>): plot the cross-validation curve produced by cv.glmnet
plot(<glmnet>) plot(<mrelnet>) plot(<multnet>) plot(<relaxed>): plot coefficients from a "glmnet" object
predict(<cv.glmnet>) predict(<cv.relaxed>): make predictions from a "cv.glmnet" object.
coef(<glmnet>) predict(<glmnet>) predict(<relaxed>): 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
response.coxnet(): Make response for coxnet
rmult(): Generate multinomial samples from a probability 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 deviation