Package index • glmnet

Fitting

Main model-fitting and cross-validation functions

glmnet() relax.glmnet(): fit a GLM with lasso or elasticnet regularization

cv.glmnet(): Cross-validation for glmnet

glmnet.control(): Internal glmnet algorithm parameters

glmnet.path(): Fit a GLM with elastic net regularization for a path of lambda values

glmnet.fit(): Fit a GLM with elastic net regularization for a single value of lambda

elnet.fit(): Solve weighted least squares (WLS) problem for a single lambda value

bigGlm(): fit a glm with all the options in glmnet

Prediction and Coefficients

coef(<glmnet>) predict(<glmnet>) predict(<relaxed>): Extract coefficients from a glmnet object

predict(<cv.glmnet>) predict(<cv.relaxed>): make predictions from a "cv.glmnet" object.

predict(<glmnetfit>): Get predictions from a glmnetfit fit object

Plotting and Printing

plot(<glmnet>) plot(<mrelnet>) plot(<multnet>) plot(<relaxed>): plot coefficients from a "glmnet" object

plot(<cv.glmnet>) plot(<cv.relaxed>): plot the cross-validation curve produced by cv.glmnet

print(<glmnet>): print a glmnet object

print(<cv.glmnet>): print a cross-validated glmnet object

Model Assessment

assess.glmnet() confusion.glmnet() roc.glmnet(): assess performance of a 'glmnet' object using test data.

deviance(<glmnet>): Extract the deviance from a glmnet object

Cindex(): compute C index for a Cox model

glmnet.measures(): Display the names of the measures used in CV for different "glmnet" families

Survival / Cox

Functions specific to Cox proportional hazards models

coxnet.deviance(): Compute deviance for Cox model

coxgrad(): Compute gradient for Cox model

coxnet(): Cox regression via penalized maximum likelihood using C++ engine

survfit(<coxnet>): Compute a survival curve from a coxnet object

survfit(<cv.glmnet>): Compute a survival curve from a cv.glmnet object

response.coxnet(): Make response for coxnet

stratifySurv(): Add strata to a Surv object

mycoxph(): Helper function to fit coxph model for survfit.coxnet

mycoxpred(): Helper function to amend ... for new data in survfit.coxnet

Utilities

makeX(): convert a data frame to a data matrix with one-hot encoding

na.replace(): Replace the missing entries in a matrix columnwise with the entries in a supplied vector

rmult(): Generate multinomial samples from a probability matrix

weighted_mean_sd(): Helper function to compute weighted mean and standard deviation

get_eta(): Helper function to get etas (linear predictions)

get_start(): Get null deviance, starting mu and lambda max

Internal

glmnet-package: Elastic net model paths for some generalized linear models

glmnet-internal auc assess.coxnet auc.mat cvtype cvstats cvcompute getcoef getcoef.multinomial fix.lam error.bars getmin elnet mrelnet lognet fishnet coefnorm cv.lognet cv.elnet cv.multnet cv.mrelnet cv.coxnet cv.fishnet cv.glmnet.raw cv.relaxed.raw blend.relaxed checkgamma.relax buildPredmat buildPredmat.mrelnetlist buildPredmat.multnetlist buildPredmat.lognetlist buildPredmat.array buildPredmat.coxnetlist buildPredmat.default lambda.interp nonzeroCoef glmnet_softmax getOptcv.glmnet getOptcv.relaxed jerr jerr.elnet jerr.lognet jerr.fishnet jerr.coxnet jerr.mrelnet plotCoef zeromat na.mean check_dots na_sparse_fix prepareX: Internal glmnet functions

dev_function(): Elastic net deviance value

obj_function(): Elastic net objective function value

pen_function(): Elastic net penalty value

fid(): Helper function for Cox deviance and gradient

Datasets

BinomialExample: Synthetic dataset with binary response

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

beta_CVX x y: Simulated data for the glmnet vignette