glmnet-package |
Elastic net model paths for some 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 |
coef.cv.glmnet |
make predictions from a "cv.glmnet" object. |
coef.cv.relaxed |
make predictions from a "cv.glmnet" object. |
coef.glmnet |
Extract coefficients from a glmnet object |
coef.relaxed |
Extract coefficients from a glmnet object |
confusion.glmnet |
assess performance of a 'glmnet' object using test data. |
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 |
CoxExample |
Synthetic dataset with right-censored survival response |
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 |
cv.glmnet |
Cross-validation for glmnet |
deviance.glmnet |
Extract the deviance from a glmnet object |
dev_function |
Elastic net deviance value |
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 |
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 |
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 entries in a supplied vector |
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.cv.relaxed |
plot the cross-validation curve produced by cv.glmnet |
plot.glmnet |
plot coefficients from a "glmnet" object |
plot.mrelnet |
plot coefficients from a "glmnet" object |
plot.multnet |
plot coefficients from a "glmnet" object |
plot.relaxed |
plot coefficients from a "glmnet" object |
PoissonExample |
Synthetic dataset with count response |
predict.coxnet |
Extract coefficients from a glmnet object |
predict.cv.glmnet |
make predictions from a "cv.glmnet" object. |
predict.cv.relaxed |
make predictions from a "cv.glmnet" object. |
predict.elnet |
Extract coefficients from a glmnet object |
predict.fishnet |
Extract coefficients from a glmnet object |
predict.glmnet |
Extract coefficients from a glmnet object |
predict.glmnetfit |
Get predictions from a 'glmnetfit' fit object |
predict.lognet |
Extract coefficients from a glmnet object |
predict.mrelnet |
Extract coefficients from a glmnet object |
predict.multnet |
Extract coefficients from a glmnet object |
predict.relaxed |
Extract coefficients from a glmnet object |
print.bigGlm |
print a glmnet object |
print.cv.glmnet |
print a cross-validated glmnet object |
print.cv.relaxed |
print a cross-validated glmnet object |
print.glmnet |
print a glmnet object |
print.relaxed |
print a glmnet object |
QuickStartExample |
Synthetic dataset with Gaussian response |
relax.glmnet |
fit a GLM with lasso or elasticnet regularization |
response.coxnet |
Make response for coxnet |
rmult |
Generate multinomial samples from a probability matrix |
roc.glmnet |
assess performance of a 'glmnet' object using test data. |
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 deviation |
x |
Simulated data for the glmnet vignette |
y |
Simulated data for the glmnet vignette |