coxnet_train: Wrapper for glmnet for censored

In EmilHvitfeldt/survnip: 'parsnip' Engines for Survival Models

Wrapper for glmnet for censored

Description

Not to be used directly by users.

Usage

coxnet_train(
  formula,
  data,
  alpha = 1,
  lambda = NULL,
  weights = NULL,
  ...,
  call = caller_env()
)

Arguments

formula	The model formula.
data	The data.
alpha	The elasticnet mixing parameter, with 0\le\alpha\le 1. The penalty is defined as (1-\alpha)/2||\beta||_2^2+\alpha||\beta||_1. alpha=1 is the lasso penalty, and alpha=0 the ridge penalty.
lambda	A user supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Supplying a value of lambda overrides this. WARNING: use with care. Avoid supplying a single value for lambda (for predictions after CV use predict() instead). Supply instead a decreasing sequence of lambda values. glmnet relies on its warms starts for speed, and its often faster to fit a whole path than compute a single fit.
weights	observation weights. Can be total counts if responses are proportion matrices. Default is 1 for each observation
...	additional parameters passed to glmnet::glmnet.
call	The call used in errors and warnings.

Details

This wrapper translates from formula interface to glmnet's matrix due to how stratification can be specified. glmnet requires that the response is stratified via glmnet::stratifySurv(). censored allows specification via a survival::strata() term on the right-hand side of the formula. The formula is used to generate the stratification information needed for stratifying the response. The formula without the strata term is used for generating the model matrix for glmnet.

The wrapper retains the original formula and the pre-processing elements including the training data to allow for predictions from the fitted model.

Value

A fitted glmnet model.

Examples

coxnet_mod <- coxnet_train(Surv(time, status) ~ age + sex, data = lung)

EmilHvitfeldt/survnip documentation built on April 18, 2024, 10:23 a.m.