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

Description

This function calls the C++ implementation of Cox regression with elastic net regularization. It handles both right-censored and left-truncated (start, stop) survival data using the Breslow or Efron method for ties. For stratified Cox models, it uses an IRLS approach with integrated C++ gradient/Hessian computation.

Usage

coxnet(
  x,
  is.sparse,
  y,
  weights,
  offset,
  alpha,
  nobs,
  nvars,
  jd,
  vp,
  cl,
  ne,
  nx,
  nlam,
  flmin,
  ulam,
  thresh,
  isd,
  vnames,
  maxit,
  pb,
  efron = FALSE
)

Value

An object of class "coxnet" with components:

a0: NULL (Cox model has no intercept)
beta: Sparse coefficient matrix
df: Number of nonzero coefficients per lambda
dim: Dimension of coefficient matrix
lambda: Lambda sequence used
dev.ratio: Fraction of null deviance explained
nulldev: Null deviance
npasses: Number of coordinate descent passes
jerr: Error code
offset: Logical indicating if offset was used

Arguments

x: Design matrix, of dimension nobs x nvars.
is.sparse: Logical, is x a sparse matrix?
y: Survival response variable, must be a Surv or stratifySurv object.
weights: Observation weights.
offset: Offset for the linear predictor.
alpha: The elastic net mixing parameter.
nobs: Number of observations.
nvars: Number of variables.
jd: Excluded variable indices (1-indexed, first element is count).
vp: Penalty factors for each coefficient.
cl: Coefficient limits matrix (2 x nvars).
ne: Maximum number of variables in the model.
nx: Maximum number of variables ever to be nonzero.
nlam: Number of lambda values.
flmin: Minimum lambda ratio.
ulam: User-supplied lambda sequence.
thresh: Convergence threshold.
isd: Standardize flag.
vnames: Variable names.
maxit: Maximum number of iterations.
pb: Progress bar object.
efron: Logical; if TRUE use Efron method for ties, otherwise Breslow.