fitit: Fit proportional hazard model with smooth baseline hazard and (optional) interval censoring

Description

Fit proportional hazard model with smooth baseline hazard and (optional) interval censoring

Usage

fitit(
  Y,
  R,
  dead,
  X,
  B,
  Ic,
  R1,
  cbx,
  Pdiff,
  Pridge,
  lambda,
  nit = 50,
  tol = 1e-06,
  tollam = 0.01,
  update_lambda = FALSE,
  ic_update = TRUE,
  monitor = FALSE
)

Value

A list with items

cbx: Vector of
ll: Poisson GLM log-likelihood
lambda: Final tuning parameter
pen: Penalty part of penalized log-likelihood
ed: Effetive dimension of the baseline hazard
nit1: Number of iterations used in first phase
nit: Total number of iterations used (first plus second phase)
tollam: Tolerance used for switching to lambda update

Arguments

Y: Events (matrix, number of bins by subjects)
R: Risk sets (matrix, number of bins by subjects)
dead: (Boolean vector, TRUE if event, FALSE if right censored)
X: Covariates (matrix, number of covariates (+1) by subjects)
B: B-spline basis matrix
Ic: Censoring interval per individual, coded as 0/1 (in columns)
R1: Left truncation interval per individual, coded as 0/1 (in columns)
cbx: Vector of starting values
Pdiff: B-spline part of penalty matrix
Pridge: Ridge part of penalty matrix (for intercept)
lambda: Smoothing parameter (number)
nit: Maximum number of iterations (integer)
tol: Tolerance for final fit
tollam: Tolerance for switching to lambda update
update_lambda: Automatic update of lambda (Boolean)
ic_update: Update risk and event probabilities (Boolean)
monitor: Monitor convergence (Boolean)