mable.reg: Mable fit of semiparametric regression model based on...

View source: R/mable-ph-model.r

mable.regR Documentation

Mable fit of semiparametric regression model based on interval censored data

Description

Wrapping all codemable fit of regression models in one function. Using maximum approximate Bernstein/Beta likelihood estimation to fit semiparametric regression models: Cox ph model, proportional odds(po) model, accelerated failure time model, and so on.

Usage

mable.reg(
  formula,
  data,
  model = c("ph", "aft"),
  M,
  g = NULL,
  pi0 = NULL,
  tau = Inf,
  x0 = NULL,
  eta = 1,
  controls = mable.ctrl(),
  progress = TRUE
)

Arguments

formula

regression formula. Response must be of the form cbind(l, u). See 'Details'.

data

a dataset

model

the model to fit. Current options are "ph" (Cox PH) or "aft" (accelerated failure time model)

M

a vector (m0, m1) specifies the set of consective integers as candidate degrees

g

an initial guess of the regression coefficients

pi0

Initial guess of \pi(x_0) = F(\tau_n|x_0). Without right censored data, pi0 = 1. See 'Details'.

tau

right endpoint of support [0, \tau) must be greater than or equal to the maximum observed time

x0

a working baseline covariate. See 'Details'.

eta

the given positive value of \eta. Used when model="po".

controls

Object of class mable.ctrl() specifying iteration limit and other control options. Default is mable.ctrl.

progress

if TRUE a text progressbar is displayed

Details

For "ph" model a missing initial guess of the regression coefficients g is obtained by ic_sp() of package icenReg. For "aft" model a missing g is imputed by the rank estimate aftsrr() of package aftgee for right-censored data. For general interval censored observations, we keep the right-censored but replace the finite interval with its midpoint and fit the data by aftsrr() as a right-censored data.

Value

A 'mable_reg' class object

Author(s)

Zhong Guan <zguan@iusb.edu>

See Also

mable.aft, mable.ph


mable documentation built on Aug. 24, 2023, 5:10 p.m.