tcensReg_newton: Newton-Raphson Algorithm for Truncated Normal Distribution...

In tcensReg: MLE of a Truncated Normal Distribution with Censored Data

Description

Iteratively solve the optimization log likelihood problem using Newton-Raphson algorithm with analytic gradient and Hessian values and step halving.

Usage

tcensReg_newton(
  y,
  X,
  a = -Inf,
  v = NULL,
  epsilon = 1e-04,
  tol_val = 1e-06,
  max_iter = 100,
  step_max = 10,
  theta_init = NULL
)

Arguments

y	Numeric vector with the observed truncated and censored outcomes
X	Numeric design matrix
a	Numeric scalar indicating the truncation value. Initial value is -Inf indicating no truncation
v	Numeric scalar indicating the censoring value. Initially set to NULL indicating no censoring
epsilon	Numeric value used to define when the algorithm should stop when the gradient is less then epsilon. Default is 0.001
tol_val	Tolerance value used to stop the algorithm if the (n+1) and (n) log likelihood is within the tolerance limit
max_iter	Maximum number of iterations for algorithm. Default is 100
step_max	Maximum number of steps when performing line search. Default is 10
theta_init	Initial values of theta provided by the user. If unspecified then calculates values from OLS regression

Value

Returns a list of final estimate of theta, total number of iterations performed, initial log-likelihood, final log-likelihood, and estimated variance covariance matrix.

tcensReg documentation built on July 8, 2020, 7:17 p.m.