LML_LEP: Log-marginal likelihood estimator for the log-exponential...

In nathansam/SMLN: Bayesian Survival Analysis Using Shape Mixtures of Log-Normal Distributions

Log-marginal likelihood estimator for the log-exponential power model

Description

Log-marginal likelihood estimator for the log-exponential power model

Usage

LML_LEP(
  thin,
  Time,
  Cens,
  X,
  chain,
  prior = 2,
  set = TRUE,
  eps_l = 0.5,
  eps_r = 0.5
)

Arguments

thin	Thinning period.
Time	Vector containing the survival times.
Cens	Censoring indication (1: observed, 0: right-censored).
X	Design matrix with dimensions n x k where n is the number of observations and k is the number of covariates (including the intercept).
chain	MCMC chains generated by a BASSLINE MCMC function
prior	Indicator of prior (1: Jeffreys, 2: Type I Ind. Jeffreys, 3: Ind. Jeffreys).
set	Indicator for the use of set observations (1: set observations, 0: point observations). The former is strongly recommended over the latter as point observations cause problems in the context of Bayesian inference (due to continuous sampling models assigning zero probability to a point).
eps_l	Lower imprecision (ε_l) for set observations (default value: 0.5).
eps_r	Upper imprecision (ε_r) for set observations (default value: 0.5)

Examples

library(BASSLINE)

# Please note: N=100 is not enough to reach convergence.
# This is only an illustration. Run longer chains for more accurate
# estimations (especially for the log-exponential power model).

LEP <- MCMC_LEP(N = 100, thin = 2, burn = 20, Time = cancer[, 1],
                Cens = cancer[, 2], X = cancer[, 3:11])
LEP.LML <- LML_LEP(thin = 2, Time = cancer[, 1], Cens = cancer[, 2],
                   X = cancer[, 3:11], chain = LEP)

nathansam/SMLN documentation built on May 14, 2022, 9:07 p.m.