DIC_LN: Deviance information criterion for the log-normal model

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

Deviance information criterion for the log-normal model

Description

Deviance information criterion is based on the deviance function D(θ, y) = -2 log(f(y|θ)) but also incorporates a penalization factor of the complexity of the model

Usage

DIC_LN(Time, Cens, X, chain, set = TRUE, eps_l = 0.5, eps_r = 0.5)

Arguments

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
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=1000 is not enough to reach convergence.
# This is only an illustration. Run longer chains for more accurate
# estimations.LM

LN <- MCMC_LN(N = 1000, thin = 20, burn = 40, Time = cancer[, 1],
              Cens = cancer[, 2], X = cancer[, 3:11])
LN.DIC <- DIC_LN(Time = cancer[, 1], Cens = cancer[, 2], X = cancer[, 3:11],
                 chain = LN)

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