Deviance information criterion is based on the deviance function \(D(\theta, y) = -2 log(f(y|\theta))\) but also incorporates a penalization factor of the complexity of the model
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 \((\epsilon_l)\) for set observations (default value: 0.5).
- eps_r
Upper imprecision \((\epsilon_r)\) 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.
LLOG <- MCMC_LLOG(N = 1000, thin = 20, burn = 40, Time = cancer[, 1],
Cens = cancer[, 2], X = cancer[, 3:11])
#> Sampling initial betas from a Normal(0, 1) distribution
#> Initial beta 1 : 0.49
#> Initial beta 2 : -0.28
#> Initial beta 3 : 1.04
#> Initial beta 4 : -1.03
#> Initial beta 5 : -0.39
#> Initial beta 6 : 0.98
#> Initial beta 7 : 1.35
#> Initial beta 8 : 0.12
#> Initial beta 9 : -1.2
#>
#> Sampling initial sigma^2 from a Gamma(2, 2) distribution
#> Initial sigma^2 : 1.1
#>
LLOG.DIC <- DIC_LLOG(Time = cancer[, 1], Cens = cancer[, 2],
X = cancer[, 3:11], chain = LLOG)
#> Effective number of parameters : 8.29
#> Actual number of parameters : 10