Log-marginal likelihood estimator for the log-Laplace model

Log-marginal likelihood estimator for the log-Laplace model

Usage

LML_LLAP(
  thin,
  Time,
  Cens,
  X,
  chain,
  Q = 1,
  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

Q

Update period for the \(\lambda_{i}\)’s

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 \((\epsilon_l)\) 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.

LLAP <- MCMC_LLAP(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 : 0.41 
#>  Initial beta 2 : -0.89 
#>  Initial beta 3 : -2.63 
#>  Initial beta 4 : -1.13 
#>  Initial beta 5 : -0.68 
#>  Initial beta 6 : 1.33 
#>  Initial beta 7 : -0.05 
#>  Initial beta 8 : -0.31 
#>  Initial beta 9 : 0.79 
#> 
#> Sampling initial sigma^2 from a Gamma(2, 2) distribution
#> Initial sigma^2 : 0.84 
#>