DIC_LLOG | R Documentation |
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
DIC_LLOG(Time, Cens, X, chain, set = TRUE, eps_l = 0.5, eps_r = 0.5)
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) |
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]) LLOG.DIC <- DIC_LLOG(Time = cancer[, 1], Cens = cancer[, 2], X = cancer[, 3:11], chain = LLOG)
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