LML_LN | R Documentation |
Log-marginal Likelihood estimator for the log-normal model
LML_LN( thin, Time, Cens, X, chain, prior = 2, set = TRUE, eps_l = 0.5, eps_r = 0.5 )
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) |
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.LML <- LML_LN(thin = 20, Time = cancer[, 1], Cens = cancer[, 2], X = cancer[, 3:11], chain = LN)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.