MCMC_LN | R Documentation |
Adaptive Metropolis-within-Gibbs algorithm with univariate Gaussian random walk proposals for the log-normal model (no mixture)
MCMC_LN( N, thin, burn, Time, Cens, X, beta0 = NULL, sigma20 = NULL, prior = 2, set = TRUE, eps_l = 0.5, eps_r = 0.5 )
N |
Total number of iterations. Must be a multiple of thin. |
thin |
Thinning period. |
burn |
Burn-in period. Must be a multiple of thin. |
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). |
beta0 |
Starting values for β. If not provided, they will be randomly generated from a normal distribution. |
sigma20 |
Starting value for σ^2. If not provided, it will be randomly generated from a gamma distribution. |
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) |
A matrix with (N - burn) / thin + 1 rows. The columns are the MCMC chains for β (k columns), σ^2 (1 column), θ (1 column, if appropriate), \log(t) (n columns, simulated via data augmentation) and the logarithm of the adaptive variances (the number varies among models). The latter allows the user to evaluate if the adaptive variances have been stabilized.
library(BASSLINE) # Please note: N=1000 is not enough to reach convergence. # This is only an illustration. Run longer chains for more accurate # estimations. LN <- MCMC_LN(N = 1000, thin = 20, burn = 40, Time = cancer[, 1], Cens = cancer[, 2], X = cancer[, 3:11])
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