View source: R/LogExponentialPower.R
BF_u_obs_LEP | R Documentation |
This returns a unique number corresponding to the Bayes Factor associated to the test M_0: Λ_{obs} = λ_{ref} versus M_1: Λ_{obs}\neq λ_{ref} (with all other Λ_j,\neq obs free). The value of λ_{ref} is required as input. The user should expect long running times for the log-Student’s t model, in which case a reduced chain given Λ_{obs} = λ_{ref} needs to be generated
BF_u_obs_LEP( N, thin, burn, ref, obs, Time, Cens, X, chain, prior = 2, set = TRUE, eps_l = 0.5, eps_r = 0.5, ar = 0.44 )
N |
Total number of iterations. Must be a multiple of thin. |
thin |
Thinning period. |
burn |
Burn-in period |
ref |
Reference value u_{ref}. Vallejos & Steel recommends this value be set to 1.6 +1_α for the LEP model. |
obs |
Indicates the number of the observation under analysis |
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) |
ar |
Optimal acceptance rate for the adaptive Metropolis-Hastings updates |
library(BASSLINE) # Please note: N=1000 is not enough to reach convergence. # This is only an illustration. Run longer chains for more accurate # estimations (especially for the log-exponential power model). LEP <- MCMC_LEP(N = 1000, thin = 20, burn = 40, Time = cancer[, 1], Cens = cancer[, 2], X = cancer[, 3:11]) alpha <- mean(LEP[, 11]) uref <- 1.6 + 1 / alpha LEP.Outlier <- BF_u_obs_LEP(N = 100, thin = 20, burn =1 , ref = uref, obs = 1, Time = cancer[, 1], Cens = cancer[, 2], cancer[, 3:11], chain = LEP)
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