BF_lambda_obs_LLOG: Outlier detection for observation for the log-logistic model

In nathansam/SMLN: Bayesian Survival Analysis Using Shape Mixtures of Log-Normal Distributions

Outlier detection for observation for the log-logistic model

Description

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

Usage

BF_lambda_obs_LLOG(ref, obs, X, chain)

Arguments

ref	Reference value λ_{ref} or u_{ref}
obs	Indicates the number of the observation under analysis
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

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.

LLOG <- MCMC_LLOG(N = 1000, thin = 20, burn = 40, Time = cancer[, 1],
                  Cens = cancer[, 2], X = cancer[, 3:11])
LLOG.Outlier <- BF_lambda_obs_LLOG(1,1, X = cancer[, 3:11], chain = LLOG)

nathansam/SMLN documentation built on May 14, 2022, 9:07 p.m.