CaseDeletion_LN: Case deletion analysis for the log-normal model

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

Case deletion analysis for the log-normal model

Description

Leave-one-out cross validation analysis. The function returns a matrix with n rows. The first column contains the logarithm of the CPO (Geisser and Eddy, 1979). Larger values of the CPO indicate better predictive accuracy of the model. The second and third columns contain the KL divergence between π(β, σ^2, θ | t_{-i}) and π(β, σ^2, θ | t) and its calibration index p_i, respectively.

Usage

CaseDeletion_LN(Time, Cens, X, chain, set = TRUE, eps_l = 0.5, eps_r = 0.5)

Arguments

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)

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.LM

LN <- MCMC_LN(N = 1000, thin = 20, burn = 40, Time = cancer[, 1],
              Cens = cancer[, 2], X = cancer[, 3:11])
LN.CD <- CaseDeletion_LN(Time = cancer[, 1], Cens = cancer[, 2],
                         X = cancer[, 3:11], chain = LN)

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