BeMRes: Markov Beta Model

Description

Posterior inference for the Bayesian non-parametric Markov beta model for discrete survival times.

Usage

BeMRes(
  times,
  delta = rep(1, length(times)),
  alpha = rep(1e-04, K),
  beta = rep(1e-04, K),
  c.r = rep(0, K - 1),
  a.eps = 0.1,
  b.eps = 0.1,
  type.c = 4,
  epsilon = 1,
  iterations = 2000,
  burn.in = floor(iterations * 0.2),
  thinning = 5,
  printtime = TRUE
)

Arguments

times: Numeric positive vector. Failure times.
delta: Logical vector. Status indicator. TRUE (1) indicates exact lifetime is known, FALSE (0) indicates that the corresponding failure time is right censored.
alpha: Nonnegative vector. Small entries are recommended in order to specify a non-informative prior distribution.
beta: Nonnegative vector. Small entries are recommended in order to specify a non-informative prior distribution.
c.r: Nonnegative vector. The higher the entries, the higher the correlation of two consecutive failure times.
a.eps: Numeric. Shape parameter for the prior gamma distribution of epsilon when type.c = 4.
b.eps: Numeric. Scale parameter for the prior gamma distribution of epsilon when type.c = 4.
type.c: Integer. 1=defines c.r as a zero-entry vector; 2=lets the user define c.r freely; 3=assigns c.r an exponential prior distribution with mean epsilon; 4=assigns c.r an exponential hierarchical distribution with mean epsilon which in turn has a a Ga(a.eps, b.eps) distribution.
epsilon: Double. Mean of the exponential distribution assigned to c.r
iterations: Integer. Number of iterations including the burn.in and thining to be computed for the Markov chain.
burn.in: Integer. Length of the burn-in period for the Markov chain.
thinning: Integer. Factor by which the chain will be thinned. Thinning the Markov chain is to reduces autocorrelation.
printtime: Logical. If TRUE, prints out the execution time.

Details

Computes the Gibbs sampler given by the full conditional distributions of u and Pi (Nieto-Barajas & Walker, 2002) and arranges the resulting Markov chain into a tibble which can be used to obtain posterior summaries.

References

- Nieto-Barajas, L. E. & Walker, S. G. (2002). Markov beta and gamma processes for modelling hazard rates. Scandinavian Journal of Statistics 29: 413-424.

Examples

Run this code




## Simulations may be time intensive. Be patient.

## Example 1
#  data(psych)
#  timesP <- psych$time
#  deltaP <- psych$death
#  BEX1 <- BeMRes(timesP, deltaP, iterations = 3000, burn.in = 300, thinning = 1)

## Example 2
#  data(gehan)
#  timesG <- gehan$time[gehan$treat == "control"]
#  deltaG <- gehan$cens[gehan$treat == "control"]
#  BEX2 <- BeMRes(timesG, deltaG, type.c = 2, c.r = rep(50, 22))