peppm {peppm}

Piecewise Exponential Product Partition Model

Description

Piecewise Exponential Product Partition Model

Usage

peppm(
  time,
  status,
  a_rates = 1,
  b_rates = 1,
  cohesion = 1,
  a_beta = 1,
  b_beta = 1,
  nburnin = 10000,
  npost = 20000,
  nlag = 10
)

Arguments

time

vector of observed failure times.

status

vector of failure indicators

a_rates

shape parameter of the gamma distribution (prior for failure rates).

b_rates

scale parameter of the gamma distribution (prior for failure rates).

cohesion

type of prior cohesion (1 to 4).

a_beta

shape1 parameter of the beta distribution (prior for p - cohesion 4).

b_beta

shape2 parameter of the beta distribution (prior for p - cohesion 4).

nburnin

number of iterations to be discarded.

npost

desired posterior sample size

nlag

number of jumps to eliminate autocorrelation of the chain.

Value

Posterior sample of the number of intervals, failure rates, the auxiliary vector U, and the logarithm of the prior predictive distribution (log data factor).

Examples

# Small chain used here due to time constraints. 
data(telecom)

# Prior cohesion 1:
fit1 <- with(telecom, peppm(time, status, cohesion=1, nburnin = 0, nlag = 1, npost = 100))
# Prior cohesion 2:
fit2 <- with(telecom, peppm(time, status, cohesion=2, nburnin = 0, nlag = 1, npost = 100))
# Prior cohesion 3:
fit3 <- with(telecom, peppm(time, status, cohesion=3, nburnin = 0, nlag = 1, npost = 100))
# Prior cohesion 4:
fit4 <- with(telecom, peppm(time, status, cohesion=4, nburnin = 0, nlag = 1, npost = 100))