BeMRes {BGPhazard}  R Documentation 
Markov Beta Model
Description
Posterior inference for the Bayesian nonparametric Markov beta model for discrete survival times.
Usage
BeMRes(
times,
delta = rep(1, length(times)),
alpha = rep(1e04, K),
beta = rep(1e04, 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. 
alpha 
Nonnegative vector. Small entries are recommended in order to specify a noninformative prior distribution. 
beta 
Nonnegative vector. Small entries are recommended in order to specify a noninformative 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 
b.eps 
Numeric. Scale parameter for the prior gamma distribution of
epsilon when 
type.c 
Integer. 1=defines 
epsilon 
Double. Mean of the exponential distribution assigned to

iterations 
Integer. Number of iterations including the 
burn.in 
Integer. Length of the burnin 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 
Details
Computes the Gibbs sampler given by the full conditional distributions of u and Pi (NietoBarajas & Walker, 2002) and arranges the resulting Markov chain into a tibble which can be used to obtain posterior summaries.
Note
It is recommended to verify chain's stationarity. This can be done by checking each partition element individually. See BePlotDiag.
References
 NietoBarajas, L. E. & Walker, S. G. (2002). Markov beta and gamma processes for modelling hazard rates. Scandinavian Journal of Statistics 29: 413424.
See Also
Examples
## 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))