GaMRes {BGPhazard}  R Documentation 
Markov Gamma Model
Description
Computes the Gibbs sampler given by the full conditional distributions of U, Lambda, C and Epsilon (NietoBarajas & Walker, 2002) and arranges the resulting Markov chain into a tibble which can be used to obtain posterior summaries.
Usage
GaMRes(
times,
delta = rep(1, length(times)),
type.t = 3,
K = 5,
utao = NULL,
alpha = rep(0.01, K),
beta = rep(0.01, K),
c.r = rep(1, (K  1)),
c.nu = 1,
a.eps = 0.1,
b.eps = 0.1,
type.c = 4,
epsilon = 1,
iterations = 1000,
burn.in = floor(iterations * 0.2),
thinning = 5,
printtime = TRUE
)
Arguments
times 
Numeric positive vector. Failure times. 
delta 
Logical vector. Status indicator. 
type.t 
Integer. 1=computes uniformlydense intervals; 2= partition arbitrarily defined by the user with parameter utao and 3=same length intervals. 
K 
Integer. Partition length for the hazard function if

utao 
vector. Partition specified by the user when type.t = 2. The first value of the vector has to be 0 and the last one the maximum observed time, either censored or uncensored. 
alpha 
Nonnegative entry vector. Small entries are recommended in order to specify a noninformative prior distribution. 
beta 
Nonnegative entry 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 intervals. 
c.nu 
Tuning parameter for the proposal distribution for c. 
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 
1=assigns 
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 reducec autocorrelation. 
printtime 
Logical. If 
Details
Posterior inference for the Bayesian nonparametric Markov gamma model in survival analysis.
Examples
## Simulations may be time intensive. Be patient.
## Example 1
data(gehan)
timesG < gehan$time[gehan$treat == "6MP"]
deltaG < gehan$cens[gehan$treat == "6MP"]
GEX1 < GaMRes(timesG, deltaG, K = 8, iterations = 3000)
## Example 2
data(leukemiaFZ)
timesFZ < leukemiaFZ$time
deltaFZ < leukemiaFZ$delta
GEX2 < GaMRes(timesFZ, deltaFZ, type.c = 4)