Markov Beta Model
Posterior inference for the Bayesian non-parametric Markov beta model for discrete survival times.
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 )
times |
Numeric positive vector. Failure times. |
delta |
Logical vector. Status indicator. |
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 |
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 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 |
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.
It is recommended to verify chain's stationarity. This can be done by checking each partition element individually. See BePlotDiag.
- Nieto-Barajas, L. E. & Walker, S. G. (2002). Markov beta and gamma processes for modelling hazard rates. Scandinavian Journal of Statistics 29: 413-424.
## 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))
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