mfsurv {BayesMFSurv}

mfsurv

Description

mfsurv fits a parametric Bayesian MF model via Markov Chain Monte Carlo (MCMC) to estimate the misclassification in the first stage and the hazard in the second stage.

Usage

mfsurv(
  formula,
  Y0,
  data = list(),
  N,
  burn,
  thin,
  w = c(1, 1, 1),
  m = 10,
  form = c("Weibull", "Exponential"),
  na.action = c("na.omit", "na.fail")
)

Arguments

formula

a formula in the form Y ~ X1 + X2... | C ~ Z1 + Z2 ... where Y is the duration until failure or censoring, and C is a binary indicator of observed failure.

Y0

the elapsed time since inception until the beginning of time period (t-1).

data

list object of data.

N

number of MCMC iterations.

burn

burn-ins to be discarded.

thin

thinning to prevent autocorrelation of chain of samples by only taking the n-th values.

w

size of the slice in the slice sampling for (betas, gammas, lambda). The default is c(1,1,1). This value may be changed by the user to meet one's needs.

m

limit on steps in the slice sampling. The default is 10. This value may be changed by the user to meet one's needs.

form

type of parametric model distribution to be used. Options are "Exponential" or "Weibull". The default is "Weibull".

na.action

a function indicating what should happen when NAs are included in the data. Options are "na.omit" or "na.fail". The default is "na.omit".

Value

mfsurv returns an object of class "mfsurv".

A "mfsurv" object has the following elements:

Y

the vector of ‘Y’.

Y0

the vector of ‘Y0’.

C

the vector of ‘C’.

X

matrix X's variables.

Z

the vector of ‘Z’.

betas

data.frame, X.intercept and X variables.

gammas

data.frame, Z.intercept and Z variables.

lambda

integer.

post

integer.

iterations

number of MCMC iterations.

burn_in

burn-ins to be discarded.

thinning

integer.

betan

integer, length of posterior sample for betas.

gamman

integer, length of posterior sample for gammas.

distribution

character, type of distribution.

call

the call.

formula

description for the model to be estimated.

Examples

set.seed(95)
bgl <- Buhaugetal_2009_JCR
bgl <- subset(bgl, coupx == 0)
bgl <- na.omit(bgl)
Y   <- bgl$Y
X   <- as.matrix(cbind(1, bgl[,1:7]))
C   <- bgl$C
Z1  <- matrix(1, nrow = nrow(bgl))
Y0  <- bgl$Y0
model1 <- mfsurv(Y ~ X | C ~ Z1, Y0 = Y0,
                N = 50,
                burn = 20,
                thin = 15,
                w = c(0.1, .1, .1),
                m = 5,
                form = "Weibull",
                na.action = 'na.omit')