mfsurv: summary.mfsurv

In Nicolas-Schmidt/BayesMFSurv: Bayesian Misclassified-Failure Survival Model

Description

Returns a summary of a mfsurv object via summary.mcmc.

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. Slice sampling is employed to draw the posterior sample of the model's split and survival stage parameters.

Returns a summary of a mfsurv object via summary.mcmc.

Usage

mfsurv.summary(object, parameter)

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")
)

## S3 method for class 'mfsurv'
summary(object, parameter = c("betas", "gammas", "lambda"), ...)

Arguments

object	an object of class mfsurv, the output of mfsurv.
parameter	one of three parameters of the mfsurv output. Indicate either "betas", "gammas" or "lambda".
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".
...	additional parameter

Value

list. Empirical mean, standard deviation and quantiles for each variable.

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.

list. Empirical mean, standard deviation and quantiles for each variable.

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")

Nicolas-Schmidt/BayesMFSurv documentation built on Sept. 12, 2020, 12:59 a.m.