Description Usage Arguments Value Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 |
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". |
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. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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')
|
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