Last data update: 2014.03.03
R: Probit model for general interval-censored data
case2probit R Documentation
Probit model for general interval-censored data
Description
Fit probit model to general interval-censored data. Use MCMC method to estimate regression coefficients, baseline survival,
and survival function at user-specified covariate values.
Usage
case2probit(L, R, status, xcov, x_user, order,
v0, a_eta, b_eta, knots, grids, niter)
Arguments
L
a numeric vector of left timepoints of observed time intervals.
R
a numeric vector of right timepoints of observed time intervals.
status
a vector of censoring indicators: 0=left-censored, 1=interval-censored, 2=right-censored.
xcov
a matrix of covariates, each column corresponds to one covariate.
x_user
a vector of user specified covariate values.
order
degree of I-splines (b_l) (see details). Recommended values are 2-4.
v0
precision of normal prior for gamma_0.
a_eta
shape parameter of Gamma prior for gamma_l (see details).
b_eta
rate parameter of Gamma prior for gamma_l (see details).
knots
a sequence of points to define I-splines.
grids
a sequence of points where baseline survival function is to be estimated. Default is minimum observed time points.
niter
total number of iterations of MCMC chains.
Details
The baseline function is modeled by a linear combination of I-splines:
gamma_0+sum_{l=1}^{k}(gamma_l*b_l).
Regression coefficient vector beta is sampled from a multivariate normal distribution.
For more information, please see reference.
Value
a list containing the following elements:
parbeta
a niter by p matrix of MCMC draws of beta_r, r=1, ..., p.
parsurv0
a niter by length(grids) matrix, each row contains the baseline survival at grids from one iteration .
grids
a sequence of points where baseline survival is estimated.
Author(s)
Lianming Wang and Xiaoyan Lin. R version by Bo Cai.
References
Lin, X. and Wang, L. (2009). A semiparametric probit model for case 2 interval-censored failure time data.
Statistics in Medicine 29 972-981.
Results
providing 
.