Fit a Bayesian semiparametric PH model with spatial frailty for spatially dependent general interval-censored data.
spatialIC(L, R, y, xcov, IC, scale.designX, scaled, area, binary, I, C, nn,
order, knots, grids, a_eta, b_eta, a_ga, b_ga, a_lamb, b_lamb, beta_iter,
phi_iter, beta_cand, beta_sig0, x_user, total, burnin, thin, conf.int, seed)The vector of left endpoints of the observed time intervals.
The vector of right endponts of the observed time intervals.
The vector of censoring indicator: 0=left-censored, 1=interval-censored, 2=right-censored, 3=exact.
The covariate matrix for the p predictors.
The vector of general interval-censored indicator: 1=general interval-censored, 0=exact.
The TRUE or FALSE indicator of whether or not to scale the design matrix X.
The vector indicating whether each covariate is to be scaled: 1=to be scaled, 0=not.
The vector of area ID.
The number of areas.
The adjacency matrix.
The vector of number of neighbors for each area.
The vector indicating whether each covariate is binary.
The degree of basis I-splines: 1=linear, 2=quadratic, 3=cubic, etc.
A sequence of knots to define the basis I-splines.
A sequence of points at which baseline survival function is to be estimated.
The shape parameter of Gamma prior for gamma_l.
The rate parameter of Gamma prior for gamma_l.
The shape parameter of Gamma prior for e^{beta_r}.
The rate parameter of Gamma prior for e^{beta_r}.
The shape parameter of Gamma prior for spatial precision lambda.
The rate parameter of Gamma prior for spatial precision lambda.
The number of initial iterations in the Metropolis-Hastings sampling for beta_r.
The number of initial iterations in the Metropolis-Hastings sampling for phi_i.
The sd of the proposal normal distribution in the MH sampling for beta_r.
The sd of the prior normal distribution for beta_r.
The user-specified covariate vector at which to estimate survival function(s).
The number of total iterations.
The number of burnin.
The frequency of thinning.
The confidence level of the CI for beta_r.
A user-specified random seed.
a list containing the following elements:
The sample size.
A total by p matrix of MCMC draws of beta_r, r=1, ..., p.
A total by length(grids) matrix, each row contains the baseline survival at grids from one iteration.
A total by length(grids)*G matrix, each row contains the survival at grids from one iteration.
G is the number of sets of user-specified covariate values.
A total by I matrix of MCMC draws of phi_i, i=1,...,I.
A total by 1 matrix of MCMC draws of lambda.
A vector of regression coefficient estimates.
A vector of sample standard deviations of regression coefficient estimates.
The credible intervals for the regression coefficients.
The estimated baseline survival at grids.
The estimated survival at grids with user-specified covariate values x_user.
The sequance of points where baseline survival functions is estimated.
Deviance information criterion.
Negative log pseudo-marginal likelihood.
The baseline cumulative hazard is approximated by a linear combination of I-splines:
sum_{l=1}^{K}(gamma_l*b_l(t)).
For a binary prdictor, we sample e^{beta_r}, with Gamma prior.
The regression coefficient beta_r for a continuous predictor is sampled
using MH algorithm. During the initial beta_iter iterations, sd of the
proposal distribution is beta_cand. Afterwards, proposal sd is set to be
the sd of available MCMC draws.
Pan, C. and Cai, B. (2020). A Bayesian model for spatial partly interval-censored data. Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2020.1839497.