Fit proportional odds model for case 1 interval-censored data. Use MCMC method to estiamte regression coefficients, baseline survival, and survival function at user-specified covariate values.
case1po(L, R, status, xcov, x_user, order, sig0, coef_range,
a_eta, b_eta, knots, grids, niter, seed)a numeric vector of left timepoints of observed time intervals.
a numeric vector of right timepoints of observed time intervals.
a vector of censoring indicators: 1=left-censored, 0=right-censored.
a matrix of covariates, each column corresponds to one covariate.
a vector of user specified covariate values.
degree of I-splines (b_l) (see details). Recommended values are 2-4.
standard deviation of normal prior for each regression coefficient beta_r.
specify support domain of target density for beta_r sampled by arms (see details).
shape parameter of Gamma prior for gamma_l (see details).
rate parameter of Gamma prior for gamma_l (see details).
a sequence of points to define I-splines.
a sequence of points where baseline survival function is to be estimated.
total number of iterations of MCMC chains.
a user specified random seed, default is NULL.
a list containing the following elements:
a niter by p matrix of MCMC draws of beta_r, r=1, ..., p.
a niter by length(grids) matrix, each row contains the baseline survival at grids from one iteration.
a niter 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 niter by n matrix, each row contains the inverse PDF of observed interval-censored data from one iteration.
This is used for computing LPML later.
The baseline odds function is approximated by a linear combination of I-splines:
sum_{l=1}^{k}(gamma_l*b_l).
Function arms is used to sample each regression coefficient beta_r, and coef_range specifies the support of
the indFunc
in arms.
Lin, X. and Wang, L. (2011). Bayesian proportional odds model for analyzing current status data: univariate, clustered, and multivariate. Communication in Statistics-Simulation and Computation, 40 1171-1181.