Calls one of the 16 functions to fit the correspoinding model.
PICBayes(L, ...)# S3 method for default
PICBayes(L,R,y,xcov,IC,model,scale.designX,scaled,xtrt,zcov,
area,binary,I,C,nn,order=3,knots,grids,a_eta=1,b_eta=1,a_ga=1,b_ga=1,a_lamb=1,
b_lamb=1,a_tau=1,b_tau=1,a_tau_trt=1,b_tau_trt=1,a_alpha=1,b_alpha=1,H=5,
a_tau_star=1,b_tau_star=1,a_alpha_trt=1,b_alpha_trt=1,H_trt=5,
a_tau_trt_star=1,b_tau_trt_star=1,beta_iter=1001,phi_iter=1001,
beta_cand,phi_cand,beta_sig0=10,x_user=NULL,
total=6000,burnin=1000,thin=1,conf.int=0.95,seed=1,...)
# S3 method for formula
PICBayes(formula, data, ...)
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.
A character string specifying the type of model. See details.
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 covariate that has a random effect.
The design matrix for the q random effects.
The vector of cluster 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 shape parameter of Gamma prior for random intercept precision tau.
The rate parameter of Gamma prior for random intercept precision tau.
The shape parameter of Gamma prior for random treatment precision tau_trt.
The rate parameter of Gamma prior for random treatment precision tau_trt.
The shape parameter of Gamma prior for alpha.
The rate parameter of Gamma prior for alpha.
The number of distinct components in DP mixture prior under blocked Gibbs sampler.
The shape parameter of G_0 in DP mixture prior.
The rate parameter of G_0 in DP mixture prior.
The shape parameter of Gamma prior for alpha_trt.
The rate parameter of Gamma prior for alpha_trt.
The number of distinct components in DP mixture prior under blocked Gibbs sampler for random treatment.
The shape parameter of G_0 in DP mixture prior for random treatment.
The rate parameter of G_0 in DP mixture prior for random treatment.
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 proposal normal distribution in the initial MH sampling for phi_i.
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 formula expression with the response returned by the Surv function in the survival package.
A data frame that contains the variables named in the formula argument.
Other arguments if any.
An object of class PICBayes. Refere to each specific function for its specific values.
Possible values are "PIC", "spatialPIC", "clusterPIC_int", "clusterPIC_int_DP", "clusterPIC_trt", "clusterPIC_trt_DP", "clusterPIC_Z", and "clusterPIC_Z_DP" for partly interval-censored data; and "IC", "spatialIC", "clusterIC_int", "clusterIC_int_DP", "clusterIC_trt", "clusterIC_trt_DP", "clusterIC_Z", and "clusterIC_Z_DP" for general interval-censored data.