PICBayes: Bayesian models for partly interval-censored data and general...
In PICBayes: Bayesian Models for Partly Interval-Censored Data

Description Usage Arguments Details Value Author(s)

Calls one of the 16 functions to fit the correspoinding model.

PICBayes(L, ...)

## Default S3 method:
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 class 'formula'
PICBayes(formula, data, ...)

`L`	The vector of left endpoints of the observed time intervals.
`R`	The vector of right endponts of the observed time intervals.
`y`	The vector of censoring indicator: 0=left-censored, 1=interval-censored, 2=right-censored, 3=exact.
`xcov`	The covariate matrix for the p predictors.
`IC`	The vector of general interval-censored indicator: 1=general interval-censored, 0=exact.
`model`	A character string specifying the type of model. See details.
`scale.designX`	The TRUE or FALSE indicator of whether or not to scale the design matrix X.
`scaled`	The vector indicating whether each covariate is to be scaled: 1=to be scaled, 0=not.
`xtrt`	The covariate that has a random effect.
`zcov`	The design matrix for the q random effects.
`area`	The vector of cluster ID.
`I`	The number of areas.
`C`	The adjacency matrix.
`nn`	The vector of number of neighbors for each area.
`binary`	The vector indicating whether each covariate is binary.
`order`	The degree of basis I-splines: 1=linear, 2=quadratic, 3=cubic, etc.
`knots`	A sequence of knots to define the basis I-splines.
`grids`	A sequence of points at which baseline survival function is to be estimated.
`a_eta`	The shape parameter of Gamma prior for `gamma_l`.
`b_eta`	The rate parameter of Gamma prior for `gamma_l`.
`a_ga`	The shape parameter of Gamma prior for `e^{beta_r}`.
`b_ga`	The rate parameter of Gamma prior for `e^{beta_r}`.
`a_lamb`	The shape parameter of Gamma prior for spatial precision `lambda`.
`b_lamb`	The rate parameter of Gamma prior for spatial precision `lambda`.
`a_tau`	The shape parameter of Gamma prior for random intercept precision `tau`.
`b_tau`	The rate parameter of Gamma prior for random intercept precision `tau`.
`a_tau_trt`	The shape parameter of Gamma prior for random treatment precision `tau_trt`.
`b_tau_trt`	The rate parameter of Gamma prior for random treatment precision `tau_trt`.
`a_alpha`	The shape parameter of Gamma prior for `alpha`.
`b_alpha`	The rate parameter of Gamma prior for `alpha`.
`H`	The number of distinct components in DP mixture prior under blocked Gibbs sampler.
`a_tau_star`	The shape parameter of `G_0` in DP mixture prior.
`b_tau_star`	The rate parameter of `G_0` in DP mixture prior.
`a_alpha_trt`	The shape parameter of Gamma prior for `alpha_trt`.
`b_alpha_trt`	The rate parameter of Gamma prior for `alpha_trt`.
`H_trt`	The number of distinct components in DP mixture prior under blocked Gibbs sampler for random treatment.
`a_tau_trt_star`	The shape parameter of `G_0` in DP mixture prior for random treatment.
`b_tau_trt_star`	The rate parameter of `G_0` in DP mixture prior for random treatment.
`beta_iter`	The number of initial iterations in the Metropolis-Hastings sampling for `beta_r`.
`phi_iter`	The number of initial iterations in the Metropolis-Hastings sampling for `phi_i`.
`beta_cand`	The sd of the proposal normal distribution in the MH sampling for `beta_r`.
`phi_cand`	The sd of the proposal normal distribution in the initial MH sampling for `phi_i`.
`beta_sig0`	The sd of the prior normal distribution for `beta_r`.
`x_user`	The user-specified covariate vector at which to estimate survival function(s).
`total`	The number of total iterations.
`burnin`	The number of burnin.
`thin`	The frequency of thinning.
`conf.int`	The confidence level of the CI for `beta_r`.
`seed`	A user-specified random seed.
`formula`	A formula expression with the response returned by the Surv function in the survival package.
`data`	A data frame that contains the variables named in the formula argument.
`...`	Other arguments if any.

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.

An object of class PICBayes. Refere to each specific function for its specific values.

Chun Pan

PICBayes documentation built on Aug. 5, 2021, 9:06 a.m.