ICARBHSampler {BayesPiecewiseICAR} R Documentation

## This function fits a piecewise hazard using a hierarchical model with a ICAR dependence applied to the hazard heights

### Description

This function fits a piecewise hazard using a hierarchical model with a ICAR dependence applied to the hazard heights

### Usage

```ICARBHSampler(Y, I, B, hyper)
```

### Arguments

 `Y` This is a n-vector containing patient survival times `I` This is a n-vector containing patient censoring indicators (0 for censored patient) `B` Number of iterations to run the sample `hyper` Vector of hyperparameters. In order, this contains a1, b1 which are the inverse gamma hyperparameters on sigma^2. Phi which is the hyperparameter on the mean number of split points. Jmax which is the maximum allowed number of split points. cl1 which is a tuning parameter greater than 0. J1 is the starting number of split points for the MCMC. Finally, clam1 which is between 0 and 1 and characterizes the spatial dependency of the baseline hazard heights.

### Value

Returns a list containing the posterior samples of the split points, split point locations, log hazard rates and hierarchical samples

### References

Lee, K. H., Haneuse, S., Schrag, D. and Dominici, F. (2015), Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64: 253-273.

### Examples

```####This generates random survival data
Y=rexp(100,1/20)
I=rbinom(100,1,.5)
###Sets hyperparameters
a1=.7
b1=.7
phi=3
Jmax=20
cl1=.25
clam1=.5
J1=3
###Combines the hyperparameters in to a vector
hyper=c(a1,b1,phi,Jmax,cl1,J1,clam1)
###Set Number of iterations
B=100
###Run the Sampler
X=ICARBHSampler(Y,I,B,hyper)
X

```

[Package BayesPiecewiseICAR version 0.2.1 Index]