## Bayesian Cox Proportional Hazards Modelling

### Description

Uses a Metropolis Hastings scheme on the proportional hazards model to draw sample from posterior. Uses a matched curvature Student's t candidate generating distribution with 4 degrees of freedom to give heavy tails.

### Usage

```BayesCPH(y, t, x, steps = 1000,
priorMean = NULL, priorVar = NULL,
mleMean = NULL, mleVar,
startValue = NULL, randomSeed = NULL,
plots = FALSE)
```

### Arguments

 `y` the Poisson censored response vector. It has value 0 when the variable is censored and 1 when it is not censored. `t` time `x` matrix of covariates `steps` the number of steps to use in the Metropolis-Hastings updating `priorMean` the mean of the prior `priorVar` the variance of the prior `mleMean` the mean of the matched curvature likelihood `mleVar` the covariance matrix of the matched curvature likelihood `startValue` a vector of starting values for all of the regression coefficients including the intercept `randomSeed` a random seed to use for different chains `plots` Plot the time series and auto correlation functions for each of the model coefficients

### Value

A list containing the following components:

 `beta` a data frame containing the sample of the model coefficients from the posterior distribution `mleMean` the mean of the matched curvature likelihood. This is useful if you've used a training set to estimate the value and wish to use it with another data set `mleVar` the covariance matrix of the matched curvature likelihood. See mleMean for why you'd want this