BayesCPH {Bolstad2} | R Documentation |
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 |