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 |
[Package
Bolstad2 version 1.0-26
Index]