BayesCPH {Bolstad2} | R Documentation |
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.
BayesCPH(y, t, x, steps = 1000, priorMean = NULL, priorVar = NULL, mleMean = NULL, mleVar, startValue = NULL, randomSeed = NULL, plots = FALSE)
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 |
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 |