BayesSurvive (version 0.1.0)

coef.BayesSurvive: Create a dataframe of estimated coefficients

Description

Estimate regression coefficients with posterior mean/median, credible intervals, standard deviation, or MPM estimates, posterior gammas

Usage

# S3 method for BayesSurvive
coef(
  object,
  MPM = FALSE,
  type = "mean",
  CI = 95,
  SD = FALSE,
  subgroup = 1,
  ...
)

Value

dataframe object

Arguments

object

an object of class BayesSurvive

MPM

logical value to obtain MPM coefficients. Default: FALSE

type

type of point estimates of regression coefficients. One of c("mean", "median"). Default is mean

CI

size (level, as a percentage) of the credible interval to report. Default: 95, i.e. a 95% credible interval

SD

logical value to show each coefficient's standard deviation over MCMC iterations

subgroup

index of the subgroup for visualizing posterior coefficients

...

other arguments

Examples

library("BayesSurvive")
set.seed(123)

# Load the example dataset
data("simData", package = "BayesSurvive")

dataset <- list(
  "X" = simData[[1]]$X,
  "t" = simData[[1]]$time,
  "di" = simData[[1]]$status
)

# Initial value: null model without covariates
initial <- list("gamma.ini" = rep(0, ncol(dataset$X)))
# Hyperparameters
hyperparPooled <- list(
  "c0"     = 2, # prior of baseline hazard
  "tau"    = 0.0375, # sd for coefficient prior
  "cb"     = 20, # sd for coefficient prior
  "pi.ga"  = 0.02, # prior variable selection probability for standard Cox models
  "a"      = -4, # hyperparameter in MRF prior
  "b"      = 0.1, # hyperparameter in MRF prior
  "G"      = simData$G # hyperparameter in MRF prior
)

# \donttest{
# run Bayesian Cox with graph-structured priors
fit <- BayesSurvive(
  survObj = dataset, hyperpar = hyperparPooled,
  initial = initial, nIter = 50
)

# show posterior coefficients
betas <- coef(fit)
head(betas)
# }