tests/testthat/test-func_MCMC_graph_cpp.R

In BayesSurvive: Bayesian Survival Models for High-Dimensional Data

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

# Run a Bayesian Cox model

## Initial value: null model without covariates
initial <- list("gamma.ini" = rep(0, ncol(dataset$X)))

# Prior parameters
hyperparPooled <- list(
  "c0" = 2, # prior of baseline hazard
  "tau" = 0.0375, # sd (spike) for coefficient prior
  "cb" = 20, # sd (slab) 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
  "lambda" = 3,
  "nu0" = 0.05,
  "nu1" = 5
)

# Run a 'Pooled' Bayesian Cox model with graphical learning

set.seed(3346141)
BayesSurvive_wrap <- function(use_cpp = FALSE) {
  suppressWarnings(
    BayesSurvive(
      survObj = list(dataset), model.type = "Pooled", MRF.G = FALSE,
      hyperpar = hyperparPooled, initial = initial, nIter = 3,
      verbose = FALSE, cpp = use_cpp
    )
  )
}
fit_R <- BayesSurvive_wrap(use_cpp = FALSE)
fit_C <- BayesSurvive_wrap(use_cpp = TRUE)

test_that("R and C++ objects are similar", {
  expect_equal(fit_R$call, fit_C$call)
  expect_equal(fit_R$input, fit_C$input)
  for (obj in names(fit_R$output)[2]) {
    expect_equal(fit_R$output[[obj]], fit_C$output[[obj]], tolerance = 1)
  }
})