tests/testthat/test_valmeta_5_oe_bayes.R

In metamisc: Meta-Analysis of Diagnosis and Prognosis Research Studies

context("valmeta 5. bayesian meta-analysis of total O:E ratio")
skip_on_cran()

library(lme4)
library(rjags)
library(runjags)
library(dplyr)
data(EuroSCORE)

test_that("Bayesian random effect meta-analysis of total O:E ratio", {
  
  pars <- list(hp.tau.dist = "dhalft",   # Prior for the between-study standard deviation
               hp.tau.sigma = 1.5,       # Standard deviation for 'hp.tau.dist'
               hp.tau.df = 3,            # Degrees of freedom for 'hp.tau.dist'
               hp.tau.max = 10)          # Maximum value for the between-study standard deviation
  
  fit1 <- valmeta(measure = "OE", 
                  O = EuroSCORE$n.events, 
                  E = EuroSCORE$e.events, 
                  N = EuroSCORE$n, 
                  method = "BAYES", 
                  slab = EuroSCORE$Study, 
                  pars = pars)
  
  expect_equal(fit1$numstudies, nrow(EuroSCORE))
  
  # Set some studies sample size to NA
  N <- EuroSCORE$n
  N[1:10] <- NA
  fit2 <- valmeta(measure = "OE", 
                  O = EuroSCORE$n.events, 
                  E = EuroSCORE$e.events, 
                  N = N, 
                  method = "BAYES", slab = EuroSCORE$Study, pars = pars)
  
  expect_equal(fit2$numstudies, nrow(EuroSCORE))
  expect_equal(as.character(class(fit2$fit)), "runjags")
  
})

test_that("Adjusting JAGS sampling parameters works", {
  
  fit1 <- valmeta(measure = "OE", 
                  O = EuroSCORE$n.events, 
                  E = EuroSCORE$e.events, 
                  N = EuroSCORE$n, 
                  method = "BAYES", 
                  slab = EuroSCORE$Study,
                  n.chains = 4,
                  burnin = 4000,
                  sample = 10000,
                  adapt = 1000)
  
  expect_equal(as.character(class(fit1$fit)), "runjags")
  
})