Nothing
context("Checking meta-analysis example: Crins dataset")
suppressPackageStartupMessages(library(rstan))
## Fitting a binomial-normal hierachical model
test_that("Results are correct for fitting binomial normal hierarchical model using WIP priors.", {
skip_on_cran()
set.seed(23344)
## Load the dataset
data('dat.Berkey1995', package = "MetaStan")
## Fitting a Binomial-Normal Hierarchical model using WIP priors
dat_long = create_MetaStan_dat(dat = dat.Berkey1995,
armVars = c(responders = "r",
sampleSize = "n"),
nArmsVar = "nd")
bnhm.wip.bnhm1.stan <- meta_stan(data = dat_long,
likelihood = "binomial",
mu_prior = c(0, 10),
delta = 250,
tau_prior = 0.5,
tau_prior_dist = "half-normal")
### compare with results
results = bnhm.wip.bnhm1.stan$fit_sum
expect_equivalent(round(results['theta', '50%'], 2), -0.75, tolerance = 0.1)
})
## Fitting a meta-regression model
test_that("Results are correct for a meta-regression model.", {
skip_on_cran()
set.seed(11112)
## Load the dataset
data('dat.Berkey1995', package = "MetaStan")
## Fitting a Binomial-Normal Hierarchical model using WIP priors
data_converted = create_MetaStan_dat(dat = dat.Berkey1995,
armVars = c(responders = "r", sampleSize = "n"))
meta.reg.stan <- meta_stan(data = data_converted,
likelihood = "binomial",
mu_prior = c(0, 10),
theta_prior = c(0, 100),
tau_prior = 0.5,
tau_prior_dist = "half-normal",
mreg = TRUE,
cov = dat.Berkey1995$Latitude)
### compare with results
results = meta.reg.stan$fit_sum
expect_equivalent(round(results['beta[1,1]', '50%'], 2), -0.03, tolerance = 0.1)
})
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