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### library(metafor); library(testthat); Sys.setenv(NOT_CRAN="true")
### see also: https://www.metafor-project.org/doku.php/analyses:henmi2010
source("settings.r")
context("Checking analysis example: henmi2010")
### load dataset
dat <- dat.lee2004
### calculate log odds ratios and corresponding sampling variances
dat <- escalc(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, data=dat)
test_that("results are correct for the random-effects model.", {
### fit random-effects model with DL estimator
res <- rma(yi, vi, data=dat, method="DL")
### compare with results on page 2978
expect_equivalent(res$tau2, 0.3325, tolerance=.tol[["var"]])
expect_equivalent(coef(res), -0.6787, tolerance=.tol[["coef"]])
expect_equivalent(res$ci.lb, -1.0664, tolerance=.tol[["ci"]])
expect_equivalent(res$ci.ub, -0.2911, tolerance=.tol[["ci"]])
})
test_that("results are correct for the Henmi & Copas method.", {
### fit random-effects model with DL estimator
res <- rma(yi, vi, data=dat, method="DL")
### apply Henmi & Copas method
sav <- hc(res)
out <- capture.output(print(sav)) ### so that print.hc.rma.uni() is run (at least once)
### compare with results on page 2978
expect_equivalent(sav$beta, -0.5145, tolerance=.tol[["coef"]])
expect_equivalent(sav$ci.lb, -0.9994, tolerance=.tol[["ci"]])
expect_equivalent(sav$ci.ub, -0.0295, tolerance=.tol[["ci"]])
})
rm(list=ls())
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