### copy data into 'dat' and examine data
dat <- dat.konstantopoulos2011
dat
if (FALSE) {
### load metafor package
library(metafor)
### fit random-effects model
res <- rma(yi, vi, data=dat)
print(res, digits=3)
### fit random-effects model using rma.mv()
res <- rma.mv(yi, vi, random = ~ 1 | study, data=dat)
print(res, digits=3)
### fit multilevel random-effects model
res.ml <- rma.mv(yi, vi, random = ~ 1 | district/school, data=dat)
print(res.ml, digits=3)
### profile variance components
profile(res.ml, progbar=FALSE)
### fit multivariate parameterization of the model
res.mv <- rma.mv(yi, vi, random = ~ school | district, data=dat)
print(res.mv, digits=3)
### tau^2 = sum of the two variance components from the multilevel model
round(sum(res.ml$sigma2), digits=3)
### rho = intraclass correlation coefficient based on the multilevel model
round(res.ml$sigma2[1] / sum(res.ml$sigma2), digits=3)
}
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