Open wwiecek opened 2 years ago
Note: frequentist heterogeneity estimates (and hence R^2) will very easily tend toward extremes and therefore be misleading. See this example:
library(metafor)
k <- 100
x <- rnorm(k,0,1)
dt <- data.frame(se = rep(2, k),
x,
tau = x + rnorm(k, 10, 1))
plot(tau ~ x, data = dt)
lm(tau~x, weights=1/se,data=dt) %>% summary()
lm(tau~x, data=dt) %>% summary()
rma(yi=tau,sei=se,data=dt,mods=~x)
Try changing SE to 0.1, 0.5, 1, 2 etc. and see how R^2 seems to only be estimated similarly to lm
when SE is very low
Reading material: