Differences

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--- analyses:konstantopoulos2011 [2022/08/03 11:18] – Wolfgang Viechtbauer
+++ analyses:konstantopoulos2011 [2022/08/22 16:00] (current) – Wolfgang Viechtbauer
@@ Line 80: / Line 80: @@
 To obtain the descriptives about the effect size estimates per district (Table 3 in the paper), we can use:
 <code rsplus>
-round(aggregate(yi ~ district, data=dat, FUN=function(x) c(mean=mean(x), sd=sd(x), min=min(x), max=max(x))), 3)
+round(aggregate(yi ~ district, data=dat, FUN=function(x)
+                c(mean=mean(x), sd=sd(x), min=min(x), max=max(x))), 3)
 </code>
 <code output>
@@ Line 288: / Line 289: @@
 The ''random = ~ study | district'' argument adds correlated random effects for the different studies within districts to the model, where the variance-covariance matrix of the random effects takes on a compound symmetric structure (''struct="CS"'' is the default). Note that the estimate of $\rho$ that is obtained is exactly the same as the ICC value we computed earlier based on the multilevel model. Also, the estimate of $\tau^2$ obtained from the multivariate parameterization is the same as the total amount of heterogeneity computed earlier based on the multilevel model. Note that ''random = ~ school | district'' would again yield the same results.
-As long as $\rho$ is estimated to be positive, the multilevel and multivariate parametrizations are in essence identical. In fact, the log likelihoods of the two models should be identical, which we can confirm with:
+As long as $\rho$ is estimated to be positive, the multilevel and multivariate parameterizations are in essence identical. In fact, the log likelihoods of the two models should be identical, which we can confirm with:
 <code rsplus>
 logLik(res.ml)