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[R-meta] H2 in the context of rma.mv

Wolfgang Viechtbauer 
Dear Marlene,

It's not there, but you can calculate it manually. You can find a discussion on computing I^2 for more complex models here:

https://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate

Using the same principles discussed there, one can also compute H^2. And yes, your calculation below is doing just that. Using the notation in the page above, H^2 for the Konstantopoulos example would be:

W <- diag(1/res$vi)
X <- model.matrix(res)
P <- W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
(sum(res$sigma2) + (res$k-res$p)/sum(diag(P))) / ((res$k-res$p)/sum(diag(P)))

Note that I put sum(res$sigma2) in the numerator instead of just res$sigma2[1]. I think for H^2, that is more sensible, since we are asking how much larger the *total variance* is compared to the amount of variance expected based on sampling variability alone. Just putting part of the variance components in the numerator leads to a rather convoluted interpretation of what the H^2 value represents.

Best,
Wolfgang

Thread (3 messages)

Werner, M.A. (Marlene) H2 in the context of rma.mv Mar 17 Wolfgang Viechtbauer H2 in the context of rma.mv Mar 18 Werner, M.A. (Marlene) H2 in the context of rma.mv Mar 18