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--- news:news [2022/08/23 09:24] – [2022-08-22: Another Multilevel Meta-Analysis Example] Wolfgang Viechtbauer
+++ news:news [2022/09/26 19:31] – Wolfgang Viechtbauer
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 ~~NOTOC~~
+==== 2022-09-26: Confidence Intervals for $R^2$ ====
+The (pseudo) $R^2$ statistic that is shown in the output for meta-regression models fitted with the ''rma()'' function provides an estimate of how much of the total amount of heterogeneity is accounted for by the moderator(s) included in the model (Raudenbush, 2009). However, it is important to realize that this statistic can be quite inaccurate, especially when the number of studies ($k$) is small. We may therefore want to construct a confidence interval to get a better sense of how precise the value may be. This can be done using bootstrapping, as illustrated [[tips:ci_for_r2|here]]. I also conducted a small simulation study to examine how well bootstrapping actually works for constructing CIs for $R^2$. The results indicate that the bias-corrected and accelerated (BCa) CI actually works quite well, as long as $k$ is at least 40 and the true value of $R^2$ is not too small.
+==== 2022-08-27: Version 3.8-1 Released on CRAN ====
+I just sent a new version (3.8-1) of the metafor package to CRAN. This update was prompted due to a small issue in the help pages (related to my use of MathJax to render nice equations in the docs), which was easy to fix. I took the opportunity to incorporate some other updates into the new version, which provide a bit of polish.
+One thing I am kind of excited about is the completely overhauled ''[[https://wviechtb.github.io/metafor/reference/vif.html|vif()]]'' function for computing variance inflation factors. One of the major difficulties with VIFs is their interpretation. Is a particular value 'large'? Commonly used cutoffs like 5 or 10 are quite arbitrary. To make it easier to gauge whether a VIF value is relatively large, one can now simulate the distribution of a VIF under independence, similar to a 'parallel analysis' that is used in factor analysis to determine the number of factors. One can then examine how extreme the actually observed VIF is under this distribution. A [[https://wviechtb.github.io/metafor/reference/plot.vif.rma.html|plot method]] is also available to visualize this.
+There is now some more support for using an identity link when fitting location-scale models with the ''[[https://wviechtb.github.io/metafor/reference/rma.uni.html|rma()]]'' function, although the default log link is typically the better choice and avoids having to use constrained optimization to fit the model.
+I also added (experimental!) support for additional measures (e.g., log risk ratios and risk differences) to ''[[https://wviechtb.github.io/metafor/reference/rma.glmm.html|rma.glmm()]]'' (by using log and identity links in the generalized linear mixed-effects model), but using these measures will often lead to estimation problems. For 2x2 table data, the log odds ratio (i.e., using a logit link) is still the preferred choice.
+Another nice feature when computing standardized mean differences with the ''[[https://wviechtb.github.io/metafor/reference/escalc.html|escalc()]]'' function is that one can now specify d-values and t-test statistics directly. This makes it easier to assemble data for a meta-analysis with SMD values, as described [[tips:assembling_data_smd|here]].
+Aside from this, there were a few smaller improvements. The full changelog can be found [[:updates#version_38-1_2022-08-26|here]].
 ==== 2022-08-22: Another Multilevel Meta-Analysis Example ====