The metafor Package

A Meta-Analysis Package for R

User Tools

Site Tools


news:news

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision
Previous revision
Next revisionBoth sides next revision
news:news [2022/08/27 16:19] Wolfgang Viechtbauernews:news [2022/09/26 19:31] Wolfgang Viechtbauer
Line 2: Line 2:
  
 ~~NOTOC~~ ~~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 ==== ==== 2022-08-27: Version 3.8-1 Released on CRAN ====
Line 13: Line 17:
 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. 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_or|here]].+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]]. Aside from this, there were a few smaller improvements. The full changelog can be found [[:updates#version_38-1_2022-08-26|here]].
news/news.txt · Last modified: 2023/09/29 07:39 by Wolfgang Viechtbauer