faq
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faq [2020/03/16 20:05] – Wolfgang Viechtbauer | faq [2021/11/05 09:40] – Wolfgang Viechtbauer | ||
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Similar (and much more thorough/ | Similar (and much more thorough/ | ||
- | It may also be useful to note that there is now an appreciable user base of the metafor package | + | It may also be useful to note that there is now an appreciable user base of the metafor package. The [[https:// |
Finally, I have become very proficient at hitting the [[https:// | Finally, I have become very proficient at hitting the [[https:// | ||
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??? Is the package development funded? | ??? Is the package development funded? | ||
- | !!! For the most part, the development of the package has been funded through my own precious time. Through some collaborative work on the 'Open Meta-Analyst' | + | !!! For the most part, the development of the package has been funded through my own precious time. Through some collaborative work on the 'Open Meta-Analyst' |
??? How do I cite the package? | ??? How do I cite the package? | ||
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!!! First of all, thanks for trying to do so in the first place. The best way of citing the package is to cite the following paper: | !!! First of all, thanks for trying to do so in the first place. The best way of citing the package is to cite the following paper: | ||
- | Viechtbauer, | + | Viechtbauer, |
By the way, try '' | By the way, try '' | ||
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??? Why can I not just use the lm() and lme(), and lmer() functions to conduct my meta-analysis? | ??? Why can I not just use the lm() and lme(), and lmer() functions to conduct my meta-analysis? | ||
- | !!! First of all, meta-analytic models (as can be fitted with the '' | + | !!! First of all, meta-analytic models (as can be fitted with the '' |
Furthermore, | Furthermore, | ||
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??? How are $I^2$ and $H^2$ computed in the metafor package? | ??? How are $I^2$ and $H^2$ computed in the metafor package? | ||
- | !!! For random-effects models, the $I^2$ statistic is computed with $$I^2 = 100\% \times \frac{\hat{\tau}^2}{\hat{\tau}^2 + \tilde{v}}, | + | !!! For random-effects models, the $I^2$ statistic is computed with $$I^2 = 100\% \times \frac{\hat{\tau}^2}{\hat{\tau}^2 + \tilde{v}}, |
Therefore, depending on the estimator of $\tau^2$ used, the values of $I^2$ and $H^2$ will change. For random-effects models, $I^2$ and $H^2$ are often computed in practice with $I^2 = 100\% \times (Q-(k-1))/ | Therefore, depending on the estimator of $\tau^2$ used, the values of $I^2$ and $H^2$ will change. For random-effects models, $I^2$ and $H^2$ are often computed in practice with $I^2 = 100\% \times (Q-(k-1))/ | ||
- | These two sets of equations for $I^2$ and $H^2$ actually coincide when using the DerSimonian-Laird estimator of $\tau^2$ (i.e., the commonly used equations are actually special cases of the more general definitions given above). Therefore, if you prefer the more conventional definitions of these statistics, use '' | + | These two sets of equations for $I^2$ and $H^2$ actually coincide when using the DerSimonian-Laird estimator of $\tau^2$ (i.e., the commonly used equations are actually special cases of the more general definitions given above). Therefore, if you prefer the more conventional definitions of these statistics, use '' |
See the analysis example for [[analyses: | See the analysis example for [[analyses: | ||
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??? For mixed-effects models, how is the $R^2$ statistic computed by the rma() function? | ??? For mixed-effects models, how is the $R^2$ statistic computed by the rma() function? | ||
- | !!! The pseudo $R^2$ statistic (Raudenbush, | + | !!! The pseudo $R^2$ statistic (Raudenbush, |
- | ??? For random-effects models fitted with the rma() function, how is the credibility/ | + | ??? For random-effects models fitted with the rma() function, how is the prediction interval computed by the predict() function? |
- | !!! By default, the interval is computed with $$\hat{\mu} \pm z_{1-\alpha/ | + | !!! By default, the interval is computed with $$\hat{\mu} \pm z_{1-\alpha/ |
- | Note that this differs from Riley et al. (2001), who suggest to use a t-distribution with $k-2$ degrees of freedom for constructing the interval. Neither a normal, nor a t-distribution with $k-1$ or $k-2$ degrees of freedom is correct; all of these are approximations. The computations in metafor are done in the way described above, so that the credibility/ | + | Note that this differs |
??? How is the Freeman-Tukey transformation of proportions and incidence rates computed? | ??? How is the Freeman-Tukey transformation of proportions and incidence rates computed? | ||
- | !!! The '' | + | !!! The '' |
For proportions, | For proportions, | ||
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??? Why do I get different results with the Mantel-Haenszel method as implemented in metafor when compared to other software? | ??? Why do I get different results with the Mantel-Haenszel method as implemented in metafor when compared to other software? | ||
- | !!! When used with the default settings, the '' | + | !!! When used with the default settings, the '' |
==== References ==== | ==== References ==== |
faq.txt · Last modified: 2023/01/24 07:56 by Wolfgang Viechtbauer