# The metafor Package

A Meta-Analysis Package for R

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tips:weights_in_rma.mv_models

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 tips:weights_in_rma.mv_models [2021/11/08 15:17]Wolfgang Viechtbauer tips:weights_in_rma.mv_models [2021/11/08 15:56] (current)Wolfgang Viechtbauer Both sides previous revision Previous revision 2021/11/08 15:56 Wolfgang Viechtbauer 2021/11/08 15:17 Wolfgang Viechtbauer 2021/11/08 15:16 Wolfgang Viechtbauer 2021/07/31 09:32 Wolfgang Viechtbauer 2021/07/31 09:10 Wolfgang Viechtbauer 2021/04/16 07:46 Wolfgang Viechtbauer 2020/06/07 20:59 Wolfgang Viechtbauer created 2021/11/08 15:56 Wolfgang Viechtbauer 2021/11/08 15:17 Wolfgang Viechtbauer 2021/11/08 15:16 Wolfgang Viechtbauer 2021/07/31 09:32 Wolfgang Viechtbauer 2021/07/31 09:10 Wolfgang Viechtbauer 2021/04/16 07:46 Wolfgang Viechtbauer 2020/06/07 20:59 Wolfgang Viechtbauer created Line 1: Line 1: ===== Weights in Models Fitted with the rma.mv() Function ===== ===== Weights in Models Fitted with the rma.mv() Function ===== - One of the fundamental concepts underlying a meta-analysis is the idea of weighting: More precise estimates are given more weight in the analysis then less precise estimates. In 'standard' fixed- and random-effects models (such as those that can be fitted with the ''rma()'' function), the weighting scheme is quite simple and covered in standard textbooks on meta-analysis. However, in more complex models (such as those that can be fitted with the ''rma.mv()'' function), the way estimates are weighted is more complex. Here, I will discuss some of those intricacies. + One of the fundamental concepts underlying a meta-analysis is the idea of weighting: More precise estimates are given more weight in the analysis then less precise estimates. In 'standard' equal- and random-effects models (such as those that can be fitted with the ''rma()'' function), the weighting scheme is quite simple and covered in standard textbooks on meta-analysis. However, in more complex models (such as those that can be fitted with the ''rma.mv()'' function), the way estimates are weighted is more complex. Here, I will discuss some of those intricacies. ==== Models Fitted with the rma() Function ==== ==== Models Fitted with the rma() Function ==== Line 33: Line 33: Variable ''yi'' contains the log risk ratios and variable ''vi'' the corresponding sampling variances. Variable ''yi'' contains the log risk ratios and variable ''vi'' the corresponding sampling variances. - We now fit fixed- and random-effects models to these estimates. + We now fit equal- and random-effects models to these estimates. 