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--- news:news [2021/04/25 13:19] – Wolfgang Viechtbauer
+++ news:news [2021/04/25 19:32] – Wolfgang Viechtbauer
@@ Line 9: / Line 9: @@
 The Knapp-Hartung method cannot be directly generalized to more complex models as can be fitted with the [[https://wviechtb.github.io/metafor/reference/rma.mv.html|rma.mv()]] function, although we can still use t- and F-distributions for conducting tests of one or multiple model coefficients in the context of such models. This is possible by setting ''test="t"''. However, this then raises the question how the (denominator) degrees of freedom for such tests should be calculated. By default, the degrees of freedom are calculated as described above. However, this method does not reflect the complexities of models that are typically fitted with the ''rma.mv()'' function. For example, in multilevel models (with multiple estimates nested within studies), a predictor (or 'moderator') may be measured at the study level (i.e., it is constant for all estimates belonging to the same study) or at the level of the individual estimates (i.e., it might vary within studies). By setting argument ''dfs="contain"'', a method is used for calculating the degrees of freedom that tends to provide tests with better control of the Type I error rate and confidence intervals with closer to nominal coverage rates. See the documentation of the function for further details.
-==== April 24th, 2021: Scatter Plots / Bubble Plots for Meta-Regression Models ====
+==== April 3rd, 2021: Scatter Plots / Bubble Plots for Meta-Regression Models ====
 I finally got around to adding a function to the package for drawing scatter plots (also known as bubble plots) for meta-regression models. See the documentation of the [[https://wviechtb.github.io/metafor/reference/regplot.html|regplot()]] function for further details. An example illustrating such a plot is provided [[plots:meta_analytic_scatterplot|here]].