Differences

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--- features [2020/08/09 16:39] – [Datasets] Wolfgang Viechtbauer
+++ features [2021/04/25 13:16] – [Plots and Figures] Wolfgang Viechtbauer
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 Below is an overview of the various features provided by the metafor package. Where applicable, function names are also indicated.
-==== Effect Size and Outcome Measures ====
+==== Calculation of Effect Sizes and Outcome Measures ====
+The package allows the user to calculate various effect sizes and outcome measures frequently used in meta-analyses (''escalc()'' function), including:
-The package allows the user to calculate various effect size and outcome measures frequently used in meta-analyses (''escalc()'' function), including:
   * risk differences, risk ratios, and odds ratios for 2×2 table data,
   * incidence rate ratios and differences for two-group person-time data,
@@ Line 18: / Line 19: @@
 The package provides a variety of models and analysis approaches, including:
   * fixed-, random-, and mixed-effects models using the inverse-variance method (''rma()'' function),
-  * Mantel-Haenszel and Peto's (one-step) method for 2×2 table and two-group person-time data (''rma.mh()'' and ''rma.peto()'' functions),
+  * the Mantel-Haenszel and Peto's (one-step) method for 2×2 table and two-group person-time data (''rma.mh()'' and ''rma.peto()'' functions),
   * generalized linear (mixed-effects) models (i.e., mixed-effects (conditional) logistic and Poisson regression models) for the analysis of 2×2 table data, two-group person-time data, proportions, and incidence rates (''rma.glmm()'' function),
   * models for multilevel and multivariate meta-analyses (''rma.mv()'' function),
   * models for network meta-analyses and mixed treatment comparisons (''rma.mv()'' function),
-  * phylogenetic meta-analysis (''rma.mv()'' function),
+  * models for phylogenetic meta-analyses (''rma.mv()'' function),
   * spatio-temporal meta-analytic models (''rma.mv()'' function),
   * subgrouping and (mixed-effects) meta-regression analyses,
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 The package provides functions for creating a variety of meta-analytic plots and figures, including:
   * funnel plots (''funnel()'' function),
   * forest plots (''forest()'' and ''addpoly()'' functions),
+  * scatter plots / bubble plots (''regplot()'' function),
   * Baujat plots (''baujat()'' function),
   * L'Abbé plots (''labbe()'' function),
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   * profile likelihood plots (''profile()'' function),
   * normal quantile-quantile (QQ) plots (''qqnorm()'' function).
 R itself also provides extensive and very flexible graphing and plotting capabilities that can be easily adapted to create further plots and figures.
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 The presence of publication bias (or more accurately, funnel plot asymmetry or "small-study effects") and its potential impact on the results can be examined via a variety of methods, including:
   * the rank correlation test (''ranktest()'' function),
   * Egger's regression test (''regtest()'' function),
@@ Line 49: / Line 55: @@
   * the Henmi and Copas approach (''hc()'' function),
   * a file drawer analysis (fail-safe N computation) using the Rosenthal, Orwin, and Rosenberg methods (''fsn()'' function),
-  * the test of excess significance (''tes()'' function).
+  * the test of excess significance (''tes()'' function),
+  * selection models (''selmodel()'' function).
 ==== Inference Methods ====
 The package provides standard and advanced methods for drawing inferences based on meta-analytic data and for assessing the model fit, including:
   * likelihood ratio and Wald-type tests (''anova()'' function),
   * confidence intervals for heterogeneity statistics (''confint()'' function),
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   * simulation of data from a fitted model (''simulate()'' function).
-==== Outlier and Influence Diagnostics ====
+The package is also compatible with the [[https://cran.r-project.org/package=glmulti|glmulti]] and [[https://cran.r-project.org/package=MuMIn|MuMIn]] packages for model selection and (multi)model inference (see [[tips:model_selection_with_glmulti|here]] for an illustration), the [[https://cran.r-project.org/package=boot|boot]] package for bootstrapping (see [[tips:bootstrapping_with_ma|here]] for an illustration), and the [[https://cran.r-project.org/package=mice|mice]] and [[https://cran.r-project.org/package=Amelia|Amelia]] packages for multiple imputation (see [[tips:multiple_imputation_with_mice_and_metafor|here]] for an illustration).
+==== Outlier/Influence Diagnostics ====
 Various methods are available to identify outliers and/or influential studies, and for conducting sensitivity analyses, including:
   * raw/standardized/studentized residuals (''residuals()'', ''rstandard()'', and ''rstudent()'' functions),
   * DFFITS values, Cook's distances, covariance ratios, and DFBETAS values (''influence()'' function),
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 ==== Notes ====
-A diagram showing the various functions in the metafor package (and how they related to each other) can be found {{docs:diagram.pdf|here}}. If the package is installed, you should also be able to open this diagram directly from R with the command ''vignette("diagram")''.
+A diagram showing the various functions in the metafor package (and how they related to each other) can be found [[diagram|here]]. If the package is installed, you should also be able to open this diagram directly from R with the command ''vignette("diagram")''.
 The metafor package is a work in progress and is updated on a regular basis with new functions and options. Under the [[updates|log of package updates]], you can see what changes/updates have been made to the package over the years.