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<h2>Project components:</h2> <h3>Data used in the meta-analysis:</h3> <p>We store <a href="https://osf.io/4c25h/" rel="nofollow">here</a> all the data used in the meta-analysis and two files that describe the characteristics of the studies (<code>issues.tsv</code> and <code>studies.tsv</code>). The data from the studies are either complete datasets or summaries from tables in the papers. The component estimates extraction describes the datasets and the exact steps we took to extract the estimates that we used in the meta-analysis. For both "Estimates extraction" script and the knitr scripts, the data needs to be saved in a <code>Data</code> folder where the script (or knitr files) are run.</p> <h2>Estimates extraction</h2> <p>We describe <a href="https://osf.io/3qmf5/" rel="nofollow">here</a> using Rmarkdown, the exact steps we took to extract the estimates that went to the meta-analyses. The data needs to be saved in <code>Data</code> folder and it requires <code>rstan</code> (<a href="http://mc-stan.org/" rel="nofollow">http://mc-stan.org/</a>) to be installed following <a href="https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started" rel="nofollow">these guidelines</a>.</p> <h2>Meta-analysis example code</h2> <p>We show <a href="https://osf.io/b6smh/" rel="nofollow">here</a> how to use the extracted estimates to obtain a meta-analytic estimate using <code>rstan</code> wrapper package <code>brms</code>. All the additional analyses and plots can be found in the knitr files component.</p> <h2>PRISMA statement</h2> <p>We show <a href="https://osf.io/wjpbg/" rel="nofollow">here</a> a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, for more information about this statement see <a href="http://www.prisma-statement.org/" rel="nofollow">http://www.prisma-statement.org/</a>.</p> <h2>knitr files</h2> <p>We store <a href="https://osf.io/5xu6v/" rel="nofollow">here</a> all the knitr (.Rnw) files needed to reproduce our analysis, plots, and paper. The data needs to be stored in a folder <code>Data</code>.</p> <h2>System info</h2> <p>The analyses and plots were done in a system with the following R and package versions:</p> <pre class="highlight"><code>&gt; sessionInfo() R version 3.4.0 (2017-04-21) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 16.04.2 LTS Matrix products: default BLAS: /usr/lib/libblas/libblas.so.3.6.0 LAPACK: /usr/lib/lapack/liblapack.so.3.6.0 locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] tibble_1.3.4 ggrepel_0.7.0 brms_2.1.0 Rcpp_0.12.15 [5] bayesplot_1.4.0 cowplot_0.8.0 stringr_1.3.0 rstan_2.17.3 [9] StanHeaders_2.17.2 MASS_7.3-47 ggplot2_2.2.1 magrittr_1.5 [13] purrr_0.2.4 tidyr_0.6.2 dplyr_0.7.4 knitr_1.20 loaded via a namespace (and not attached): [1] mvtnorm_1.0-6 lattice_0.20-35 zoo_1.8-0 [4] gtools_3.5.0 assertthat_0.2.0 digest_0.6.15 [7] mime_0.5 R6_2.2.2 plyr_1.8.4 [10] stats4_3.4.0 coda_0.19-1 colourpicker_0.3 [13] rlang_0.2.0 lazyeval_0.2.0 miniUI_0.1.1 [16] Matrix_1.2-10 DT_0.2 shinythemes_1.1.1 [19] shinyjs_0.9 htmlwidgets_0.9 loo_1.1.0 [22] munsell_0.4.3 shiny_1.0.3 compiler_3.4.0 [25] httpuv_1.3.3 pkgconfig_2.0.1 base64enc_0.1-3 [28] rstantools_1.4.0 htmltools_0.3.6 gridExtra_2.3 [31] threejs_0.2.2 matrixStats_0.53.1 grid_3.4.0 [34] nlme_3.1-131 jsonlite_1.5 xtable_1.8-2 [37] gtable_0.2.0 scales_0.4.1 stringi_1.1.6 [40] reshape2_1.4.3 bindrcpp_0.2 dygraphs_1.1.1.4 [43] xts_0.9-7 tools_3.4.0 glue_1.2.0 [46] shinystan_2.4.0 markdown_0.8 rsconnect_0.8 [49] abind_1.4-5 parallel_3.4.0 inline_0.3.14 [52] colorspace_1.3-2 bridgesampling_0.3-0 bindr_0.1 [55] Brobdingnag_1.2-4</code></pre>
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