├── .Rbuildignore ├── .gitattributes ├── .github └── workflows │ └── build.yml ├── .gitignore ├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── NEWS.md ├── R ├── BGGM-package.R ├── RcppExports.R ├── bggm_missing.R ├── bma_posterior.R ├── coef.estimate.R ├── confirm.R ├── constrained_post.R ├── convergence.R ├── datasets.R ├── estimate.R ├── explore.default.R ├── fisher_r2z.R ├── fisher_z2r.R ├── gen_ordinal.R ├── gen_pcors.R ├── ggm_compare_bf.default.R ├── ggm_compare_confirm.R ├── ggm_compare_estimate.default.R ├── ggm_compare_ppc.default.R ├── ggm_search.R ├── helpers.R ├── map.R ├── mvn_imputation.R ├── pcor_2_cor.BGGM.R ├── pcor_mat.R ├── plot.select.R ├── plot_prior.R ├── posterior_predict.R ├── posterior_samples.R ├── posterior_sum.R ├── precision.R ├── pred_prob.R ├── predict.estimate.R ├── predictability.R ├── print_BGGM.R ├── prior_belief_ggm.R ├── prior_belief_var.R ├── regression_summary.R ├── roll_your_own.R ├── select.VAR_estimate.R ├── select.estimate.R ├── select.explore.R ├── select.ggm_compare_bf.R ├── select.ggm_compare_estimate.R ├── var_estimate.R ├── weighted_adj_mat.R └── zero_order.R ├── README.Rmd ├── README.md ├── _pkgdown.yml ├── cleanup ├── configure ├── configure.ac ├── cran-comments.md ├── data-raw ├── bfi_data.R ├── csws_data.R ├── ptsd_cor1.R ├── ptsd_cor2.R ├── ptsd_cor3.R ├── ptsd_cor4.R └── ptsd_data.R ├── data ├── Sachs.rda ├── asd_ocd.rda ├── bfi.rda ├── csws.rda ├── depression_anxiety_t1.rda ├── depression_anxiety_t2.rda ├── gss.rda ├── ifit.rda ├── iri.rda ├── ptsd.csv ├── ptsd.rda ├── ptsd_cor1.csv ├── ptsd_cor1.rda ├── ptsd_cor2.rda ├── ptsd_cor3.rda ├── ptsd_cor4.rda ├── rsa.rda ├── tas.rda └── women_math.rda ├── docs ├── 404.html ├── LICENSE-text.html ├── apple-touch-icon-120x120.png ├── apple-touch-icon-152x152.png ├── apple-touch-icon-180x180.png ├── apple-touch-icon-60x60.png ├── apple-touch-icon-76x76.png ├── apple-touch-icon.png ├── articles │ ├── control.html │ ├── hyp_3_ways.html │ ├── in_tandem.html │ ├── index.html │ ├── mcmc_diagnostics.html │ ├── netplot.html │ ├── netstat_custom.html │ ├── ppc_custom.html │ ├── ppc_custom_files │ │ └── figure-html │ │ │ ├── unnamed-chunk-13-1.png │ │ │ ├── unnamed-chunk-18-1.png │ │ │ ├── unnamed-chunk-23-1.png │ │ │ ├── unnamed-chunk-28-1.png │ │ │ └── unnamed-chunk-8-1.png │ ├── predictability.html │ ├── test_sum.html │ └── var_model.html ├── authors.html ├── deps │ ├── bootstrap-5.3.1 │ │ ├── bootstrap.bundle.min.js │ │ ├── bootstrap.bundle.min.js.map │ │ ├── bootstrap.min.css │ │ ├── font.css │ │ └── fonts │ │ │ ├── 07d40e985ad7c747025dabb9f22142c4.woff2 │ │ │ ├── 1Ptug8zYS_SKggPNyC0ITw.woff2 │ │ │ ├── 1Ptug8zYS_SKggPNyCAIT5lu.woff2 │ │ │ ├── 1Ptug8zYS_SKggPNyCIIT5lu.woff2 │ │ │ ├── 1Ptug8zYS_SKggPNyCMIT5lu.woff2 │ │ │ ├── 1Ptug8zYS_SKggPNyCkIT5lu.woff2 │ │ │ ├── 1f5e011d6aae0d98fc0518e1a303e99a.woff2 │ │ │ ├── 4iCs6KVjbNBYlgoKcQ72j00.woff2 │ │ │ ├── 4iCs6KVjbNBYlgoKcg72j00.woff2 │ │ │ ├── 4iCs6KVjbNBYlgoKcw72j00.woff2 │ │ │ ├── 4iCs6KVjbNBYlgoKew72j00.woff2 │ │ │ ├── 4iCs6KVjbNBYlgoKfA72j00.woff2 │ │ │ ├── 4iCs6KVjbNBYlgoKfw72.woff2 │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjs2yNL4U.woff2 │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjsGyN.woff2 │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjtGyNL4U.woff2 │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjvGyNL4U.woff2 │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjvWyNL4U.woff2 │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjvmyNL4U.woff2 │ │ │ ├── 626330658504e338ee86aec8e957426b.woff2 │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7jsDJT9g.woff2 │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7ksDJT9g.woff2 │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDI.woff2 │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7osDJT9g.woff2 │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7psDJT9g.woff2 │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7qsDJT9g.woff2 │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7rsDJT9g.woff2 │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qN67lqDY.woff2 │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qNK7lqDY.woff2 │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qNa7lqDY.woff2 │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qNq7lqDY.woff2 │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qO67lqDY.woff2 │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7l.woff2 │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qPK7lqDY.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwkxduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlBduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlxdu.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmBduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmRduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmhduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmxduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwkxduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlBduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdu.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmBduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmRduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmhduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmxduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwkxduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlBduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdu.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmBduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmRduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmhduz8A.woff2 │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmxduz8A.woff2 │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvWNReuQ.woff2 │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvWNpeudwk.woff2 │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_fO4KTet_.woff2 │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_fOAKTQ.woff2 │ │ │ ├── HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvQlMIXxw.woff2 │ │ │ ├── HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvUlMI.woff2 │ │ │ ├── HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvXlMIXxw.woff2 │ │ │ ├── HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvYlMIXxw.woff2 │ │ │ ├── HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvZlMIXxw.woff2 │ │ │ ├── HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvalMIXxw.woff2 │ │ │ ├── HI_QiYsKILxRpg3hIP6sJ7fM7PqlONvblMIXxw.woff2 │ │ │ ├── HI_SiYsKILxRpg3hIP6sJ7fM7PqlM-vWjMY.woff2 │ │ │ ├── HI_SiYsKILxRpg3hIP6sJ7fM7PqlMOvWjMY.woff2 │ │ │ ├── HI_SiYsKILxRpg3hIP6sJ7fM7PqlMevWjMY.woff2 │ │ │ ├── HI_SiYsKILxRpg3hIP6sJ7fM7PqlMuvWjMY.woff2 │ │ │ ├── HI_SiYsKILxRpg3hIP6sJ7fM7PqlOevWjMY.woff2 │ │ │ ├── HI_SiYsKILxRpg3hIP6sJ7fM7PqlPevW.woff2 │ │ │ ├── HI_SiYsKILxRpg3hIP6sJ7fM7PqlPuvWjMY.woff2 │ │ │ ├── JTUSjIg1_i6t8kCHKm459W1hyzbi.woff2 │ │ │ ├── JTUSjIg1_i6t8kCHKm459WRhyzbi.woff2 │ │ │ ├── JTUSjIg1_i6t8kCHKm459WZhyzbi.woff2 │ │ │ ├── JTUSjIg1_i6t8kCHKm459Wdhyzbi.woff2 │ │ │ ├── JTUSjIg1_i6t8kCHKm459Wlhyw.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmEU9fABc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmEU9fBBc4.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmEU9fBxc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmEU9fCBc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmEU9fCRc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmEU9fChc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmEU9fCxc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmSU5fABc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmSU5fBBc4.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmSU5fBxc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmSU5fCBc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmSU5fCRc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmSU5fChc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmSU5fCxc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmWUlfABc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmWUlfBBc4.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmWUlfBxc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmWUlfCBc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmWUlfCRc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmWUlfChc4EsA.woff2 │ │ │ ├── KFOlCnqEu92Fr1MmWUlfCxc4EsA.woff2 │ │ │ ├── KFOmCnqEu92Fr1Mu4WxKOzY.woff2 │ │ │ ├── KFOmCnqEu92Fr1Mu4mxK.woff2 │ │ │ ├── KFOmCnqEu92Fr1Mu5mxKOzY.woff2 │ │ │ ├── KFOmCnqEu92Fr1Mu72xKOzY.woff2 │ │ │ ├── KFOmCnqEu92Fr1Mu7GxKOzY.woff2 │ │ │ ├── KFOmCnqEu92Fr1Mu7WxKOzY.woff2 │ │ │ ├── KFOmCnqEu92Fr1Mu7mxKOzY.woff2 │ │ │ ├── QGYpz_kZZAGCONcK2A4bGOj8mNhN.woff2 │ │ │ ├── S6u8w4BMUTPHjxsAUi-qJCY.woff2 │ │ │ ├── S6u8w4BMUTPHjxsAXC-q.woff2 │ │ │ ├── S6u9w4BMUTPHh6UVSwaPGR_p.woff2 │ │ │ ├── S6u9w4BMUTPHh6UVSwiPGQ.woff2 │ │ │ ├── S6u9w4BMUTPHh7USSwaPGR_p.woff2 │ │ │ ├── S6u9w4BMUTPHh7USSwiPGQ.woff2 │ │ │ ├── S6uyw4BMUTPHjx4wXg.woff2 │ │ │ ├── S6uyw4BMUTPHjxAwXjeu.woff2 │ │ │ ├── UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa0ZL7SUc.woff2 │ │ │ ├── UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa1ZL7.woff2 │ │ │ ├── UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa1pL7SUc.woff2 │ │ │ ├── UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa25L7SUc.woff2 │ │ │ ├── UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2JL7SUc.woff2 │ │ │ ├── UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2ZL7SUc.woff2 │ │ │ ├── UcC73FwrK3iLTeHuS_fvQtMwCp50KnMa2pL7SUc.woff2 │ │ │ ├── XRXV3I6Li01BKofIMeaBXso.woff2 │ │ │ ├── XRXV3I6Li01BKofINeaB.woff2 │ │ │ ├── XRXV3I6Li01BKofIO-aBXso.woff2 │ │ │ ├── XRXV3I6Li01BKofIOOaBXso.woff2 │ │ │ ├── XRXV3I6Li01BKofIOuaBXso.woff2 │ │ │ ├── c2f002b3a87d3f9bfeebb23d32cfd9f8.woff2 │ │ │ ├── ee91700cdbf7ce16c054c2bb8946c736.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqW106F15M.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWt06F15M.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtE6F15M.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtU6F15M.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWtk6F15M.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWu06F15M.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuU6F.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWuk6F15M.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWvU6F15M.woff2 │ │ │ ├── memtYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWqWxU6F15M.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS-muw.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTS2mu1aB.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSCmu1aB.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSGmu1aB.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSKmu1aB.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSOmu1aB.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSumu1aB.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTSymu1aB.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTUGmu1aB.woff2 │ │ │ ├── memvYaGs126MiZpBA-UvWbX2vVnXBbObj2OVTVOmu1aB.woff2 │ │ │ ├── q5uGsou0JOdh94bfuQltOxU.woff2 │ │ │ └── q5uGsou0JOdh94bfvQlt.woff2 │ ├── bootstrap-toc-1.0.1 │ │ └── bootstrap-toc.min.js │ ├── clipboard.js-2.0.11 │ │ └── clipboard.min.js │ ├── data-deps.txt │ ├── font-awesome-6.4.2 │ │ ├── css │ │ │ ├── all.css │ │ │ ├── all.min.css │ │ │ ├── v4-shims.css │ │ │ └── v4-shims.min.css │ │ └── webfonts │ │ │ ├── fa-brands-400.ttf │ │ │ ├── fa-brands-400.woff2 │ │ │ ├── fa-regular-400.ttf │ │ │ ├── fa-regular-400.woff2 │ │ │ ├── fa-solid-900.ttf │ │ │ ├── fa-solid-900.woff2 │ │ │ ├── fa-v4compatibility.ttf │ │ │ └── fa-v4compatibility.woff2 │ ├── headroom-0.11.0 │ │ ├── headroom.min.js │ │ └── jQuery.headroom.min.js │ ├── jquery-3.6.0 │ │ ├── jquery-3.6.0.js │ │ ├── jquery-3.6.0.min.js │ │ └── jquery-3.6.0.min.map │ └── search-1.0.0 │ │ ├── autocomplete.jquery.min.js │ │ ├── fuse.min.js │ │ └── mark.min.js ├── favicon-16x16.png ├── favicon-32x32.png ├── favicon.ico ├── index.html ├── katex-auto.js ├── lightswitch.js ├── link.svg ├── logo.png ├── news │ └── index.html ├── paper.html ├── pkgdown.js ├── pkgdown.yml ├── reference │ ├── BGGM-package.html │ ├── Rplot001.png │ ├── Rplot002.png │ ├── Sachs.html │ ├── _PACKAGE.html │ ├── asd_ocd.html │ ├── bfi.html │ ├── bggm_missing-1.png │ ├── bggm_missing.html │ ├── coef.estimate.html │ ├── coef.explore.html │ ├── confirm.html │ ├── constrained_posterior.html │ ├── convergence.html │ ├── csws.html │ ├── depression_anxiety_t1.html │ ├── depression_anxiety_t2.html │ ├── estimate.html │ ├── explore-1.png │ ├── explore.html │ ├── figures │ │ ├── acf.png │ │ ├── acf_new.png │ │ ├── acf_new2.png │ │ ├── acf_problem.png │ │ ├── binary_r2_error.png │ │ ├── binary_r2_ridge.png │ │ ├── est_net.png │ │ ├── hyp_3ways_hist.png │ │ ├── hyp_3ways_pie.png │ │ ├── hyp_3ways_ppc.png │ │ ├── hyp_plot.png │ │ ├── index_hist.png │ │ ├── index_summ.png │ │ ├── logo.png │ │ ├── netplot_1.png │ │ ├── netplot_2.png │ │ ├── netplot_3.png │ │ ├── netplot_index.png │ │ ├── netstat_assort.png │ │ ├── netstat_bridge.png │ │ ├── netstat_ridge.png │ │ ├── ordinal_r2_error.png │ │ ├── test_sum_hist.png │ │ ├── trace.png │ │ ├── var_coef.png │ │ └── var_ridgeline.png │ ├── fisher_r_to_z.html │ ├── fisher_z_to_r.html │ ├── gen_net.html │ ├── gen_ordinal.html │ ├── ggm_compare_confirm.html │ ├── ggm_compare_estimate.html │ ├── ggm_compare_explore.html │ ├── ggm_compare_ppc.html │ ├── gss.html │ ├── ifit.html │ ├── impute_data.html │ ├── index.html │ ├── iri.html │ ├── map.html │ ├── pcor_mat.html │ ├── pcor_sum-1.png │ ├── pcor_sum.html │ ├── pcor_to_cor.html │ ├── plot.confirm-1.png │ ├── plot.confirm.html │ ├── plot.ggm_compare_ppc-1.png │ ├── plot.ggm_compare_ppc-2.png │ ├── plot.ggm_compare_ppc.html │ ├── plot.pcor_sum.html │ ├── plot.predictability-1.png │ ├── plot.predictability.html │ ├── plot.roll_your_own-1.png │ ├── plot.roll_your_own.html │ ├── plot.select.html │ ├── plot.summary.estimate-1.png │ ├── plot.summary.estimate.html │ ├── plot.summary.explore-1.png │ ├── plot.summary.explore.html │ ├── plot.summary.ggm_compare_estimate-1.png │ ├── plot.summary.ggm_compare_estimate.html │ ├── plot.summary.ggm_compare_explore-1.png │ ├── plot.summary.ggm_compare_explore.html │ ├── plot.summary.select.explore-1.png │ ├── plot.summary.select.explore.html │ ├── plot.summary.var_estimate-1.png │ ├── plot.summary.var_estimate.html │ ├── plot_prior-1.png │ ├── plot_prior.html │ ├── posterior_predict.html │ ├── posterior_samples-1.png │ ├── posterior_samples.html │ ├── precision.html │ ├── predict.estimate.html │ ├── predict.explore.html │ ├── predict.var_estimate.html │ ├── predictability.html │ ├── predicted_probability.html │ ├── print.BGGM.html │ ├── prior_belief_ggm.html │ ├── prior_belief_var.html │ ├── ptsd.html │ ├── ptsd_cor1.html │ ├── ptsd_cor2.html │ ├── ptsd_cor3.html │ ├── ptsd_cor4.html │ ├── regression_summary.html │ ├── roll_your_own.html │ ├── rsa.html │ ├── select.estimate.html │ ├── select.explore.html │ ├── select.ggm_compare_estimate.html │ ├── select.ggm_compare_explore.html │ ├── select.html │ ├── select.var_estimate.html │ ├── summary.coef.html │ ├── summary.estimate.html │ ├── summary.explore.html │ ├── summary.ggm_compare_estimate.html │ ├── summary.ggm_compare_explore.html │ ├── summary.predictability.html │ ├── summary.select.explore.html │ ├── summary.var_estimate.html │ ├── tas.html │ ├── var_estimate.html │ ├── weighted_adj_mat.html │ ├── women_math.html │ └── zero_order_cors.html ├── search.json └── sitemap.xml ├── index.Rmd ├── index.md ├── inst ├── CITATION └── REFERENCES.bib ├── man ├── BGGM-package.Rd ├── Sachs.Rd ├── asd_ocd.Rd ├── bfi.Rd ├── bggm_missing.Rd ├── bma_posterior.Rd ├── coef.estimate.Rd ├── coef.explore.Rd ├── confirm.Rd ├── constrained_posterior.Rd ├── convergence.Rd ├── csws.Rd ├── depression_anxiety_t1.Rd ├── depression_anxiety_t2.Rd ├── estimate.Rd ├── explore.Rd ├── figures │ ├── acf.png │ ├── acf_new.png │ ├── acf_new2.png │ ├── acf_problem.png │ ├── binary_r2_error.png │ ├── binary_r2_ridge.png │ ├── est_net.png │ ├── hyp_3ways_hist.png │ ├── hyp_3ways_pie.png │ ├── hyp_3ways_ppc.png │ ├── hyp_plot.png │ ├── index_hist.png │ ├── index_summ.png │ ├── logo.png │ ├── netplot_1.png │ ├── netplot_2.png │ ├── netplot_3.png │ ├── netplot_index.png │ ├── netstat_assort.png │ ├── netstat_bridge.png │ ├── netstat_ridge.png │ ├── ordinal_r2_error.png │ ├── test_sum_hist.png │ ├── trace.png │ ├── var_coef.png │ └── var_ridgeline.png ├── fisher_r_to_z.Rd ├── fisher_z_to_r.Rd ├── gen_net.Rd ├── gen_ordinal.Rd ├── ggm_compare_confirm.Rd ├── ggm_compare_estimate.Rd ├── ggm_compare_explore.Rd ├── ggm_compare_ppc.Rd ├── ggm_search.Rd ├── gss.Rd ├── ifit.Rd ├── impute_data.Rd ├── iri.Rd ├── map.Rd ├── pcor_mat.Rd ├── pcor_sum.Rd ├── pcor_to_cor.Rd ├── plot.confirm.Rd ├── plot.ggm_compare_ppc.Rd ├── plot.pcor_sum.Rd ├── plot.predictability.Rd ├── plot.roll_your_own.Rd ├── plot.select.Rd ├── plot.summary.estimate.Rd ├── plot.summary.explore.Rd ├── plot.summary.ggm_compare_estimate.Rd ├── plot.summary.ggm_compare_explore.Rd ├── plot.summary.select.explore.Rd ├── plot.summary.var_estimate.Rd ├── plot_prior.Rd ├── posterior_predict.Rd ├── posterior_samples.Rd ├── precision.Rd ├── predict.estimate.Rd ├── predict.explore.Rd ├── predict.var_estimate.Rd ├── predictability.Rd ├── predicted_probability.Rd ├── print.BGGM.Rd ├── prior_belief_ggm.Rd ├── prior_belief_var.Rd ├── ptsd.Rd ├── ptsd_cor1.Rd ├── ptsd_cor2.Rd ├── ptsd_cor3.Rd ├── ptsd_cor4.Rd ├── regression_summary.Rd ├── roll_your_own.Rd ├── rsa.Rd ├── select.Rd ├── select.estimate.Rd ├── select.explore.Rd ├── select.ggm_compare_estimate.Rd ├── select.ggm_compare_explore.Rd ├── select.var_estimate.Rd ├── summary.coef.Rd ├── summary.estimate.Rd ├── summary.explore.Rd ├── summary.ggm_compare_estimate.Rd ├── summary.ggm_compare_explore.Rd ├── summary.predictability.Rd ├── summary.select.explore.Rd ├── summary.var_estimate.Rd ├── tas.Rd ├── var_estimate.Rd ├── weighted_adj_mat.Rd ├── women_math.Rd └── zero_order_cors.Rd ├── paper.md ├── pkgdown └── favicon │ ├── apple-touch-icon-120x120.png │ ├── apple-touch-icon-152x152.png │ ├── apple-touch-icon-180x180.png │ ├── apple-touch-icon-60x60.png │ ├── apple-touch-icon-76x76.png │ ├── apple-touch-icon.png │ ├── favicon-16x16.png │ ├── favicon-32x32.png │ └── favicon.ico ├── readme_models ├── binary_cors.rda ├── bridge.png ├── confirm_hyp.png ├── fit_explore.rda ├── fit_ggm_compare_confirm.rda ├── fit_hyp1.rda ├── fit_mvn_ordinal.rda ├── fit_plt_ex.rda ├── fit_ppc1.rda ├── fit_sample.rda ├── ggm_compare_estimate.png ├── global_hd.rda ├── hex.jpg ├── mv_probit.rda ├── obs.rda ├── pcor_ex.rda ├── plt_est_net.png ├── plt_ggm_compare_explore.png ├── plt_net_example.png ├── ppc_1.png └── predictability.png ├── revdep ├── .gitignore ├── README.md ├── cran.md ├── failures.md └── problems.md ├── src ├── Makevars.in ├── Makevars.win ├── RcppExports.cpp └── bggm_fast.cpp ├── tests ├── testthat.R └── testthat │ ├── test-bggm_missing.R │ ├── test-bma_posterior.R │ ├── test-estimate.R │ ├── test-select.explore.R │ └── test-summary.estimate.R └── vignettes ├── .gitignore ├── control.Rmd ├── hyp_3_ways.Rmd ├── in_tandem.Rmd ├── mcmc_diagnostics.Rmd ├── netplot.Rmd ├── netstat_custom.Rmd ├── netstat_fit.rda ├── ppc_custom.Rmd ├── predictability.Rmd ├── test_sum.Rmd └── var_model.Rmd /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | ^/\.gitattributes$ 4 | ^\.travis\.yml$ 5 | ^Meta$ 6 | ^data-raw$ 7 | ^README\.Rmd$ 8 | ^README\.md$ 9 | ^README\.html$ 10 | ^paper\.md$ 11 | ^LICENSE$ 12 | ^readme_models$ 13 | ^_pkgdown\.yml$ 14 | ^pkgdown$ 15 | ^index\.md$ 16 | ^index\.Rmd$ 17 | ^doc$ 18 | ^docs$ 19 | ^config.status$ 20 | ^tests$ 21 | ^local$ 22 | ^cran-comments\.md$ 23 | ^CRAN-SUBMISSION$ 24 | ^\.github$ 25 | ^inst/auto$ 26 | ^revdep$ 27 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Declare files that will always have LF line endings on checkout. 2 | ^configure\.ac$ text eol=lf 3 | ^cleanup$ text eol=lf 4 | 5 | * text=auto 6 | *.sh text eol=lf 7 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | ^/\.gitattributes$ 6 | .joss_paper/ 7 | .data-raw/ 8 | src/*.o 9 | src/*.so 10 | src/*.dll 11 | doc 12 | Meta 13 | custom.css 14 | /doc/ 15 | /Meta/ 16 | /src/Makevars 17 | /config.log 18 | config.status 19 | /inst/auto/ 20 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: BGGM 2 | Type: Package 3 | Title: Bayesian Gaussian Graphical Models 4 | Version: 2.1.4.9000 5 | Date: 2024-12-12 6 | Authors@R: c(person("Donald", "Williams", email = "drwwilliams@ucdavis.edu", role = "aut"), 7 | person("Joris", "Mulder", email = "J.Mulder3@uvt.nl", role = "aut"), 8 | person("Philippe", "Rast", email = "rast.ph@gmail.com", role = c("aut", "cre"))) 9 | Description: Fit Bayesian Gaussian graphical models. The methods are separated into 10 | two Bayesian approaches for inference: hypothesis testing and estimation. There are 11 | extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, 12 | and node wise predictability. These methods were recently introduced in the Gaussian 13 | graphical model literature, including 14 | Williams (2019) , 15 | Williams and Mulder (2019) , 16 | Williams, Rast, Pericchi, and Mulder (2019) . 17 | Depends: R (>= 4.0.0) 18 | License: GPL-2 19 | Imports: 20 | BFpack (>= 1.2.3), 21 | GGally (>= 1.4.0), 22 | ggplot2 (>= 3.2.1), 23 | ggridges (>= 0.5.1), 24 | grDevices, 25 | MASS (>= 7.3-51.5), 26 | methods, 27 | mvnfast (>= 0.2.5), 28 | network (>= 1.15), 29 | reshape (>= 0.8.8), 30 | Rcpp (>= 1.0.4.6), 31 | Rdpack (>= 0.11-1), 32 | sna (>= 2.5), 33 | stats, 34 | utils, 35 | Suggests: 36 | abind (>= 1.4-5), 37 | assortnet (>= 0.12), 38 | networktools (>= 1.3.0), 39 | mice (>= 3.8.0), 40 | psych, 41 | knitr, 42 | rmarkdown, 43 | testthat (>= 3.0.0) 44 | Encoding: UTF-8 45 | LazyData: true 46 | VignetteBuilder: 47 | knitr 48 | RoxygenNote: 7.3.2 49 | LinkingTo: Rcpp, 50 | RcppArmadillo, 51 | RcppDist, 52 | RcppProgress 53 | RdMacros: Rdpack 54 | BugReports: https://github.com/donaldRwilliams/BGGM/issues 55 | Config/testthat/edition: 3 56 | URL: https://donaldrwilliams.github.io/BGGM/ 57 | -------------------------------------------------------------------------------- /R/fisher_r2z.R: -------------------------------------------------------------------------------- 1 | #' Fisher Z Transformation 2 | #' 3 | #' @description Tranform correlations to Fisher's Z 4 | #' @param r correlation (can be a vector) 5 | #' 6 | #' @return Fisher Z transformed correlation(s) 7 | #' @export 8 | #' 9 | #' @examples 10 | #' fisher_r_to_z(0.5) 11 | fisher_r_to_z <- function(r){ 12 | z <- fisher_z(r) 13 | return(z) 14 | } 15 | 16 | 17 | -------------------------------------------------------------------------------- /R/fisher_z2r.R: -------------------------------------------------------------------------------- 1 | #' Fisher Z Back Transformation 2 | #' @description Back tranform Fisher's Z to correlations 3 | #' @param z Fisher Z 4 | #' 5 | #' @return Correlation (s) (backtransformed) 6 | #' @export 7 | #' 8 | #' @examples 9 | #' fisher_z_to_r(0.5) 10 | fisher_z_to_r <- function(z){ 11 | r <- z2r(z) 12 | return(r) 13 | } 14 | -------------------------------------------------------------------------------- /R/gen_pcors.R: -------------------------------------------------------------------------------- 1 | #' Simulate a Partial Correlation Matrix 2 | #' 3 | #' @param p number of variables (nodes) 4 | #' 5 | #' @param edge_prob connectivity 6 | #' 7 | #' @param lb lower bound for the partial correlations 8 | #' 9 | #' @param ub upper bound for the partial correlations 10 | #' 11 | #' @note The function checks for a valid matrix (positive definite), 12 | #' but sometimes this will still fail. For example, for 13 | #' larger \code{p}, to have large partial correlations this 14 | #' requires a sparse GGM 15 | #' (accomplished by setting \code{edge_prob} 16 | #' to a small value). 17 | #' 18 | #' @return A list containing the following: 19 | #' 20 | #' \itemize{ 21 | #' 22 | #' \item{\strong{pcor}}: Partial correlation matrix, encoding 23 | #' the conditional (in)dependence structure. 24 | #' 25 | #' \item{\strong{cors}}: Correlation matrix. 26 | #' 27 | #' \item{\strong{adj}}: Adjacency matrix. 28 | #' 29 | #' \item{\strong{trys}}: Number of attempts to obtain a 30 | #' positive definite matrix. 31 | #' 32 | #' } 33 | #' 34 | #' @export 35 | #' 36 | #' @importFrom stats runif 37 | #' 38 | #' @examples 39 | #' 40 | #' true_net <- gen_net(p = 10) 41 | gen_net <- function(p = 20, 42 | edge_prob = 0.3, 43 | lb = 0.05, 44 | ub = 0.3) { 45 | 46 | # negative determinant 47 | d <- -1 48 | 49 | # number of trys 50 | trys <- 0 51 | 52 | # until d is positive 53 | while (d < 0) { 54 | 55 | trys <- trys + 1 56 | 57 | effects <- p * (p - 1) * 0.5 58 | 59 | mat <- matrix(1, p, p) 60 | 61 | prob_zero <- 1 - edge_prob 62 | 63 | pool <- c(rep(0, effects * prob_zero), 64 | runif(effects * edge_prob, lb, ub)) 65 | 66 | if (length(pool) != effects) { 67 | pool <- c(0, pool) 68 | } 69 | 70 | mat[upper.tri(mat)] <- sample(pool, size = effects) 71 | 72 | pcs <- symm_mat(mat) 73 | 74 | pcs <- -pcs 75 | 76 | diag(pcs) <- -diag(pcs) 77 | 78 | d <- det(pcs) 79 | 80 | } 81 | 82 | cors <- cov2cor(solve(pcs)) 83 | 84 | inv <- solve(cors) 85 | 86 | pcors <- cov2cor(inv) * -1 87 | 88 | diag(pcors) <- 1 89 | 90 | adj <- ifelse(pcs == 0, 0, 1) 91 | 92 | returned_object <- list( 93 | pcors = pcors * adj, 94 | cors = cors, 95 | adj = adj, 96 | trys = trys 97 | ) 98 | 99 | return(returned_object) 100 | 101 | } 102 | -------------------------------------------------------------------------------- /R/map.R: -------------------------------------------------------------------------------- 1 | #' Maximum A Posteriori Precision Matrix 2 | #' 3 | #' 4 | #' @param Y Matrix (or data frame) of dimensions \emph{n} (observations) by \emph{p} (variables). 5 | #' 6 | #' @return An object of class \code{map}, including the precision matrix, 7 | #' partial correlation matrix, and regression parameters. 8 | #' 9 | #' @export 10 | #' 11 | #' @examples 12 | #' Y <- BGGM::bfi[, 1:5] 13 | #' 14 | #' # map 15 | #' map <- map(Y) 16 | #' map 17 | map <- function(Y){ 18 | 19 | Y <- na.omit(Y) 20 | 21 | p <- ncol(Y) 22 | 23 | fit <- analytic_solve(Y) 24 | 25 | map <- fit$inv_map 26 | 27 | pcor <- fit$pcor_mat 28 | 29 | betas <- lapply(1:p, function(z) -1 * (map[z,-z] / map[z,z]) ) 30 | betas <- do.call(rbind, betas) 31 | 32 | returned_object <- list(precision = round(map, 3), 33 | pcor = round(pcor, 3), 34 | betas = betas, 35 | dat = Y) 36 | 37 | class(returned_object) <- c("BGGM", 38 | "estimate", 39 | "map") 40 | 41 | return(returned_object) 42 | } 43 | 44 | -------------------------------------------------------------------------------- /R/plot_prior.R: -------------------------------------------------------------------------------- 1 | #' @title Plot: Prior Distribution 2 | #' 3 | #' @description Visualize the implied prior distribution for the partial correlations. This is 4 | #' particularly useful for the Bayesian hypothesis testing methods. 5 | #' 6 | #' @name plot_prior 7 | #' 8 | #' @param prior_sd Scale of the prior distribution, approximately the standard deviation 9 | #' of a beta distribution (defaults to 0.5). 10 | #' 11 | #' @param iter Number of iterations (prior samples; defaults to 5000). 12 | #' 13 | #' @return A \code{ggplot} object. 14 | #' 15 | #' @examples 16 | #' # note: iter = 250 for demonstrative purposes 17 | #' 18 | #' plot_prior(prior_sd = 0.25, iter = 250) 19 | #' @export 20 | plot_prior <- function(prior_sd = 0.5, iter = 5000){ 21 | 22 | 23 | # matrix dimensions for prior 24 | Y_dummy <- matrix(rnorm(10 * 3), 25 | nrow = 10, ncol = 3) 26 | 27 | delta <- delta_solve(prior_sd) 28 | 29 | # sample prior 30 | prior_samp <- .Call('_BGGM_sample_prior', 31 | PACKAGE = 'BGGM', 32 | Y = Y_dummy, 33 | iter = iter, 34 | delta = delta, 35 | epsilon = 0.01, 36 | prior_only = 1, 37 | explore = 1, 38 | progress = FALSE 39 | ) 40 | 41 | qplot(prior_samp$pcors[1,2,], geom = "density") + 42 | xlab("Implied Prior Distribution") 43 | 44 | } 45 | -------------------------------------------------------------------------------- /R/precision.R: -------------------------------------------------------------------------------- 1 | #' @title Precision Matrix Posterior Distribution 2 | #' 3 | #' @description Transform the sampled correlation matrices to 4 | #' precision matrices (i.e., inverse covariance matrices). 5 | #' 6 | #' @param object An object of class \code{estimate}. 7 | #' 8 | #' @param progress Logical. Should a progress bar be included (defaults to \code{TRUE}) ? 9 | #' 10 | #' @note The estimated precision matrix is the inverse of the \strong{correlation} matrix. 11 | #' 12 | #' @return 13 | #' 14 | #' \itemize{ 15 | #' 16 | #' \item \code{precision_mean} The mean of the precision matrix (\code{p} by \code{p} matrix). 17 | #' 18 | #' \item \code{precision} 3d array of dimensions \code{p} by \code{p} by \code{iter} 19 | #' including \strong{unconstrained} (i.e., from th full graph) 20 | #' precision matrices. 21 | #' 22 | #' } 23 | #' 24 | #' @examples 25 | #' \donttest{ 26 | #' # data 27 | #' Y <- ptsd 28 | #' 29 | #' # fit model 30 | #' fit <- estimate(Y) 31 | #' 32 | #' # precision matrix 33 | #' Theta <- precision(fit) 34 | #' 35 | #' } 36 | #' 37 | #' @export 38 | precision <- function(object, 39 | progress = TRUE){ 40 | 41 | if(is(object,"estimate") & is(object,"default")){ 42 | 43 | iter <- object$iter 44 | 45 | p <- object$p 46 | 47 | cors <- pcor_to_cor(object)$R 48 | 49 | if(isTRUE(progress)){ 50 | pb <- utils::txtProgressBar(min = 0, max = iter, style = 3) 51 | } 52 | 53 | precision <- vapply(1:iter, function(s){ 54 | 55 | Theta <- solve(cors[,,s]) 56 | 57 | if(isTRUE(progress)){ 58 | utils::setTxtProgressBar(pb, s) 59 | } 60 | Theta 61 | 62 | }, FUN.VALUE = matrix(0, p, p)) 63 | 64 | 65 | } else { 66 | 67 | stop("class not currently supported") 68 | 69 | } 70 | 71 | precision_mean = apply(precision, 1:2, mean) 72 | 73 | returned_object <- list(precision_mean = precision_mean, 74 | precision = precision) 75 | 76 | class(returned_object) <- c("BGGM", 77 | "precision") 78 | 79 | return(returned_object) 80 | 81 | } 82 | 83 | 84 | print_precision <- function(x,...) { 85 | mat <- x$precision_mean 86 | p <- ncol(mat) 87 | colnames(mat) <- 1:p 88 | row.names(mat) <- 1:p 89 | cat("BGGM: Bayesian Gaussian Graphical Models \n") 90 | cat("--- \n") 91 | cat("Estimate:\n\n") 92 | print(round(mat, 3)) 93 | } 94 | -------------------------------------------------------------------------------- /R/weighted_adj_mat.R: -------------------------------------------------------------------------------- 1 | #' Extract the Weighted Adjacency Matrix 2 | #' 3 | #' @description Extract the weighted adjacency matrix (posterior mean) from 4 | #' \code{\link{estimate}}, \code{\link{explore}}, \code{\link{ggm_compare_estimate}}, 5 | #' and \code{\link{ggm_compare_explore}} objects. 6 | #' 7 | #' @param object A model estimated with \strong{BGGM}. All classes are supported, assuming 8 | #' there is matrix to be extracted. 9 | #' 10 | #' @param ... Currently ignored. 11 | #' 12 | #' @return The weighted adjacency matrix (partial correlation matrix with zeros). 13 | #' 14 | #' @examples 15 | #' \donttest{ 16 | #' # note: iter = 250 for demonstrative purposes 17 | #' Y <- bfi[,1:5] 18 | #' 19 | #' # estimate 20 | #' fit <- estimate(Y, iter = 250, 21 | #' progress = FALSE) 22 | #' 23 | #' # select graph 24 | #' E <- select(fit) 25 | #' 26 | #' # extract weighted adj matrix 27 | #' weighted_adj_mat(E) 28 | #' 29 | #' } 30 | #' @export 31 | weighted_adj_mat <- function(object, ...){ 32 | 33 | 34 | if(all(c("select.estimate", "estimate") %in% class(object))){ 35 | 36 | weighted_adj_mat <- round(object$pcor_adj, 3) 37 | weighted_adj_mat 38 | 39 | } else if(all(c("select.estimate", "estimate") %in% class(object))){ 40 | 41 | weighted_adj_mat <- round(object$pcor_mat_zero, 3) 42 | weighted_adj_mat 43 | 44 | } else if(all(c("select.ggm_compare_estimate", "estimate") %in% class(object))){ 45 | 46 | contrasts <- length(object$pcor_adj) 47 | 48 | weighted_adj_mat <- lapply(1:length(contrasts), function(x) round(object$pcor_adj[[x]], 3)) 49 | names(weighted_adj_mat) <- names(object$object$pcor_mats) 50 | weighted_adj_mat 51 | 52 | } else if(c("select.ggm_compare_bf") %in% class(object)){ 53 | 54 | if(object$object$groups > 2){ 55 | 56 | stop("weigthed adjacency only available for two groups") 57 | } 58 | 59 | weighted_adj_mat <- round(object$pcor_mat_10, 3) 60 | weighted_adj_mat 61 | 62 | } else { 63 | 64 | stop("weighted adjacency matrix not found.") 65 | 66 | } 67 | 68 | } 69 | 70 | 71 | 72 | -------------------------------------------------------------------------------- /cleanup: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | rm -f config.* src/Makevars 4 | 5 | rm -f config.log config.status confdefs.h src/*.o src/*.so src/Makevars 6 | -------------------------------------------------------------------------------- /configure.ac: -------------------------------------------------------------------------------- 1 | ## -*- mode: autoconf; autoconf-indentation: 4; -*- 2 | AC_PREREQ(2.61) 3 | 4 | AC_INIT([BGGM], m4_esyscmd_s([awk -e '/^Version:/ {print $2}' DESCRIPTION])) 5 | 6 | ## Set R_HOME 7 | : ${R_HOME=$(R RHOME)} 8 | if test -z "${R_HOME}"; then 9 | AC_MSG_ERROR([Could not determine R_HOME.]) 10 | fi 11 | 12 | ## Use R to configure compilers 13 | CXX=`${R_HOME}/bin/R CMD config CXX` 14 | CXXFLAGS=`${R_HOME}/bin/R CMD config CXXFLAGS` 15 | 16 | ## Specify C++ as the programming language 17 | AC_LANG(C++) 18 | 19 | ## Check for OpenMP support using R macros 20 | #openmp_flag="" 21 | #openmp_cflag="" 22 | 23 | ## Use R's OpenMP flags 24 | openmp_flag='$(SHLIB_OPENMP_CXXFLAGS)' 25 | openmp_cflag='$(SHLIB_OPENMP_CFLAGS)' 26 | 27 | ## Substitute OpenMP flags 28 | AC_SUBST([OPENMP_CFLAG], ["${openmp_cflag}"]) 29 | AC_SUBST([OPENMP_FLAG], ["${openmp_flag}"]) 30 | 31 | ## Configure output files 32 | AC_CONFIG_FILES([src/Makevars]) 33 | AC_OUTPUT 34 | -------------------------------------------------------------------------------- /cran-comments.md: -------------------------------------------------------------------------------- 1 | # Version 2.1.5 2 | ## Addressing CRAN errors 3 | - Adjusted autoconf.ac to avoid openMP comments and build comments 4 | 5 | # Version 2.1.4 6 | ## Addressing CRAN submission errors 7 | - CRAN server running example in bggm_missing returned `Error: inv(): matrix is singular` 8 | Error can not be locally reproduced using R CMD check --as-cran or rhub. 9 | Temporary workaround is to remove offending example 10 | 11 | ## R CMD check results 12 | 13 | ❯ checking installed package size ... NOTE 14 | installed size is 12.2Mb 15 | sub-directories of 1Mb or more: 16 | doc 3.3Mb 17 | help 1.1Mb 18 | libs 7.0Mb 19 | 20 | ❯ checking compilation flags used ... NOTE 21 | Compilation used the following non-portable flag(s): 22 | ‘-Werror=format-security’ ‘-Wformat’ ‘-Wp,-D_FORTIFY_SOURCE=3’ 23 | ‘-Wp,-D_GLIBCXX_ASSERTIONS’ ‘-march=x86-64’ 24 | ‘-mno-omit-leaf-frame-pointer’ 25 | 26 | 0 errors ✔ | 0 warnings ✔ | 2 notes ✖ 27 | 28 | ## revdepcheck results 29 | Checked 1 rev dep: zero problems. 30 | 31 | # Version 2.1.3 32 | ## R CMD check results 33 | 34 | ❯ checking installed package size ... NOTE 35 | installed size is 12.1Mb 36 | sub-directories of 1Mb or more: 37 | doc 3.4Mb 38 | help 1.1Mb 39 | libs 6.9Mb 40 | 41 | ❯ checking compilation flags used ... NOTE 42 | Compilation used the following non-portable flag(s): 43 | ‘-Werror=format-security’ ‘-Wformat’ ‘-Wp,-D_FORTIFY_SOURCE=3’ 44 | ‘-Wp,-D_GLIBCXX_ASSERTIONS’ ‘-march=x86-64’ 45 | ‘-mno-omit-leaf-frame-pointer’ 46 | 47 | 0 errors ✔ | 0 warnings ✔ | 2 notes ✖ 48 | 49 | 50 | # Version 2.1.2 51 | ## R CMD check results 52 | 53 | ❯ checking installed package size ... NOTE 54 | installed size is 8.8Mb 55 | sub-directories of 1Mb or more: 56 | help 1.1Mb 57 | libs 6.9Mb 58 | 59 | ❯ checking compilation flags used ... NOTE 60 | Compilation used the following non-portable flag(s): 61 | ‘-Werror=format-security’ ‘-Wformat’ ‘-Wp,-D_FORTIFY_SOURCE=3’ 62 | ‘-Wp,-D_GLIBCXX_ASSERTIONS’ ‘-march=x86-64’ 63 | ‘-mno-omit-leaf-frame-pointer’ 64 | 65 | 0 errors ✔ | 0 warnings ✔ | 2 notes ✖ 66 | 67 | # Version 2.1.1 68 | ## R CMD check results 69 | 70 | 0 errors | 0 warnings | 1 note 71 | 72 | * This package was archived on 2023-10-13 as it requires the _then_ archived package 'BFpack'. 73 | This error has now been fixed. 74 | -------------------------------------------------------------------------------- /data-raw/bfi_data.R: -------------------------------------------------------------------------------- 1 | bfi <- read.csv('bfi.csv') 2 | #devtools::use_data(bfi, overwrite = T) 3 | -------------------------------------------------------------------------------- /data-raw/csws_data.R: -------------------------------------------------------------------------------- 1 | csws <- read.csv("csws.csv") 2 | 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for Gaussian graphical models: 17 | Structure learning, predictability, and network comparisons", 18 | author = c(person(c("Donald", "R."), "Williams")), 19 | year = "2018", 20 | journal = "PsyArXiv", 21 | url = "https://osf.io/preprints/psyarxiv/x8dpr/", 22 | doi = "10.31234/osf.io/x8dpr") 23 | 24 | 25 | bibentry(bibtype = "Article", 26 | title = "Bayesian hypothesis testing for Gaussian graphical 27 | models: Conditional independence and order constraints", 28 | author = c(person(c("Donald", "R."), "Williams"), 29 | person("Joris", "Mulder")), 30 | year = "2019", 31 | journal = "PsyArXiv", 32 | url = "https://osf.io/preprints/psyarxiv/ypxd8/", 33 | doi = "10.31234/osf.io/ypxd8") 34 | 35 | bibentry(bibtype = "Article", 36 | title = "Comparing Gaussian graphical models with the posterior predictive 37 | distribution and Bayesian model selection.", 38 | author = c(person(c("Donald", "R."), "Williams"), 39 | person("Rast", "Philipe"), 40 | person(c("Pericchi", "R."), "Luis"), 41 | person("Joris", "Mulder")), 42 | year = "2020", 43 | journal = "Psychological Methods", 44 | url = "https://doi.org/10.1037/met0000254", 45 | doi = "10.1037/met0000254") 46 | -------------------------------------------------------------------------------- /man/Sachs.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{Sachs} 5 | \alias{Sachs} 6 | \title{Data: Sachs Network} 7 | \format{ 8 | A data frame containing 7466 cells (n = 7466) and flow cytometry 9 | measurements of 11 (p = 11) phosphorylated proteins and phospholipids 10 | 11 | @references 12 | Sachs, K., Gifford, D., Jaakkola, T., Sorger, P., & Lauffenburger, D. A. (2002). 13 | Bayesian network approach to cell signaling pathway modeling. Sci. STKE, 2002(148), pe38-pe38. 14 | } 15 | \usage{ 16 | data("Sachs") 17 | } 18 | \description{ 19 | Protein expression in human immune system cells 20 | } 21 | \examples{ 22 | data("Sachs") 23 | 24 | } 25 | \keyword{datasets} 26 | -------------------------------------------------------------------------------- /man/asd_ocd.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{asd_ocd} 5 | \alias{asd_ocd} 6 | \title{Data: Autism and Obssesive Compulsive Disorder} 7 | \format{ 8 | A correlation matrix including 17 variables. These data were measured on a 4 level likert scale. 9 | } 10 | \usage{ 11 | data("asd_ocd") 12 | } 13 | \description{ 14 | A correlation matrix with 17 variables in total (autsim: 9; OCD: 8). 15 | The sample size was 213. 16 | } 17 | \details{ 18 | \strong{Autism}: 19 | 20 | \itemize{ 21 | 22 | \item \code{CI} Circumscribed interests 23 | \item \code{UP} Unusual preoccupations 24 | \item \code{RO} Repetitive use of objects or interests in parts of objects 25 | \item \code{CR} Compulsions and/or rituals 26 | \item \code{CI} Unusual sensory interests 27 | \item \code{SM} Complex mannerisms or stereotyped body movements 28 | \item \code{SU} Stereotyped utterances/delayed echolalia 29 | \item \code{NIL} Neologisms and/or idiosyncratic language 30 | \item \code{VR} Verbal rituals 31 | } 32 | 33 | \strong{OCD} 34 | 35 | \itemize{ 36 | \item \code{CD} Concern with things touched due to dirt/bacteria 37 | \item \code{TB} Thoughts of doing something bad around others 38 | \item \code{CT} Continual thoughts that do not go away 39 | \item \code{HP} Belief that someone/higher power put reoccurring thoughts in their head 40 | \item \code{CW} Continual washing 41 | \item \code{CCh} Continual checking CntCheck 42 | \item \code{CC} Continual counting/repeating 43 | \item \code{RD} Repeatedly do things until it feels good or just right 44 | 45 | } 46 | } 47 | \examples{ 48 | data("asd_ocd") 49 | 50 | # generate continuous 51 | Y <- MASS::mvrnorm(n = 213, 52 | mu = rep(0, 17), 53 | Sigma = asd_ocd, 54 | empirical = TRUE) 55 | 56 | 57 | } 58 | \references{ 59 | Jones, P. J., Ma, R., & McNally, R. J. (2019). Bridge centrality: 60 | A network approach 61 | to understanding comorbidity. Multivariate behavioral research, 1-15. 62 | 63 | Ruzzano, L., Borsboom, D., & Geurts, H. M. (2015). 64 | Repetitive behaviors in autism and obsessive-compulsive 65 | disorder: New perspectives from a network analysis. 66 | Journal of Autism and Developmental Disorders, 45(1), 67 | 192-202. doi:10.1007/s10803-014-2204-9 68 | } 69 | \keyword{datasets} 70 | -------------------------------------------------------------------------------- /man/bfi.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{bfi} 5 | \alias{bfi} 6 | \title{Data: 25 Personality items representing 5 factors} 7 | \format{ 8 | A data frame with 25 variables and 2800 observations (including missing values) 9 | } 10 | \usage{ 11 | data("bfi") 12 | } 13 | \description{ 14 | This dataset and the corresponding documentation was taken from the \strong{psych} package. We refer users to that 15 | package for further details \insertCite{psych}{BGGM}. 16 | } 17 | \details{ 18 | \itemize{ 19 | \item \code{A1} Am indifferent to the feelings of others. (q_146) 20 | \item \code{A2} Inquire about others' well-being. (q_1162) 21 | \item \code{A3} Know how to comfort others. (q_1206) 22 | \item \code{A4} Love children. (q_1364) 23 | \item \code{A5} Make people feel at ease. (q_1419) 24 | \item \code{C1} Am exacting in my work. (q_124) 25 | \item \code{C2} Continue until everything is perfect. (q_530) 26 | \item \code{C3} Do things according to a plan. (q_619) 27 | \item \code{C4} Do things in a half-way manner. (q_626) 28 | \item \code{C5} Waste my time. (q_1949) 29 | \item \code{E1} Don't talk a lot. (q_712) 30 | \item \code{E2} Find it difficult to approach others. (q_901) 31 | \item \code{E3} Know how to captivate people. (q_1205) 32 | \item \code{E4} Make friends easily. (q_1410) 33 | \item \code{E5} Take charge. (q_1768) 34 | \item \code{N1} Get angry easily. (q_952) 35 | \item \code{N2} Get irritated easily. (q_974) 36 | \item \code{N3} Have frequent mood swings. (q_1099) 37 | \item \code{N4} Often feel blue. (q_1479) 38 | \item \code{N5} Panic easily. (q_1505) 39 | \item \code{o1} Am full of ideas. (q_128) 40 | \item \code{o2} Avoid difficult reading material.(q_316) 41 | \item \code{o3} Carry the conversation to a higher level. (q_492) 42 | \item \code{o4} Spend time reflecting on things. (q_1738) 43 | \item \code{o5} Will not probe deeply into a subject. (q_1964) 44 | \item \code{gender} Males = 1, Females =2 45 | \item \code{education} 1 = HS, 2 = finished HS, 3 = some college, 4 = college graduate 5 = graduate degree 46 | } 47 | } 48 | \references{ 49 | \insertAllCited{} 50 | } 51 | \keyword{datasets} 52 | -------------------------------------------------------------------------------- /man/bma_posterior.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/bma_posterior.R 3 | \name{bma_posterior} 4 | \alias{bma_posterior} 5 | \title{Compute Posterior Distributions from Graph Search Results} 6 | \usage{ 7 | bma_posterior(object, param = "pcor", iter = 5000, progress = TRUE) 8 | } 9 | \arguments{ 10 | \item{object}{A ggm_search object} 11 | 12 | \item{param}{Compute BMA on either partial correlations "pcor" (default) or on precision matrix "Theta".} 13 | 14 | \item{iter}{Number of samples to be drawn, defaults to 5,000} 15 | 16 | \item{progress}{Show progress bar, defaults to TRUE} 17 | } 18 | \value{ 19 | A list containing posterior samples and the Bayesian Model Averaged 20 | parameter estimates. 21 | } 22 | \description{ 23 | The `bma_posterior` function samples posterior distributions of graph 24 | parameters (e.g., partial correlations or precision matrices) based on the 25 | graph structures sampled during a Bayesian graph search performed by 26 | \code{\link{ggm_search}}. 27 | } 28 | \details{ 29 | This function incorporates uncertainty in both graph structure and parameter 30 | estimation, providing Bayesian Model Averaged (BMA) parameter estimates. 31 | 32 | Use `bma_posterior` when detailed posterior inference on graph parameters 33 | is needed, or to refine results obtained from `ggm_search`. 34 | } 35 | \seealso{ 36 | \code{\link{ggm_search}} 37 | } 38 | -------------------------------------------------------------------------------- /man/coef.estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/coef.estimate.R 3 | \name{coef.estimate} 4 | \alias{coef.estimate} 5 | \title{Compute Regression Parameters for \code{estimate} Objects} 6 | \usage{ 7 | \method{coef}{estimate}(object, iter = NULL, progress = TRUE, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An Object of class \code{estimate}} 11 | 12 | \item{iter}{Number of iterations (posterior samples; defaults to the number in the object).} 13 | 14 | \item{progress}{Logical. Should a progress bar be included (defaults to \code{TRUE}) ?} 15 | 16 | \item{...}{Currently ignored.} 17 | } 18 | \value{ 19 | An object of class \code{coef}, containting two lists. 20 | 21 | 22 | \itemize{ 23 | 24 | \item \code{betas} A list of length \emph{p}, each containing a \emph{p} - 1 by \code{iter} matrix of 25 | posterior samples 26 | 27 | \item \code{object} An object of class \code{estimate} (the fitted model). 28 | } 29 | } 30 | \description{ 31 | There is a direct correspondence between the inverse covariance matrix and 32 | multiple regression \insertCite{kwan2014regression,Stephens1998}{BGGM}. This readily allows 33 | for converting the GGM parameters to regression coefficients. All data types are supported. 34 | } 35 | \examples{ 36 | \donttest{ 37 | # note: iter = 250 for demonstrative purposes 38 | 39 | ######################### 40 | ### example 1: binary ### 41 | ######################### 42 | # data 43 | Y = matrix( rbinom(100, 1, .5), ncol=4) 44 | 45 | # fit model 46 | fit <- estimate(Y, type = "binary", 47 | iter = 250, 48 | progress = TRUE) 49 | 50 | # summarize the partial correlations 51 | reg <- coef(fit, progress = FALSE) 52 | 53 | # summary 54 | summ <- summary(reg) 55 | 56 | summ 57 | } 58 | } 59 | \references{ 60 | \insertAllCited{} 61 | } 62 | -------------------------------------------------------------------------------- /man/coef.explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/coef.estimate.R 3 | \name{coef.explore} 4 | \alias{coef.explore} 5 | \title{Compute Regression Parameters for \code{explore} Objects} 6 | \usage{ 7 | \method{coef}{explore}(object, iter = NULL, progress = TRUE, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An Object of class \code{explore}.} 11 | 12 | \item{iter}{Number of iterations (posterior samples; defaults to the number in the object).} 13 | 14 | \item{progress}{Logical. Should a progress bar be included (defaults to \code{TRUE}) ?} 15 | 16 | \item{...}{Currently ignored.} 17 | } 18 | \value{ 19 | An object of class \code{coef}, containting two lists. 20 | 21 | \itemize{ 22 | 23 | \item \code{betas} A list of length \emph{p}, each containing a \emph{p} - 1 by \code{iter} matrix of 24 | posterior samples 25 | 26 | \item \code{object} An object of class \code{explore} (the fitted model). 27 | } 28 | } 29 | \description{ 30 | There is a direct correspondence between the inverse covariance matrix and 31 | multiple regression \insertCite{kwan2014regression,Stephens1998}{BGGM}. This readily allows 32 | for converting the GGM parameters to regression coefficients. All data types are supported. 33 | } 34 | \examples{ 35 | \donttest{ 36 | # note: iter = 250 for demonstrative purposes 37 | 38 | # data 39 | Y <- ptsd[,1:4] 40 | 41 | ########################## 42 | ### example 1: ordinal ### 43 | ########################## 44 | 45 | # fit model (note + 1, due to zeros) 46 | fit <- explore(Y + 1, 47 | type = "ordinal", 48 | iter = 250, 49 | progress = FALSE, 50 | seed = 1234) 51 | 52 | # summarize the partial correlations 53 | reg <- coef(fit, progress = FALSE) 54 | 55 | # summary 56 | summ <- summary(reg) 57 | 58 | summ 59 | } 60 | } 61 | \references{ 62 | \insertAllCited{} 63 | } 64 | -------------------------------------------------------------------------------- /man/constrained_posterior.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/constrained_post.R 3 | \name{constrained_posterior} 4 | \alias{constrained_posterior} 5 | \title{Constrained Posterior Distribution} 6 | \usage{ 7 | constrained_posterior( 8 | object, 9 | adj, 10 | method = "direct", 11 | iter = 5000, 12 | progress = TRUE, 13 | ... 14 | ) 15 | } 16 | \arguments{ 17 | \item{object}{An object of class \code{estimate} or \code{explore}} 18 | 19 | \item{adj}{A \code{p} by \code{p} adjacency matrix. The zero entries denote the 20 | elements that should be constrained to zero.} 21 | 22 | \item{method}{Character string. Which method should be used ? Defaults to 23 | the "direct sampler" (i.e., \code{method = "direct"}) described in 24 | \insertCite{@page 122, section 2.4, @lenkoski2013direct;textual}{BGGM}. The other 25 | option is a Metropolis-Hastings algorithm (\code{MH}). 26 | See details.} 27 | 28 | \item{iter}{Number of iterations (posterior samples; defaults to 5000).} 29 | 30 | \item{progress}{Logical. Should a progress bar be included (defaults to \code{TRUE}) ?} 31 | 32 | \item{...}{Currently ignored.} 33 | } 34 | \value{ 35 | An object of class \code{contrained}, including 36 | 37 | \itemize{ 38 | 39 | \item \code{precision_mean} The posterior mean for the precision matrix. 40 | 41 | \item \code{pcor_mean} The posterior mean for the precision matrix. 42 | 43 | \item \code{precision_samps} A 3d array of dimension \code{p} by \code{p} by \code{iter} 44 | including the sampled precision matrices. 45 | 46 | \item \code{pcor_samps} A 3d array of dimension \code{p} by \code{p} by \code{iter} 47 | including sampled partial correlations matrices. 48 | } 49 | } 50 | \description{ 51 | Compute the posterior distribution 52 | with off-diagonal elements of the precision matrix constrained 53 | to zero. 54 | } 55 | \examples{ 56 | \donttest{ 57 | 58 | # data 59 | Y <- bfi[,1:10] 60 | 61 | # sample posterior 62 | fit <- estimate(Y, iter = 100) 63 | 64 | # select graph 65 | sel <- select(fit) 66 | 67 | # constrained posterior 68 | post <- constrained_posterior(object = fit, 69 | adj = sel$adj, 70 | iter = 100, 71 | progress = FALSE) 72 | 73 | } 74 | } 75 | \references{ 76 | \insertAllCited{} 77 | } 78 | -------------------------------------------------------------------------------- /man/convergence.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/convergence.R 3 | \name{convergence} 4 | \alias{convergence} 5 | \title{MCMC Convergence} 6 | \usage{ 7 | convergence(object, param = NULL, type = "trace", print_names = FALSE) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate} or \code{explore}} 11 | 12 | \item{param}{Character string. Names of parameters for which to monitor MCMC convergence.} 13 | 14 | \item{type}{Character string. Which type of convergence plot ? The current 15 | options are \code{trace} (default) and \code{acf}.} 16 | 17 | \item{print_names}{Logical. Should the parameter names be printed (defaults to \code{FALSE})? This 18 | can be used to first determine the parameter names to specify in \code{type}.} 19 | } 20 | \value{ 21 | A list of \code{ggplot} objects. 22 | } 23 | \description{ 24 | Monitor convergence of the MCMC algorithms. 25 | } 26 | \note{ 27 | An overview of MCMC diagnostics can be found \href{https://sbfnk.github.io/mfiidd/mcmc_diagnostics.html}{here}. 28 | } 29 | \examples{ 30 | \donttest{ 31 | # note: iter = 250 for demonstrative purposes 32 | 33 | # data 34 | Y <- ptsd[,1:5] 35 | 36 | ######################### 37 | ###### continuous ####### 38 | ######################### 39 | fit <- estimate(Y, iter = 250, 40 | progress = FALSE) 41 | 42 | # print names first 43 | convergence(fit, print_names = TRUE) 44 | 45 | # trace plots 46 | convergence(fit, type = "trace", 47 | param = c("B1--B2", "B1--B3"))[[1]] 48 | 49 | # acf plots 50 | convergence(fit, type = "acf", 51 | param = c("B1--B2", "B1--B3"))[[1]] 52 | } 53 | } 54 | 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12 | \value{ 13 | Fisher Z transformed correlation(s) 14 | } 15 | \description{ 16 | Tranform correlations to Fisher's Z 17 | } 18 | \examples{ 19 | fisher_r_to_z(0.5) 20 | } 21 | -------------------------------------------------------------------------------- /man/fisher_z_to_r.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fisher_z2r.R 3 | \name{fisher_z_to_r} 4 | \alias{fisher_z_to_r} 5 | \title{Fisher Z Back Transformation} 6 | \usage{ 7 | fisher_z_to_r(z) 8 | } 9 | \arguments{ 10 | \item{z}{Fisher Z} 11 | } 12 | \value{ 13 | Correlation (s) (backtransformed) 14 | } 15 | \description{ 16 | Back tranform Fisher's Z to correlations 17 | } 18 | \examples{ 19 | fisher_z_to_r(0.5) 20 | } 21 | -------------------------------------------------------------------------------- /man/gen_net.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/gen_pcors.R 3 | \name{gen_net} 4 | \alias{gen_net} 5 | \title{Simulate a Partial Correlation Matrix} 6 | \usage{ 7 | gen_net(p = 20, edge_prob = 0.3, lb = 0.05, ub = 0.3) 8 | } 9 | \arguments{ 10 | \item{p}{number of variables (nodes)} 11 | 12 | \item{edge_prob}{connectivity} 13 | 14 | \item{lb}{lower bound for the partial correlations} 15 | 16 | \item{ub}{upper bound for the partial correlations} 17 | } 18 | \value{ 19 | A list containing the following: 20 | 21 | \itemize{ 22 | 23 | \item{\strong{pcor}}: Partial correlation matrix, encoding 24 | the conditional (in)dependence structure. 25 | 26 | \item{\strong{cors}}: Correlation matrix. 27 | 28 | \item{\strong{adj}}: Adjacency matrix. 29 | 30 | \item{\strong{trys}}: Number of attempts to obtain a 31 | positive definite matrix. 32 | 33 | } 34 | } 35 | \description{ 36 | Simulate a Partial Correlation Matrix 37 | } 38 | \note{ 39 | The function checks for a valid matrix (positive definite), 40 | but sometimes this will still fail. For example, for 41 | larger \code{p}, to have large partial correlations this 42 | requires a sparse GGM 43 | (accomplished by setting \code{edge_prob} 44 | to a small value). 45 | } 46 | \examples{ 47 | 48 | true_net <- gen_net(p = 10) 49 | } 50 | -------------------------------------------------------------------------------- /man/ggm_search.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggm_search.R 3 | \name{ggm_search} 4 | \alias{ggm_search} 5 | \title{Perform Bayesian Graph Search and Optional Model Averaging} 6 | \usage{ 7 | ggm_search( 8 | x, 9 | n = NULL, 10 | method = "mc3", 11 | prior_prob = 0.3, 12 | iter = 5000, 13 | stop_early = 1000, 14 | bma_mean = TRUE, 15 | seed = NULL, 16 | progress = TRUE, 17 | ... 18 | ) 19 | } 20 | \arguments{ 21 | \item{x}{Data, either raw data or covariance matrix} 22 | 23 | \item{n}{For x = covariance matrix, provide number of observations} 24 | 25 | \item{method}{mc3 defaults to MH sampling} 26 | 27 | \item{prior_prob}{Prior prbability of sparseness.} 28 | 29 | \item{iter}{Number of iterations 30 | #@param burn_in Burn in. Defaults to iter/2} 31 | 32 | \item{stop_early}{Default to 1000. Stop MH algorithm if proposals keep being rejected (stopping by default after 1000 rejections).} 33 | 34 | \item{bma_mean}{Compute Bayesian Model Averaged solution} 35 | 36 | \item{seed}{Set seed. Current default is to set R's random seed.} 37 | 38 | \item{progress}{Show progress bar, defaults to TRUE} 39 | 40 | \item{...}{Not currently in use} 41 | } 42 | \value{ 43 | A list containing the MAP graph structure, BMA solution (if specified), 44 | and posterior probabilities of the sampled graphs. 45 | } 46 | \description{ 47 | The `ggm_search` function performs a Bayesian graph search to identify the 48 | most probable graph structure (MAP solution) using the Metropolis-Hastings 49 | algorithm. It also computes an optional Bayesian Model Averaged (BMA) solution 50 | across the graph structures sampled during the search. 51 | } 52 | \details{ 53 | This function is ideal for exploring the graph space and obtaining an initial 54 | estimate of the graph structure or adjacency matrix. 55 | 56 | To refine the results or compute posterior distributions of graph parameters 57 | (e.g., partial correlations), use the \code{\link{bma_posterior}} function, 58 | which builds on the output of `ggm_search` to account for parameter uncertainty. 59 | } 60 | \seealso{ 61 | \code{\link{bma_posterior}} 62 | } 63 | \author{ 64 | Donny Williams and Philippe Rast 65 | } 66 | -------------------------------------------------------------------------------- /man/gss.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{gss} 5 | \alias{gss} 6 | \title{Data: 1994 General Social Survey} 7 | \format{ 8 | A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale 9 | \insertCite{fowlkes1988evaluating}{BGGM}. The variable descriptions were copied from 10 | \insertCite{@section 4, @hoff2007extending;textual}{BGGM} 11 | } 12 | \usage{ 13 | data("gss") 14 | } 15 | \description{ 16 | A data frame containing 1002 rows and 7 variables measured on various scales, 17 | including binary and ordered cateogrical (with varying numbers of categories). 18 | There are also missing values in each variable 19 | 20 | \itemize{ 21 | \item \code{Inc} Income of the respondent in 1000s of dollars, binned into 21 ordered categories. 22 | \item \code{DEG} Highest degree ever obtained (none, HS, Associates, Bachelors, or Graduate) 23 | \item \code{CHILD} Number of children ever had. 24 | \item \code{PINC} Financial status of respondent's parents when respondent was 16 (on a 5-point scale). 25 | \item \code{PDEG} Maximum of mother's and father's highest degree 26 | \item \code{PCHILD} Number of siblings of the respondent plus one 27 | \item \code{AGE} Age of the respondent in years. 28 | } 29 | } 30 | \examples{ 31 | data("gss") 32 | } 33 | \references{ 34 | \insertAllCited{} 35 | } 36 | \keyword{datasets} 37 | -------------------------------------------------------------------------------- /man/ifit.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{ifit} 5 | \alias{ifit} 6 | \title{Data: ifit Intensive Longitudinal Data} 7 | \format{ 8 | A data frame containing 197 observations and 8 variables. The data have been used in 9 | \insertCite{o2020use}{BGGM} and \insertCite{williams2019bayesian}{BGGM} 10 | } 11 | \usage{ 12 | data("ifit") 13 | } 14 | \description{ 15 | A data frame containing 8 variables and nearly 200 observations. There are 16 | two subjects, each of which provided data every data for over 90 days. Six variables are from 17 | the PANAS scale (positive and negative affect), the daily number of steps, and the subject id. 18 | 19 | \itemize{ 20 | \item \code{id} Subject id 21 | \item \code{interested} 22 | \item \code{disinterested} 23 | \item \code{excited} 24 | \item \code{upset} 25 | \item \code{strong} 26 | \item \code{stressed} 27 | \item \code{steps} steps recorded by a fit bit 28 | } 29 | } 30 | \examples{ 31 | data("ifit") 32 | } 33 | \references{ 34 | \insertAllCited{} 35 | } 36 | \keyword{datasets} 37 | -------------------------------------------------------------------------------- /man/map.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/map.R 3 | \name{map} 4 | \alias{map} 5 | \title{Maximum A Posteriori Precision Matrix} 6 | \usage{ 7 | map(Y) 8 | } 9 | \arguments{ 10 | \item{Y}{Matrix (or data frame) of dimensions \emph{n} (observations) by \emph{p} (variables).} 11 | } 12 | \value{ 13 | An object of class \code{map}, including the precision matrix, 14 | partial correlation matrix, and regression parameters. 15 | } 16 | \description{ 17 | Maximum A Posteriori Precision Matrix 18 | } 19 | \examples{ 20 | Y <- BGGM::bfi[, 1:5] 21 | 22 | # map 23 | map <- map(Y) 24 | map 25 | } 26 | -------------------------------------------------------------------------------- /man/pcor_mat.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/pcor_mat.R 3 | \name{pcor_mat} 4 | \alias{pcor_mat} 5 | \title{Extract the Partial Correlation Matrix} 6 | \usage{ 7 | pcor_mat(object, difference = FALSE, ...) 8 | } 9 | \arguments{ 10 | \item{object}{A model estimated with \strong{BGGM}. All classes are supported, assuming 11 | there is matrix to be extracted.} 12 | 13 | \item{difference}{Logical. Should the difference be returned (defaults to \code{FALSE}) ? Note 14 | that this assumes there is a difference (e.g., an object of class \code{ggm_compare_estimate}) 15 | and ignored otherwise.} 16 | 17 | \item{...}{Currently ignored.} 18 | } 19 | \value{ 20 | The estimated partial correlation matrix. 21 | } 22 | \description{ 23 | Extract the partial correlation matrix (posterior mean) 24 | from \code{\link{estimate}}, \code{\link{explore}}, \code{\link{ggm_compare_estimate}}, 25 | and \code{\link{ggm_compare_explore}} objects. It is also possible to extract the 26 | partial correlation differences for \code{\link{ggm_compare_estimate}} and 27 | \code{\link{ggm_compare_explore}} objects. 28 | } 29 | \examples{ 30 | \donttest{ 31 | # note: iter = 250 for demonstrative purposes 32 | 33 | # data 34 | Y <- ptsd[,1:5] + 1 35 | 36 | # ordinal 37 | fit <- estimate(Y, type = "ordinal", 38 | iter = 250, 39 | progress = FALSE) 40 | 41 | pcor_mat(fit) 42 | } 43 | } 44 | -------------------------------------------------------------------------------- /man/pcor_to_cor.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/pcor_2_cor.BGGM.R 3 | \name{pcor_to_cor} 4 | \alias{pcor_to_cor} 5 | \title{Compute Correlations from the Partial Correlations} 6 | \usage{ 7 | pcor_to_cor(object, iter = NULL) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate} or \code{explore}} 11 | 12 | \item{iter}{numeric. How many iterations (i.e., posterior samples) should be used ? 13 | The default uses all of the samples, but note that this can take a long 14 | time with large matrices.} 15 | } 16 | \value{ 17 | \itemize{ 18 | 19 | \item \code{R} An array including the correlation matrices 20 | (of dimensions \emph{p} by \emph{p} by \emph{iter}) 21 | 22 | \item \code{R_mean} Posterior mean of the correlations (of dimensions \emph{p} by \emph{p}) 23 | } 24 | } 25 | \description{ 26 | Convert the partial correlation matrices into correlation matrices. To our knowledge, 27 | this is the only Bayesian 28 | implementation in \code{R} that can estiamte Pearson's, tetrachoric (binary), polychoric 29 | (ordinal with more than two cateogries), and rank based correlation coefficients. 30 | } 31 | \note{ 32 | The 'default' prior distributions are specified for partial correlations in particular. This 33 | means that the implied prior distribution will not be the same for the correlations. 34 | } 35 | \examples{ 36 | \donttest{ 37 | # note: iter = 250 for demonstrative purposes 38 | 39 | # data 40 | Y <- BGGM::ptsd 41 | 42 | ######################### 43 | ###### continuous ####### 44 | ######################### 45 | 46 | # estimate the model 47 | fit <- estimate(Y, iter = 250, 48 | progress = FALSE) 49 | 50 | # compute correlations 51 | cors <- pcor_to_cor(fit) 52 | 53 | 54 | ######################### 55 | ###### ordinal ######### 56 | ######################### 57 | 58 | # first level must be 1 ! 59 | Y <- Y + 1 60 | 61 | # estimate the model 62 | fit <- estimate(Y, type = "ordinal", 63 | iter = 250, 64 | progress = FALSE) 65 | 66 | # compute correlations 67 | cors <- pcor_to_cor(fit) 68 | 69 | 70 | ######################### 71 | ####### mixed ###### 72 | ######################### 73 | 74 | # rank based correlations 75 | 76 | # estimate the model 77 | fit <- estimate(Y, type = "mixed", 78 | iter = 250, 79 | progress = FALSE) 80 | 81 | # compute correlations 82 | cors <- pcor_to_cor(fit) 83 | } 84 | 85 | } 86 | -------------------------------------------------------------------------------- /man/plot.confirm.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggm_compare_confirm.R 3 | \name{plot.confirm} 4 | \alias{plot.confirm} 5 | \title{Plot \code{confirm} objects} 6 | \usage{ 7 | \method{plot}{confirm}(x, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{confirm}} 11 | 12 | \item{...}{Currently ignored.} 13 | } 14 | \value{ 15 | A \code{ggplot} object. 16 | } 17 | \description{ 18 | Plot the posterior hypothesis probabilities as a pie chart, with 19 | each slice corresponding the probability of a given hypothesis. 20 | } 21 | \examples{ 22 | 23 | \donttest{ 24 | 25 | ##################################### 26 | ##### example 1: many relations ##### 27 | ##################################### 28 | 29 | # data 30 | Y <- bfi 31 | 32 | hypothesis <- c("g1_A1--A2 > g2_A1--A2 & g1_A1--A3 = g2_A1--A3; 33 | g1_A1--A2 = g2_A1--A2 & g1_A1--A3 = g2_A1--A3; 34 | g1_A1--A2 = g2_A1--A2 = g1_A1--A3 = g2_A1--A3") 35 | 36 | Ymale <- subset(Y, gender == 1, 37 | select = -c(education, 38 | gender))[,1:5] 39 | 40 | 41 | # females 42 | Yfemale <- subset(Y, gender == 2, 43 | select = -c(education, 44 | gender))[,1:5] 45 | 46 | test <- ggm_compare_confirm(Ymale, 47 | Yfemale, 48 | hypothesis = hypothesis, 49 | iter = 250, 50 | progress = FALSE) 51 | 52 | 53 | # plot 54 | plot(test) 55 | } 56 | } 57 | -------------------------------------------------------------------------------- /man/plot.ggm_compare_ppc.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggm_compare_ppc.default.R 3 | \name{plot.ggm_compare_ppc} 4 | \alias{plot.ggm_compare_ppc} 5 | \title{Plot \code{ggm_compare_ppc} Objects} 6 | \usage{ 7 | \method{plot}{ggm_compare_ppc}( 8 | x, 9 | critical = 0.05, 10 | col_noncritical = "#84e184A0", 11 | col_critical = "red", 12 | point_size = 2, 13 | ... 14 | ) 15 | } 16 | \arguments{ 17 | \item{x}{An object of class \code{ggm_compare_ppc}} 18 | 19 | \item{critical}{Numeric. The 'significance' level 20 | (defaults to \code{0.05}).} 21 | 22 | \item{col_noncritical}{Character string. Fill color for the non-critical region 23 | (defaults to \code{"#84e184A0"}).} 24 | 25 | \item{col_critical}{Character string. Fill color for the critical region 26 | (defaults to \code{"red"}).} 27 | 28 | \item{point_size}{Numeric. The point size for the observed score 29 | (defaults to \code{2}).} 30 | 31 | \item{...}{Currently ignored.} 32 | } 33 | \value{ 34 | An object (or list of objects) of class \code{ggplot}. 35 | } 36 | \description{ 37 | Plot the predictive check with \code{\link[ggridges]{ggridges}} 38 | } 39 | \note{ 40 | See 41 | \href{https://CRAN.R-project.org/package=ggridges/vignettes/introduction.html}{ggridges} for 42 | many examples. 43 | } 44 | \examples{ 45 | \donttest{ 46 | # data 47 | Y <- bfi 48 | 49 | ############################# 50 | ######### global ############ 51 | ############################# 52 | # males 53 | Ym <- subset(Y, gender == 1, 54 | select = - c(gender, education)) 55 | 56 | # females 57 | 58 | Yf <- subset(Y, gender == 2, 59 | select = - c(gender, education)) 60 | 61 | 62 | global_test <- ggm_compare_ppc(Ym, Yf, 63 | iter = 250, 64 | progress = FALSE) 65 | 66 | plot(global_test) 67 | } 68 | } 69 | \seealso{ 70 | \code{\link{ggm_compare_ppc}} 71 | } 72 | -------------------------------------------------------------------------------- /man/plot.pcor_sum.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/posterior_sum.R 3 | \name{plot.pcor_sum} 4 | \alias{plot.pcor_sum} 5 | \title{Plot \code{pcor_sum} Object} 6 | \usage{ 7 | \method{plot}{pcor_sum}(x, fill = "#CC79A7", ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{posterior_sum}} 11 | 12 | \item{fill}{Character string. What fill for the histogram 13 | (defaults to colorblind "pink")?} 14 | 15 | \item{...}{Currently ignored.} 16 | } 17 | \value{ 18 | A list of \code{ggplot} objects 19 | } 20 | \description{ 21 | Plot \code{pcor_sum} Object 22 | } 23 | \note{ 24 | \strong{Examples}: 25 | } 26 | \seealso{ 27 | pcor_sum 28 | } 29 | -------------------------------------------------------------------------------- /man/plot.predictability.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/predictability.R 3 | \name{plot.predictability} 4 | \alias{plot.predictability} 5 | \title{Plot \code{predictability} Objects} 6 | \usage{ 7 | \method{plot}{predictability}( 8 | x, 9 | type = "error_bar", 10 | cred = 0.95, 11 | alpha = 0.5, 12 | scale = 1, 13 | width = 0, 14 | size = 1, 15 | color = "blue", 16 | ... 17 | ) 18 | } 19 | \arguments{ 20 | \item{x}{An object of class \code{predictability}} 21 | 22 | \item{type}{Character string. Which type of plot ? The options 23 | are \code{"error_bar"} or \code{"ridgeline"} (defaults to \code{"error_bar"}).} 24 | 25 | \item{cred}{Numeric. The credible interval width for summarizing the posterior 26 | distributions (defaults to 0.95; must be between 0 and 1).} 27 | 28 | \item{alpha}{Numeric. Transparancey of the ridges} 29 | 30 | \item{scale}{Numeric. This controls the overlap of densities 31 | for \code{type = "ridgeline"} (defaults to 1).} 32 | 33 | \item{width}{Numeric. The width of error bar ends (defaults to \code{0}) 34 | for \code{type = "error_bar"}.} 35 | 36 | \item{size}{Numeric. The size for the points (defaults to \code{2}) 37 | for \code{type = "error_bar"}.} 38 | 39 | \item{color}{Character string. What color for the point (\code{type = "error_bar"}) or 40 | tail region (\code{type = "ridgeline"} ) ? Defaults to \code{"blue"}.} 41 | 42 | \item{...}{Currently ignored.} 43 | } 44 | \value{ 45 | An object of class \code{ggplot}. 46 | } 47 | \description{ 48 | Plot \code{predictability} Objects 49 | } 50 | \examples{ 51 | \donttest{ 52 | Y <- ptsd[,1:5] 53 | 54 | fit <- explore(Y, iter = 250, 55 | progress = FALSE) 56 | 57 | r2 <- predictability(fit, iter = 250, 58 | progress = FALSE) 59 | 60 | plot(r2) 61 | 62 | } 63 | } 64 | -------------------------------------------------------------------------------- /man/plot.roll_your_own.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/roll_your_own.R 3 | \name{plot.roll_your_own} 4 | \alias{plot.roll_your_own} 5 | \title{Plot \code{roll_your_own} Objects} 6 | \usage{ 7 | \method{plot}{roll_your_own}(x, fill = "#CC79A7", alpha = 0.5, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{roll_your_own}} 11 | 12 | \item{fill}{Character string specifying the color for the ridges.} 13 | 14 | \item{alpha}{Numeric. Transparancey of the ridges} 15 | 16 | \item{...}{Currently ignored} 17 | } 18 | \value{ 19 | An object of class \code{ggplot} 20 | } 21 | \description{ 22 | Plot \code{roll_your_own} Objects 23 | } 24 | \examples{ 25 | \donttest{ 26 | #################################### 27 | ###### example 1: assortment ####### 28 | #################################### 29 | # assortment 30 | library(assortnet) 31 | 32 | Y <- BGGM::bfi[,1:10] 33 | membership <- c(rep("a", 5), rep("c", 5)) 34 | 35 | # fit model 36 | fit <- estimate(Y = Y, iter = 250, 37 | progress = FALSE) 38 | 39 | # membership 40 | membership <- c(rep("a", 5), rep("c", 5)) 41 | 42 | # define function 43 | f <- function(x,...){ 44 | assortment.discrete(x, ...)$r 45 | } 46 | 47 | net_stat <- roll_your_own(object = fit, 48 | FUN = f, 49 | types = membership, 50 | weighted = TRUE, 51 | SE = FALSE, M = 1, 52 | progress = FALSE) 53 | 54 | # plot 55 | plot(net_stat) 56 | 57 | } 58 | } 59 | -------------------------------------------------------------------------------- /man/plot.summary.estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/estimate.R 3 | \name{plot.summary.estimate} 4 | \alias{plot.summary.estimate} 5 | \title{Plot \code{summary.estimate} Objects} 6 | \usage{ 7 | \method{plot}{summary.estimate}(x, color = "black", size = 2, width = 0, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{summary.estimate}} 11 | 12 | \item{color}{Character string. The color for the error bars. 13 | (defaults to \code{"black"}).} 14 | 15 | \item{size}{Numeric. The size for the points (defaults to \code{2}).} 16 | 17 | \item{width}{Numeric. The width of error bar ends (defaults to \code{0}).} 18 | 19 | \item{...}{Currently ignored} 20 | } 21 | \value{ 22 | A \code{ggplot} object. 23 | } 24 | \description{ 25 | Visualize the posterior distributions for each partial correlation. 26 | } 27 | \examples{ 28 | \donttest{ 29 | # data 30 | Y <- ptsd[,1:5] 31 | 32 | fit <- estimate(Y, iter = 250, 33 | progress = FALSE) 34 | 35 | 36 | plot(summary(fit)) 37 | 38 | } 39 | 40 | } 41 | \seealso{ 42 | \code{\link{estimate}} 43 | } 44 | -------------------------------------------------------------------------------- /man/plot.summary.explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/explore.default.R 3 | \name{plot.summary.explore} 4 | \alias{plot.summary.explore} 5 | \title{Plot \code{summary.explore} Objects} 6 | \usage{ 7 | \method{plot}{summary.explore}(x, color = "black", size = 2, width = 0, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{summary.explore}} 11 | 12 | \item{color}{Character string. The color for the error bars. 13 | (defaults to \code{"black"}).} 14 | 15 | \item{size}{Numeric. The size for the points (defaults to \code{2}).} 16 | 17 | \item{width}{Numeric. The width of error bar ends (defaults to \code{0} ).} 18 | 19 | \item{...}{Currently ignored} 20 | } 21 | \value{ 22 | A \code{ggplot} object 23 | } 24 | \description{ 25 | Visualize the posterior distributions for each partial correlation. 26 | } 27 | \examples{ 28 | \donttest{ 29 | # note: iter = 250 for demonstrative purposes 30 | 31 | Y <- ptsd[,1:5] 32 | 33 | fit <- explore(Y, iter = 250, 34 | progress = FALSE) 35 | 36 | plt <- plot(summary(fit)) 37 | 38 | plt 39 | } 40 | } 41 | \seealso{ 42 | \code{\link{explore}} 43 | } 44 | -------------------------------------------------------------------------------- /man/plot.summary.ggm_compare_estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggm_compare_estimate.default.R 3 | \name{plot.summary.ggm_compare_estimate} 4 | \alias{plot.summary.ggm_compare_estimate} 5 | \title{Plot \code{summary.ggm_compare_estimate} Objects} 6 | \usage{ 7 | \method{plot}{summary.ggm_compare_estimate}(x, color = "black", size = 2, width = 0, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{ggm_compare_estimate}.} 11 | 12 | \item{color}{Character string. The color of the points 13 | (defaults to \code{"black"}).} 14 | 15 | \item{size}{Numeric. The size of the points (defaults to 2).} 16 | 17 | \item{width}{Numeric. The width of error bar ends (defaults to \code{0}).} 18 | 19 | \item{...}{Currently ignored.} 20 | } 21 | \value{ 22 | An object of class \code{ggplot} 23 | } 24 | \description{ 25 | Visualize the posterior distribution differences. 26 | } 27 | \examples{ 28 | \donttest{ 29 | # note: iter = 250 for demonstrative purposes 30 | # data 31 | Y <- bfi[complete.cases(bfi),] 32 | 33 | # males and females 34 | Ymale <- subset(Y, gender == 1, 35 | select = -c(gender, 36 | education))[,1:10] 37 | 38 | Yfemale <- subset(Y, gender == 2, 39 | select = -c(gender, 40 | education))[,1:10] 41 | 42 | # fit model 43 | fit <- ggm_compare_estimate(Ymale, Yfemale, 44 | type = "ordinal", 45 | iter = 250, 46 | prior_sd = 0.25, 47 | progress = FALSE) 48 | 49 | plot(summary(fit)) 50 | } 51 | 52 | } 53 | \seealso{ 54 | \code{\link{ggm_compare_estimate}} 55 | } 56 | -------------------------------------------------------------------------------- /man/plot.summary.ggm_compare_explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggm_compare_bf.default.R 3 | \name{plot.summary.ggm_compare_explore} 4 | \alias{plot.summary.ggm_compare_explore} 5 | \title{Plot \code{summary.ggm_compare_explore} Objects} 6 | \usage{ 7 | \method{plot}{summary.ggm_compare_explore}(x, size = 2, color = "black", ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{summary.ggm_compare_explore}} 11 | 12 | \item{size}{Numeric. The size of the points (defaults to 2).} 13 | 14 | \item{color}{Character string. The color of the points 15 | (defaults to \code{"black"}).} 16 | 17 | \item{...}{Currently ignored.} 18 | } 19 | \value{ 20 | A \code{ggplot} object 21 | } 22 | \description{ 23 | Visualize the posterior hypothesis probabilities. 24 | } 25 | \examples{ 26 | \donttest{ 27 | # note: iter = 250 for demonstrative purposes 28 | 29 | # data 30 | Y <- bfi[complete.cases(bfi),] 31 | 32 | # males and females 33 | Ymale <- subset(Y, gender == 1, 34 | select = -c(gender, 35 | education))[,1:10] 36 | 37 | Yfemale <- subset(Y, gender == 2, 38 | select = -c(gender, 39 | education))[,1:10] 40 | 41 | ########################## 42 | ### example 1: ordinal ### 43 | ########################## 44 | 45 | # fit model 46 | fit <- ggm_compare_explore(Ymale, Yfemale, 47 | type = "ordinal", 48 | iter = 250, 49 | progress = FALSE) 50 | # summary 51 | summ <- summary(fit) 52 | 53 | plot(summ) 54 | } 55 | } 56 | \seealso{ 57 | \code{\link{ggm_compare_explore}} 58 | } 59 | -------------------------------------------------------------------------------- /man/plot.summary.select.explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/select.explore.R 3 | \name{plot.summary.select.explore} 4 | \alias{plot.summary.select.explore} 5 | \title{Plot \code{summary.select.explore} Objects} 6 | \usage{ 7 | \method{plot}{summary.select.explore}(x, size = 2, color = "black", ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{summary.select.explore}} 11 | 12 | \item{size}{Numeric. The size for the points (defaults to 2).} 13 | 14 | \item{color}{Character string. The Color for the points} 15 | 16 | \item{...}{Currently ignored} 17 | } 18 | \value{ 19 | A \code{ggplot} object 20 | } 21 | \description{ 22 | Visualize the posterior hypothesis probabilities. 23 | } 24 | \examples{ 25 | \donttest{ 26 | # data 27 | Y <- bfi[,1:10] 28 | 29 | # fit model 30 | fit <- explore(Y, iter = 250, 31 | progress = FALSE) 32 | 33 | # edge set 34 | E <- select(fit, 35 | alternative = "exhaustive") 36 | 37 | plot(summary(E)) 38 | 39 | } 40 | } 41 | -------------------------------------------------------------------------------- /man/plot.summary.var_estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/var_estimate.R 3 | \name{plot.summary.var_estimate} 4 | \alias{plot.summary.var_estimate} 5 | \title{Plot \code{summary.var_estimate} Objects} 6 | \usage{ 7 | \method{plot}{summary.var_estimate}(x, color = "black", size = 2, width = 0, param = "all", order = TRUE, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{summary.var_estimate}} 11 | 12 | \item{color}{Character string. The color for the error bars. 13 | (defaults to \code{"black"}).} 14 | 15 | \item{size}{Numeric. The size for the points (defaults to \code{2}).} 16 | 17 | \item{width}{Numeric. The width of error bar ends (defaults to \code{0}).} 18 | 19 | \item{param}{Character string. Which parameters should be plotted ? The options 20 | are \code{pcor}, \code{beta}, or \code{all} (default).} 21 | 22 | \item{order}{Logical. Should the relations be ordered by size (defaults to \code{TRUE}) ?} 23 | 24 | \item{...}{Currently ignored} 25 | } 26 | \value{ 27 | A list of \code{ggplot} objects. 28 | } 29 | \description{ 30 | Visualize the posterior distributions of each partial correlation and 31 | regression coefficient. 32 | } 33 | \examples{ 34 | \donttest{ 35 | 36 | # data 37 | Y <- subset(ifit, id == 1)[,-1] 38 | 39 | # fit model with alias (var_estimate also works) 40 | fit <- var_estimate(Y, progress = FALSE) 41 | 42 | plts <- plot(summary(fit)) 43 | plts$pcor_plt 44 | } 45 | 46 | } 47 | -------------------------------------------------------------------------------- /man/plot_prior.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/plot_prior.R 3 | \name{plot_prior} 4 | \alias{plot_prior} 5 | \title{Plot: Prior Distribution} 6 | \usage{ 7 | plot_prior(prior_sd = 0.5, iter = 5000) 8 | } 9 | \arguments{ 10 | \item{prior_sd}{Scale of the prior distribution, approximately the standard deviation 11 | of a beta distribution (defaults to 0.5).} 12 | 13 | \item{iter}{Number of iterations (prior samples; defaults to 5000).} 14 | } 15 | \value{ 16 | A \code{ggplot} object. 17 | } 18 | \description{ 19 | Visualize the implied prior distribution for the partial correlations. This is 20 | particularly useful for the Bayesian hypothesis testing methods. 21 | } 22 | \examples{ 23 | # note: iter = 250 for demonstrative purposes 24 | 25 | plot_prior(prior_sd = 0.25, iter = 250) 26 | } 27 | -------------------------------------------------------------------------------- /man/posterior_predict.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/posterior_predict.R 3 | \name{posterior_predict} 4 | \alias{posterior_predict} 5 | \title{Posterior Predictive Distribution} 6 | \usage{ 7 | posterior_predict(object, iter = 1000, progress = TRUE) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate} or \code{explore}} 11 | 12 | \item{iter}{Numeric. Number of samples from the predictive distribution} 13 | 14 | \item{progress}{Logical. Should a progress bar be included (defaults to \code{TRUE})} 15 | } 16 | \value{ 17 | A 3D array containing the predicted datasets 18 | } 19 | \description{ 20 | Draw samples from the posterior predictive distribution. 21 | } 22 | \note{ 23 | Currently only implemented for \code{type = "mixed"}, \code{type = "ordinal"}, 24 | and \code{type = "binary"}. Note the term mixed is confusing, in that it can 25 | be used with only, say, ordinal data. In this case, reestimate the model with \code{type = "mixed"} 26 | until all data types are supported. 27 | } 28 | \examples{ 29 | \donttest{ 30 | Y <- gss 31 | 32 | fit <- estimate(as.matrix(Y), 33 | impute = TRUE, 34 | iter = 150, type = "mixed") 35 | 36 | yrep <- posterior_predict(fit, iter = 100) 37 | } 38 | } 39 | -------------------------------------------------------------------------------- /man/posterior_samples.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/posterior_samples.R 3 | \name{posterior_samples} 4 | \alias{posterior_samples} 5 | \title{Extract Posterior Samples} 6 | \usage{ 7 | posterior_samples(object, ...) 8 | } 9 | \arguments{ 10 | \item{object}{an object of class \code{estimate} or \code{explore}.} 11 | 12 | \item{...}{currently ignored.} 13 | } 14 | \value{ 15 | A matrix of posterior samples for the partial correlation. Note that if controlling for 16 | variables (e.g., formula \code{~ age}), the matrix also includes the coefficients from each 17 | multivariate regression. 18 | } 19 | \description{ 20 | Extract posterior samples for all parameters. 21 | } 22 | \examples{ 23 | \donttest{ 24 | # note: iter = 250 for demonstrative purposes 25 | 26 | ######################################## 27 | ### example 1: control with formula ### 28 | ######################################## 29 | # (the following works with all data types) 30 | 31 | # controlling for gender 32 | Y <- bfi 33 | 34 | # to control for only gender 35 | # (remove education) 36 | Y <- subset(Y, select = - education) 37 | 38 | # fit model 39 | fit <- estimate(Y, formula = ~ gender, 40 | iter = 250) 41 | 42 | # note regression coefficients 43 | samps <- posterior_samples(fit) 44 | 45 | hist(samps[,1]) 46 | } 47 | 48 | } 49 | -------------------------------------------------------------------------------- /man/precision.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/precision.R 3 | \name{precision} 4 | \alias{precision} 5 | \title{Precision Matrix Posterior Distribution} 6 | \usage{ 7 | precision(object, progress = TRUE) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate}.} 11 | 12 | \item{progress}{Logical. Should a progress bar be included (defaults to \code{TRUE}) ?} 13 | } 14 | \value{ 15 | \itemize{ 16 | 17 | \item \code{precision_mean} The mean of the precision matrix (\code{p} by \code{p} matrix). 18 | 19 | \item \code{precision} 3d array of dimensions \code{p} by \code{p} by \code{iter} 20 | including \strong{unconstrained} (i.e., from th full graph) 21 | precision matrices. 22 | 23 | } 24 | } 25 | \description{ 26 | Transform the sampled correlation matrices to 27 | precision matrices (i.e., inverse covariance matrices). 28 | } 29 | \note{ 30 | The estimated precision matrix is the inverse of the \strong{correlation} matrix. 31 | } 32 | \examples{ 33 | \donttest{ 34 | # data 35 | Y <- ptsd 36 | 37 | # fit model 38 | fit <- estimate(Y) 39 | 40 | # precision matrix 41 | Theta <- precision(fit) 42 | 43 | } 44 | 45 | } 46 | -------------------------------------------------------------------------------- /man/predict.estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/predict.estimate.R 3 | \name{predict.estimate} 4 | \alias{predict.estimate} 5 | \title{Model Predictions for \code{estimate} Objects} 6 | \usage{ 7 | \method{predict}{estimate}( 8 | object, 9 | newdata = NULL, 10 | summary = TRUE, 11 | cred = 0.95, 12 | iter = NULL, 13 | progress = TRUE, 14 | ... 15 | ) 16 | } 17 | \arguments{ 18 | \item{object}{object of class \code{estimate}} 19 | 20 | \item{newdata}{an optional data frame for obtaining predictions (e.g., on test data)} 21 | 22 | \item{summary}{summarize the posterior samples (defaults to \code{TRUE}).} 23 | 24 | \item{cred}{credible interval used for summarizing} 25 | 26 | \item{iter}{number of posterior samples (defaults to all in the object).} 27 | 28 | \item{progress}{Logical. Should a progress bar be included (defaults to \code{TRUE}) ?} 29 | 30 | \item{...}{currently ignored} 31 | } 32 | \value{ 33 | \code{summary = TRUE}: 3D array of dimensions n (observations), 34 | 4 (posterior summary), 35 | p (number of nodes). \code{summary = FALSE}: 36 | list containing predictions for each variable 37 | } 38 | \description{ 39 | Model Predictions for \code{estimate} Objects 40 | } 41 | \examples{ 42 | \donttest{ 43 | # # data 44 | Y <- ptsd 45 | 46 | fit <- estimate(Y, iter = 250, 47 | progress = FALSE) 48 | 49 | pred <- predict(fit, 50 | progress = FALSE) 51 | } 52 | 53 | } 54 | -------------------------------------------------------------------------------- /man/predict.explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/predict.estimate.R 3 | \name{predict.explore} 4 | \alias{predict.explore} 5 | \title{Model Predictions for \code{explore} Objects} 6 | \usage{ 7 | \method{predict}{explore}( 8 | object, 9 | newdata = NULL, 10 | summary = TRUE, 11 | cred = 0.95, 12 | iter = NULL, 13 | progress = TRUE, 14 | ... 15 | ) 16 | } 17 | \arguments{ 18 | \item{object}{object of class \code{explore}} 19 | 20 | \item{newdata}{an optional data frame for obtaining predictions (e.g., on test data)} 21 | 22 | \item{summary}{summarize the posterior samples (defaults to \code{TRUE}).} 23 | 24 | \item{cred}{credible interval used for summarizing} 25 | 26 | \item{iter}{number of posterior samples (defaults to all in the object).} 27 | 28 | \item{progress}{Logical. Should a progress bar be included (defaults to \code{TRUE}) ?} 29 | 30 | \item{...}{currently ignored} 31 | } 32 | \value{ 33 | \code{summary = TRUE}: 3D array of dimensions n (observations), 34 | 4 (posterior summary), 35 | p (number of nodes). \code{summary = FALSE}: 36 | list containing predictions for each variable 37 | } 38 | \description{ 39 | Model Predictions for \code{explore} Objects 40 | } 41 | \examples{ 42 | \donttest{ 43 | # data 44 | Y <- ptsd 45 | 46 | # fit model 47 | fit <- explore(Y, iter = 250, 48 | progress = FALSE) 49 | 50 | # predict 51 | pred <- predict(fit, 52 | progress = FALSE) 53 | 54 | } 55 | } 56 | -------------------------------------------------------------------------------- /man/predict.var_estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/predict.estimate.R 3 | \name{predict.var_estimate} 4 | \alias{predict.var_estimate} 5 | \title{Model Predictions for \code{var_estimate} Objects} 6 | \usage{ 7 | \method{predict}{var_estimate}(object, summary = TRUE, cred = 0.95, iter = NULL, progress = TRUE, ...) 8 | } 9 | \arguments{ 10 | \item{object}{object of class \code{var_estimate}} 11 | 12 | \item{summary}{summarize the posterior samples (defaults to \code{TRUE}).} 13 | 14 | \item{cred}{credible interval used for summarizing} 15 | 16 | \item{iter}{number of posterior samples (defaults to all in the object).} 17 | 18 | \item{progress}{Logical. Should a progress bar be included (defaults to \code{TRUE}) ?} 19 | 20 | \item{...}{Currently ignored} 21 | } 22 | \value{ 23 | The predicted values for each regression model. 24 | } 25 | \description{ 26 | Model Predictions for \code{var_estimate} Objects 27 | } 28 | \examples{ 29 | \donttest{ 30 | # data 31 | Y <- subset(ifit, id == 1)[,-1] 32 | 33 | # fit model with alias (var_estimate also works) 34 | fit <- var_estimate(Y, progress = FALSE) 35 | 36 | # fitted values 37 | pred <- predict(fit, progress = FALSE) 38 | 39 | # predicted values (1st outcome) 40 | pred[,,1] 41 | 42 | } 43 | } 44 | -------------------------------------------------------------------------------- /man/predicted_probability.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/pred_prob.R 3 | \name{predicted_probability} 4 | \alias{predicted_probability} 5 | \title{Predicted Probabilities} 6 | \usage{ 7 | predicted_probability(object, outcome, Y, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{posterior_predict}} 11 | 12 | \item{outcome}{Character string. Node for which the probabilities are computed.} 13 | 14 | \item{Y}{Matrix (or data frame) of dimensions \emph{n} (observations) by \emph{p} (variables). 15 | This must include the column names.} 16 | 17 | \item{...}{Compute conditional probabilities by specifying a column name in \code{Y} 18 | (besides the \code{outcome}) and a fixed value. This can include 19 | any number of nodes. See example below. Leave this blank to compute 20 | unconditional probabilities for \code{outcome}.} 21 | } 22 | \value{ 23 | A list containing a matrix with the computed probabilities 24 | (a row for each predictive sample and a column for each category). 25 | } 26 | \description{ 27 | Compute the predicted probabilities for discrete data, with the possibility 28 | of conditional predictive probabilities (i.e., at fixed values of other nodes) 29 | } 30 | \note{ 31 | There are no checks that the conditional probability exists, i.e., suppose 32 | you wish to condition on, say, B3 = 2 and B4 = 1, yet there is no instance in 33 | which B3 is 2 AND B4 is 1. This will result in an uninformative error. 34 | } 35 | \examples{ 36 | \donttest{ 37 | Y <- ptsd 38 | fit <- estimate(as.matrix(Y), iter = 150, type = "mixed") 39 | 40 | pred <- posterior_predict(fit, iter = 100) 41 | 42 | prob <- predicted_probability(pred, 43 | Y = Y, 44 | outcome = "B3", 45 | B4 = 0, 46 | B5 = 0) 47 | 48 | } 49 | } 50 | -------------------------------------------------------------------------------- /man/print.BGGM.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/print_BGGM.R 3 | \name{print.BGGM} 4 | \alias{print.BGGM} 5 | \title{Print method for \code{BGGM} objects} 6 | \usage{ 7 | \method{print}{BGGM}(x, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object of class \code{BGGM}} 11 | 12 | \item{...}{currently ignored} 13 | } 14 | \description{ 15 | Mainly used to avoid a plethora of different print 16 | functions that overcrowded the documentation in previous versions 17 | of \strong{BGGM}. 18 | } 19 | -------------------------------------------------------------------------------- /man/ptsd.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{ptsd} 5 | \alias{ptsd} 6 | \title{Data: Post-Traumatic Stress Disorder} 7 | \format{ 8 | A dataframe with 221 rows and 20 variables 9 | } 10 | \usage{ 11 | data("ptsd") 12 | } 13 | \description{ 14 | A dataset containing items that measure Post-traumatic stress disorder symptoms \insertCite{armour2017network}{BGGM}. 15 | There are 20 variables (\emph{p}) and 221 observations (\emph{n}). 16 | } 17 | \details{ 18 | \itemize{ 19 | \item Intrusive Thoughts 20 | \item Nightmares 21 | \item Flashbacks 22 | \item Emotional cue reactivity 23 | \item Psychological cue reactivity 24 | \item Avoidance of thoughts 25 | \item Avoidance of reminders 26 | \item Trauma-related amnesia 27 | \item Negative beliefs 28 | \item Negative trauma-related emotions 29 | \item Loss of interest 30 | \item Detachment 31 | \item Restricted affect 32 | \item Irritability/anger 33 | \item Self-destructive/reckless behavior 34 | \item Hypervigilance 35 | \item Exaggerated startle response 36 | \item Difficulty concentrating 37 | \item Sleep disturbance 38 | } 39 | } 40 | \references{ 41 | \insertAllCited{} 42 | } 43 | \keyword{datasets} 44 | -------------------------------------------------------------------------------- /man/ptsd_cor1.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{ptsd_cor1} 5 | \alias{ptsd_cor1} 6 | \title{Data: Post-Traumatic Stress Disorder (Sample # 1)} 7 | \format{ 8 | A correlation matrix with 16 variables 9 | } 10 | \description{ 11 | A correlation matrix that includes 16 variables. The correlation matrix was estimated from 526 12 | individuals \insertCite{fried2018replicability}{BGGM}. 13 | } 14 | \details{ 15 | \itemize{ 16 | \item Intrusive Thoughts 17 | \item Nightmares 18 | \item Flashbacks 19 | \item Physiological/psychological reactivity 20 | \item Avoidance of thoughts 21 | \item Avoidance of situations 22 | \item Amnesia 23 | \item Disinterest in activities 24 | \item Feeling detached 25 | \item Emotional numbing 26 | \item Foreshortened future 27 | \item Sleep problems 28 | \item Irritability 29 | \item Concentration problems 30 | \item Hypervigilance 31 | \item Startle response 32 | } 33 | } 34 | \examples{ 35 | 36 | data(ptsd_cor1) 37 | 38 | Y <- MASS::mvrnorm(n = 526, 39 | mu = rep(0, 16), 40 | Sigma = ptsd_cor1, 41 | empirical = TRUE) 42 | 43 | } 44 | \references{ 45 | \insertAllCited{} 46 | } 47 | \keyword{datasets} 48 | -------------------------------------------------------------------------------- /man/ptsd_cor2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{ptsd_cor2} 5 | \alias{ptsd_cor2} 6 | \title{Data: Post-Traumatic Stress Disorder (Sample # 2)} 7 | \format{ 8 | A correlation matrix with 16 variables 9 | } 10 | \description{ 11 | A correlation matrix that includes 16 variables. The correlation matrix 12 | was estimated from 365 individuals \insertCite{fried2018replicability}{BGGM}. 13 | } 14 | \details{ 15 | \itemize{ 16 | \item Intrusive Thoughts 17 | \item Nightmares 18 | \item Flashbacks 19 | \item Physiological/psychological reactivity 20 | \item Avoidance of thoughts 21 | \item Avoidance of situations 22 | \item Amnesia 23 | \item Disinterest in activities 24 | \item Feeling detached 25 | \item Emotional numbing 26 | \item Foreshortened future 27 | \item Sleep problems 28 | \item Irritability 29 | \item Concentration problems 30 | \item Hypervigilance 31 | \item Startle response 32 | } 33 | } 34 | \examples{ 35 | data(ptsd_cor2) 36 | Y <- MASS::mvrnorm(n = 365, 37 | mu = rep(0, 16), 38 | Sigma = ptsd_cor2, 39 | empirical = TRUE) 40 | } 41 | \references{ 42 | \insertAllCited{} 43 | } 44 | \keyword{datasets} 45 | -------------------------------------------------------------------------------- /man/ptsd_cor3.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{ptsd_cor3} 5 | \alias{ptsd_cor3} 6 | \title{Data: Post-Traumatic Stress Disorder (Sample # 3)} 7 | \format{ 8 | A correlation matrix with 16 variables 9 | } 10 | \description{ 11 | A correlation matrix that includes 16 variables. The correlation matrix 12 | was estimated from 926 individuals \insertCite{fried2018replicability}{BGGM}. 13 | } 14 | \details{ 15 | \itemize{ 16 | 17 | \item Intrusive Thoughts 18 | \item Nightmares 19 | \item Flashbacks 20 | \item Physiological/psychological reactivity 21 | \item Avoidance of thoughts 22 | \item Avoidance of situations 23 | \item Amnesia 24 | \item Disinterest in activities 25 | \item Feeling detached 26 | \item Emotional numbing 27 | \item Foreshortened future 28 | \item Sleep problems 29 | \item Irritability 30 | \item Concentration problems 31 | \item Hypervigilance 32 | \item Startle response 33 | } 34 | } 35 | \examples{ 36 | data(ptsd_cor3) 37 | Y <- MASS::mvrnorm(n = 926, 38 | mu = rep(0, 16), 39 | Sigma = ptsd_cor3, 40 | empirical = TRUE) 41 | 42 | } 43 | \references{ 44 | \insertAllCited{} 45 | } 46 | \keyword{datasets} 47 | -------------------------------------------------------------------------------- /man/ptsd_cor4.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{ptsd_cor4} 5 | \alias{ptsd_cor4} 6 | \title{Data: Post-Traumatic Stress Disorder (Sample # 4)} 7 | \format{ 8 | A correlation matrix with 16 variables 9 | } 10 | \description{ 11 | A correlation matrix that includes 16 variables. The correlation matrix 12 | was estimated from 965 individuals \insertCite{fried2018replicability}{BGGM}. 13 | } 14 | \details{ 15 | \itemize{ 16 | \item Intrusive Thoughts 17 | \item Nightmares 18 | \item Flashbacks 19 | \item Physiological/psychological reactivity 20 | \item Avoidance of thoughts 21 | \item Avoidance of situations 22 | \item Amnesia 23 | \item Disinterest in activities 24 | \item Feeling detached 25 | \item Emotional numbing 26 | \item Foreshortened future 27 | \item Sleep problems 28 | \item Irritability 29 | \item Concentration problems 30 | \item Hypervigilance 31 | \item Startle response 32 | } 33 | } 34 | \examples{ 35 | data(ptsd_cor4) 36 | Y <- MASS::mvrnorm(n = 965, 37 | mu = rep(0, 16), 38 | Sigma = ptsd_cor4, 39 | empirical = TRUE) 40 | 41 | } 42 | \references{ 43 | \insertAllCited{} 44 | } 45 | \keyword{datasets} 46 | -------------------------------------------------------------------------------- /man/regression_summary.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/regression_summary.R 3 | \name{regression_summary} 4 | \alias{regression_summary} 5 | \title{Summarary Method for Multivariate or Univarate Regression} 6 | \usage{ 7 | regression_summary(object, cred = 0.95, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate}} 11 | 12 | \item{cred}{Numeric. The credible interval width for summarizing the posterior 13 | distributions (defaults to 0.95; must be between 0 and 1).} 14 | 15 | \item{...}{Currently ignored} 16 | } 17 | \value{ 18 | A list of length \emph{p} including the 19 | summaries for each regression. 20 | } 21 | \description{ 22 | Summarary Method for Multivariate or Univarate Regression 23 | } 24 | \examples{ 25 | \donttest{ 26 | # note: iter = 250 for demonstrative purposes 27 | 28 | # data 29 | Y <- bfi 30 | 31 | Y <- subset(Y, select = c("A1", "A2", 32 | "gender", "education")) 33 | 34 | fit_mv_ordinal <- estimate(Y, formula = ~ gender + as.factor(education), 35 | type = "continuous", 36 | iter = 250, 37 | progress = TRUE) 38 | 39 | regression_summary(fit_mv_ordinal) 40 | } 41 | } 42 | -------------------------------------------------------------------------------- /man/select.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/select.estimate.R 3 | \name{select} 4 | \alias{select} 5 | \title{S3 \code{select} method} 6 | \usage{ 7 | select(object, ...) 8 | } 9 | \arguments{ 10 | \item{object}{object of class \code{estimate} or\code{explore}} 11 | 12 | \item{...}{not currently used} 13 | } 14 | \value{ 15 | \code{select} works with the following methods: 16 | \itemize{ 17 | \item \code{\link{select.estimate}} 18 | \item \code{\link{select.explore}} 19 | \item \code{\link{select.ggm_compare_estimate}} 20 | } 21 | } 22 | \description{ 23 | S3 select method 24 | } 25 | -------------------------------------------------------------------------------- /man/select.estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/select.estimate.R 3 | \name{select.estimate} 4 | \alias{select.estimate} 5 | \title{Graph Selection for \code{estimate} Objects} 6 | \usage{ 7 | \method{select}{estimate}(object, cred = 0.95, alternative = "two.sided", ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate.default}.} 11 | 12 | \item{cred}{Numeric. The credible interval width for selecting the graph 13 | (defaults to 0.95; must be between 0 and 1).} 14 | 15 | \item{alternative}{A character string specifying the alternative hypothesis. It 16 | must be one of "two.sided" (default), "greater" or "less". 17 | See note for futher details.} 18 | 19 | \item{...}{Currently ignored.} 20 | } 21 | \value{ 22 | The returned object of class \code{select.estimate} contains a lot of information that 23 | is used for printing and plotting the results. For users of \strong{BGGM}, the following 24 | are the useful objects: 25 | 26 | \itemize{ 27 | 28 | \item \code{pcor_adj} Selected partial correlation matrix (weighted adjacency). 29 | \item \code{adj} Adjacency matrix for the selected edges 30 | \item \code{object} An object of class \code{estimate} (the fitted model). 31 | 32 | } 33 | } 34 | \description{ 35 | Provides the selected graph based on credible intervals for 36 | the partial correlations that did not contain zero 37 | \insertCite{Williams2019}{BGGM}. 38 | } 39 | \details{ 40 | This package was built for the social-behavioral sciences in particular. In these applications, there is 41 | strong theory that expects \emph{all} effects to be positive. This is known as a "positive manifold" and 42 | this notion has a rich tradition in psychometrics. Hence, this can be incorporated into the graph with 43 | \code{alternative = "greater"}. This results in the estimated structure including only positive edges. 44 | } 45 | \examples{ 46 | \donttest{ 47 | # note: iter = 250 for demonstrative purposes 48 | 49 | # data 50 | Y <- bfi[,1:10] 51 | 52 | # estimate 53 | fit <- estimate(Y, iter = 250, 54 | progress = FALSE) 55 | 56 | 57 | # select edge set 58 | E <- select(fit) 59 | 60 | } 61 | 62 | } 63 | \references{ 64 | \insertAllCited{} 65 | } 66 | \seealso{ 67 | \code{\link{estimate}} and \code{\link{ggm_compare_estimate}} for several examples. 68 | } 69 | -------------------------------------------------------------------------------- /man/select.ggm_compare_estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/select.ggm_compare_estimate.R 3 | \name{select.ggm_compare_estimate} 4 | \alias{select.ggm_compare_estimate} 5 | \title{Graph Selection for \code{ggm_compare_estimate} Objects} 6 | \usage{ 7 | \method{select}{ggm_compare_estimate}(object, cred = 0.95, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate.default}.} 11 | 12 | \item{cred}{Numeric. The credible interval width for selecting the graph 13 | (defaults to 0.95; must be between 0 and 1).} 14 | 15 | \item{...}{not currently used} 16 | } 17 | \value{ 18 | The returned object of class \code{select.ggm_compare_estimate} contains a lot of information that 19 | is used for printing and plotting the results. For users of \strong{BGGM}, the following 20 | are the useful objects: 21 | 22 | 23 | \itemize{ 24 | 25 | \item \code{mean_diff} A list of matrices for each group comparsion (partial correlation differences). 26 | 27 | \item \code{pcor_adj} A list of weighted adjacency matrices for each group comparsion. 28 | 29 | \item \code{adj} A list of adjacency matrices for each group comparsion. 30 | 31 | } 32 | } 33 | \description{ 34 | Provides the selected graph (of differences) based on credible intervals for 35 | the partial correlations that did not contain zero 36 | \insertCite{Williams2019}{BGGM}. 37 | } 38 | \examples{ 39 | \donttest{ 40 | # note: iter = 250 for demonstrative purposes 41 | ################## 42 | ### example 1: ### 43 | ################## 44 | # data 45 | Y <- bfi 46 | 47 | # males and females 48 | Ymale <- subset(Y, gender == 1, 49 | select = -c(gender, 50 | education)) 51 | 52 | Yfemale <- subset(Y, gender == 2, 53 | select = -c(gender, 54 | education)) 55 | 56 | # fit model 57 | fit <- ggm_compare_estimate(Ymale, Yfemale, 58 | type = "continuous", 59 | iter = 250, 60 | progress = FALSE) 61 | 62 | 63 | E <- select(fit) 64 | 65 | } 66 | } 67 | -------------------------------------------------------------------------------- /man/select.ggm_compare_explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/select.ggm_compare_bf.R 3 | \name{select.ggm_compare_explore} 4 | \alias{select.ggm_compare_explore} 5 | \title{Graph selection for \code{ggm_compare_explore} Objects} 6 | \usage{ 7 | \method{select}{ggm_compare_explore}(object, BF_cut = 3, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{ggm_compare_explore}.} 11 | 12 | \item{BF_cut}{Numeric. Threshold for including an edge (defaults to 3).} 13 | 14 | \item{...}{Currently ignored.} 15 | } 16 | \value{ 17 | The returned object of class \code{select.ggm_compare_explore} contains 18 | a lot of information that is used for printing and plotting the results. 19 | For users of \strong{BGGM}, the following are the useful objects: 20 | 21 | 22 | \itemize{ 23 | 24 | \item \code{adj_10} Adjacency matrix for which there was evidence for a difference. 25 | 26 | \item \code{adj_10} Adjacency matrix for which there was evidence for a null relation 27 | 28 | \item \code{pcor_mat_10} Selected partial correlation matrix (weighted adjacency; only for two groups). 29 | 30 | } 31 | } 32 | \description{ 33 | Provides the selected graph (of differences) based on the Bayes factor 34 | \insertCite{williams2020comparing}{BGGM}. 35 | } 36 | \examples{ 37 | \donttest{ 38 | 39 | ################## 40 | ### example 1: ### 41 | ################## 42 | # data 43 | Y <- bfi 44 | 45 | # males and females 46 | Ymale <- subset(Y, gender == 1, 47 | select = -c(gender, 48 | education))[,1:10] 49 | 50 | Yfemale <- subset(Y, gender == 2, 51 | select = -c(gender, 52 | education))[,1:10] 53 | 54 | # fit model 55 | fit <- ggm_compare_explore(Ymale, Yfemale, 56 | iter = 250, 57 | type = "continuous", 58 | progress = FALSE) 59 | 60 | 61 | E <- select(fit, post_prob = 0.50) 62 | 63 | } 64 | 65 | } 66 | \seealso{ 67 | \code{\link{explore}} and \code{\link{ggm_compare_explore}} for several examples. 68 | } 69 | -------------------------------------------------------------------------------- /man/select.var_estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/select.VAR_estimate.R 3 | \name{select.var_estimate} 4 | \alias{select.var_estimate} 5 | \title{Graph Selection for \code{var.estimate} Object} 6 | \usage{ 7 | \method{select}{var_estimate}(object, cred = 0.95, alternative = "two.sided", ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{VAR.estimate}.} 11 | 12 | \item{cred}{Numeric. The credible interval width for selecting the graph 13 | (defaults to 0.95; must be between 0 and 1).} 14 | 15 | \item{alternative}{A character string specifying the alternative hypothesis. It 16 | must be one of "two.sided" (default), "greater" or "less". 17 | See note for futher details.} 18 | 19 | \item{...}{Currently ignored.} 20 | } 21 | \value{ 22 | An object of class \code{select.var_estimate}, including 23 | 24 | \itemize{ 25 | 26 | \item {pcor_adj} Adjacency matrix for the partial correlations. 27 | 28 | \item {beta_adj} Adjacency matrix for the regression coefficients. 29 | 30 | \item {pcor_weighted_adj} Weighted adjacency matrix for the partial correlations. 31 | 32 | \item {beta_weighted_adj} Weighted adjacency matrix for the regression coefficients. 33 | 34 | \item \code{pcor_mu} Partial correlation matrix (posterior mean). 35 | 36 | \item \code{beta_mu} A matrix including the regression coefficients (posterior mean). 37 | 38 | } 39 | } 40 | \description{ 41 | Graph Selection for \code{var.estimate} Object 42 | } 43 | \examples{ 44 | \donttest{ 45 | # data 46 | Y <- subset(ifit, id == 1)[,-1] 47 | 48 | # fit model with alias (var_estimate also works) 49 | fit <- var_estimate(Y, progress = FALSE) 50 | 51 | # select graphs 52 | select(fit, cred = 0.95) 53 | 54 | } 55 | } 56 | -------------------------------------------------------------------------------- /man/summary.coef.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/coef.estimate.R 3 | \name{summary.coef} 4 | \alias{summary.coef} 5 | \title{Summarize \code{coef} Objects} 6 | \usage{ 7 | \method{summary}{coef}(object, cred = 0.95, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{coef}.} 11 | 12 | \item{cred}{Numeric. The credible interval width for summarizing the posterior 13 | distributions (defaults to 0.95; must be between 0 and 1).} 14 | 15 | \item{...}{Currently ignored} 16 | } 17 | \value{ 18 | A list of length \emph{p} including the 19 | summaries for each multiple regression. 20 | } 21 | \description{ 22 | Summarize regression parameters with the posterior mean, 23 | standard deviation, and credible interval. 24 | } 25 | \note{ 26 | See \code{\link{coef.estimate}} and \code{\link{coef.explore}} for examples. 27 | } 28 | -------------------------------------------------------------------------------- /man/summary.estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/estimate.R 3 | \name{summary.estimate} 4 | \alias{summary.estimate} 5 | \title{Summary method for \code{estimate.default} objects} 6 | \usage{ 7 | \method{summary}{estimate}(object, col_names = TRUE, cred = 0.95, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate}} 11 | 12 | \item{col_names}{Logical. Should the summary include the column names (default is \code{TRUE})? 13 | Setting to \code{FALSE} includes the column numbers (e.g., \code{1--2}).} 14 | 15 | \item{cred}{Numeric. The credible interval width for summarizing the posterior 16 | distributions (defaults to 0.95; must be between 0 and 1).} 17 | 18 | \item{...}{Currently ignored.} 19 | } 20 | \value{ 21 | A dataframe containing the summarized posterior distributions. 22 | } 23 | \description{ 24 | Summarize the posterior distribution of each partial correlation 25 | with the posterior mean and standard deviation. 26 | } 27 | \examples{ 28 | \donttest{ 29 | # data 30 | Y <- ptsd[,1:5] 31 | 32 | fit <- estimate(Y, iter = 250, 33 | progress = FALSE) 34 | 35 | summary(fit) 36 | 37 | } 38 | 39 | } 40 | \seealso{ 41 | \code{\link{estimate}} 42 | } 43 | -------------------------------------------------------------------------------- /man/summary.explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/explore.default.R 3 | \name{summary.explore} 4 | \alias{summary.explore} 5 | \title{Summary Method for \code{explore.default} Objects} 6 | \usage{ 7 | \method{summary}{explore}(object, col_names = TRUE, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{estimate}} 11 | 12 | \item{col_names}{Logical. Should the summary include the column names (default is \code{TRUE})? 13 | Setting to \code{FALSE} includes the column numbers (e.g., \code{1--2}).} 14 | 15 | \item{...}{Currently ignored} 16 | } 17 | \value{ 18 | A dataframe containing the summarized posterior distributions. 19 | } 20 | \description{ 21 | Summarize the posterior distribution for each partial correlation 22 | with the posterior mean and standard deviation. 23 | } 24 | \examples{ 25 | \donttest{ 26 | # note: iter = 250 for demonstrative purposes 27 | 28 | Y <- ptsd[,1:5] 29 | 30 | fit <- explore(Y, iter = 250, 31 | progress = FALSE) 32 | 33 | summ <- summary(fit) 34 | 35 | summ 36 | } 37 | } 38 | \seealso{ 39 | \code{\link{select.explore}} 40 | } 41 | -------------------------------------------------------------------------------- /man/summary.ggm_compare_estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggm_compare_estimate.default.R 3 | \name{summary.ggm_compare_estimate} 4 | \alias{summary.ggm_compare_estimate} 5 | \title{Summary method for \code{ggm_compare_estimate} objects} 6 | \usage{ 7 | \method{summary}{ggm_compare_estimate}(object, col_names = TRUE, cred = 0.95, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{ggm_compare_estimate}.} 11 | 12 | \item{col_names}{Logical. Should the summary include the column names (default is \code{TRUE})? 13 | Setting to \code{FALSE} includes the column numbers (e.g., \code{1--2}).} 14 | 15 | \item{cred}{Numeric. The credible interval width for summarizing the posterior 16 | distributions (defaults to 0.95; must be between 0 and 1).} 17 | 18 | \item{...}{Currently ignored.} 19 | } 20 | \value{ 21 | A list containing the summarized posterior distributions. 22 | } 23 | \description{ 24 | Summarize the posterior distribution of each partial correlation 25 | difference with the posterior mean and standard deviation. 26 | } 27 | \examples{ 28 | \donttest{ 29 | # note: iter = 250 for demonstrative purposes 30 | # data 31 | Y <- bfi 32 | 33 | # males and females 34 | Ymale <- subset(Y, gender == 1, 35 | select = -c(gender, 36 | education))[,1:5] 37 | 38 | Yfemale <- subset(Y, gender == 2, 39 | select = -c(gender, 40 | education))[,1:5] 41 | 42 | # fit model 43 | fit <- ggm_compare_estimate(Ymale, Yfemale, 44 | type = "continuous", 45 | iter = 250, 46 | progress = FALSE) 47 | 48 | summary(fit) 49 | } 50 | } 51 | \seealso{ 52 | \code{\link{ggm_compare_estimate}} 53 | } 54 | -------------------------------------------------------------------------------- /man/summary.ggm_compare_explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggm_compare_bf.default.R 3 | \name{summary.ggm_compare_explore} 4 | \alias{summary.ggm_compare_explore} 5 | \title{Summary Method for \code{ggm_compare_explore} Objects} 6 | \usage{ 7 | \method{summary}{ggm_compare_explore}(object, col_names = TRUE, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{ggm_compare_explore}.} 11 | 12 | \item{col_names}{Logical. Should the summary include the column names (default is \code{TRUE})? 13 | Setting to \code{FALSE} includes the column numbers (e.g., \code{1--2}).} 14 | 15 | \item{...}{Currently ignored.} 16 | } 17 | \value{ 18 | An object of class \code{summary.ggm_compare_explore} 19 | } 20 | \description{ 21 | Summarize the posterior hypothesis probabilities 22 | } 23 | \examples{ 24 | \donttest{ 25 | # note: iter = 250 for demonstrative purposes 26 | 27 | # data 28 | Y <- bfi[complete.cases(bfi),] 29 | 30 | # males and females 31 | Ymale <- subset(Y, gender == 1, 32 | select = -c(gender, 33 | education))[,1:10] 34 | 35 | Yfemale <- subset(Y, gender == 2, 36 | select = -c(gender, 37 | education))[,1:10] 38 | 39 | ########################## 40 | ### example 1: ordinal ### 41 | ########################## 42 | 43 | # fit model 44 | fit <- ggm_compare_explore(Ymale, Yfemale, 45 | type = "ordinal", 46 | iter = 250, 47 | progress = FALSE) 48 | # summary 49 | summ <- summary(fit) 50 | 51 | summ 52 | } 53 | } 54 | \seealso{ 55 | \code{\link{ggm_compare_explore}} 56 | } 57 | -------------------------------------------------------------------------------- /man/summary.predictability.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/predictability.R 3 | \name{summary.predictability} 4 | \alias{summary.predictability} 5 | \title{Summary Method for \code{predictability} Objects} 6 | \usage{ 7 | \method{summary}{predictability}(object, cred = 0.95, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{predictability}.} 11 | 12 | \item{cred}{Numeric. The credible interval width for summarizing the posterior 13 | distributions (defaults to 0.95; must be between 0 and 1).} 14 | 15 | \item{...}{Currently ignored} 16 | } 17 | \description{ 18 | Summary Method for \code{predictability} Objects 19 | } 20 | \examples{ 21 | \donttest{ 22 | Y <- ptsd[,1:5] 23 | 24 | fit <- explore(Y, iter = 250, 25 | progress = FALSE) 26 | 27 | r2 <- predictability(fit, iter = 250, 28 | progress = FALSE) 29 | 30 | summary(r2) 31 | 32 | } 33 | 34 | } 35 | -------------------------------------------------------------------------------- /man/summary.select.explore.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/select.explore.R 3 | \name{summary.select.explore} 4 | \alias{summary.select.explore} 5 | \title{Summary Method for \code{select.explore} Objects} 6 | \usage{ 7 | \method{summary}{select.explore}(object, col_names = TRUE, ...) 8 | } 9 | \arguments{ 10 | \item{object}{object of class \code{select.explore}.} 11 | 12 | \item{col_names}{Logical.} 13 | 14 | \item{...}{Currently ignored.} 15 | } 16 | \value{ 17 | a data frame including the posterior mean, standard deviation, 18 | and posterior hypothesis probabilities for each relation. 19 | } 20 | \description{ 21 | Summary Method for \code{select.explore} Objects 22 | } 23 | \examples{ 24 | \donttest{ 25 | # data 26 | Y <- bfi[,1:10] 27 | 28 | # fit model 29 | fit <- explore(Y, iter = 250, 30 | progress = FALSE) 31 | 32 | # edge set 33 | E <- select(fit, 34 | alternative = "exhaustive") 35 | 36 | summary(E) 37 | 38 | } 39 | } 40 | -------------------------------------------------------------------------------- /man/summary.var_estimate.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/var_estimate.R 3 | \name{summary.var_estimate} 4 | \alias{summary.var_estimate} 5 | \title{Summary Method for \code{var_estimate} Objects} 6 | \usage{ 7 | \method{summary}{var_estimate}(object, cred = 0.95, ...) 8 | } 9 | \arguments{ 10 | \item{object}{An object of class \code{var_estimate}} 11 | 12 | \item{cred}{Numeric. The credible interval width for summarizing the posterior 13 | distributions (defaults to 0.95; must be between 0 and 1).} 14 | 15 | \item{...}{Currently ignored.} 16 | } 17 | \value{ 18 | A dataframe containing the summarized posterior distributions, 19 | including both the partial correlations and the regression coefficients. 20 | 21 | \itemize{ 22 | 23 | \item \code{pcor_results} A data frame including the summarized partial correlations 24 | 25 | \item \code{beta_results} A list containing the summarized regression coefficients (one 26 | data frame for each outcome) 27 | } 28 | } 29 | \description{ 30 | Summarize the posterior distribution of each partial correlation 31 | and regression coefficient with the posterior mean, standard deviation, and 32 | credible intervals. 33 | } 34 | \examples{ 35 | \donttest{ 36 | # data 37 | Y <- subset(ifit, id == 1)[,-1] 38 | 39 | # fit model with alias (var_estimate also works) 40 | fit <- var_estimate(Y, progress = FALSE) 41 | 42 | # summary ('pcor') 43 | print( 44 | summary(fit, cred = 0.95), 45 | param = "pcor", 46 | ) 47 | 48 | 49 | # summary ('beta') 50 | print( 51 | summary(fit, cred = 0.95), 52 | param = "beta", 53 | ) 54 | 55 | } 56 | } 57 | \seealso{ 58 | \code{\link{var_estimate}} 59 | } 60 | -------------------------------------------------------------------------------- /man/weighted_adj_mat.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/weighted_adj_mat.R 3 | \name{weighted_adj_mat} 4 | \alias{weighted_adj_mat} 5 | \title{Extract the Weighted Adjacency Matrix} 6 | \usage{ 7 | weighted_adj_mat(object, ...) 8 | } 9 | \arguments{ 10 | \item{object}{A model estimated with \strong{BGGM}. All classes are supported, assuming 11 | there is matrix to be extracted.} 12 | 13 | \item{...}{Currently ignored.} 14 | } 15 | \value{ 16 | The weighted adjacency matrix (partial correlation matrix with zeros). 17 | } 18 | \description{ 19 | Extract the weighted adjacency matrix (posterior mean) from 20 | \code{\link{estimate}}, \code{\link{explore}}, \code{\link{ggm_compare_estimate}}, 21 | and \code{\link{ggm_compare_explore}} objects. 22 | } 23 | \examples{ 24 | \donttest{ 25 | # note: iter = 250 for demonstrative purposes 26 | Y <- bfi[,1:5] 27 | 28 | # estimate 29 | fit <- estimate(Y, iter = 250, 30 | progress = FALSE) 31 | 32 | # select graph 33 | E <- select(fit) 34 | 35 | # extract weighted adj matrix 36 | weighted_adj_mat(E) 37 | 38 | } 39 | } 40 | -------------------------------------------------------------------------------- /man/women_math.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasets.R 3 | \docType{data} 4 | \name{women_math} 5 | \alias{women_math} 6 | \title{Data: Women and Mathematics} 7 | \format{ 8 | A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale 9 | \insertCite{fowlkes1988evaluating}{BGGM}. These data have been analyzed in \insertCite{tarantola2004mcmc;textual}{BGGM} 10 | and in \insertCite{madigan1994model}{BGGM}. The variable descriptions were copied from (section 5.2 ) 11 | \insertCite{@section 5.2, @talhouk2012efficient}{BGGM} 12 | } 13 | \usage{ 14 | data("women_math") 15 | } 16 | \description{ 17 | A data frame containing 1190 observations (n = 1190) and 6 variables (p = 6) measured on the binary scale. 18 | } 19 | \details{ 20 | \itemize{ 21 | \item \code{1} Lecture attendance (attend/did not attend) 22 | \item \code{2} Gender (male/female) 23 | \item \code{3} School type (urban/suburban) 24 | \item \code{4} “I will be needing Mathematics in my future work” (agree/disagree) 25 | \item \code{5} Subject preference (math/science vs. liberal arts) 26 | \item \code{6} Future plans (college/job) 27 | } 28 | } 29 | \examples{ 30 | data("women_math") 31 | } 32 | \references{ 33 | \insertAllCited{} 34 | } 35 | \keyword{datasets} 36 | -------------------------------------------------------------------------------- /pkgdown/favicon/apple-touch-icon-120x120.png: 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-------------------------------------------------------------------------------- 1 | *Wow, no problems at all. :)* -------------------------------------------------------------------------------- /revdep/problems.md: -------------------------------------------------------------------------------- 1 | *Wow, no problems at all. :)* -------------------------------------------------------------------------------- /src/Makevars.in: -------------------------------------------------------------------------------- 1 | # Specify the C++ standard to use. C++17 is required for compatibility with R >= 4.3.0 2 | CXX_STD = CXX17 3 | 4 | # Disables Armadillo's debug mode for faster execution. 5 | PKG_CPPFLAGS = -DARMA_NO_DEBUG 6 | 7 | # Use OpenMP flags provided by R and include custom headers located in inst/include 8 | PKG_CXXFLAGS = @OPENMP_FLAG@ -I../inst/include 9 | 10 | # Link against OpenMP and required numerical libraries (LAPACK, BLAS, and Fortran libraries) 11 | PKG_LIBS= @OPENMP_FLAG@ $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) 12 | -------------------------------------------------------------------------------- /src/Makevars.win: -------------------------------------------------------------------------------- 1 | # Specify the C++ standard to use. C++17 is required for compatibility with R >= 4.3.0 2 | CXX_STD = CXX17 3 | 4 | # Use OpenMP flags provided by R and include custom headers located in inst/include 5 | PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) -I../inst/include 6 | 7 | # Link against OpenMP and required numerical libraries (LAPACK, BLAS, and Fortran libraries) 8 | PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) 9 | -------------------------------------------------------------------------------- /tests/testthat.R: -------------------------------------------------------------------------------- 1 | t# This file is part of the standard setup for testthat. 2 | # It is recommended that you do not modify it. 3 | # 4 | # Where should you do additional test configuration? 5 | #t Learn more about the roles of various files in: 6 | # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview 7 | # * https://testthat.r-lib.org/articles/special-files.html 8 | 9 | library(testthat) 10 | library(BGGM) 11 | 12 | test_check("BGGM") 13 | -------------------------------------------------------------------------------- /tests/testthat/test-bggm_missing.R: -------------------------------------------------------------------------------- 1 | library(testthat) 2 | library(BGGM) 3 | 4 | test_that("bggm_missing handles missing data with method 'estimate'", { 5 | set.seed(123) 6 | 7 | # Create a sample dataset with missing values 8 | Y <- matrix(rnorm(100), ncol = 5) 9 | Y[sample(length(Y), 20)] <- NA 10 | 11 | # Impute missing data 12 | imp <- mice::mice(Y, m = 5, print = FALSE) 13 | 14 | # Test bggm_missing with method 'estimate' 15 | result <- bggm_missing(imp, method = "estimate", iter = 250, progress = FALSE) 16 | 17 | expect_s3_class(result, "estimate") 18 | expect_true("post_samp" %in% names(result)) 19 | expect_true("pcors" %in% names(result$post_samp)) 20 | }) 21 | 22 | test_that("bggm_missing handles missing data with method 'explore'", { 23 | set.seed(123) 24 | 25 | # Create a sample dataset with missing values 26 | Y <- matrix(rnorm(100), ncol = 5) 27 | Y[sample(length(Y), 20)] <- NA 28 | 29 | # Impute missing data 30 | imp <- mice::mice(Y, m = 5, print = FALSE) 31 | 32 | # Test bggm_missing with method 'explore' 33 | result <- bggm_missing(imp, method = "explore", iter = 250, progress = FALSE) 34 | 35 | expect_s3_class(result, "explore") 36 | expect_true("post_samp" %in% names(result)) 37 | expect_true("pcors" %in% names(result$post_samp)) 38 | }) 39 | -------------------------------------------------------------------------------- /tests/testthat/test-bma_posterior.R: -------------------------------------------------------------------------------- 1 | # test-bma_posterior.R 2 | 3 | library(testthat) 4 | library(BGGM) 5 | 6 | test_that("bma_posterior returns expected output", { 7 | # Load example data 8 | Y <- ptsd[,1:4] 9 | 10 | # Fit the model using ggm_search 11 | fit <- ggm_search(x = Y, prior_prob = 0.9, iter = 5000) 12 | 13 | # Generate posterior samples using bma_posterior 14 | bma_result <- bma_posterior(fit, iter = 100, progress = FALSE) 15 | 16 | # Check that the result is a list 17 | expect_type(bma_result, "list") 18 | 19 | # Check that the list contains 'bma_mean' and 'samples' 20 | expect_named(bma_result, c("bma_mean", "samples")) 21 | 22 | # Check that 'bma_mean' is a matrix with appropriate dimensions 23 | expect_true(is.matrix(bma_result$bma_mean)) 24 | expect_equal(dim(bma_result$bma_mean), c(ncol(Y), ncol(Y))) 25 | 26 | # Check that 'samples' is a 3D array with appropriate dimensions 27 | expect_true(length(dim(bma_result$samples)) == 3) 28 | expect_equal(dim(bma_result$samples), c(ncol(Y), ncol(Y), 100)) 29 | }) 30 | -------------------------------------------------------------------------------- /tests/testthat/test-select.explore.R: -------------------------------------------------------------------------------- 1 | library(testthat) 2 | library(BGGM) 3 | 4 | test_that("select.explore returns expected structure for two-sided alternative", { 5 | Y <- matrix(rnorm(100), ncol = 4) 6 | fit <- explore(Y, progress = FALSE) 7 | result <- select.explore(fit, alternative = "two.sided") 8 | 9 | expect_s3_class(result, "select.explore") 10 | expect_named(result, c("pcor_mat_zero", "pcor_mat", "pcor_sd_fisher","Adj_10", "Adj_01", "BF_10", "BF_01", "BF_cut", "alternative", "call", "type", "formula", "analytic", "object")) 11 | expect_true(is.matrix(result$pcor_mat_zero)) 12 | expect_true(is.matrix(result$pcor_mat)) 13 | expect_true(is.matrix(result$Adj_10)) 14 | expect_true(is.matrix(result$Adj_01)) 15 | }) 16 | 17 | test_that("select.explore returns expected structure for greater alternative", { 18 | Y <- matrix(rnorm(100), ncol = 4) 19 | fit <- explore(Y, progress = FALSE) 20 | result <- select.explore(fit, alternative = "greater") 21 | 22 | expect_s3_class(result, "select.explore") 23 | expect_named(result, c("pcor_mat_zero", "pcor_mat", "pcor_sd_fisher", "Adj_20", "Adj_02", "BF_20", "BF_02", "BF_cut", "alternative", "call", "type", "formula", "analytic", "object")) 24 | expect_true(is.matrix(result$pcor_mat_zero)) 25 | expect_true(is.matrix(result$pcor_mat)) 26 | expect_true(is.matrix(result$Adj_20)) 27 | expect_true(is.matrix(result$Adj_02)) 28 | }) 29 | 30 | test_that("select.explore returns expected structure for exhaustive alternative", { 31 | Y <- matrix(rnorm(100), ncol = 4) 32 | fit <- explore(Y, progress = FALSE) 33 | result <- select.explore(fit, alternative = "exhaustive") 34 | 35 | expect_s3_class(result, "select.explore") 36 | expect_named(result, c("post_prob", "neg_mat", "pos_mat", "null_mat", "alternative", "pcor_mat", "pcor_sd_fisher", "call", "prob", "type", "formula", "analytic", "object")) 37 | expect_true(is.data.frame(result$post_prob)) 38 | expect_true(is.matrix(result$neg_mat)) 39 | expect_true(is.matrix(result$pos_mat)) 40 | expect_true(is.matrix(result$null_mat)) 41 | }) 42 | -------------------------------------------------------------------------------- /tests/testthat/test-summary.estimate.R: -------------------------------------------------------------------------------- 1 | library(testthat) 2 | library(BGGM) 3 | 4 | test_data <- ptsd[, 1:5] 5 | fit <- estimate(test_data) 6 | 7 | # Test summary call 8 | test_that("summary.estimate basic functionality", { 9 | summary_result <- summary.estimate(fit) 10 | expect_s3_class(summary_result, c("BGGM", "estimate", "summary_estimate", "summary.estimate")) 11 | expect_true("Relation" %in% colnames(summary_result$dat_results)) 12 | }) 13 | 14 | # Test col_names = FALSE 15 | test_that("summary.estimate handles col_names properly", { 16 | summary_result <- summary.estimate(fit, col_names = FALSE) 17 | expected_relation_format <- grepl("--", summary_result$dat_results$Relation[1]) 18 | expect_true(expected_relation_format) 19 | }) 20 | 21 | # Add more tests... 22 | -------------------------------------------------------------------------------- /vignettes/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | *.R 3 | -------------------------------------------------------------------------------- /vignettes/in_tandem.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "In Tandem: Confirmatory and Exploratory Testing" 3 | author: "Donny Williams" 4 | date: "5/23/2020" 5 | bibliography: ../inst/REFERENCES.bib 6 | output: 7 | rmarkdown::html_vignette: 8 | toc: yes 9 | vignette: > 10 | %\VignetteIndexEntry{In Tandem: Confirmatory and Exploratory Testing} 11 | %\VignetteEngine{knitr::rmarkdown} 12 | \usepackage[utf8]{inputenc} 13 | --- 14 | 15 | ```{r setup, include=FALSE} 16 | knitr::opts_chunk$set(echo = TRUE) 17 | ``` 18 | 19 | 20 | 21 | The blog post, "Tutorial: Bayesian Testing of Central Structures in Psychological Networks," is hosted on a different website. 22 | 23 | # [External Link](https://josue.rbind.io/post/tutorial-bayesian-testing/) 24 | -------------------------------------------------------------------------------- /vignettes/netstat_fit.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/donaldRwilliams/BGGM/e6e8366c9ce1a964deb769b5b2fa64878bf10dcf/vignettes/netstat_fit.rda --------------------------------------------------------------------------------