├── .Rbuildignore ├── .gitattributes ├── .github └── workflows │ └── draft-pdf.yml ├── .gitignore ├── CODE_OF_CONDUCT.md ├── CRAN-SUBMISSION ├── DESCRIPTION ├── LICENSE ├── LICENSE.md ├── NAMESPACE ├── NEWS.md ├── R ├── SpATS.R ├── checkConection.R ├── checkDesign.R ├── extractG.R ├── extractRcov.R ├── fa.R ├── fitMET.R ├── fitSingle.R ├── ggCor.R ├── global.R ├── heritability.R ├── icREML.R ├── lme4.R ├── parametersGG.R ├── utils-pipe.R └── utils_S3_methods.R ├── README.Rmd ├── README.md ├── _pkgdown.yml ├── agriutilities.Rproj ├── cran-comments.md ├── docs ├── 404.html ├── CODE_OF_CONDUCT.html ├── LICENSE-text.html ├── LICENSE.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 │ ├── how-to-start.html │ ├── images │ │ └── equation.png │ ├── index.html │ ├── repeated_measurements.html │ └── repeated_measurements_files │ │ └── figure-html │ │ ├── unnamed-chunk-1-1.png │ │ ├── unnamed-chunk-10-1.png │ │ ├── unnamed-chunk-11-1.png │ │ ├── unnamed-chunk-12-1.png │ │ ├── unnamed-chunk-13-1.png │ │ ├── unnamed-chunk-14-1.png │ │ ├── unnamed-chunk-15-1.png │ │ ├── unnamed-chunk-17-1.png │ │ ├── unnamed-chunk-19-1.png │ │ ├── unnamed-chunk-2-1.png │ │ ├── unnamed-chunk-4-1.png │ │ ├── unnamed-chunk-5-1.png │ │ ├── unnamed-chunk-6-1.png │ │ ├── unnamed-chunk-7-1.png │ │ └── unnamed-chunk-8-1.png ├── authors.html ├── bootstrap-toc.css ├── bootstrap-toc.js ├── deps │ ├── bootstrap-5.1.3 │ │ ├── bootstrap.bundle.min.js │ │ ├── bootstrap.bundle.min.js.map │ │ ├── bootstrap.min.css │ │ ├── font.css │ │ └── fonts │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff │ │ │ ├── 4iCs6KVjbNBYlgo6ew.woff │ │ │ ├── 4iCs6KVjbNBYlgoKfw7w.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvTtA.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjsGyL.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDQ.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPa7j.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3aPA.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7j.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdo.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdo.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvWNRevw.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvaNA.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_TOQ.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_fOAKSw.woff │ │ │ ├── JTURjIg1_i6t8kCHKm45_ZpC7g0.woff │ │ │ ├── JTURjIg1_i6t8kCHKm45_dJE7g0.woff │ │ │ ├── JTUSjIg1_i6t8kCHKm45xW0.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlfBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlvAA.woff │ │ │ ├── KFOmCnqEu92Fr1Me5g.woff │ │ │ ├── KFOmCnqEu92Fr1Mu4mxM.woff │ │ │ ├── QGYpz_kZZAGCONcK2A4bGOj8mNhL.woff │ │ │ ├── S6u8w4BMUTPHjxsAXC-s.woff │ │ │ ├── S6u8w4BMUTPHjxswWA.woff │ │ │ ├── S6u9w4BMUTPHh6UVSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh6UVeww.woff │ │ │ ├── S6u9w4BMUTPHh7USSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh7USeww.woff │ │ │ ├── S6uyw4BMUTPHjx4wWA.woff │ │ │ ├── S6uyw4BMUTPHvxo.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuFuYMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuI6fMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuLyfMZs.woff │ │ │ ├── XRXV3I6Li01BKof4MQ.woff │ │ │ ├── XRXW3I6Li01BKofA6sKkZQ.woff │ │ │ ├── XRXW3I6Li01BKofAjsOkZQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1x4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1y4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0C4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0C4k.woff │ │ │ ├── pe03MImSLYBIv1o4X1M8cc9iB_5p.woff │ │ │ ├── pe0qMImSLYBIv1o4X1M8cfe5.woff │ │ │ └── q5uGsou0JOdh94bfvQlr.woff │ ├── bootstrap-5.2.2 │ │ ├── bootstrap.bundle.min.js │ │ ├── bootstrap.bundle.min.js.map │ │ ├── bootstrap.min.css │ │ ├── font.css │ │ └── fonts │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff │ │ │ ├── 4iCs6KVjbNBYlgo6ew.woff │ │ │ ├── 4iCs6KVjbNBYlgoKfw7w.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvTtA.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjsGyL.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDQ.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPa7j.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3aPA.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7j.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdo.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdo.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvWNRevw.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvaNA.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_TOQ.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_fOAKSw.woff │ │ │ ├── JTUHjIg1_i6t8kCHKm4532VJOt5-QNFgpCtZ6Ew9.woff │ │ │ ├── JTUHjIg1_i6t8kCHKm4532VJOt5-QNFgpCtr6Ew9.woff │ │ │ ├── JTUHjIg1_i6t8kCHKm4532VJOt5-QNFgpCuM70w9.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlfBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlvAA.woff │ │ │ ├── KFOmCnqEu92Fr1Me5g.woff │ │ │ ├── KFOmCnqEu92Fr1Mu4mxM.woff │ │ │ ├── QGYpz_kZZAGCONcK2A4bGOj8mNhL.woff │ │ │ ├── S6u8w4BMUTPHjxsAXC-s.woff │ │ │ ├── S6u8w4BMUTPHjxswWA.woff │ │ │ ├── S6u9w4BMUTPHh6UVSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh6UVeww.woff │ │ │ ├── S6u9w4BMUTPHh7USSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh7USeww.woff │ │ │ ├── S6uyw4BMUTPHjx4wWA.woff │ │ │ ├── S6uyw4BMUTPHvxo.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuFuYMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuI6fMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuLyfMZs.woff │ │ │ ├── XRXI3I6Li01BKofiOc5wtlZ2di8HDFwmRTA.woff │ │ │ ├── XRXI3I6Li01BKofiOc5wtlZ2di8HDGUmRTA.woff │ │ │ ├── XRXI3I6Li01BKofiOc5wtlZ2di8HDLshRTA.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1x4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1y4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0C4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0C4k.woff │ │ │ ├── pe03MImSLYBIv1o4X1M8cc9iB_5p.woff │ │ │ ├── pe0qMImSLYBIv1o4X1M8cfe5.woff │ │ │ └── q5uGsou0JOdh94bfvQlr.woff │ ├── 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 │ ├── data-deps.txt │ └── jquery-3.6.0 │ │ ├── jquery-3.6.0.js │ │ ├── jquery-3.6.0.min.js │ │ └── jquery-3.6.0.min.map ├── docsearch.css ├── docsearch.js ├── favicon-16x16.png ├── favicon-32x32.png ├── favicon.ico ├── index.html ├── link.svg ├── logo.png ├── news │ └── index.html ├── pkgdown.css ├── pkgdown.js ├── pkgdown.yml ├── reference │ ├── Rplot001.png │ ├── Rplot002.png │ ├── Rplot003.png │ ├── Rplot004.png │ ├── Rplot005.png │ ├── Rplot006.png │ ├── Rplot007.png │ ├── Rplot008.png │ ├── check_connectivity.html │ ├── check_design_met-1.png │ ├── check_design_met-2.png │ ├── check_design_met.html │ ├── check_design_met_1.html │ ├── connectivity_matrix.html │ ├── covcor_heat-1.png │ ├── covcor_heat.html │ ├── extract_rcov.html │ ├── extract_vcov.html │ ├── fa2_summary.html │ ├── fa_summary-1.png │ ├── fa_summary-2.png │ ├── fa_summary-3.png │ ├── fa_summary-4.png │ ├── fa_summary.html │ ├── figures │ │ ├── README-unnamed-chunk-10-1.png │ │ ├── README-unnamed-chunk-10-2.png │ │ ├── README-unnamed-chunk-11-1.png │ │ ├── README-unnamed-chunk-11-2.png │ │ ├── README-unnamed-chunk-2-1.png │ │ ├── README-unnamed-chunk-2-2.png │ │ ├── README-unnamed-chunk-3-1.png │ │ ├── README-unnamed-chunk-3-2.png │ │ ├── README-unnamed-chunk-4-1.png │ │ ├── README-unnamed-chunk-4-2.png │ │ ├── README-unnamed-chunk-5-1.png │ │ ├── README-unnamed-chunk-5-2.png │ │ ├── README-unnamed-chunk-6-1.png │ │ ├── README-unnamed-chunk-6-2.png │ │ ├── README-unnamed-chunk-7-1.png │ │ ├── README-unnamed-chunk-7-2.png │ │ ├── README-unnamed-chunk-8-1.png │ │ ├── README-unnamed-chunk-8-2.png │ │ ├── README-unnamed-chunk-9-1.png │ │ ├── README-unnamed-chunk-9-2.png │ │ ├── equation.png │ │ └── logo.png │ ├── ggCor-1.png │ ├── ggCor.html │ ├── gg_cor-1.png │ ├── gg_cor.html │ ├── h_cullis.html │ ├── h_cullis_spt.html │ ├── heritability_fa.html │ ├── ic_reml_asr.html │ ├── ic_reml_spt.html │ ├── index.html │ ├── met_analysis-1.png │ ├── met_analysis.html │ ├── parameters_gg-1.png │ ├── parameters_gg.html │ ├── pipe.html │ ├── plot.checkAgri-1.png │ ├── plot.checkAgri-2.png │ ├── plot.checkAgri.html │ ├── plot.metAgri.html │ ├── plot.smaAgri-1.png │ ├── plot.smaAgri-2.png │ ├── plot.smaAgri-3.png │ ├── plot.smaAgri-4.png │ ├── plot.smaAgri-5.png │ ├── plot.smaAgri-6.png │ ├── plot.smaAgri-7.png │ ├── plot.smaAgri-8.png │ ├── plot.smaAgri.html │ ├── print.checkAgri.html │ ├── print.metAgri.html │ ├── print.smaAgri.html │ ├── single_trial_analysis.html │ └── stability.html ├── search.json └── sitemap.xml ├── index.Rmd ├── index.md ├── joss ├── paper.bib ├── paper.md └── summaryPlot.png ├── man ├── check_connectivity.Rd ├── check_design_met.Rd ├── covcor_heat.Rd ├── extract_rcov.Rd ├── extract_vcov.Rd ├── fa_summary.Rd ├── figures │ ├── README-unnamed-chunk-10-1.png │ ├── README-unnamed-chunk-10-2.png │ ├── README-unnamed-chunk-11-1.png │ ├── README-unnamed-chunk-11-2.png │ ├── README-unnamed-chunk-2-1.png │ ├── README-unnamed-chunk-2-2.png │ ├── README-unnamed-chunk-3-1.png │ ├── README-unnamed-chunk-3-2.png │ ├── README-unnamed-chunk-4-1.png │ ├── README-unnamed-chunk-4-2.png │ ├── README-unnamed-chunk-5-1.png │ ├── README-unnamed-chunk-5-2.png │ ├── README-unnamed-chunk-6-1.png │ ├── README-unnamed-chunk-6-2.png │ ├── README-unnamed-chunk-7-1.png │ ├── README-unnamed-chunk-7-2.png │ ├── README-unnamed-chunk-8-1.png │ ├── README-unnamed-chunk-8-2.png │ ├── README-unnamed-chunk-9-1.png │ ├── README-unnamed-chunk-9-2.png │ └── logo.png ├── gg_cor.Rd ├── h_cullis.Rd ├── h_cullis_spt.Rd ├── heritability_fa.Rd ├── ic_reml_asr.Rd ├── ic_reml_spt.Rd ├── met_analysis.Rd ├── parameters_gg.Rd ├── pipe.Rd ├── plot.checkAgri.Rd ├── plot.metAgri.Rd ├── plot.smaAgri.Rd ├── print.checkAgri.Rd ├── print.metAgri.Rd ├── print.smaAgri.Rd ├── single_trial_analysis.Rd └── stability.Rd ├── 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 ├── revdep ├── README.md ├── cran.md ├── failures.md └── problems.md └── vignettes ├── .gitignore ├── bibliography.bib ├── how-to-start.Rmd ├── images └── equation.png └── repeated_measurements.Rmd /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | ^_pkgdown\.yml$ 4 | ^docs$ 5 | ^joss$ 6 | ^pkgdown$ 7 | ^README\.Rmd$ 8 | ^\.github$ 9 | ^LICENSE\.md$ 10 | ^cran-comments\.md$ 11 | ^CODE_OF_CONDUCT\.md$ 12 | ^CRAN-SUBMISSION$ 13 | ^NEWS\.Rmd$ 14 | ^NEWS\.md$ 15 | ^index\.md$ 16 | ^index\.Rmd$ 17 | ^cran-comments\.md$ 18 | ^revdep$ 19 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.github/workflows/draft-pdf.yml: -------------------------------------------------------------------------------- 1 | on: [push] 2 | 3 | jobs: 4 | paper: 5 | runs-on: ubuntu-latest 6 | name: Paper Draft 7 | steps: 8 | - name: Checkout 9 | uses: actions/checkout@v3 10 | - name: Build draft PDF 11 | uses: openjournals/openjournals-draft-action@master 12 | with: 13 | journal: joss 14 | # This should be the path to the paper within your repo. 15 | paper-path: joss/paper.md 16 | - name: Upload 17 | uses: actions/upload-artifact@v1 18 | with: 19 | name: paper 20 | # This is the output path where Pandoc will write the compiled 21 | # PDF. Note, this should be the same directory as the input 22 | # paper.md 23 | path: joss/paper.pdf 24 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .Rapp.history 4 | .RData 5 | .Ruserdata 6 | inst/doc 7 | .Rproj.user/ -------------------------------------------------------------------------------- /CRAN-SUBMISSION: -------------------------------------------------------------------------------- 1 | Version: 1.2.1 2 | Date: 2025-01-17 19:50:47 UTC 3 | SHA: e7a743e30d03e6d64ad2f9123ce080e38e1a8aeb 4 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: agriutilities 2 | Type: Package 3 | Title: Utilities for Data Analysis in Agriculture 4 | Version: 1.2.2 5 | Authors@R: 6 | c(person(given = "Johan", 7 | family = "Aparicio", 8 | role = c("aut", "cre"), 9 | email = "johanstevenapa@gmail.com"), 10 | person(given = "Alexia", 11 | family = "Bornhorst", 12 | role = "aut", 13 | email = "alexiabornhorst@gmail.com"), 14 | person(given = "The Alliance of Bioversity International and CIAT", 15 | role = "cph")) 16 | Description: Utilities designed to make the analysis of field trials easier and 17 | more accessible for everyone working in plant breeding. It provides a simple 18 | and intuitive interface for conducting single and multi-environmental trial 19 | analysis, with minimal coding required. Whether you're a beginner or an 20 | experienced user, 'agriutilities' will help you quickly and easily carry out 21 | complex analyses with confidence. With built-in functions for fitting Linear 22 | Mixed Models, 'agriutilities' is the ideal choice for anyone who wants to save 23 | time and focus on interpreting their results. 24 | Some of the functions require the R package 'asreml' for the 'ASReml' software, 25 | this can be obtained upon purchase from 'VSN' international . 26 | License: MIT + file LICENSE 27 | Imports: 28 | ggplot2, 29 | psych, 30 | dplyr, 31 | tidyr, 32 | lme4, 33 | Matrix, 34 | ggpubr, 35 | lmerTest, 36 | data.table, 37 | stats, 38 | magrittr, 39 | emmeans, 40 | ggrepel, 41 | tibble, 42 | rlang, 43 | statgenSTA, 44 | SpATS 45 | Enhances: asreml 46 | Encoding: UTF-8 47 | RoxygenNote: 7.3.3 48 | URL: https://github.com/AparicioJohan/agriutilities, 49 | https://apariciojohan.github.io/agriutilities/ 50 | BugReports: https://github.com/AparicioJohan/agriutilities/issues 51 | ByteCompile: TRUE 52 | Suggests: 53 | knitr, 54 | lattice, 55 | cluster, 56 | rmarkdown, 57 | agridat, 58 | gt 59 | VignetteBuilder: knitr 60 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | YEAR: 2023 2 | COPYRIGHT HOLDER: agriutilities authors 3 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | # MIT License 2 | 3 | Copyright (c) 2023 agriutilities authors 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | S3method(plot,checkAgri) 4 | S3method(plot,metAgri) 5 | S3method(plot,smaAgri) 6 | S3method(print,checkAgri) 7 | S3method(print,metAgri) 8 | S3method(print,smaAgri) 9 | export("%>%") 10 | export(check_connectivity) 11 | export(check_design_met) 12 | export(covcor_heat) 13 | export(extract_rcov) 14 | export(extract_vcov) 15 | export(fa_summary) 16 | export(gg_cor) 17 | export(h_cullis) 18 | export(h_cullis_spt) 19 | export(heritability_fa) 20 | export(ic_reml_asr) 21 | export(ic_reml_spt) 22 | export(met_analysis) 23 | export(parameters_gg) 24 | export(single_trial_analysis) 25 | export(stability) 26 | import(dplyr) 27 | import(ggplot2) 28 | import(ggrepel) 29 | import(tibble) 30 | import(tidyr) 31 | importFrom(SpATS,plot.SpATS) 32 | importFrom(ggpubr,ggarrange) 33 | importFrom(magrittr,"%>%") 34 | importFrom(rlang,.data) 35 | importFrom(statgenSTA,STAtoTD) 36 | importFrom(statgenSTA,createTD) 37 | importFrom(statgenSTA,extractSTA) 38 | importFrom(statgenSTA,fitTD) 39 | importFrom(statgenSTA,outlierSTA) 40 | importFrom(stats,anova) 41 | importFrom(stats,cov2cor) 42 | importFrom(stats,median) 43 | importFrom(stats,na.omit) 44 | importFrom(stats,sd) 45 | importFrom(utils,head) 46 | importFrom(utils,str) 47 | importFrom(utils,type.convert) 48 | -------------------------------------------------------------------------------- /NEWS.md: -------------------------------------------------------------------------------- 1 | # agriutilities 1.2.1 2 | 3 | * `extract_rcov()` does not require any other parameter but the model. 4 | * `ic_reml_asr()` implementation of the information criteria proposed by Verbyla (2019). 5 | * `ic_reml_spt()` implementation of the information criteria proposed by Verbyla (2019) for SpATS models. 6 | * Add `h_cullis_spt()` to calculate the generalized heritability proposed by Cullis (2006). 7 | * `remove_outliers` FALSE by default in `single_trial_analysis()`. 8 | * Minor changes in `check_design_met()`. 9 | * Fix absolute value of the mean in CV for `single_trial_analysis()`. 10 | 11 | # agriutilities 1.2.0 12 | 13 | * Add `extract_rcov()` to extract variance-covariance matrices. 14 | * Add `method` to be passed to cor function in `gg_cor()`. 15 | * Change style for naming the parameters in `gg_cor()`. 16 | * Minor changes in the writing style. 17 | * Add `reorder` a logical value to reorder by a Hierarchical Clustering in `covcor_heat()`. 18 | 19 | # agriutilities 1.1.0 20 | 21 | ## Minor changes/fixes 22 | 23 | * Add `type = "spatial"` to `plot.smaAgri()` method. 24 | * Add restriction when identifying row-column designs. 25 | * Add minimum and maximum value when returning summary in `check_design_met()`. 26 | * Add S3 method to plot an object of class metAgri `plot.metAgri()`. 27 | * Add S3 method to plot an object of class smaAgri `plot.smaAgri()`. 28 | * Fix argument not working in `covcor_heat()`. 29 | 30 | # agriutilities 1.0.0 31 | 32 | -------------------------------------------------------------------------------- /R/checkConection.R: -------------------------------------------------------------------------------- 1 | #' Check connectivity between trials 2 | #' 3 | #' @param data A data.frame in a wide format. 4 | #' @param genotype A character string indicating the column in data that 5 | #' contains genotypes. 6 | #' @param trial A character string indicating the column in data that contains 7 | #' trials. 8 | #' @param response A character string specifying the trait. 9 | #' @param all Whether or not print all the table. 10 | #' @param return_matrix A logical value indicating if the user wants to return 11 | #' a (n_trial x n_trial) matrix with the amount of genotypes shared between each 12 | #' pair of trial. (\code{FALSE} by default) 13 | #' 14 | #' @return A data.frame with the genotype connectivity. If return_matrix is 15 | #' \code{TRUE}, it will return a n_trial x n_trial matrix with the amount of 16 | #' genotypes shared between each pair of trial. 17 | #' @export 18 | #' 19 | #' @examples 20 | #' library(agridat) 21 | #' library(agriutilities) 22 | #' data(besag.met) 23 | #' dat <- besag.met 24 | #' head( 25 | #' check_connectivity( 26 | #' data = dat, 27 | #' genotype = "gen", 28 | #' trial = "county", 29 | #' response = "yield", 30 | #' all = TRUE, 31 | #' return_matrix = FALSE 32 | #' ) 33 | #' ) 34 | #' @importFrom rlang .data 35 | #' @import dplyr tidyr tibble 36 | check_connectivity <- function(data = NULL, 37 | genotype = "line", 38 | trial = "Experiment", 39 | response = NULL, 40 | all = FALSE, 41 | return_matrix = FALSE) { 42 | tmp_data <- data %>% 43 | { 44 | if (!is.null(response)) { 45 | filter(.data = ., !is.na(.data[[response]])) 46 | } else { 47 | . 48 | } 49 | } %>% 50 | select(all_of(c(genotype, trial))) %>% 51 | unique.data.frame() %>% 52 | mutate(value = 1) %>% 53 | tidyr::spread(all_of(trial), value = value) 54 | 55 | if (return_matrix) { 56 | conn <- tmp_data %>% 57 | tibble::column_to_rownames(genotype) %>% 58 | as.matrix() 59 | conn[is.na(conn)] <- 0 60 | conectivity <- t(conn) %*% conn 61 | return(conectivity) 62 | } else { 63 | connection_table <- tmp_data %>% 64 | mutate( 65 | total = rowSums( 66 | x = select(., -all_of(genotype)), 67 | na.rm = TRUE 68 | ), 69 | n = ncol(.) - 1, 70 | percent = total / n 71 | ) %>% 72 | arrange(desc(total)) 73 | if (all) { 74 | return(connection_table) 75 | } else { 76 | connection_table <- connection_table %>% 77 | select(all_of(genotype), total, n, percent) 78 | return(connection_table) 79 | } 80 | } 81 | } 82 | -------------------------------------------------------------------------------- /R/global.R: -------------------------------------------------------------------------------- 1 | utils::globalVariables( 2 | names = c( 3 | "fa1", 4 | "fa2", 5 | "Vg", 6 | "BLUE", 7 | "site", 8 | "x", 9 | "y", 10 | "vx", 11 | "vy", 12 | "blup", 13 | ".", 14 | "Score", 15 | "Genotype", 16 | "value", 17 | "total", 18 | "percent", 19 | "SD", 20 | "Mean", 21 | "n_miss", 22 | "gen_reps", 23 | "n_gen", 24 | "n_rep", 25 | "rcbd", 26 | "n_block", 27 | "num_of_reps", 28 | "n_col", 29 | "n_row", 30 | "miss_perc", 31 | "var_total", 32 | "spatial", 33 | "prep", 34 | "exp_design", 35 | "solution", 36 | "std.error", 37 | "Var2", 38 | "Var1", 39 | "fa1_scaled", 40 | "fa2_scaled", 41 | "name.x", 42 | "label", 43 | "Effect", 44 | "Line", 45 | "predicted.value", 46 | "Ls", 47 | "Lu", 48 | "predicted.values", 49 | "standard.errors", 50 | "checks", 51 | "r_square", 52 | "Classify", 53 | "fit", 54 | "Residuals", 55 | "Index", 56 | "..density..", 57 | "alpha_lattice", 58 | "trial", 59 | ":=", 60 | "genotype", 61 | "coef", 62 | "CV_spats", 63 | "outlier", 64 | "seBLUEs", 65 | "residual", 66 | "mean_gen", 67 | "mean_trial", 68 | "wt", 69 | "trait", 70 | "heritability", 71 | "trial_y", 72 | "trial_x", 73 | "size_reps", 74 | "reps_equal", 75 | "BLUPs", 76 | "component", 77 | "design" 78 | ) 79 | ) 80 | -------------------------------------------------------------------------------- /R/heritability.R: -------------------------------------------------------------------------------- 1 | #' Heritability for Factor Analytic Models in ASReml-R 2 | #' 3 | #' @param model_fa Factor Analytic ASReml model 4 | #' @param genotype A character string indicating the column in data that 5 | #' contains genotypes. 6 | #' @param env A character string indicating the column in data that contains 7 | #' environments or trials. 8 | #' @param vc_model A character string indicating the variance-covariance 9 | #' structure. Can be "fa1", "fa2", "fa3", "fa4" or "us". 10 | #' @param diag \code{TRUE} or \code{FALSE} depending on the user if they want to 11 | #' take the elements on the diagonal of the variance-covariance matrix or the 12 | #' elements out of the diagonal to estimate the heritability. \code{FALSE} by 13 | #' default. 14 | #' 15 | #' @return An object with a list of: 16 | #' \item{h2_cullis}{A numerical value of the Cullis heritability estimate.} 17 | #' \item{h2_se}{A numerical value of the Cullis heritability estimate based on 18 | #' the standard error.} 19 | #' @export 20 | #' 21 | #' @examples 22 | #' \dontrun{ 23 | #' library(agridat) 24 | #' library(agriutilities) 25 | #' data(besag.met) 26 | #' dat <- besag.met 27 | #' results <- check_design_met( 28 | #' data = dat, 29 | #' genotype = "gen", 30 | #' trial = "county", 31 | #' traits = c("yield"), 32 | #' rep = "rep", 33 | #' block = "block", 34 | #' col = "col", 35 | #' row = "row" 36 | #' ) 37 | #' out <- single_trial_analysis(results, progress = FALSE) 38 | #' met_results <- met_analysis(out, progress = FALSE) 39 | #' model <- met_results$met_models$yield 40 | #' heritability_fa( 41 | #' model_fa = model, 42 | #' genotype = "genotype", 43 | #' env = "trial", 44 | #' vc_model = "us" 45 | #' ) 46 | #' } 47 | heritability_fa <- function(model_fa = NULL, 48 | genotype = "line", 49 | env = "loc", 50 | vc_model = c("fa2"), 51 | diag = FALSE) { 52 | G <- extract_vcov( 53 | model = model_fa, 54 | gen = genotype, 55 | env = env, 56 | vc_model = vc_model 57 | )$VCOV 58 | 59 | if (diag) { 60 | Gvar <- mean(diag(G)) 61 | } else { 62 | Gvar <- mean(G[upper.tri(G, diag = FALSE)]) 63 | } 64 | pr <- suppressWarnings( 65 | asreml::predict.asreml(model_fa, classify = genotype, sed = TRUE, trace = 0) 66 | ) 67 | vdBLUP.mat <- pr$sed^2 68 | vdBLUP.avg <- mean(vdBLUP.mat[upper.tri(vdBLUP.mat, diag = FALSE)]) 69 | h2_cullis <- 1 - (vdBLUP.avg / 2 / Gvar) 70 | h2_cullis 71 | avsed <- pr$pvals %>% 72 | summarise(mean = mean(std.error^2)) %>% 73 | as.numeric() 74 | h2_js <- 1 - avsed / Gvar 75 | return(list(h2_cullis = h2_cullis, h2_se = h2_js)) 76 | } 77 | -------------------------------------------------------------------------------- /R/parametersGG.R: -------------------------------------------------------------------------------- 1 | #' Genetic Gain Parameters 2 | #' 3 | #' @param model Linear regression model (\code{lm} object) 4 | #' @param trait A character string indicating the column in data that contains 5 | #' trials. 6 | #' 7 | #' @return A data.frame with some parameters from the linear regression (Slope, 8 | #' se_Slope, Intercept, r2, Pr(>F)) and the percentage of Genetic Gain. 9 | #' @export 10 | #' 11 | #' @examples 12 | #' \donttest{ 13 | #' library(ggplot2) 14 | #' library(agridat) 15 | #' library(magrittr) 16 | #' library(agriutilities) 17 | #' 18 | #' data(baker.barley.uniformity) 19 | #' dat <- baker.barley.uniformity 20 | #' head(dat) 21 | #' 22 | #' model <- lm(yield ~ year, dat) 23 | #' 24 | #' dat %>% 25 | #' na.omit() %>% 26 | #' ggplot( 27 | #' aes(x = year, y = yield) 28 | #' ) + 29 | #' geom_point() + 30 | #' geom_smooth(method = "lm") + 31 | #' theme_bw() 32 | #' 33 | #' parameters_gg(model = model, trait = "yield") 34 | #' } 35 | #' @importFrom stats anova 36 | parameters_gg <- function(model, trait = "trait") { 37 | summ <- summary(model) 38 | first_year <- min(model$model[, 2], na.rm = TRUE) 39 | last_year <- max(model$model[, 2], na.rm = TRUE) 40 | intercept <- summ$coefficients[1, 1] 41 | slope <- summ$coefficients[2, 1] 42 | se_slope <- summ$coefficients[2, 2] 43 | r2_lm <- summ$r.squared 44 | p_value <- as.data.frame(anova(model))$`Pr(>F)`[1] 45 | table_report <- data.frame( 46 | trait = trait, 47 | first_year = first_year, 48 | last_year = last_year, 49 | Slope = slope, 50 | se_Slope = se_slope, 51 | Intercept = intercept, 52 | r2 = r2_lm, 53 | `Pr(>F)` = p_value, 54 | `Genetic_Gain%` = slope / (intercept + first_year * slope) * 100, 55 | check.names = FALSE, 56 | row.names = "Regression" 57 | ) 58 | return(table_report) 59 | } 60 | -------------------------------------------------------------------------------- /R/utils-pipe.R: -------------------------------------------------------------------------------- 1 | #' Pipe operator 2 | #' 3 | #' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. 4 | #' 5 | #' @name %>% 6 | #' @rdname pipe 7 | #' @keywords internal 8 | #' @export 9 | #' @importFrom magrittr %>% 10 | #' @usage lhs \%>\% rhs 11 | #' @param lhs A value or the magrittr placeholder. 12 | #' @param rhs A function call using the magrittr semantics. 13 | #' @return The result of calling `rhs(lhs)`. 14 | NULL 15 | -------------------------------------------------------------------------------- /_pkgdown.yml: -------------------------------------------------------------------------------- 1 | template: 2 | bootstrap: 5 3 | bootswatch: materia 4 | theme: a11y-light 5 | bslib: 6 | pkgdown-nav-height: 100px 7 | url: https://apariciojohan.github.io/agriutilities/ 8 | navbar: 9 | title: agriutilities 10 | type: default 11 | bg: secondary 12 | left: 13 | - icon: fa-home fa-lg 14 | text: Home 15 | href: index.html 16 | - icon: fa-code 17 | text: Reference 18 | href: reference/index.html 19 | - icon: fa-book 20 | text: Articles 21 | menu: 22 | - text: How to Start 23 | href: articles/how-to-start.html 24 | - text: Repeated Measurements 25 | href: articles/repeated_measurements.html 26 | - icon: fa-newspaper 27 | text: News 28 | href: news/index.html 29 | right: 30 | - icon: fa-github 31 | text: Github 32 | href: https://github.com/AparicioJohan/agriutilities 33 | reference: 34 | - title: Data Analysis Pipeline 35 | desc: Identify the design and fit the models automatically 36 | - contents: 37 | - check_design_met 38 | - single_trial_analysis 39 | - met_analysis 40 | - print.checkAgri 41 | - print.smaAgri 42 | - print.metAgri 43 | - plot.checkAgri 44 | - plot.smaAgri 45 | - plot.metAgri 46 | - title: Lme4 47 | desc: Additional functions for lme4 models 48 | - contents: 49 | - h_cullis 50 | - title: ASReml 51 | desc: Additional functions for ASReml models 52 | - contents: 53 | - extract_vcov 54 | - extract_rcov 55 | - fa_summary 56 | - heritability_fa 57 | - ic_reml_asr 58 | - title: SpATS 59 | desc: Additional functions for SpATS models 60 | - contents: 61 | - h_cullis_spt 62 | - ic_reml_spt 63 | - title: Others 64 | - contents: 65 | - check_connectivity 66 | - covcor_heat 67 | - gg_cor 68 | - parameters_gg 69 | - stability 70 | 71 | -------------------------------------------------------------------------------- /agriutilities.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | ProjectId: 6dba61dc-f247-481f-988d-6b0de35908c4 3 | 4 | RestoreWorkspace: Default 5 | SaveWorkspace: Default 6 | AlwaysSaveHistory: Default 7 | 8 | EnableCodeIndexing: Yes 9 | UseSpacesForTab: Yes 10 | NumSpacesForTab: 2 11 | Encoding: UTF-8 12 | 13 | RnwWeave: Sweave 14 | LaTeX: pdfLaTeX 15 | 16 | AutoAppendNewline: Yes 17 | StripTrailingWhitespace: Yes 18 | 19 | BuildType: Package 20 | PackageUseDevtools: Yes 21 | PackageInstallArgs: --no-multiarch --with-keep.source 22 | PackageRoxygenize: rd,collate,namespace 23 | -------------------------------------------------------------------------------- /cran-comments.md: -------------------------------------------------------------------------------- 1 | ## R CMD check results 2 | 3 | 0 errors | 0 warnings | 1 note 4 | 5 | * This is a new release. 6 | -------------------------------------------------------------------------------- /docs/apple-touch-icon-120x120.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/docs/apple-touch-icon-120x120.png 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118 | -------------------------------------------------------------------------------- /index.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | output: github_document 3 | bibliography: ./vignettes/bibliography.bib 4 | --- 5 | 6 | 7 | 8 | ```{r, include = FALSE} 9 | knitr::opts_chunk$set( 10 | warning = FALSE, 11 | message = FALSE, 12 | collapse = TRUE, 13 | comment = "", 14 | fig.path = "man/figures/README-", 15 | out.width = "100%" 16 | ) 17 | ``` 18 | 19 | # agriutilities 20 | 21 | 22 | agriutilities is an `R` package designed to make the analysis of 23 | field trials easier and more accessible for everyone working in plant breeding. 24 | It provides a simple and intuitive interface for conducting **single** and 25 | **multi-environmental** trial analysis, with minimal coding required. Whether 26 | you're a beginner or an experienced user, agriutilities will help you quickly 27 | and easily carry out complex analyses with confidence. With built-in functions 28 | for fitting Linear Mixed Models (**LMM**), agriutilities is the ideal choice for 29 | anyone who wants to save time and focus on interpreting their results. 30 | 31 | ## Installation 32 | 33 | ### From CRAN 34 | 35 | ``` r 36 | install.packages("agriutilities") 37 | ``` 38 | 39 | ### From GitHub 40 | 41 | You can install the development version of agriutilities from 42 | [GitHub](https://github.com/AparicioJohan/agriutilities) with: 43 | 44 | ``` r 45 | remotes::install_github("AparicioJohan/agriutilities") 46 | ``` 47 | 48 | 49 | ## Automatic Data Analysis Pipeline 50 | 51 | This is a basic example which shows you how to use some of the functions of the 52 | package. 53 | 54 | ### Identify the Experimental Design 55 | 56 | The function `check_design_met` helps us to check the quality of the data and 57 | also to identify the experimental design of the trials. This works as a quality 58 | check or quality control before we fit any model. 59 | 60 | ```{r example} 61 | library(agriutilities) 62 | library(agridat) 63 | data(besag.met) 64 | dat <- besag.met 65 | results <- check_design_met( 66 | data = dat, 67 | genotype = "gen", 68 | trial = "county", 69 | traits = "yield", 70 | rep = "rep", 71 | block = "block", 72 | col = "col", 73 | row = "row" 74 | ) 75 | ``` 76 | ```{r, fig.dpi=600} 77 | plot(results, type = "connectivity") 78 | plot(results, type = "missing") 79 | ``` 80 | 81 | 82 | Inspecting the output. 83 | 84 | ```{r} 85 | print(results) 86 | ``` 87 | 88 | 89 | ### Single Trial Analysis (STA) 90 | 91 | The results of the previous function are used in `single_trial_analysis()` to 92 | fit single trial models. This function can fit, Completely Randomized Designs 93 | (**CRD**), Randomized Complete Block Designs (**RCBD**), Resolvable Incomplete 94 | Block Designs (**res-IBD**), Non-Resolvable Row-Column Designs (**Row-Col**) 95 | and Resolvable Row-Column Designs (**res-Row-Col**). 96 | 97 | > **NOTE**: It fits models based on the randomization detected. 98 | 99 | ```{r} 100 | obj <- single_trial_analysis(results, progress = FALSE) 101 | ``` 102 | 103 | Inspecting the output. 104 | 105 | ```{r} 106 | print(obj) 107 | ``` 108 | 109 | ```{r, fig.dpi=600} 110 | plot(obj, horizontal = TRUE, nudge_y_h2 = 0.12) 111 | plot(obj, type = "correlation") 112 | ``` 113 | 114 | The returning object is a set of lists with trial summary, BLUEs, BLUPs, 115 | heritability, variance components, potential extreme observations, residuals, 116 | the models fitted and the data used. 117 | 118 | ### Two-Stage Analysis (MET) 119 | The results of the previous function are used in `met_analysis()` to 120 | fit multi-environmental trial models. 121 | 122 | ```{r, message=FALSE, warning=FALSE} 123 | met_results <- met_analysis(obj, vcov = "fa2", progress = FALSE) 124 | ``` 125 | 126 | Inspecting the output. 127 | 128 | ```{r} 129 | print(met_results) 130 | ``` 131 | 132 | ### Exploring Factor Analytic in MET analysis. 133 | 134 | ```{r} 135 | pvals <- met_results$trial_effects 136 | model <- met_results$met_models$yield 137 | fa_objt <- fa_summary( 138 | model = model, 139 | trial = "trial", 140 | genotype = "genotype", 141 | BLUEs_trial = pvals, 142 | k_biplot = 8, 143 | size_label_var = 4, 144 | filter_score = 1 145 | ) 146 | ``` 147 | 148 | ```{r, fig.dpi=600} 149 | fa_objt$plots$loadings_c 150 | fa_objt$plots$biplot 151 | ``` 152 | 153 | For more information and to learn more about what is described here you may find 154 | useful the following sources: @isik2017genetic; @rodriguez2018correcting. 155 | 156 | ## Code of Conduct 157 | 158 | Please note that the agriutilities project is released with a [Contributor Code of Conduct](https://apariciojohan.github.io/agriutilities/CODE_OF_CONDUCT.html). 159 | By contributing to this project, you agree to abide by its terms. 160 | 161 | # References 162 | 163 | -------------------------------------------------------------------------------- /joss/paper.bib: -------------------------------------------------------------------------------- 1 | @book{isik2017genetic, 2 | title={Genetic Data Analysis for Plant and Animal Breeding}, 3 | author={Fikret Isik and James Holland and Christian Maltecca}, 4 | volume={400}, 5 | year={2017}, 6 | publisher={Springer} 7 | } 8 | 9 | @article{rodriguez2018, 10 | title={Correcting for spatial heterogeneity in plant breeding experiments with P-splines}, 11 | author={María Xosé Rodríguez-Álvarez and Martin P Boer and Fred A van Eeuwijk and Paul HC Eilers}, 12 | journal={Spatial Statistics}, 13 | volume={23}, 14 | pages={52-71}, 15 | year={2018}, 16 | publisher={Elsevier} 17 | } 18 | 19 | @Article{Cullis2006, 20 | author = {B. R. Cullis and A. B. Smith and N. E. Coombes}, 21 | title = {On the design of early generation variety trials with correlated data.}, 22 | journal = {Journal of Agricultural, Biological, and Environmental Statistics}, 23 | volume = {11}, 24 | pages = {381-393}, 25 | year={2006}, 26 | keywords = {Genetic gain, Grid plot design, REML, optimal design, Spatial correlation}, 27 | doi = {10.1198/108571106X154443}, 28 | url = {https://doi.org/10.1198/108571106X154443} 29 | } 30 | 31 | @book{kuehl2001, 32 | title={Design of Experiments: Statistical Principles of Research Design and Analysis}, 33 | author={Robert Kuehl}, 34 | publisher = {Thomson Learning, México}, 35 | edition = {Second edition}, 36 | year={2001} 37 | } 38 | 39 | @Manual{asreml, 40 | title = {asreml: Fits the Linear Mixed Model}, 41 | author = {David Butler}, 42 | year = {2022}, 43 | note = {R package version 4.1.0.176}, 44 | url = {www.vsni.co.uk}, 45 | } 46 | -------------------------------------------------------------------------------- /joss/summaryPlot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/joss/summaryPlot.png -------------------------------------------------------------------------------- /man/check_connectivity.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/checkConection.R 3 | \name{check_connectivity} 4 | \alias{check_connectivity} 5 | \title{Check connectivity between trials} 6 | \usage{ 7 | check_connectivity( 8 | data = NULL, 9 | genotype = "line", 10 | trial = "Experiment", 11 | response = NULL, 12 | all = FALSE, 13 | return_matrix = FALSE 14 | ) 15 | } 16 | \arguments{ 17 | \item{data}{A data.frame in a wide format.} 18 | 19 | \item{genotype}{A character string indicating the column in data that 20 | contains genotypes.} 21 | 22 | \item{trial}{A character string indicating the column in data that contains 23 | trials.} 24 | 25 | \item{response}{A character string specifying the trait.} 26 | 27 | \item{all}{Whether or not print all the table.} 28 | 29 | \item{return_matrix}{A logical value indicating if the user wants to return 30 | a (n_trial x n_trial) matrix with the amount of genotypes shared between each 31 | pair of trial. (\code{FALSE} by default)} 32 | } 33 | \value{ 34 | A data.frame with the genotype connectivity. If return_matrix is 35 | \code{TRUE}, it will return a n_trial x n_trial matrix with the amount of 36 | genotypes shared between each pair of trial. 37 | } 38 | \description{ 39 | Check connectivity between trials 40 | } 41 | \examples{ 42 | library(agridat) 43 | library(agriutilities) 44 | data(besag.met) 45 | dat <- besag.met 46 | head( 47 | check_connectivity( 48 | data = dat, 49 | genotype = "gen", 50 | trial = "county", 51 | response = "yield", 52 | all = TRUE, 53 | return_matrix = FALSE 54 | ) 55 | ) 56 | } 57 | -------------------------------------------------------------------------------- /man/check_design_met.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/checkDesign.R 3 | \name{check_design_met} 4 | \alias{check_design_met} 5 | \title{Check Experimental Design} 6 | \usage{ 7 | check_design_met( 8 | data = NULL, 9 | genotype = NULL, 10 | trial = NULL, 11 | traits = NULL, 12 | rep = NULL, 13 | block = NULL, 14 | row = NULL, 15 | col = NULL 16 | ) 17 | } 18 | \arguments{ 19 | \item{data}{A data.frame in a wide format.} 20 | 21 | \item{genotype}{A character string indicating the column in data that 22 | contains genotypes.} 23 | 24 | \item{trial}{A character string indicating the column in data that contains 25 | trials.} 26 | 27 | \item{traits}{A character vector specifying the traits for which the models 28 | should be fitted.} 29 | 30 | \item{rep}{A character string indicating the column in data that contains 31 | replicates.} 32 | 33 | \item{block}{A character string indicating the column in data that contains 34 | sub blocks.} 35 | 36 | \item{row}{A character string indicating the column in data that contains the 37 | row coordinates.} 38 | 39 | \item{col}{A character string indicating the column in data that contains the 40 | column coordinates.} 41 | } 42 | \value{ 43 | An object of class \code{checkAgri}, with a list of: 44 | \item{summ_traits}{A data.frame containing a summary of the traits.} 45 | \item{exp_design_resum}{A data.frame containing a summary of the experimental 46 | design.} 47 | \item{filter}{A list by trait containing the filtered trials.} 48 | \item{exp_design_list}{A data.frame containing the experimental design of 49 | each trial.} 50 | \item{check_connectivity}{A data.frame with the genotype connectivity.} 51 | \item{connectivity_matrix}{A matrix with the amount of genotypes shared 52 | between each pair of trial.} 53 | \item{data_design}{A data frame containing the data used with two additional 54 | columns, one realted to the experimental design and a sequential number (id)} 55 | \item{inputs}{A list containing the character string that indicates the 56 | column in data that contains the genotype, trial, traits, rep, block, row 57 | and col.} 58 | } 59 | \description{ 60 | This function helps to identify the experimental design of each 61 | trial, filters the data and then provide a summary for the traits and the 62 | experimental design. This works as a quality check before we fit any model. 63 | Returns an object of class \code{checkAgri}. 64 | } 65 | \examples{ 66 | library(agridat) 67 | library(agriutilities) 68 | data(besag.met) 69 | dat <- besag.met 70 | results <- check_design_met( 71 | data = dat, 72 | genotype = "gen", 73 | trial = "county", 74 | traits = c("yield"), 75 | rep = "rep", 76 | block = "block", 77 | col = "col", 78 | row = "row" 79 | ) 80 | print(results) 81 | plot(results, type = "connectivity") 82 | plot(results, type = "missing") 83 | } 84 | -------------------------------------------------------------------------------- /man/covcor_heat.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fa.R 3 | \name{covcor_heat} 4 | \alias{covcor_heat} 5 | \title{Correlation Covariance Heatmap} 6 | \usage{ 7 | covcor_heat( 8 | matrix, 9 | corr = TRUE, 10 | size = 4, 11 | digits = 3, 12 | legend = c(0.6, 0.7), 13 | upper_tri = FALSE, 14 | reorder = FALSE 15 | ) 16 | } 17 | \arguments{ 18 | \item{matrix}{A numeric matrix.} 19 | 20 | \item{corr}{A logical value indicating if the matrix is in a scaled form 21 | (\code{TRUE} by default, correlation matrix)} 22 | 23 | \item{size}{A numeric value to define the letter size.} 24 | 25 | \item{digits}{A numeric integer to define the number of digits to plot.} 26 | 27 | \item{legend}{the position of legends ("none", "left", "right", "bottom", 28 | "top", or two-element numeric vector)} 29 | 30 | \item{upper_tri}{A logical value to plot the Lower or Upper Triangular Part of 31 | the matrix. FALSE by default.} 32 | 33 | \item{reorder}{A logical value to reorder by a Hierarchical Clustering. FALSE 34 | by default.} 35 | } 36 | \value{ 37 | A ggplot object showing the upper triangular elements of the matrix. 38 | } 39 | \description{ 40 | Correlation Covariance Heatmap 41 | } 42 | \examples{ 43 | library(agriutilities) 44 | data(iris) 45 | M <- cor(iris[, -5]) 46 | covcor_heat(matrix = M, corr = TRUE) 47 | } 48 | -------------------------------------------------------------------------------- /man/extract_vcov.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/extractG.R 3 | \name{extract_vcov} 4 | \alias{extract_vcov} 5 | \title{Extract Variance-Covariance from ASReml-R} 6 | \usage{ 7 | extract_vcov(model = NULL, gen = "genotype", env = "trial", vc_model = "corv") 8 | } 9 | \arguments{ 10 | \item{model}{ASReml object} 11 | 12 | \item{gen}{A character string indicating the column in data that contains 13 | genotypes.} 14 | 15 | \item{env}{A character string indicating the column in data that contains 16 | environments or trials.} 17 | 18 | \item{vc_model}{A character string indicating the variance-covariance fitted. 19 | Can be 'diag', 'corv', 'corh', 'corgv', 'fa1', 'fa2', 'fa3', 'fa4', 'corgh', 20 | 'us' or 'rr2'.} 21 | } 22 | \value{ 23 | An object with a list of: 24 | \item{VCOV}{A matrix of the estimated variance-covariance between trials.} 25 | \item{CORR}{A n_trial x n_trial matrix with the correlation between trials.} 26 | \item{vc_model}{A character string indicating the variance-covariance fitted.} 27 | } 28 | \description{ 29 | Extract Variance-Covariance from ASReml-R 30 | } 31 | \examples{ 32 | \dontrun{ 33 | library(agridat) 34 | library(agriutilities) 35 | 36 | data(besag.met) 37 | dat <- besag.met 38 | results <- check_design_met( 39 | data = dat, 40 | genotype = "gen", 41 | trial = "county", 42 | traits = c("yield"), 43 | rep = "rep", 44 | block = "block", 45 | col = "col", 46 | row = "row" 47 | ) 48 | out <- single_trial_analysis(results, progress = FALSE) 49 | met_results <- met_analysis(out, progress = FALSE) 50 | extract_vcov( 51 | model = met_results$met_models$yield, 52 | vc_model = "us" 53 | ) 54 | } 55 | } 56 | -------------------------------------------------------------------------------- /man/fa_summary.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fa.R 3 | \name{fa_summary} 4 | \alias{fa_summary} 5 | \title{Factor Analytic Summary} 6 | \usage{ 7 | fa_summary( 8 | model = NULL, 9 | trial = "trial", 10 | genotype = "genotype", 11 | BLUEs_trial = NULL, 12 | mult_fa1 = -1, 13 | mult_fa2 = 1, 14 | filter_score = 1.5, 15 | k_biplot = 1, 16 | size_label_var = 2, 17 | alpha_label_var = 0.2, 18 | size_label_ind = 2, 19 | alpha_label_ind = 0.8, 20 | size_arrow = 0.2, 21 | alpha_arrow = 0.2, 22 | base_size = 12 23 | ) 24 | } 25 | \arguments{ 26 | \item{model}{Factor Analytic Model (ASReml object)} 27 | 28 | \item{trial}{A character string indicating the column in data that contains 29 | trials.} 30 | 31 | \item{genotype}{A character string indicating the column in data that 32 | contains genotypes.} 33 | 34 | \item{BLUEs_trial}{A data.frame containing BLUEs for each trial.} 35 | 36 | \item{mult_fa1}{A constant to multiply the first loading. Must be 1 or -1. 37 | (-1 by default)} 38 | 39 | \item{mult_fa2}{A constant to multiply the second loading. Must be 1 or -1. 40 | (1 by default)} 41 | 42 | \item{filter_score}{A numeric value to filter genotypes by the distance from 43 | the origin.} 44 | 45 | \item{k_biplot}{A numeric value to multiply the scores in the biplot.} 46 | 47 | \item{size_label_var}{A numeric value to define the label size for the 48 | variables.} 49 | 50 | \item{alpha_label_var}{A numeric value between (0,1) to define the label for 51 | the variables.} 52 | 53 | \item{size_label_ind}{A numeric value to define the label size for the 54 | individuals.} 55 | 56 | \item{alpha_label_ind}{A numeric value between (0,1) to define the label for 57 | the individuals.} 58 | 59 | \item{size_arrow}{A numeric value to define the arrow size.} 60 | 61 | \item{alpha_arrow}{A numeric value between (0,1) to define the arrow.} 62 | 63 | \item{base_size}{A numeric value to define the base size.} 64 | } 65 | \value{ 66 | An object with a list of: 67 | \item{loadings}{A data.frame containing the first and second loading for each 68 | trial.} 69 | \item{loading_star}{A data.frame containing the first and second loading 70 | rotated for each trial.} 71 | \item{Gvar}{A matrix of the estimated variance-covariance between trials.} 72 | \item{Cmat}{A matrix of the correlation between trials.} 73 | \item{summary_loading}{A data.frame containing a summary of the loadings.} 74 | \item{paf_site}{A data.frame containing the percentage of variance explained 75 | for each component and for each trial.} 76 | \item{var_tot}{A numeric value of the total variance.} 77 | \item{scores}{A data.frame containing the scores for each genotype.} 78 | \item{plots}{A list with different plots. Includes a plot for the loadings, 79 | biplot, biplot_scaled and loadings_c.} 80 | } 81 | \description{ 82 | Factor Analytic Summary 83 | } 84 | \examples{ 85 | \dontrun{ 86 | library(agridat) 87 | library(agriutilities) 88 | data(besag.met) 89 | dat <- besag.met 90 | results <- check_design_met( 91 | data = dat, 92 | genotype = "gen", 93 | trial = "county", 94 | traits = c("yield"), 95 | rep = "rep", 96 | block = "block", 97 | col = "col", 98 | row = "row" 99 | ) 100 | out <- single_trial_analysis(results, progress = FALSE) 101 | met_results <- met_analysis(out, vcov = "fa2", progress = FALSE) 102 | 103 | pp <- met_results$trial_effects 104 | model <- met_results$met_models$yield 105 | fa_summary( 106 | model = model, 107 | trial = "trial", 108 | genotype = "genotype", 109 | BLUEs_trial = pp, 110 | mult_fa1 = -1, 111 | mult_fa2 = -1, 112 | filter_score = 1, 113 | k_biplot = 10, 114 | size_label_var = 3, 115 | alpha_label_var = 0.5, 116 | size_label_ind = 3, 117 | alpha_label_ind = 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roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggCor.R 3 | \name{gg_cor} 4 | \alias{gg_cor} 5 | \title{Triangular Correlation Plot} 6 | \usage{ 7 | gg_cor( 8 | data, 9 | colours = c("#db4437", "white", "#4285f4"), 10 | blackLabs = c(-0.7, 0.7), 11 | show_signif = TRUE, 12 | p_breaks = c(0, 0.001, 0.01, 0.05, Inf), 13 | p_labels = c("***", "**", "*", "ns"), 14 | show_diagonal = FALSE, 15 | diag = NULL, 16 | return_table = FALSE, 17 | return_n = FALSE, 18 | adjusted = TRUE, 19 | label_size = 3, 20 | method = "pearson" 21 | ) 22 | } 23 | \arguments{ 24 | \item{data}{A data.frame with numerical columns for each variable to be 25 | compared.} 26 | 27 | \item{colours}{A vector of size three with the colors to be used for values 28 | -1, 0 and 1.} 29 | 30 | \item{blackLabs}{A numeric vector of size two, with min and max correlation 31 | coefficient.} 32 | 33 | \item{show_signif}{Logical scalar. Display significance values ?} 34 | 35 | \item{p_breaks}{Passed to function 'cut'. Either a numeric vector of two or 36 | more unique cut points or a single number (greater than or equal to 2) giving 37 | the number of intervals into which x is to be cut.} 38 | 39 | \item{p_labels}{Passed to function 'cut'. labels for the levels of the 40 | resulting category. By default, labels are constructed using "(a,b]" interval 41 | notation. If \code{p_labels = FALSE}, simple integer codes are returned 42 | instead of a factor.} 43 | 44 | \item{show_diagonal}{Logical scalar. Display main diagonal values ?} 45 | 46 | \item{diag}{A named vector of labels to display in the main diagonal. The 47 | names are used to place each value in the corresponding coordinates of the 48 | diagonal. Hence, these names must be the same as the colnames of data.} 49 | 50 | \item{return_table}{Return the table to display instead of a ggplot object.} 51 | 52 | \item{return_n}{Return plot with shared information.} 53 | 54 | \item{adjusted}{Use the adjusted p values for multiple testing instead of 55 | raw coeffs. \code{TRUE} by default.} 56 | 57 | \item{label_size}{Numeric value indicating the label size. 3 by default.} 58 | 59 | \item{method}{method="pearson" is the default value. 60 | The alternatives to be passed to cor are "spearman" and "kendall". 61 | These last two are much slower, particularly for big data sets.} 62 | } 63 | \value{ 64 | A ggplot object containing a triangular correlation figure with all 65 | numeric variables in data. If return_table is \code{TRUE}, the table used to 66 | produce the figure is returned instead. 67 | } 68 | \description{ 69 | Return a ggplot object to plot a triangular correlation figure 70 | between 2 or more variables. 71 | } 72 | \examples{ 73 | library(agriutilities) 74 | data(iris) 75 | gg_cor( 76 | data = iris, 77 | colours = c("#db4437", "white", "#4285f4"), 78 | label_size = 6 79 | ) 80 | } 81 | \author{ 82 | Daniel Ariza, Johan Aparicio. 83 | } 84 | -------------------------------------------------------------------------------- /man/h_cullis.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/lme4.R 3 | \name{h_cullis} 4 | \alias{h_cullis} 5 | \title{Cullis heritability for lme4 models} 6 | \usage{ 7 | h_cullis(model, genotype, re_MME = FALSE) 8 | } 9 | \arguments{ 10 | \item{model}{Object of class \code{lmer}.} 11 | 12 | \item{genotype}{A character string indicating the column in data that 13 | contains genotypes.} 14 | 15 | \item{re_MME}{A logical value to ask if we want to reconstruct the mixed models 16 | equations to estimate the Cullis heritability. (\code{FALSE} by default)} 17 | } 18 | \value{ 19 | A numerical value of the Cullis heritability estimate. If 20 | \code{re_MME} is \code{TRUE}, a list with matrices of the mixed models 21 | equations is returned. 22 | } 23 | \description{ 24 | Cullis heritability for lme4 models 25 | } 26 | \examples{ 27 | \donttest{ 28 | library(lme4) 29 | library(agridat) 30 | library(agriutilities) 31 | dat <- john.alpha 32 | g.ran <- lmer( 33 | formula = yield ~ rep + (1 | gen) + (1 | rep:block), 34 | data = dat 35 | ) 36 | h_cullis(model = g.ran, genotype = "gen") 37 | } 38 | } 39 | \author{ 40 | Paul Schmidt, Johan Aparicio. 41 | } 42 | -------------------------------------------------------------------------------- /man/h_cullis_spt.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/SpATS.R 3 | \name{h_cullis_spt} 4 | \alias{h_cullis_spt} 5 | \title{Calculate Cullis heritabilities from SpATS objects} 6 | \usage{ 7 | h_cullis_spt(model) 8 | } 9 | \arguments{ 10 | \item{model}{an object of class SpATS as produced by SpATS()} 11 | } 12 | \value{ 13 | A data frame. The data frame has the following components 14 | \itemize{ 15 | \item \code{trait} : Character string with the trait being analyzed 16 | \item \code{H2Cullis} : Generalized heritability proposed by Cullis (2006) 17 | \item \code{H2Oakey} : Generalized heritability proposed by Oakey (2006) 18 | \item \code{reBLUP_avg} : Average BLUP reliability 19 | \item \code{vdBLUP_avg} : Average pairwise prediction error variance of genotype effects 20 | \item \code{PEV_avg} : Average predictive error variance (PEV) of genotype effects 21 | \item \code{var_G} : Genotypic Variance 22 | } 23 | } 24 | \description{ 25 | Calculate Cullis heritabilities from SpATS objects 26 | } 27 | \examples{ 28 | \donttest{ 29 | library(SpATS) 30 | library(agriutilities) 31 | data(wheatdata) 32 | wheatdata$R <- as.factor(wheatdata$row) 33 | wheatdata$C <- as.factor(wheatdata$col) 34 | 35 | m1 <- SpATS( 36 | response = "yield", 37 | spatial = ~ PSANOVA(col, row, nseg = c(10, 20), nest.div = 2), 38 | genotype = "geno", 39 | genotype.as.random = TRUE, 40 | fixed = ~ colcode + rowcode, 41 | random = ~ R + C, 42 | data = wheatdata, 43 | control = list(tolerance = 1e-03, monitoring = 0) 44 | ) 45 | 46 | h_cullis_spt(m1) 47 | } 48 | } 49 | \references{ 50 | Cullis, B. R., Smith, A. B., & Coombes, N. E. (2006). 51 | On the design of early generation variety trials with correlated data. 52 | Journal of agricultural, biological, and environmental statistics, 11, 381-393. 53 | 54 | Oakey, H., A. Verbyla, W. Pitchford, B. Cullis, and H. Kuchel (2006). 55 | Joint modeling of additive and non-additive genetic line effects in single 56 | field trials. Theoretical and Applied Genetics, 113, 809 - 819. 57 | } 58 | \author{ 59 | Johan Aparicio 60 | } 61 | -------------------------------------------------------------------------------- /man/heritability_fa.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/heritability.R 3 | \name{heritability_fa} 4 | \alias{heritability_fa} 5 | \title{Heritability for Factor Analytic Models in ASReml-R} 6 | \usage{ 7 | heritability_fa( 8 | model_fa = NULL, 9 | genotype = "line", 10 | env = "loc", 11 | vc_model = c("fa2"), 12 | diag = FALSE 13 | ) 14 | } 15 | \arguments{ 16 | \item{model_fa}{Factor Analytic ASReml model} 17 | 18 | \item{genotype}{A character string indicating the column in data that 19 | contains genotypes.} 20 | 21 | \item{env}{A character string indicating the column in data that contains 22 | environments or trials.} 23 | 24 | \item{vc_model}{A character string indicating the variance-covariance 25 | structure. Can be "fa1", "fa2", "fa3", "fa4" or "us".} 26 | 27 | \item{diag}{\code{TRUE} or \code{FALSE} depending on the user if they want to 28 | take the elements on the diagonal of the variance-covariance matrix or the 29 | elements out of the diagonal to estimate the heritability. \code{FALSE} by 30 | default.} 31 | } 32 | \value{ 33 | An object with a list of: 34 | \item{h2_cullis}{A numerical value of the Cullis heritability estimate.} 35 | \item{h2_se}{A numerical value of the Cullis heritability estimate based on 36 | the standard error.} 37 | } 38 | \description{ 39 | Heritability for Factor Analytic Models in ASReml-R 40 | } 41 | \examples{ 42 | \dontrun{ 43 | library(agridat) 44 | library(agriutilities) 45 | data(besag.met) 46 | dat <- besag.met 47 | results <- check_design_met( 48 | data = dat, 49 | genotype = "gen", 50 | trial = "county", 51 | traits = c("yield"), 52 | rep = "rep", 53 | block = "block", 54 | col = "col", 55 | row = "row" 56 | ) 57 | out <- single_trial_analysis(results, progress = FALSE) 58 | met_results <- met_analysis(out, progress = FALSE) 59 | model <- met_results$met_models$yield 60 | heritability_fa( 61 | model_fa = model, 62 | genotype = "genotype", 63 | env = "trial", 64 | vc_model = "us" 65 | ) 66 | } 67 | } 68 | -------------------------------------------------------------------------------- /man/ic_reml_asr.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/icREML.R 3 | \name{ic_reml_asr} 4 | \alias{ic_reml_asr} 5 | \title{Find the AIC and BIC for a set of models fitted using asreml} 6 | \usage{ 7 | ic_reml_asr(fm, scale = 1) 8 | } 9 | \arguments{ 10 | \item{fm}{A \code{list} of asreml fitted model objects} 11 | 12 | \item{scale}{A scalar to scale the variance matrix of the estimated 13 | fixed effects (to ensure numerical stability of a log-determinant). 14 | Default value is 1.} 15 | } 16 | \value{ 17 | A data frame. The data frame has the following components 18 | \itemize{ 19 | \item \code{model} : the names of the models 20 | \item \code{loglik} : the full log-likelihood for each model 21 | \item \code{p} : the number of fixed effects parameters for each model 22 | \item \code{q} : the number of (non-zero) variance parameters for each model. 23 | \item \code{b} : the number of variance parameters that are fixed or on the 24 | boundary. These parameters are not counted in the AIC or BIC. 25 | \item \code{AIC} : the AIC for each model 26 | \item \code{BIC} : the BIC for each model 27 | \item \code{logdet} : the log-determinant used in adjusting the residual 28 | log-likelihood for each model 29 | } 30 | } 31 | \description{ 32 | The function is a wrapper for 'icREML' function described 33 | in Verbyla (2019). 34 | } 35 | \references{ 36 | Verbyla, A. P. (2019). A note on model selection using information 37 | criteria for general linear models estimated using REML. Australian & 38 | New Zealand Journal of Statistics, 61(1), 39-50. 39 | } 40 | \author{ 41 | Ari Verbyla (averbyla at avdataanalytics.com.au) 42 | } 43 | -------------------------------------------------------------------------------- /man/ic_reml_spt.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/icREML.R 3 | \name{ic_reml_spt} 4 | \alias{ic_reml_spt} 5 | \title{Find the AIC and BIC for a model fitted using SpATS} 6 | \usage{ 7 | ic_reml_spt(model, scale = 1, k = 2, label = "spats") 8 | } 9 | \arguments{ 10 | \item{model}{A model fitted using SpATS.} 11 | 12 | \item{scale}{A scalar to scale the variance matrix of the estimated 13 | fixed effects (to ensure numerical stability of a log-determinant). 14 | Default value is 1.} 15 | 16 | \item{k}{An integer value to round ratios when identifying boundary variance 17 | parameters. Default value is 2.} 18 | 19 | \item{label}{A string to label the model. Default value is "spats".} 20 | } 21 | \value{ 22 | A data frame. The data frame has the following components 23 | \itemize{ 24 | \item \code{model} : the name of the models 25 | \item \code{loglik} : the full log-likelihood for each model 26 | \item \code{p} : the number of fixed effects parameters for each model 27 | \item \code{q} : the number of (non-zero) variance parameters for each model. 28 | \item \code{b} : the number of variance parameters that are fixed or on the 29 | boundary. These parameters are not counted in the AIC or BIC. 30 | \item \code{AIC} : the AIC for each model 31 | \item \code{BIC} : the BIC for each model 32 | \item \code{logdet} : the log-determinant used in adjusting the residual 33 | log-likelihood for each model 34 | } 35 | } 36 | \description{ 37 | This function calculates the AIC and BIC for a model fitted in 38 | SpATS following the methodology proposed by Verbyla (2019). 39 | } 40 | \examples{ 41 | \donttest{ 42 | library(SpATS) 43 | library(agriutilities) 44 | data(wheatdata) 45 | wheatdata$R <- as.factor(wheatdata$row) 46 | wheatdata$C <- as.factor(wheatdata$col) 47 | 48 | m1 <- SpATS( 49 | response = "yield", 50 | spatial = ~ PSANOVA(col, row, nseg = c(10, 20), nest.div = 2), 51 | genotype = "geno", 52 | genotype.as.random = TRUE, 53 | fixed = ~ colcode + rowcode, 54 | random = ~ R + C, 55 | data = wheatdata, 56 | control = list(tolerance = 1e-03, monitoring = 0) 57 | ) 58 | 59 | m2 <- SpATS( 60 | response = "yield", 61 | spatial = ~ PSANOVA(col, row, nseg = c(10, 20), nest.div = 2), 62 | genotype = "geno", 63 | genotype.as.random = TRUE, 64 | fixed = ~colcode, 65 | random = ~ R + C, 66 | data = wheatdata, 67 | control = list(tolerance = 1e-03, monitoring = 0) 68 | ) 69 | 70 | rbind.data.frame( 71 | ic_reml_spt(m1, label = "colcode_rowcode"), 72 | ic_reml_spt(m2, label = "colcode_no_rowcode") 73 | ) 74 | 75 | rbind.data.frame( 76 | h_cullis_spt(m1), 77 | h_cullis_spt(m2) 78 | ) 79 | } 80 | } 81 | \references{ 82 | Verbyla, A. P. (2019). A note on model selection using information 83 | criteria for general linear models estimated using REML. Australian & 84 | New Zealand Journal of Statistics, 61(1), 39-50. 85 | } 86 | \author{ 87 | Johan Aparicio 88 | } 89 | -------------------------------------------------------------------------------- /man/met_analysis.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fitMET.R 3 | \name{met_analysis} 4 | \alias{met_analysis} 5 | \title{Multi-Environmental Trial Analysis} 6 | \usage{ 7 | met_analysis( 8 | sma_output = NULL, 9 | h2_filter = 0.2, 10 | workspace = "1gb", 11 | vcov = NULL, 12 | filter_traits = NULL, 13 | remove_trials = NULL, 14 | progress = TRUE 15 | ) 16 | } 17 | \arguments{ 18 | \item{sma_output}{Object of class \code{smaAgri} resulting of executing 19 | \code{single_trial_analysis()} function.} 20 | 21 | \item{h2_filter}{Numeric value to filter trials with poor heritability. 22 | 0.2 by default.} 23 | 24 | \item{workspace}{Sets the workspace for the core \code{REML} routines in the 25 | form of a number optionally followed directly by a valid measurement unit. 26 | "128mb" by default.} 27 | 28 | \item{vcov}{A character string specifying the Variance-Covariance structure 29 | to be fitted. Can be "fa2", "fa1", "us", "corh" or "corv". If \code{NULL} the 30 | function will try to fit an "us" Variance-Covariance and if it fails, it will 31 | try with "fa2" and then with "fa1".} 32 | 33 | \item{filter_traits}{A character vector with traits to filter. \code{NULL} by 34 | default.} 35 | 36 | \item{remove_trials}{A character vector with trials to remove. \code{NULL} by 37 | default.} 38 | 39 | \item{progress}{Should the progress of the modeling be printed. 40 | If \code{TRUE}, for every trait a line is output indicating that the model is 41 | being fitted.} 42 | } 43 | \value{ 44 | An object of class \code{metAgri}, with a list of: 45 | \item{trial_effects}{A data.frame containing Trial BLUEs.} 46 | \item{overall_BLUPs}{A data.frame containing Genotypic BLUPs across trials, 47 | by trait.} 48 | \item{BLUPs_GxE}{A data.frame containing Genotypic BLUPs by trial/trait.} 49 | \item{VCOV}{A list by trait contanining the variance-covariance fitted.} 50 | \item{stability}{A data.frame containing several Stability coefficients 51 | resulting of executing the function \code{stability()}.} 52 | \item{heritability}{A data.frame containing overall heritabilities by trait.} 53 | \item{met_models}{A list by trait containing the fitted models.} 54 | } 55 | \description{ 56 | The results of the \code{single_trial_analysis()} function are 57 | used in \code{met_analysis()} to fit multi-environmental trial models. 58 | Returns an object of class \code{metAgri}, with a list of trial effects, 59 | BLUPs, heritability, variance components, stability and the models fitted. 60 | } 61 | \examples{ 62 | \dontrun{ 63 | library(agridat) 64 | library(agriutilities) 65 | data(besag.met) 66 | dat <- besag.met 67 | results <- check_design_met( 68 | data = dat, 69 | genotype = "gen", 70 | trial = "county", 71 | traits = c("yield"), 72 | rep = "rep", 73 | block = "block", 74 | col = "col", 75 | row = "row" 76 | ) 77 | out <- single_trial_analysis(results, progress = FALSE) 78 | met_results <- met_analysis(out, progress = FALSE) 79 | print(met_results) 80 | covcor_heat(matrix = met_results$VCOV$yield$CORR) 81 | } 82 | } 83 | -------------------------------------------------------------------------------- /man/parameters_gg.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/parametersGG.R 3 | \name{parameters_gg} 4 | \alias{parameters_gg} 5 | \title{Genetic Gain Parameters} 6 | \usage{ 7 | parameters_gg(model, trait = "trait") 8 | } 9 | \arguments{ 10 | \item{model}{Linear regression model (\code{lm} object)} 11 | 12 | \item{trait}{A character string indicating the column in data that contains 13 | trials.} 14 | } 15 | \value{ 16 | A data.frame with some parameters from the linear regression (Slope, 17 | se_Slope, Intercept, r2, Pr(>F)) and the percentage of Genetic Gain. 18 | } 19 | \description{ 20 | Genetic Gain Parameters 21 | } 22 | \examples{ 23 | \donttest{ 24 | library(ggplot2) 25 | library(agridat) 26 | library(magrittr) 27 | library(agriutilities) 28 | 29 | data(baker.barley.uniformity) 30 | dat <- baker.barley.uniformity 31 | head(dat) 32 | 33 | model <- lm(yield ~ year, dat) 34 | 35 | dat \%>\% 36 | na.omit() \%>\% 37 | ggplot( 38 | aes(x = year, y = yield) 39 | ) + 40 | geom_point() + 41 | geom_smooth(method = "lm") + 42 | theme_bw() 43 | 44 | parameters_gg(model = model, trait = "yield") 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /man/pipe.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils-pipe.R 3 | \name{\%>\%} 4 | \alias{\%>\%} 5 | \title{Pipe operator} 6 | \usage{ 7 | lhs \%>\% rhs 8 | } 9 | \arguments{ 10 | \item{lhs}{A value or the magrittr placeholder.} 11 | 12 | \item{rhs}{A function call using the magrittr semantics.} 13 | } 14 | \value{ 15 | The result of calling `rhs(lhs)`. 16 | } 17 | \description{ 18 | See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. 19 | } 20 | \keyword{internal} 21 | -------------------------------------------------------------------------------- /man/plot.checkAgri.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils_S3_methods.R 3 | \name{plot.checkAgri} 4 | \alias{plot.checkAgri} 5 | \title{Plot an object of class \code{checkAgri}} 6 | \usage{ 7 | \method{plot}{checkAgri}( 8 | x, 9 | type = c("connectivity", "missing", "boxplot"), 10 | axis_size = 15, 11 | text_size = 5, 12 | ... 13 | ) 14 | } 15 | \arguments{ 16 | \item{x}{An object inheriting from class \code{checkAgri} resulting of 17 | executing the function \code{check_design_met()}} 18 | 19 | \item{type}{A character string specifiying the type of plot. "connectivity", 20 | "missing" or "boxplot".} 21 | 22 | \item{axis_size}{Numeric input to define the axis size.} 23 | 24 | \item{text_size}{Numeric input to define the text size.} 25 | 26 | \item{...}{Further graphical parameters. For future improvements.} 27 | } 28 | \value{ 29 | A ggplot object. 30 | } 31 | \description{ 32 | Create several plots for an object of class \code{checkAgri} 33 | } 34 | \examples{ 35 | library(agridat) 36 | library(agriutilities) 37 | data(besag.met) 38 | dat <- besag.met 39 | results <- check_design_met( 40 | data = dat, 41 | genotype = "gen", 42 | trial = "county", 43 | traits = c("yield"), 44 | rep = "rep", 45 | block = "block", 46 | col = "col", 47 | row = "row" 48 | ) 49 | plot(results, type = "missing") 50 | plot(results, type = "boxplot") 51 | 52 | } 53 | \author{ 54 | Johan Aparicio [aut] 55 | } 56 | -------------------------------------------------------------------------------- /man/plot.metAgri.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils_S3_methods.R 3 | \name{plot.metAgri} 4 | \alias{plot.metAgri} 5 | \title{Plot an object of class \code{metAgri}} 6 | \usage{ 7 | \method{plot}{metAgri}( 8 | x, 9 | type = c("correlation", "covariance", "multi_traits"), 10 | filter_traits = NULL, 11 | text_size = 4, 12 | ... 13 | ) 14 | } 15 | \arguments{ 16 | \item{x}{An object inheriting from class \code{metAgri} resulting of 17 | executing the function \code{met_analysis()}} 18 | 19 | \item{type}{A character string specifying the type of plot. "correlation", 20 | "covariance" or "multi_traits"} 21 | 22 | \item{filter_traits}{An optional character vector to filter traits.} 23 | 24 | \item{text_size}{Numeric input to define the text size.} 25 | 26 | \item{...}{Further graphical parameters passed to \code{covcor_heat()}.} 27 | } 28 | \value{ 29 | A ggplot object. 30 | } 31 | \description{ 32 | Create several plots for an object of class \code{metAgri} 33 | } 34 | \examples{ 35 | \dontrun{ 36 | library(agridat) 37 | library(agriutilities) 38 | data(besag.met) 39 | dat <- besag.met 40 | results <- check_design_met( 41 | data = dat, 42 | genotype = "gen", 43 | trial = "county", 44 | traits = c("yield"), 45 | rep = "rep", 46 | block = "block", 47 | col = "col", 48 | row = "row" 49 | ) 50 | out <- single_trial_analysis(results, progress = FALSE) 51 | met_results <- met_analysis(out, progress = FALSE) 52 | print(met_results) 53 | plot(met_results, type = "correlation") 54 | plot(met_results, type = "covariance") 55 | } 56 | } 57 | \author{ 58 | Johan Aparicio [aut] 59 | } 60 | -------------------------------------------------------------------------------- /man/plot.smaAgri.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils_S3_methods.R 3 | \name{plot.smaAgri} 4 | \alias{plot.smaAgri} 5 | \title{Plot an object of class \code{smaAgri}} 6 | \usage{ 7 | \method{plot}{smaAgri}( 8 | x, 9 | type = c("summary", "correlation", "spatial"), 10 | filter_traits = NULL, 11 | nudge_y_cv = 3, 12 | nudge_y_h2 = 0.07, 13 | horizontal = FALSE, 14 | theme_size = 15, 15 | axis_size = 8, 16 | text_size = 4, 17 | ... 18 | ) 19 | } 20 | \arguments{ 21 | \item{x}{An object inheriting from class \code{smaAgri} resulting of 22 | executing the function \code{single_trial_analysis()}} 23 | 24 | \item{type}{A character string specifiying the type of plot. "summary", 25 | "correlation" or "spatial".} 26 | 27 | \item{filter_traits}{An optional character vector to filter traits.} 28 | 29 | \item{nudge_y_cv}{Vertical adjustment to nudge labels by when plotting CV 30 | bars. Only works if the argument type is "summary". 3 by default.} 31 | 32 | \item{nudge_y_h2}{Vertical adjustment to nudge labels by when plotting h2 33 | bars. Only works if the argument type is "summary". 0.07 by default.} 34 | 35 | \item{horizontal}{If \code{FALSE}, the default, the labels are plotted 36 | vertically. If \code{TRUE}, the labels are plotted horizontally.} 37 | 38 | \item{theme_size}{Base font size, given in pts. 15 by default.} 39 | 40 | \item{axis_size}{Numeric input to define the axis size.} 41 | 42 | \item{text_size}{Numeric input to define the text size.} 43 | 44 | \item{...}{Further graphical parameters. For future improvements.} 45 | } 46 | \value{ 47 | A ggplot object. 48 | } 49 | \description{ 50 | Create several plots for an object of class \code{smaAgri} 51 | } 52 | \examples{ 53 | \donttest{ 54 | library(agridat) 55 | library(agriutilities) 56 | data(besag.met) 57 | dat <- besag.met 58 | results <- check_design_met( 59 | data = dat, 60 | genotype = "gen", 61 | trial = "county", 62 | traits = c("yield"), 63 | rep = "rep", 64 | block = "block", 65 | col = "col", 66 | row = "row" 67 | ) 68 | out <- single_trial_analysis(results, progress = FALSE) 69 | print(out) 70 | plot(out, type = "summary", horizontal = TRUE) 71 | plot(out, type = "correlation") 72 | plot(out, type = "spatial") 73 | } 74 | } 75 | \author{ 76 | Johan Aparicio [aut] 77 | } 78 | -------------------------------------------------------------------------------- /man/print.checkAgri.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils_S3_methods.R 3 | \name{print.checkAgri} 4 | \alias{print.checkAgri} 5 | \title{Print an object of class \code{checkAgri}} 6 | \usage{ 7 | \method{print}{checkAgri}(x, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object fitted with the function \code{check_design_met()}.} 11 | 12 | \item{...}{Options used by the tibble package to format the output. See 13 | `tibble::print()` for more details.} 14 | } 15 | \value{ 16 | an object inheriting from class \code{checkAgri}. 17 | } 18 | \description{ 19 | Prints information about \code{check_design_met()} function. 20 | } 21 | \examples{ 22 | library(agridat) 23 | library(agriutilities) 24 | data(besag.met) 25 | dat <- besag.met 26 | results <- check_design_met( 27 | data = dat, 28 | genotype = "gen", 29 | trial = "county", 30 | traits = c("yield"), 31 | rep = "rep", 32 | block = "block", 33 | col = "col", 34 | row = "row" 35 | ) 36 | print(results) 37 | } 38 | \author{ 39 | Johan Aparicio [aut] 40 | } 41 | -------------------------------------------------------------------------------- /man/print.metAgri.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils_S3_methods.R 3 | \name{print.metAgri} 4 | \alias{print.metAgri} 5 | \title{Print an object of class \code{metAgri}} 6 | \usage{ 7 | \method{print}{metAgri}(x, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object fitted with the function \code{met_analysis()}.} 11 | 12 | \item{...}{Options used by the tibble package to format the output. See 13 | `tibble::print()` for more details.} 14 | } 15 | \value{ 16 | an object inheriting from class \code{metAgri}. 17 | } 18 | \description{ 19 | Prints information about \code{met_analysis()} function. 20 | } 21 | \examples{ 22 | \dontrun{ 23 | library(agridat) 24 | library(agriutilities) 25 | data(besag.met) 26 | dat <- besag.met 27 | results <- check_design_met( 28 | data = dat, 29 | genotype = "gen", 30 | trial = "county", 31 | traits = c("yield"), 32 | rep = "rep", 33 | block = "block", 34 | col = "col", 35 | row = "row" 36 | ) 37 | out <- single_trial_analysis(results, progress = FALSE) 38 | met_results <- met_analysis(out, progress = FALSE) 39 | print(met_results) 40 | } 41 | } 42 | \author{ 43 | Johan Aparicio [aut] 44 | } 45 | -------------------------------------------------------------------------------- /man/print.smaAgri.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils_S3_methods.R 3 | \name{print.smaAgri} 4 | \alias{print.smaAgri} 5 | \title{Print an object of class \code{smaAgri}} 6 | \usage{ 7 | \method{print}{smaAgri}(x, ...) 8 | } 9 | \arguments{ 10 | \item{x}{An object fitted with the function \code{single_trial_analysis()}.} 11 | 12 | \item{...}{Options used by the tibble package to format the output. See 13 | `tibble::print()` for more details.} 14 | } 15 | \value{ 16 | an object inheriting from class \code{smaAgri}. 17 | } 18 | \description{ 19 | Prints information about \code{single_trial_analysis} function. 20 | } 21 | \examples{ 22 | \donttest{ 23 | library(agridat) 24 | library(agriutilities) 25 | data(besag.met) 26 | dat <- besag.met 27 | results <- check_design_met( 28 | data = dat, 29 | genotype = "gen", 30 | trial = "county", 31 | traits = c("yield"), 32 | rep = "rep", 33 | block = "block", 34 | col = "col", 35 | row = "row" 36 | ) 37 | out <- single_trial_analysis(results, progress = FALSE) 38 | print(out) 39 | } 40 | } 41 | \author{ 42 | Johan Aparicio [aut] 43 | } 44 | -------------------------------------------------------------------------------- /man/single_trial_analysis.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fitSingle.R 3 | \name{single_trial_analysis} 4 | \alias{single_trial_analysis} 5 | \title{Single Trial Analysis} 6 | \usage{ 7 | single_trial_analysis( 8 | results = NULL, 9 | progress = TRUE, 10 | engine = "asreml", 11 | remove_outliers = FALSE 12 | ) 13 | } 14 | \arguments{ 15 | \item{results}{Object of class \code{checkAgri} resulting of executing 16 | \code{check_design_met()} function.} 17 | 18 | \item{progress}{Should the progress of the modeling be printed. 19 | If \code{TRUE}, for every trial a line is output indicating the traits fitted 20 | for the particular trial.} 21 | 22 | \item{engine}{A character string specifying the name of the mixed modeling 23 | engine to use, either \code{lme4} or \code{asreml}. For spatial designs, 24 | \code{SpATS} is always used, for other designs \code{asreml} as a default.} 25 | 26 | \item{remove_outliers}{Should outliers be removed? \code{FALSE} by default.} 27 | } 28 | \value{ 29 | An object of class \code{smaAgri}, with a list of: 30 | \item{fitted_models}{A list containing the fitted models. (Both models, the 31 | one with Genotype as Random and the one with Genotype as Fixed)} 32 | \item{resum_fitted_model}{A data.frame containing a summary of the fitted 33 | models.} 34 | \item{outliers}{A data.frame containing extreme observations. If 35 | \code{remove_outliers} is \code{TRUE}, this data.frame will contain the 36 | observations removed.} 37 | \item{blues_blups}{A data.frame containing BLUPs/BLUEs for all the genotypes 38 | in each trial.} 39 | \item{std_residuals}{A data.frame containing the standardized residuals for 40 | the model with genotype as random component.} 41 | \item{data}{A data.frame containing the data used. If \code{remove_outliers} 42 | is \code{TRUE}, data will have missing values for the outliers detected.} 43 | } 44 | \description{ 45 | The results of the \code{check_design_met()} function are used in 46 | \code{single_trial_analysis()} to fit single trial models. This function can 47 | fit, Completely Randomized Designs (CRD), Randomized Complete Block Designs 48 | (RCBD), Resolvable Incomplete Block Designs (res-IBD), Non-Resolvable 49 | Row-Column Designs (Row-Col) and Resolvable Row-Column Designs (res-Row-Col). 50 | 51 | Returns an object of class \code{smaAgri}, with a list of trial summary, 52 | BLUEs, BLUPs, heritability, variance components, potential extreme 53 | observations, residuals, 54 | the models fitted and the data used. 55 | This function will generate the required output to be used in the two-stage 56 | analysis. 57 | } 58 | \examples{ 59 | \donttest{ 60 | library(agridat) 61 | library(agriutilities) 62 | data(besag.met) 63 | dat <- besag.met 64 | results <- check_design_met( 65 | data = dat, 66 | genotype = "gen", 67 | trial = "county", 68 | traits = c("yield"), 69 | rep = "rep", 70 | block = "block", 71 | col = "col", 72 | row = "row" 73 | ) 74 | out <- single_trial_analysis(results, progress = FALSE) 75 | print(out) 76 | } 77 | } 78 | -------------------------------------------------------------------------------- /man/stability.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fitMET.R 3 | \name{stability} 4 | \alias{stability} 5 | \title{Stability Coefficients} 6 | \usage{ 7 | stability( 8 | predictions = NULL, 9 | genotype = NULL, 10 | trial = NULL, 11 | response = NULL, 12 | best = "max" 13 | ) 14 | } 15 | \arguments{ 16 | \item{predictions}{A data.frame with one value per GxE combination.} 17 | 18 | \item{genotype}{A character string indicating the column in predictions that 19 | contains genotypes.} 20 | 21 | \item{trial}{A character string indicating the column in predictions that 22 | contains trials.} 23 | 24 | \item{response}{A character string specifying the response variable.} 25 | 26 | \item{best}{A character string specifying how to define the best genotype 27 | by numeric value ("min", "max"). "max" by default.} 28 | } 29 | \value{ 30 | A data.frame with several stability measures. 31 | "superiority" (cultivar-superiority measure), "static" (Shukla's stability 32 | variance) and "wricke" (Wricke's ecovalence). 33 | } 34 | \description{ 35 | Stability Coefficients 36 | } 37 | \examples{ 38 | \dontrun{ 39 | library(agridat) 40 | library(agriutilities) 41 | 42 | data(besag.met) 43 | dat <- besag.met 44 | results <- check_design_met( 45 | data = dat, 46 | genotype = "gen", 47 | trial = "county", 48 | traits = c("yield"), 49 | rep = "rep", 50 | block = "block", 51 | col = "col", 52 | row = "row" 53 | ) 54 | out <- single_trial_analysis(results, progress = FALSE) 55 | met_results <- met_analysis(out, progress = FALSE) 56 | 57 | head( 58 | stability( 59 | predictions = met_results$BLUPs_GxE, 60 | genotype = "genotype", 61 | trial = "trial", 62 | response = "predicted.value" 63 | ) 64 | ) 65 | } 66 | } 67 | -------------------------------------------------------------------------------- /pkgdown/favicon/apple-touch-icon-120x120.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/apple-touch-icon-120x120.png -------------------------------------------------------------------------------- /pkgdown/favicon/apple-touch-icon-152x152.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/apple-touch-icon-152x152.png -------------------------------------------------------------------------------- /pkgdown/favicon/apple-touch-icon-180x180.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/apple-touch-icon-180x180.png -------------------------------------------------------------------------------- /pkgdown/favicon/apple-touch-icon-60x60.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/apple-touch-icon-60x60.png -------------------------------------------------------------------------------- /pkgdown/favicon/apple-touch-icon-76x76.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/apple-touch-icon-76x76.png -------------------------------------------------------------------------------- /pkgdown/favicon/apple-touch-icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/apple-touch-icon.png -------------------------------------------------------------------------------- /pkgdown/favicon/favicon-16x16.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/favicon-16x16.png -------------------------------------------------------------------------------- /pkgdown/favicon/favicon-32x32.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/favicon-32x32.png -------------------------------------------------------------------------------- /pkgdown/favicon/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/pkgdown/favicon/favicon.ico -------------------------------------------------------------------------------- /revdep/cran.md: -------------------------------------------------------------------------------- 1 | ## revdepcheck results 2 | 3 | We checked 0 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package. 4 | 5 | * We saw 0 new problems 6 | * We failed to check 0 packages 7 | 8 | -------------------------------------------------------------------------------- /revdep/failures.md: -------------------------------------------------------------------------------- 1 | *Wow, no problems at all. :)* -------------------------------------------------------------------------------- /revdep/problems.md: -------------------------------------------------------------------------------- 1 | *Wow, no problems at all. :)* -------------------------------------------------------------------------------- /vignettes/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | *.R 3 | -------------------------------------------------------------------------------- /vignettes/bibliography.bib: -------------------------------------------------------------------------------- 1 | @book{isik2017genetic, 2 | title={Genetic data analysis for plant and animal breeding}, 3 | author={Isik, Fikret and Holland, James and Maltecca, Christian}, 4 | volume={400}, 5 | year={2017}, 6 | publisher={Springer} 7 | } 8 | 9 | @article{rodriguez2018correcting, 10 | title={Correcting for spatial heterogeneity in plant breeding experiments with P-splines}, 11 | author={Rodriguez-Alvarez, Maria Xose and Boer, Martin P and van Eeuwijk, Fred A and Eilers, Paul HC}, 12 | journal={Spatial Statistics}, 13 | volume={23}, 14 | pages={52--71}, 15 | year={2018}, 16 | publisher={Elsevier} 17 | } 18 | -------------------------------------------------------------------------------- /vignettes/how-to-start.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "How to Start" 3 | output: rmarkdown::html_vignette 4 | vignette: > 5 | %\VignetteIndexEntry{How to Start} 6 | %\VignetteEngine{knitr::rmarkdown} 7 | %\VignetteEncoding{UTF-8} 8 | --- 9 | 10 | ```{r, include = FALSE} 11 | knitr::opts_chunk$set( 12 | collapse = TRUE, 13 | comment = "#>" 14 | ) 15 | ``` 16 | 17 | # Identify the Experimental Design 18 | 19 | The function `check_design_met` helps us to check the quality of the data and 20 | also to identify the experimental design of the trials. This works as a quality 21 | check or quality control before we fit any model. 22 | 23 | ```{r setup} 24 | library(agriutilities) 25 | library(agridat) 26 | data(besag.met) 27 | dat <- besag.met 28 | results <- check_design_met( 29 | data = dat, 30 | genotype = "gen", 31 | trial = "county", 32 | traits = "yield", 33 | rep = "rep", 34 | block = "block", 35 | col = "col", 36 | row = "row" 37 | ) 38 | ``` 39 | 40 | ```{r} 41 | print(results) 42 | ``` 43 | 44 | # Single Trial Analysis 45 | The results of the previous function are used in `single_trial_analysis()` to 46 | fit single trial models. 47 | 48 | ```{r} 49 | obj <- single_trial_analysis(results, progress = FALSE) 50 | print(obj) 51 | ``` 52 | 53 | # Multi-Environmental Trial Analysis 54 | The results of the previous function are used in `met_analysis()` to 55 | fit multi-environmental trial models. 56 | 57 | ```{r, eval=FALSE} 58 | met_results <- met_analysis(obj) 59 | print(met_results) 60 | ``` 61 | 62 | 63 | ```{r, echo=FALSE} 64 | if (requireNamespace("asreml", quietly = TRUE)) { 65 | met_results <- met_analysis(obj) 66 | print(met_results) 67 | } 68 | ``` 69 | -------------------------------------------------------------------------------- /vignettes/images/equation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AparicioJohan/agriutilities/ba8dc94e13bcec2ae29797b0b2d3861cc1aed6ed/vignettes/images/equation.png --------------------------------------------------------------------------------