実行環境
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Japanese_Japan.utf8 LC_CTYPE=Japanese_Japan.utf8
## [3] LC_MONETARY=Japanese_Japan.utf8 LC_NUMERIC=C
## [5] LC_TIME=Japanese_Japan.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] coxme_2.2-18.1 bdsmatrix_1.3-6 MASS_7.3-58.1 cmprsk_2.2-11
## [5] survminer_0.4.9 ggpubr_0.5.0 ggplotify_0.1.0 flextable_0.9.1
## [9] flexsurv_2.2.2 eha_2.10.3 ggeffects_1.1.4 rstanarm_2.21.4
## [13] brms_2.18.0 Rcpp_1.0.9 fontregisterer_0.3 systemfonts_1.0.4
## [17] extrafont_0.18 lemon_0.4.6 ggsci_2.9 stargazer_5.2.3
## [21] kableExtra_1.3.4 knitr_1.42 DT_0.27 patchwork_1.1.2
## [25] data.table_1.14.6 see_0.7.5.5 report_0.5.7.4 parameters_0.20.3
## [29] performance_0.10.3 modelbased_0.8.6.3 insight_0.19.1.4 effectsize_0.8.3.6
## [33] datawizard_0.7.1.1 correlation_0.8.4 bayestestR_0.13.1 easystats_0.6.0.8
## [37] haven_2.5.1 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
## [41] purrr_1.0.0 readr_2.1.3 tidyr_1.2.1 tibble_3.2.1
## [45] tidyverse_1.3.2 ggplot2_3.4.2 ggsurvfit_0.3.0 survival_3.5-5
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.2.0 lme4_1.1-31
## [4] htmlwidgets_1.6.1 grid_4.2.2 munsell_0.5.0
## [7] ragg_1.2.4 codetools_0.2-18 statmod_1.5.0
## [10] miniUI_0.1.1.1 withr_2.5.0 Brobdingnag_1.2-9
## [13] colorspace_2.0-3 muhaz_1.2.6.4 uuid_1.1-0
## [16] rstudioapi_0.14 stats4_4.2.2 ggsignif_0.6.4
## [19] officer_0.6.2 Rttf2pt1_1.3.8 fontLiberation_0.1.0
## [22] bayesplot_1.10.0 emmeans_1.8.3 rstan_2.26.13
## [25] KMsurv_0.1-5 farver_2.1.1 bridgesampling_1.1-2
## [28] coda_0.19-4 vctrs_0.6.2 generics_0.1.3
## [31] xfun_0.36 timechange_0.1.1 fontquiver_0.2.1
## [34] R6_2.5.1 markdown_1.7 gridGraphics_0.5-1
## [37] cachem_1.0.6 assertthat_0.2.1 promises_1.2.0.1
## [40] scales_1.2.1 googlesheets4_1.0.1 gtable_0.3.3
## [43] processx_3.8.0 rlang_1.1.1 splines_4.2.2
## [46] rstatix_0.7.1 extrafontdb_1.0 gargle_1.2.1
## [49] broom_1.0.2 checkmate_2.1.0 inline_0.3.19
## [52] yaml_2.3.7 reshape2_1.4.4 abind_1.4-5
## [55] modelr_0.1.10 threejs_0.3.3 crosstalk_1.2.0
## [58] backports_1.4.1 httpuv_1.6.7 tensorA_0.36.2
## [61] tools_4.2.2 bookdown_0.31 ellipsis_0.3.2
## [64] jquerylib_0.1.4 posterior_1.3.1 plyr_1.8.8
## [67] base64enc_0.1-3 ps_1.7.2 prettyunits_1.1.1
## [70] openssl_2.0.5 deSolve_1.34 zoo_1.8-11
## [73] fs_1.5.2 crul_1.3 magrittr_2.0.3
## [76] colourpicker_1.2.0 reprex_2.0.2 googledrive_2.0.0
## [79] mvtnorm_1.1-3 matrixStats_0.63.0 hms_1.1.2
## [82] shinyjs_2.1.0 mime_0.12 evaluate_0.20
## [85] xtable_1.8-4 shinystan_2.6.0 readxl_1.4.1
## [88] gridExtra_2.3 rstantools_2.2.0 compiler_4.2.2
## [91] fontBitstreamVera_0.1.1 V8_4.2.2 crayon_1.5.2
## [94] minqa_1.2.5 StanHeaders_2.26.13 htmltools_0.5.4
## [97] later_1.3.0 tzdb_0.3.0 RcppParallel_5.1.6
## [100] lubridate_1.9.0 DBI_1.1.3 dbplyr_2.2.1
## [103] boot_1.3-28 car_3.1-1 Matrix_1.5-1
## [106] cli_3.6.0 quadprog_1.5-8 parallel_4.2.2
## [109] igraph_1.3.5 km.ci_0.5-6 pkgconfig_2.0.3
## [112] numDeriv_2016.8-1.1 xml2_1.3.3 dygraphs_1.1.1.6
## [115] svglite_2.1.1 bslib_0.4.2 webshot_0.5.4
## [118] estimability_1.4.1 rvest_1.0.3 yulab.utils_0.0.6
## [121] distributional_0.3.1 callr_3.7.3 digest_0.6.31
## [124] httpcode_0.3.0 rmarkdown_2.20 cellranger_1.1.0
## [127] survMisc_0.5.6 gdtools_0.3.3 curl_4.3.3
## [130] shiny_1.7.4 gtools_3.9.4 nloptr_2.0.3
## [133] lifecycle_1.0.3 nlme_3.1-160 jsonlite_1.8.4
## [136] mstate_0.3.2 carData_3.0-5 askpass_1.1
## [139] viridisLite_0.4.1 fansi_1.0.3 pillar_1.9.0
## [142] lattice_0.20-45 loo_2.5.1 fastmap_1.1.0
## [145] httr_1.4.4 pkgbuild_1.4.0 glue_1.6.2
## [148] xts_0.12.2 zip_2.2.2 shinythemes_1.2.0
## [151] stringi_1.7.8 sass_0.4.5 textshaping_0.3.6
## [154] gfonts_0.2.0References
Allison, P. D. (2014). Event history and survival analysis: Regression for longitudinal event data. SAGE Publications.
Broström, G. (2021). Event history analysis with R. CRC Press.
Chang, W. (2018). R graphics cookbook: Practical recipes for visualizing data. “O’Reilly Media, Inc.”
Cox, D. R. (1972). Regression models and life-tables. J. R. Stat. Soc., 34(2), 187–202.
Ellis, S., Snyder-Mackler, N., Ruiz-Lambides, A., Platt, M. L., & Brent, L. J. N. (2019). Deconstructing sociality: The types of social connections that predict longevity in a group-living primate. Proc. Biol. Sci., 286(1917), 20191991.
Rossi, P. H., Berk, R. A., & Lenihan, K. J. (1980). Money, work and crime: Some experimental results. New York: Academic Press.
Silk, J. B., Beehner, J. C., Bergman, T. J., Crockford, C., Engh, A. L., Moscovice, L. R., Wittig, R. M., Seyfarth, R. M., & Cheney, D. L. (2010). Strong and consistent social bonds enhance the longevity of female baboons. Curr. Biol., 20(15), 1359–1361.
Swedell, L., Saunders, J., Schreier, A., Davis, B., Tesfaye, T., & Pines, M. (2011). Female “dispersal” in hamadryas baboons: Transfer among social units in a multilevel society. Am. J. Phys. Anthropol., 145(3), 360–370.
Thompson, N. A., & Cords, M. (2018). Stronger social bonds do not always predict greater longevity in a gregarious primate. Ecol. Evol., 8(3), 1604–1614.
Tung, J., Archie, E. A., Altmann, J., & Alberts, S. C. (2016). Cumulative early life adversity predicts longevity in wild baboons. Nat. Commun., 7.
Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. “O’Reilly Media, Inc.”
アリソンポール=デイビット. (2021). イベント・ヒストリー分析 (福田亘孝., Trans.). 共立出版.
久保拓弥. (2012). データ解析のための統計モデリング入門. 岩波書店.
大東健太郎. (2010). 線形モデルから一般化線形モデル（GLM）へ. 雑草研究, 55(4), 268–274.
大橋靖雄., 浜田知久馬., & 魚住龍史. (2021). 生存時間解析 第2版 SASによる生物統計. 東京大学出版.
杉本知之. (2021). 生存時間解析. 朝倉書店.
松村優哉., 湯谷啓明., 紀ノ定保礼., & 前田和. (2021). RユーザのためのRstudio[実践]入門 tidyverseによるモダンな分析フローの世界 改訂2版. 技術評論社.
粕谷英一. (2012). 一般化線形モデル. 共立出版.