├── .Rbuildignore ├── .github ├── .gitignore └── workflows │ ├── check-standard.yaml │ ├── pkgdown.yaml │ └── test-coverage.yaml ├── .gitignore ├── DESCRIPTION ├── LICENSE.md ├── NAMESPACE ├── NEWS.md ├── R ├── curve_cont.r ├── curve_cont_auc.r ├── get_kaplan_meier.r ├── helper_functions.r ├── input_checks.r ├── plot_surv_3Dsurface.r ├── plot_surv_animated.r ├── plot_surv_area.r ├── plot_surv_at_t.r ├── plot_surv_contour.r ├── plot_surv_heatmap.r ├── plot_surv_lines.r ├── plot_surv_matrix.r ├── plot_surv_quantiles.r ├── plot_surv_rmst.r ├── plot_surv_rmtl.r └── zzz.r ├── README.md ├── TODO.txt ├── _pkgdown.yml ├── codecov.yml ├── codemeta.json ├── contsurvplot.Rproj ├── cran-comments.md ├── docs ├── .nojekyll ├── 404.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 │ ├── index.html │ ├── introduction.html │ └── introduction_files │ │ └── figure-html │ │ ├── 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-16-1.png │ │ ├── unnamed-chunk-17-1.png │ │ ├── unnamed-chunk-18-1.png │ │ ├── unnamed-chunk-19-1.png │ │ ├── unnamed-chunk-20-1.png │ │ ├── unnamed-chunk-21-1.png │ │ ├── unnamed-chunk-22-1.png │ │ ├── unnamed-chunk-26-1.png │ │ ├── unnamed-chunk-27-1.png │ │ ├── unnamed-chunk-7-1.png │ │ ├── unnamed-chunk-8-1.png │ │ └── unnamed-chunk-9-1.png ├── authors.html ├── deps │ ├── bootstrap-5.1.3 │ │ ├── bootstrap.bundle.min.js │ │ ├── bootstrap.bundle.min.js.map │ │ └── bootstrap.min.css │ ├── data-deps.txt │ └── jquery-3.6.0 │ │ ├── jquery-3.6.0.js │ │ ├── jquery-3.6.0.min.js │ │ └── jquery-3.6.0.min.map ├── favicon-16x16.png ├── favicon-32x32.png ├── favicon.ico ├── index.html ├── link.svg ├── logo.png ├── pkgdown.js ├── pkgdown.yml ├── reference │ ├── Rplot001.png │ ├── Rplot002.png │ ├── Rplot003.png │ ├── Rplot004.png │ ├── contsurvplot-package.html │ ├── curve_cont.html │ ├── figures │ │ ├── example_area.png │ │ ├── example_contour.png │ │ ├── example_quantiles.png │ │ └── logo.png │ ├── gganim_plot0001.png │ ├── gganim_plot0002.png │ ├── gganim_plot0003.png │ ├── gganim_plot0004.png │ ├── gganim_plot0005.png │ ├── gganim_plot0006.png │ ├── gganim_plot0007.png │ ├── gganim_plot0008.png │ ├── gganim_plot0009.png │ ├── gganim_plot0010.png │ ├── gganim_plot0011.png │ ├── gganim_plot0012.png │ ├── gganim_plot0013.png │ ├── gganim_plot0014.png │ ├── gganim_plot0015.png │ ├── gganim_plot0016.png │ ├── gganim_plot0017.png │ ├── gganim_plot0018.png │ ├── gganim_plot0019.png │ ├── gganim_plot0020.png │ ├── gganim_plot0021.png │ ├── gganim_plot0022.png │ ├── gganim_plot0023.png │ ├── gganim_plot0024.png │ ├── gganim_plot0025.png │ ├── gganim_plot0026.png │ ├── gganim_plot0027.png │ ├── gganim_plot0028.png │ ├── gganim_plot0029.png │ ├── gganim_plot0030.png │ ├── gganim_plot0031.png │ ├── gganim_plot0032.png │ ├── gganim_plot0033.png │ ├── gganim_plot0034.png │ ├── gganim_plot0035.png │ ├── gganim_plot0036.png │ ├── gganim_plot0037.png │ ├── gganim_plot0038.png │ ├── gganim_plot0039.png │ ├── gganim_plot0040.png │ ├── gganim_plot0041.png │ ├── gganim_plot0042.png │ ├── gganim_plot0043.png │ ├── gganim_plot0044.png │ ├── gganim_plot0045.png │ ├── gganim_plot0046.png │ ├── gganim_plot0047.png │ ├── gganim_plot0048.png │ ├── gganim_plot0049.png │ ├── gganim_plot0050.png │ ├── gganim_plot0051.png │ ├── gganim_plot0052.png │ ├── gganim_plot0053.png │ ├── gganim_plot0054.png │ ├── gganim_plot0055.png │ ├── gganim_plot0056.png │ ├── gganim_plot0057.png │ ├── gganim_plot0058.png │ ├── gganim_plot0059.png │ ├── gganim_plot0060.png │ ├── gganim_plot0061.png │ ├── gganim_plot0062.png │ ├── gganim_plot0063.png │ ├── gganim_plot0064.png │ ├── gganim_plot0065.png │ ├── gganim_plot0066.png │ ├── gganim_plot0067.png │ ├── gganim_plot0068.png │ ├── gganim_plot0069.png │ ├── gganim_plot0070.png │ ├── gganim_plot0071.png │ ├── gganim_plot0072.png │ ├── gganim_plot0073.png │ ├── gganim_plot0074.png │ ├── gganim_plot0075.png │ ├── gganim_plot0076.png │ ├── gganim_plot0077.png │ ├── gganim_plot0078.png │ ├── gganim_plot0079.png │ ├── gganim_plot0080.png │ ├── gganim_plot0081.png │ ├── gganim_plot0082.png │ ├── gganim_plot0083.png │ ├── gganim_plot0084.png │ ├── gganim_plot0085.png │ ├── gganim_plot0086.png │ ├── gganim_plot0087.png │ ├── gganim_plot0088.png │ ├── gganim_plot0089.png │ ├── gganim_plot0090.png │ ├── gganim_plot0091.png │ ├── gganim_plot0092.png │ ├── gganim_plot0093.png │ ├── gganim_plot0094.png │ ├── gganim_plot0095.png │ ├── gganim_plot0096.png │ ├── gganim_plot0097.png │ ├── gganim_plot0098.png │ ├── gganim_plot0099.png │ ├── gganim_plot0100.png │ ├── index.html │ ├── libs │ │ ├── crosstalk-1.2.0 │ │ │ ├── css │ │ │ │ └── crosstalk.min.css │ │ │ ├── js │ │ │ │ ├── crosstalk.js │ │ │ │ ├── crosstalk.js.map │ │ │ │ ├── crosstalk.min.js │ │ │ │ └── crosstalk.min.js.map │ │ │ └── scss │ │ │ │ └── crosstalk.scss │ │ ├── htmlwidgets-1.5.4 │ │ │ └── htmlwidgets.js │ │ ├── plotly-binding-4.10.0 │ │ │ └── plotly.js │ │ ├── plotly-htmlwidgets-css-2.5.1 │ │ │ └── plotly-htmlwidgets.css │ │ ├── plotly-main-2.5.1 │ │ │ └── plotly-latest.min.js │ │ └── typedarray-0.1 │ │ │ └── typedarray.min.js │ ├── plot_surv_3Dsurface-1.png │ ├── plot_surv_3Dsurface-2.png │ ├── plot_surv_3Dsurface.html │ ├── plot_surv_animated.html │ ├── plot_surv_area-1.png │ ├── plot_surv_area-2.png │ ├── plot_surv_area-3.png │ ├── plot_surv_area-4.png │ ├── plot_surv_area.html │ ├── plot_surv_at_t-1.png │ ├── plot_surv_at_t-2.png │ ├── plot_surv_at_t.html │ ├── plot_surv_contour-1.png │ ├── plot_surv_contour-2.png │ ├── plot_surv_contour-3.png │ ├── plot_surv_contour.html │ ├── plot_surv_heatmap-1.png │ ├── plot_surv_heatmap-2.png │ ├── plot_surv_heatmap-3.png │ ├── plot_surv_heatmap-4.png │ ├── plot_surv_heatmap.html │ ├── plot_surv_lines-1.png │ ├── plot_surv_lines-2.png │ ├── plot_surv_lines-3.png │ ├── plot_surv_lines.html │ ├── plot_surv_quantiles-1.png │ ├── plot_surv_quantiles-2.png │ ├── plot_surv_quantiles-3.png │ ├── plot_surv_quantiles.html │ ├── plot_surv_rmst-1.png │ ├── plot_surv_rmst-2.png │ ├── plot_surv_rmst-3.png │ ├── plot_surv_rmst.html │ ├── plot_surv_rmtl-1.png │ ├── plot_surv_rmtl-2.png │ ├── plot_surv_rmtl-3.png │ └── plot_surv_rmtl.html ├── search.json └── sitemap.xml ├── inst ├── CITATION └── testdata │ ├── sim150.Rds │ └── sim500.Rds ├── man ├── contsurvplot-package.Rd ├── curve_cont.Rd ├── figures │ ├── example_area.png │ ├── example_contour.png │ ├── example_quantiles.png │ └── logo.png ├── plot_surv_3Dsurface.Rd ├── plot_surv_animated.Rd ├── plot_surv_area.Rd ├── plot_surv_at_t.Rd ├── plot_surv_contour.Rd ├── plot_surv_heatmap.Rd ├── plot_surv_lines.Rd ├── plot_surv_matrix.Rd ├── plot_surv_quantiles.Rd ├── plot_surv_rmst.Rd └── plot_surv_rmtl.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 ├── tests ├── testthat.R └── testthat │ ├── Rplots.pdf │ ├── _snaps │ ├── plot_surv_3Dsurface │ │ ├── plot-at-t.svg │ │ ├── plot-change-horizon.svg │ │ ├── plot-cif.svg │ │ ├── plot-custom-non-interactive.svg │ │ └── plot-defaults.svg │ ├── plot_surv_area │ │ ├── plot-at-t.svg │ │ ├── plot-change-horizon.svg │ │ ├── plot-cif.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-discrete-bins.svg │ │ ├── plot-discrete-round.svg │ │ ├── plot-lots-of-stuff.svg │ │ ├── plot-sep-lines.svg │ │ ├── plot-with-group.svg │ │ ├── plot-with-kaplan-meier.svg │ │ └── plot-with-nonmonotonic.svg │ ├── plot_surv_at_t │ │ ├── plot-change-horizon.svg │ │ ├── plot-cif.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-lots-of-stuff.svg │ │ ├── plot-with-ci-multiple-t.svg │ │ ├── plot-with-ci-one-t.svg │ │ └── plot-with-group.svg │ ├── plot_surv_contour │ │ ├── plot-at-t.svg │ │ ├── plot-change-horizon.svg │ │ ├── plot-cif.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-lots-of-stuff.svg │ │ ├── plot-panel-border-axis-dist.svg │ │ └── plot-with-group.svg │ ├── plot_surv_heatmap │ │ ├── plot-at-t.svg │ │ ├── plot-change-horizon.svg │ │ ├── plot-cif.svg │ │ ├── plot-contour-lines.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-lots-of-stuff.svg │ │ ├── plot-panel-border-axis-dist.svg │ │ └── plot-with-group.svg │ ├── plot_surv_lines │ │ ├── plot-at-t.svg │ │ ├── plot-change-horizon.svg │ │ ├── plot-cif.svg │ │ ├── plot-cont-color.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-kaplan-meier.svg │ │ ├── plot-lots-of-stuff.svg │ │ ├── plot-with-ci.svg │ │ └── plot-with-group.svg │ ├── plot_surv_matrix │ │ ├── plot-change-border-stuff.svg │ │ ├── plot-change-fixed-t.svg │ │ ├── plot-change-horizon.svg │ │ ├── plot-change-n-col-and-n-row.svg │ │ ├── plot-change-numbers-stuff.svg │ │ ├── plot-cif.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-panel-border-axis-dist.svg │ │ └── plot-using-group.svg │ ├── plot_surv_quantiles │ │ ├── plot-change-horizon.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-lots-of-stuff.svg │ │ ├── plot-single-colors.svg │ │ └── plot-with-group.svg │ ├── plot_surv_rmst │ │ ├── plot-change-horizon.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-lots-of-stuff.svg │ │ └── plot-with-group.svg │ └── plot_surv_rmtl │ │ ├── plot-change-horizon.svg │ │ ├── plot-custom-colors.svg │ │ ├── plot-defaults.svg │ │ ├── plot-lots-of-stuff.svg │ │ └── plot-with-group.svg │ ├── test_.onAttach.r │ ├── test_check_horizon.r │ ├── test_check_inputs_curve_cont.r │ ├── test_check_inputs_plots.r │ ├── test_check_inputs_surv_matrix.r │ ├── test_curve_cont.r │ ├── test_get_kaplan_meier.r │ ├── test_plot_surv_3Dsurface.r │ ├── test_plot_surv_animated.r │ ├── test_plot_surv_area.r │ ├── test_plot_surv_at_t.r │ ├── test_plot_surv_contour.r │ ├── test_plot_surv_heatmap.r │ ├── test_plot_surv_lines.r │ ├── test_plot_surv_matrix.r │ ├── test_plot_surv_quantiles.r │ ├── test_plot_surv_rmst.r │ ├── test_plot_surv_rmtl.r │ ├── test_prepare_inputdata.r │ └── test_read_from_step_function.r └── vignettes ├── .gitignore └── introduction.Rmd /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | ^LICENSE\.md$ 4 | ^\.github$ 5 | ^codemeta\.json$ 6 | ^_pkgdown\.yml$ 7 | ^docs$ 8 | ^pkgdown$ 9 | ^codecov\.yml$ 10 | ^cran-comments\.md$ 11 | ^CRAN-SUBMISSION$ 12 | ^TODO\.txt$ 13 | -------------------------------------------------------------------------------- /.github/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | -------------------------------------------------------------------------------- /.github/workflows/check-standard.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | 9 | name: R-CMD-check 10 | 11 | jobs: 12 | R-CMD-check: 13 | runs-on: ${{ matrix.config.os }} 14 | 15 | name: ${{ matrix.config.os }} (${{ matrix.config.r }}) 16 | 17 | strategy: 18 | fail-fast: false 19 | matrix: 20 | config: 21 | - {os: macos-latest, r: 'release'} 22 | - {os: windows-latest, r: 'release'} 23 | - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} 24 | - {os: ubuntu-latest, r: 'release'} 25 | - {os: ubuntu-latest, r: 'oldrel-1'} 26 | 27 | env: 28 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 29 | R_KEEP_PKG_SOURCE: yes 30 | 31 | steps: 32 | - uses: actions/checkout@v3 33 | 34 | - uses: r-lib/actions/setup-pandoc@v2 35 | 36 | - uses: r-lib/actions/setup-r@v2 37 | with: 38 | r-version: ${{ matrix.config.r }} 39 | http-user-agent: ${{ matrix.config.http-user-agent }} 40 | use-public-rspm: true 41 | 42 | - uses: r-lib/actions/setup-r-dependencies@v2 43 | with: 44 | extra-packages: any::rcmdcheck 45 | needs: check 46 | 47 | - uses: r-lib/actions/check-r-package@v2 48 | with: 49 | upload-snapshots: true 50 | -------------------------------------------------------------------------------- /.github/workflows/pkgdown.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | release: 9 | types: [published] 10 | workflow_dispatch: 11 | 12 | name: pkgdown 13 | 14 | jobs: 15 | pkgdown: 16 | runs-on: ubuntu-latest 17 | # Only restrict concurrency for non-PR jobs 18 | concurrency: 19 | group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} 20 | env: 21 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 22 | steps: 23 | - uses: actions/checkout@v3 24 | 25 | - uses: r-lib/actions/setup-pandoc@v2 26 | 27 | - uses: r-lib/actions/setup-r@v2 28 | with: 29 | use-public-rspm: true 30 | 31 | - uses: r-lib/actions/setup-r-dependencies@v2 32 | with: 33 | extra-packages: any::pkgdown, local::. 34 | needs: website 35 | 36 | - name: Build site 37 | run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) 38 | shell: Rscript {0} 39 | 40 | - name: Deploy to GitHub pages 🚀 41 | if: github.event_name != 'pull_request' 42 | uses: JamesIves/github-pages-deploy-action@v4.4.1 43 | with: 44 | clean: false 45 | branch: gh-pages 46 | folder: docs 47 | -------------------------------------------------------------------------------- /.github/workflows/test-coverage.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | 9 | name: test-coverage 10 | 11 | jobs: 12 | test-coverage: 13 | runs-on: ubuntu-latest 14 | env: 15 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 16 | 17 | steps: 18 | - uses: actions/checkout@v3 19 | 20 | - uses: r-lib/actions/setup-r@v2 21 | with: 22 | use-public-rspm: true 23 | 24 | - uses: r-lib/actions/setup-r-dependencies@v2 25 | with: 26 | extra-packages: any::covr 27 | needs: coverage 28 | 29 | - name: Test coverage 30 | run: covr::codecov(quiet = FALSE) 31 | shell: Rscript {0} 32 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | docs 6 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: contsurvplot 2 | Title: Visualize the Effect of a Continuous Variable on a Time-to-Event Outcome 3 | Version: 0.2.1 4 | Authors@R: 5 | person("Robin", "Denz", , "robin.denz@rub.de", role = c("aut", "cre")) 6 | Maintainer: Robin Denz 7 | Description: Graphically display the (causal) effect of a continuous variable on a time-to-event outcome 8 | using multiple different types of plots based on g-computation. Those functions 9 | include, among others, survival area plots, survival contour plots, survival quantile plots and 10 | 3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally. 11 | For details, see Robin Denz, Nina Timmesfeld (2023) . 12 | License: GPL (>= 3) 13 | URL: https://github.com/RobinDenz1/contsurvplot, 14 | https://robindenz1.github.io/contsurvplot/ 15 | BugReports: https://github.com/RobinDenz1/contsurvplot/issues 16 | Imports: 17 | ggplot2 (>= 3.4.0), 18 | dplyr, 19 | rlang, 20 | riskRegression, 21 | foreach 22 | Suggests: 23 | survival, 24 | pammtools, 25 | gganimate, 26 | transformr, 27 | plotly, 28 | reshape2, 29 | doParallel, 30 | knitr, 31 | rmarkdown, 32 | testthat (>= 3.0.0), 33 | vdiffr (>= 1.0.0), 34 | covr 35 | VignetteBuilder: 36 | knitr 37 | Config/testthat/edition: 3 38 | Contact: 39 | Encoding: UTF-8 40 | Roxygen: list(markdown = TRUE) 41 | RoxygenNote: 7.2.3 42 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(curve_cont) 4 | export(plot_surv_3Dsurface) 5 | export(plot_surv_animated) 6 | export(plot_surv_area) 7 | export(plot_surv_at_t) 8 | export(plot_surv_contour) 9 | export(plot_surv_heatmap) 10 | export(plot_surv_lines) 11 | export(plot_surv_matrix) 12 | export(plot_surv_quantiles) 13 | export(plot_surv_rmst) 14 | export(plot_surv_rmtl) 15 | importFrom(dplyr,"%>%") 16 | importFrom(foreach,"%dopar%") 17 | importFrom(rlang,.data) 18 | -------------------------------------------------------------------------------- /NEWS.md: -------------------------------------------------------------------------------- 1 | # contsurvplot 0.1.0 2 | 3 | * This is the first release of this package 4 | * Added a reference to the main paper to the description file 5 | 6 | # contsurvplot 0.2.0 7 | 8 | Enhancements: 9 | 10 | * Installation instructions in vignette and README were updated to include CRAN 11 | * Re-worked the documentation page of the `curve_cont` function 12 | 13 | Bug fixes: 14 | 15 | * plot_surv_animated now uses `gganimate::transition_manual()` internally, ruling out interpolation errors 16 | * There are no slight differences in the outlines of the areas when using `plot_surv_area` with `discrete=TRUE` and `discrete=FALSE` anymore 17 | 18 | New Features: 19 | 20 | * Added `plot_surv_matrix` function 21 | * Added support for bootstrap standard error and confidence interval calculation in the `curve_cont` function 22 | * Added support for causal contrasts in `curve_cont` function, which also makes it possible to use those directly in all plot functions 23 | * Added support for bootstrap confidence intervals in the `plot_surv_at_t`, `plot_surv_lines` and `plot_surv_animated` functions 24 | * Added support for plotting Kaplan-Meier curves as reference in `plot_surv_area`, `plot_surv_animated`, `plot_surv_lines` 25 | * Added the `monotonic` argument in the `plot_surv_area` function to allow plotting curved relationships 26 | * Vignette now includes `plot_surv_matrix` function 27 | 28 | # contsurvplot 0.2.1 29 | 30 | Documentation: 31 | 32 | * Updated citation information to include published manuscript 33 | -------------------------------------------------------------------------------- /R/curve_cont_auc.r: -------------------------------------------------------------------------------- 1 | 2 | ## small function to estimate rmtl from plotdata 3 | cont_surv_auc <- function(plotdata, tau) { 4 | 5 | plotdata$group <- as.factor(plotdata$cont) 6 | plotdata$surv <- plotdata$est 7 | plotdata$est <- NULL 8 | plotdata$cont <- NULL 9 | 10 | levs <- levels(plotdata$group) 11 | 12 | out <- vector(mode="list", length=length(tau)*length(levs)) 13 | count <- 0 14 | for (i in seq_len(length(tau))) { 15 | for (j in seq_len(length(levs))) { 16 | count <- count + 1 17 | 18 | data_j <- plotdata[plotdata$group==levs[j], ] 19 | 20 | # constrain function end 21 | latest <- read_from_step_function(tau[i], data_j, est="surv", time="time") 22 | data_j <- data_j[data_j$time <= tau[i], ] 23 | data_j$group <- NULL 24 | 25 | if (!tau[i] %in% data_j$time) { 26 | temp <- data.frame(time=tau[i], 27 | surv=latest) 28 | data_j <- rbind(data_j, temp) 29 | } 30 | 31 | # calculate rmst 32 | area <- stepfun_integral(x=data_j$time, y=data_j$surv) 33 | 34 | row <- data.frame(cont=as.numeric(as.character(levs[j])), 35 | auc=area, 36 | tau=tau[i]) 37 | out[[count]] <- row 38 | } 39 | } 40 | out <- dplyr::bind_rows(out) 41 | out$tau <- as.factor(out$tau) 42 | 43 | return(out) 44 | } 45 | 46 | curve_cont_auc <- function(data, variable, model, horizon=NULL, 47 | group=NULL, cause=1, cif=FALSE, 48 | tau, ...) { 49 | 50 | # default horizon 51 | if (is.null(horizon)) { 52 | horizon <- seq(min(data[, variable]), max(data[, variable]), 53 | length.out=100) 54 | } 55 | 56 | # needed points in time 57 | fixed_t <- c(0, sort(unique(data[, time][data[, status] >= 1]))) 58 | 59 | # get survival probabilities / CIFs 60 | plotdata <- curve_cont(data=data, 61 | variable=variable, 62 | group=group, 63 | model=model, 64 | horizon=horizon, 65 | times=fixed_t, 66 | na.action="na.fail", 67 | event_time=time, 68 | event_status=status, 69 | cif=cif, 70 | cause=cause, 71 | ...) 72 | 73 | if (is.null(group)) { 74 | out <- cont_surv_auc(plotdata=plotdata, tau=tau) 75 | } else { 76 | group_levs <- levels(plotdata$group) 77 | out <- vector(mode="list", length=length(group_levs)) 78 | for (i in seq_len(length(group_levs))) { 79 | temp <- plotdata[plotdata$group==group_levs[i], ] 80 | out_i <- cont_surv_auc(plotdata=temp, tau=tau) 81 | out_i$group <- group_levs[i] 82 | out[[i]] <- out_i 83 | } 84 | out <- dplyr::bind_rows(out) 85 | } 86 | 87 | return(out) 88 | } 89 | 90 | -------------------------------------------------------------------------------- /R/get_kaplan_meier.r: -------------------------------------------------------------------------------- 1 | 2 | ## wrapper function to get an overall kaplan-meier estimate 3 | get_kaplan_meier <- function(time, status, group=NULL, data, conf_int=FALSE, 4 | conf_type="plain", conf_level=0.95, 5 | cif=FALSE, fixed_t=NULL, ...) { 6 | # get formula 7 | if (is.null(group)) { 8 | form <- stats::as.formula(paste0("survival::Surv(", time, ", ", 9 | status, ") ~ 1")) 10 | } else { 11 | form <- stats::as.formula(paste0("survival::Surv(", time, ", ", 12 | status, ") ~ ", group)) 13 | } 14 | 15 | # estimate kaplan-meier 16 | survf <- survival::survfit(formula=form, data=data, se.fit=conf_int, 17 | conf.int=conf_level, conf.type=conf_type, ...) 18 | 19 | km_dat <- data.frame(time=survf$time, 20 | est=survf$surv) 21 | 22 | # add group variable, if specified 23 | if (!is.null(group)) { 24 | km_group <- c() 25 | for (strat in names(survf$strata)) { 26 | km_group <- c(km_group, rep(strat, survf$strata[strat])) 27 | } 28 | km_group <- gsub(paste0(group, "="), "", km_group) 29 | km_dat$group <- factor(km_group, levels=levels(data[, group])) 30 | } 31 | 32 | # add confidence interval 33 | if (conf_int) { 34 | km_dat$ci_lower <- survf$lower 35 | km_dat$ci_upper <- survf$upper 36 | } 37 | 38 | # add rows at t = 0 39 | if (is.null(group) & conf_int) { 40 | row_0 <- data.frame(time=0, est=1, ci_lower=1, ci_upper=1) 41 | } else if (is.null(group)) { 42 | row_0 <- data.frame(time=0, est=1) 43 | } else if (!is.null(group) & conf_int) { 44 | row_0 <- data.frame(time=0, est=1, group=levels(data[, group]), 45 | ci_lower=1, ci_upper=1) 46 | } else { 47 | row_0 <- data.frame(time=0, est=1, group=levels(data[, group])) 48 | } 49 | km_dat <- rbind(row_0, km_dat) 50 | 51 | # read at specific points in time 52 | # NOTE: this ignores confidence intervals and se, because those are never 53 | # used when using fixed_t 54 | if (!is.null(fixed_t)) { 55 | km_dat <- km_at_t(data=km_dat, times=fixed_t, group=group) 56 | } 57 | 58 | # turn to cif 59 | if (cif) { 60 | km_dat$est <- 1 - km_dat$est 61 | if ("ci_lower" %in% colnames(km_dat)) { 62 | km_dat$ci_lower <- 1 - km_dat$ci_lower 63 | km_dat$ci_upper <- 1 - km_dat$ci_upper 64 | } 65 | } 66 | 67 | return(km_dat) 68 | } 69 | 70 | ## function to get kaplan-meier estimates at t using interpolation 71 | km_at_t <- function(data, times, group) { 72 | 73 | d_ref <- data[, c("time", "est")] 74 | 75 | if (is.null(group)) { 76 | d_ref <- d_ref[order(d_ref$time), ] 77 | d_ref <- data.frame(time=times, 78 | est=vapply(X=times, 79 | FUN=read_from_step_function, 80 | FUN.VALUE=numeric(1), 81 | data=d_ref, 82 | est="est")) 83 | } else { 84 | levs <- unique(data$group) 85 | out <- vector(mode="list", length=length(levs)) 86 | for (i in seq_len(length(levs))) { 87 | d_temp <- d_ref[data$group==levs[i], ] 88 | d_temp <- d_temp[order(d_temp$time), ] 89 | d_temp <- data.frame(time=times, 90 | est=vapply(X=times, 91 | FUN=read_from_step_function, 92 | FUN.VALUE=numeric(1), 93 | data=d_temp, 94 | est="est")) 95 | d_temp$group <- levs[i] 96 | out[[i]] <- d_temp 97 | } 98 | d_ref <- dplyr::bind_rows(out) 99 | } 100 | 101 | return(d_ref) 102 | } 103 | -------------------------------------------------------------------------------- /R/helper_functions.r: -------------------------------------------------------------------------------- 1 | 2 | ## keep only the covariates needed for the analysis 3 | ## this has to be done in order to correctly use na.action 4 | remove_unnecessary_covars <- function(data, time, status, variable, 5 | group, model) { 6 | 7 | # extract variables from outcome model 8 | if (inherits(model, c("coxph", "mexhaz"))) { 9 | model_vars <- all.vars(model$formula) 10 | } else if (inherits(model, c("CauseSpecificCox", "FGR", "aalen", 11 | "cox.aalen", "flexsurvreg", 12 | "pecCforest", "prodlim", 13 | "psm", "randomForest", 14 | "riskRegression", "selectCox", 15 | "glm", "ols", "rfsrc", 16 | "penfitS3", "gbm", 17 | "singleEventCB", "fcrr", 18 | "comprisk"))) { 19 | model_vars <- all.vars(model$call$formula) 20 | } else if (inherits(model, "pecRpart")) { 21 | model_vars <- all.vars(model$rpart$terms) 22 | } else if (inherits(model, "ranger")) { 23 | model_vars <- all.vars(model$call[[2]]) 24 | } else { 25 | model_vars <- NULL 26 | } 27 | 28 | # covariates that are always needed 29 | needed_covars <- c(time, status, variable, model_vars, group) 30 | 31 | # remove duplicates 32 | needed_covars <- unique(needed_covars) 33 | 34 | # filter data 35 | data <- dplyr::select(data, dplyr::all_of(needed_covars)) 36 | 37 | return(data) 38 | } 39 | 40 | ## composite function to prepare the data for further use 41 | prepare_inputdata <- function(data, time, status, variable, group, model, 42 | na.action) { 43 | 44 | # keep only needed columns 45 | data <- remove_unnecessary_covars(data=data, time=time, status=status, 46 | variable=variable, model=model, 47 | group=group) 48 | 49 | # perform na.action 50 | if (is.function(na.action)) { 51 | data <- na.action(data) 52 | } else { 53 | na.action <- get(na.action) 54 | data <- na.action(data) 55 | } 56 | 57 | if (nrow(data)==0) { 58 | stop("There is no data left after removing the missing values.") 59 | } 60 | 61 | return(data) 62 | } 63 | 64 | ## use only data.frame methods, no tibbles etc. 65 | use_data.frame <- function(data) { 66 | # correct data type 67 | if (!inherits(data, "data.frame")) { 68 | stop("'data' must be a data.frame object.") 69 | } else { 70 | data <- as.data.frame(data) 71 | } 72 | return(data) 73 | } 74 | 75 | ## takes a value x at which to read from the step function 76 | ## and step function data from which to read it 77 | read_from_step_function <- function(x, data, est="surv", time="time") { 78 | 79 | # keep only data with non-missing est 80 | data <- data[which(!is.na(data[, est])), ] 81 | 82 | # no extrapolation 83 | if (x > max(data[, time])) { 84 | return(NA) 85 | } 86 | 87 | # otherwise get value 88 | check <- data[which(data[, time] <= x), ] 89 | if (nrow(check)==0) { 90 | if (est=="surv") { 91 | val <- 1 92 | } else if (est=="cif") { 93 | val <- 0 94 | } else { 95 | val <- NA 96 | } 97 | } else { 98 | val <- check[, est][which(check[, time]==max(check[, time]))][1] 99 | } 100 | return(val) 101 | } 102 | 103 | ## calculate exact integral of a step function 104 | stepfun_integral <- function(x, y) { 105 | area <- 0 106 | for (i in seq_len((length(x)-1))) { 107 | x1 <- x[i] 108 | x2 <- x[i+1] 109 | rect_area <- (x2 - x1) * y[i] 110 | area <- area + rect_area 111 | } 112 | return(area) 113 | } 114 | -------------------------------------------------------------------------------- /R/plot_surv_3Dsurface.r: -------------------------------------------------------------------------------- 1 | 2 | ## function to plot the continuous survival area as a 3D surface 3 | #' @importFrom dplyr %>% 4 | #' @export 5 | plot_surv_3Dsurface <- function(time, status, variable, data, model, 6 | cif=FALSE, na.action=options()$na.action, 7 | horizon=NULL, fixed_t=NULL, max_t=Inf, 8 | interactive=FALSE, 9 | xlab="Time", ylab="Survival Probability", 10 | zlab=variable, ticktype="detailed", 11 | theta=120, phi=20, col="green", shade=0.5, 12 | ...) { 13 | requireNamespace("reshape2") 14 | 15 | data <- use_data.frame(data) 16 | 17 | check_inputs_plots(time=time, status=status, variable=variable, 18 | data=data, model=model, na.action=na.action, 19 | horizon=horizon, fixed_t=fixed_t, max_t=max_t, 20 | discrete=TRUE, panel_border=TRUE, t=1, tau=1, 21 | group=NULL) 22 | 23 | data <- prepare_inputdata(data=data, time=time, status=status, 24 | variable=variable, model=model, 25 | group=NULL, na.action=na.action) 26 | 27 | if (is.null(fixed_t)) { 28 | fixed_t <- seq(min(data[, time]), max(data[, time]), length.out=100) 29 | } 30 | if (is.null(horizon)) { 31 | horizon <- seq(min(data[, variable]), max(data[, variable]), length.out=40) 32 | } 33 | 34 | # only show up to max_t 35 | fixed_t <- fixed_t[fixed_t <= max_t] 36 | 37 | # get plotdata 38 | plotdata <- curve_cont(data=data, 39 | variable=variable, 40 | group=NULL, 41 | model=model, 42 | horizon=horizon, 43 | times=fixed_t, 44 | na.action="na.fail", 45 | cif=cif, 46 | event_time=time, 47 | event_status=status, 48 | ...) 49 | # transform 50 | plot_matrix <- t(reshape2::acast(plotdata, cont~time, value.var="est")) 51 | 52 | if (interactive) { 53 | requireNamespace("plotly") 54 | 55 | p <- plotly::plot_ly(x=as.numeric(colnames(plot_matrix)), 56 | y=as.numeric(rownames(plot_matrix)), 57 | z=plot_matrix) %>% 58 | plotly::add_surface() %>% 59 | plotly::layout( 60 | scene=list( 61 | xaxis=list(title=zlab), 62 | yaxis=list(title=xlab), 63 | zaxis=list(title=ylab) 64 | )) 65 | return(p) 66 | } else { 67 | p <- graphics::persp(x=as.numeric(colnames(plot_matrix)), 68 | y=as.numeric(rownames(plot_matrix)), 69 | z=t(plot_matrix), 70 | xlab=zlab, 71 | ylab=xlab, 72 | zlab=ylab, 73 | ticktype=ticktype, 74 | theta=theta, 75 | phi=phi, 76 | col=col, 77 | shade=shade) 78 | return(invisible(p)) 79 | } 80 | } 81 | -------------------------------------------------------------------------------- /R/plot_surv_animated.r: -------------------------------------------------------------------------------- 1 | 2 | ## function to draw one survival curve or CIF, and animate the continuous 3 | ## variable either as a gif or a slider 4 | #' @importFrom rlang .data 5 | #' @export 6 | plot_surv_animated <- function(time, status, variable, group=NULL, data, model, 7 | cif=FALSE, conf_int=FALSE, conf_level=0.95, 8 | n_boot=300, na.action=options()$na.action, 9 | horizon=NULL, fixed_t=NULL, max_t=Inf, 10 | slider=TRUE, 11 | size=1, color="black", linetype="solid", alpha=1, 12 | xlab="Time", ylab="Survival Probability", 13 | title=NULL, subtitle=NULL, 14 | gg_theme=ggplot2::theme_bw(), 15 | facet_args=list(), ci_alpha=0.4, 16 | kaplan_meier=FALSE, km_size=0.5, 17 | km_linetype="solid", km_alpha=1, 18 | km_color="black", km_ci=FALSE, 19 | km_ci_type="plain", km_ci_level=0.95, 20 | km_ci_alpha=0.4, ...) { 21 | 22 | data <- use_data.frame(data) 23 | 24 | check_inputs_plots(time=time, status=status, variable=variable, 25 | data=data, model=model, na.action=na.action, 26 | horizon=horizon, fixed_t=fixed_t, max_t=max_t, 27 | discrete=TRUE, panel_border=TRUE, t=1, tau=1, 28 | group=group) 29 | 30 | data <- prepare_inputdata(data=data, time=time, status=status, 31 | variable=variable, model=model, 32 | group=group, na.action=na.action) 33 | 34 | if (is.null(fixed_t)) { 35 | fixed_t <- c(0, sort(unique(data[, time][data[, status]==1]))) 36 | } 37 | if (is.null(horizon)) { 38 | horizon <- seq(min(data[, variable]), max(data[, variable]), length.out=40) 39 | } 40 | 41 | # only show up to max_t 42 | fixed_t <- fixed_t[fixed_t <= max_t] 43 | 44 | # get plotdata 45 | plotdata <- curve_cont(data=data, 46 | variable=variable, 47 | group=group, 48 | model=model, 49 | horizon=horizon, 50 | times=fixed_t, 51 | na.action="na.fail", 52 | cif=cif, 53 | event_time=time, 54 | event_status=status, 55 | conf_int=conf_int, 56 | conf_level=conf_level, 57 | n_boot=n_boot, 58 | ...) 59 | 60 | # correct label 61 | if (cif & ylab=="Survival Probability") { 62 | ylab <- "Cumulative Incidence" 63 | } 64 | 65 | # plot it 66 | if (slider) { 67 | p <- ggplot2::ggplot(plotdata, ggplot2::aes(x=.data$time, y=.data$est, 68 | frame=.data$cont)) 69 | } else { 70 | p <- ggplot2::ggplot(plotdata, ggplot2::aes(x=.data$time, y=.data$est)) 71 | } 72 | 73 | if (conf_int & slider) { 74 | stop("Showing confidence intervals is currently not supported when using", 75 | " slider=TRUE.") 76 | } else if (conf_int) { 77 | requireNamespace("pammtools") 78 | 79 | p <- p + pammtools::geom_stepribbon(ggplot2::aes(x=.data$time, 80 | y=.data$est, 81 | ymin=.data$ci_lower, 82 | ymax=.data$ci_upper), 83 | alpha=ci_alpha, inherit.aes=FALSE, 84 | fill=color) 85 | } 86 | 87 | p <- p + ggplot2::geom_step(linewidth=size, color=color, linetype=linetype, 88 | alpha=alpha) + 89 | ggplot2::labs(x=xlab, y=ylab, title=title, subtitle=subtitle) + 90 | gg_theme 91 | 92 | # add kaplan-meier curve if specified 93 | if (kaplan_meier) { 94 | km_dat <- get_kaplan_meier(time=time, status=status, group=group, 95 | data=data, conf_int=km_ci, 96 | conf_type=km_ci_type, conf_level=km_ci_level, 97 | cif=cif) 98 | if (km_ci & slider) { 99 | stop("Showing confidence intervals is currently not supported when using", 100 | " slider=TRUE.") 101 | } else if (km_ci) { 102 | requireNamespace("pammtools") 103 | 104 | p <- p + pammtools::geom_stepribbon(data=km_dat, 105 | ggplot2::aes(x=.data$time, 106 | y=.data$est, 107 | ymin=.data$ci_lower, 108 | ymax=.data$ci_upper), 109 | fill=km_color, alpha=km_ci_alpha, 110 | inherit.aes=FALSE) 111 | } 112 | p <- p + ggplot2::geom_step(data=km_dat, ggplot2::aes(x=.data$time, 113 | y=.data$est), 114 | linewidth=km_size, color=km_color, 115 | alpha=km_alpha, linetype=km_linetype, 116 | inherit.aes=FALSE) 117 | } 118 | 119 | # facet plot by factor variable 120 | if (!is.null(group)) { 121 | facet_args$facets <- stats::as.formula("~ group") 122 | facet_obj <- do.call(ggplot2::facet_wrap, facet_args) 123 | p <- p + facet_obj 124 | } 125 | 126 | if (slider) { 127 | requireNamespace("plotly") 128 | 129 | return(plotly::ggplotly(p)) 130 | } else { 131 | requireNamespace("gganimate") 132 | requireNamespace("transformr") 133 | 134 | if (is.null(title)) { 135 | title <- variable 136 | } 137 | 138 | p <- p + gganimate::transition_manual(frames=.data$cont) + 139 | ggplot2::labs(title=paste0(title, ": {frame}")) 140 | } 141 | 142 | return(p) 143 | } 144 | -------------------------------------------------------------------------------- /R/plot_surv_at_t.r: -------------------------------------------------------------------------------- 1 | 2 | ## function to plot the survival or failure probability of a fixed point in time 3 | ## as a function of a continuous variable 4 | #' @importFrom rlang .data 5 | #' @export 6 | plot_surv_at_t <- function(time, status, variable, group=NULL, data, model, 7 | cif=FALSE, conf_int=FALSE, conf_level=0.95, 8 | n_boot=300, na.action=options()$na.action, 9 | t, horizon=NULL, 10 | size=1, linetype="solid", alpha=1, 11 | xlab=variable, ylab="Survival Probability at t", 12 | title=NULL, subtitle=NULL, 13 | legend.title="t", legend.position="right", 14 | gg_theme=ggplot2::theme_bw(), 15 | facet_args=list(), ci_alpha=0.4, ...) { 16 | 17 | data <- use_data.frame(data) 18 | 19 | check_inputs_plots(time=time, status=status, variable=variable, 20 | data=data, model=model, na.action=na.action, 21 | horizon=horizon, fixed_t=NULL, max_t=Inf, 22 | discrete=TRUE, panel_border=TRUE, t=t, tau=1, 23 | group=group) 24 | 25 | data <- prepare_inputdata(data=data, time=time, status=status, 26 | variable=variable, model=model, 27 | group=group, na.action=na.action) 28 | 29 | if (is.null(horizon)) { 30 | horizon <- seq(min(data[, variable]), max(data[, variable]), 31 | length.out=100) 32 | } 33 | 34 | # get plotdata 35 | plotdata <- curve_cont(data=data, 36 | variable=variable, 37 | group=group, 38 | model=model, 39 | horizon=horizon, 40 | times=t, 41 | na.action="na.fail", 42 | cif=cif, 43 | event_time=time, 44 | event_status=status, 45 | conf_int=conf_int, 46 | conf_level=conf_level, 47 | n_boot=n_boot, 48 | ...) 49 | plotdata$time <- as.factor(plotdata$time) 50 | 51 | # plot them 52 | p <- ggplot2::ggplot(plotdata, ggplot2::aes(x=.data$cont, y=.data$est, 53 | color=.data$time)) 54 | 55 | if (length(t)==1) { 56 | p$mapping$colour <- NULL 57 | } 58 | 59 | # correct label 60 | if (cif & ylab=="Survival Probability at t") { 61 | ylab <- "Cumulative Incidence at t" 62 | } 63 | 64 | if (conf_int & length(t)==1) { 65 | p <- p + ggplot2::geom_ribbon(ggplot2::aes(ymin=.data$ci_lower, 66 | ymax=.data$ci_upper), 67 | fill="grey", alpha=ci_alpha, linewidth=0) 68 | } else if (conf_int) { 69 | p <- p + ggplot2::geom_ribbon(ggplot2::aes(x=.data$cont, 70 | y=.data$est, 71 | ymin=.data$ci_lower, 72 | ymax=.data$ci_upper, 73 | fill=.data$time), 74 | alpha=ci_alpha, linewidth=0, 75 | inherit.aes=FALSE) 76 | } 77 | 78 | p <- p + ggplot2::geom_line(linewidth=size, linetype=linetype, alpha=alpha) + 79 | ggplot2::labs(x=xlab, y=ylab, title=title, subtitle=subtitle, 80 | fill=legend.title, color=legend.title) + 81 | gg_theme + 82 | ggplot2::theme(legend.position=legend.position) 83 | 84 | # facet plot by factor variable 85 | if (!is.null(group)) { 86 | facet_args$facets <- stats::as.formula("~ group") 87 | facet_obj <- do.call(ggplot2::facet_wrap, facet_args) 88 | p <- p + facet_obj 89 | } 90 | 91 | return(p) 92 | } 93 | -------------------------------------------------------------------------------- /R/plot_surv_contour.r: -------------------------------------------------------------------------------- 1 | 2 | ## A contour plot of time vs. continuous covariate with color grading 3 | ## showing the survival or failure probability 4 | #' @importFrom rlang .data 5 | #' @export 6 | plot_surv_contour <- function(time, status, variable, group=NULL, data, model, 7 | cif=FALSE, na.action=options()$na.action, 8 | horizon=NULL, fixed_t=NULL, max_t=Inf, 9 | size=0.1, linetype="solid", alpha=1, 10 | bins=NULL, binwidth=NULL, breaks=NULL, 11 | custom_colors=NULL, 12 | xlab="Time", ylab=variable, 13 | title=NULL, subtitle=NULL, 14 | legend.title="S(t)", legend.position="right", 15 | gg_theme=ggplot2::theme_bw(), facet_args=list(), 16 | panel_border=FALSE, axis_dist=0, 17 | ...) { 18 | 19 | data <- use_data.frame(data) 20 | 21 | check_inputs_plots(time=time, status=status, variable=variable, 22 | data=data, model=model, na.action=na.action, 23 | horizon=horizon, fixed_t=fixed_t, max_t=max_t, 24 | discrete=TRUE, panel_border=panel_border, t=1, tau=1, 25 | group=group) 26 | 27 | data <- prepare_inputdata(data=data, time=time, status=status, 28 | variable=variable, model=model, 29 | group=group, na.action=na.action) 30 | 31 | if (is.null(fixed_t)) { 32 | fixed_t <- seq(min(data[, time]), max(data[, time]), length.out=100) 33 | } 34 | if (is.null(horizon)) { 35 | horizon <- seq(min(data[, variable]), max(data[, variable]), length.out=40) 36 | } 37 | 38 | # only show up to max_t 39 | fixed_t <- fixed_t[fixed_t <= max_t] 40 | 41 | # get plotdata 42 | plotdata <- curve_cont(data=data, 43 | variable=variable, 44 | model=model, 45 | group=group, 46 | horizon=horizon, 47 | times=fixed_t, 48 | na.action="na.fail", 49 | cif=cif, 50 | event_time=time, 51 | event_status=status, 52 | ...) 53 | 54 | # correct label 55 | if (cif & legend.title=="S(t)") { 56 | legend.title <- "F(t)" 57 | } 58 | 59 | # plot it 60 | p <- ggplot2::ggplot(plotdata, ggplot2::aes(x=.data$time, y=.data$cont, 61 | z=.data$est)) + 62 | ggplot2::geom_contour_filled(linewidth=size, alpha=alpha, bins=bins, 63 | binwidth=binwidth, breaks=breaks) + 64 | ggplot2::labs(x=xlab, y=ylab, title=title, subtitle=subtitle, 65 | fill=legend.title) + 66 | gg_theme + 67 | ggplot2::theme(legend.position=legend.position) + 68 | ggplot2::scale_x_continuous(expand=c(axis_dist, axis_dist)) + 69 | ggplot2::scale_y_continuous(expand=c(axis_dist, axis_dist)) 70 | 71 | if (!panel_border) { 72 | p <- p + ggplot2::theme(panel.border=ggplot2::element_blank()) 73 | } 74 | if (!is.null(custom_colors)) { 75 | p <- p + ggplot2::scale_fill_manual(values=custom_colors) 76 | } 77 | # facet plot by factor variable 78 | if (!is.null(group)) { 79 | facet_args$facets <- stats::as.formula("~ group") 80 | facet_obj <- do.call(ggplot2::facet_wrap, facet_args) 81 | p <- p + facet_obj 82 | } 83 | return(p) 84 | } 85 | -------------------------------------------------------------------------------- /R/plot_surv_heatmap.r: -------------------------------------------------------------------------------- 1 | 2 | ## A heatmap of time vs. continuous covariate with color grading 3 | ## showing the survival or failure probability 4 | #' @importFrom rlang .data 5 | #' @export 6 | plot_surv_heatmap <- function(time, status, variable, group=NULL, data, model, 7 | cif=FALSE, na.action=options()$na.action, 8 | horizon=NULL, fixed_t=NULL, max_t=Inf, 9 | start_color=NULL, end_color=NULL, 10 | alpha=1, xlab="Time", ylab=variable, 11 | title=NULL, subtitle=NULL, 12 | legend.title="S(t)", legend.position="right", 13 | gg_theme=ggplot2::theme_bw(), facet_args=list(), 14 | panel_border=FALSE, axis_dist=0, 15 | interpolate=TRUE, 16 | contour_lines=FALSE, contour_color="white", 17 | contour_size=0.3, contour_linetype="dashed", 18 | ...) { 19 | 20 | data <- use_data.frame(data) 21 | 22 | check_inputs_plots(time=time, status=status, variable=variable, 23 | data=data, model=model, na.action=na.action, 24 | horizon=horizon, fixed_t=fixed_t, max_t=max_t, 25 | discrete=TRUE, panel_border=panel_border, t=1, tau=1, 26 | group=group) 27 | 28 | data <- prepare_inputdata(data=data, time=time, status=status, 29 | variable=variable, model=model, 30 | group=group, na.action=na.action) 31 | 32 | if (is.null(fixed_t)) { 33 | fixed_t <- seq(min(data[, time]), max(data[, time]), length.out=100) 34 | } 35 | if (is.null(horizon)) { 36 | horizon <- seq(min(data[, variable]), max(data[, variable]), length.out=40) 37 | } 38 | 39 | # only show up to max_t 40 | fixed_t <- fixed_t[fixed_t <= max_t] 41 | 42 | # get plotdata 43 | plotdata <- curve_cont(data=data, 44 | variable=variable, 45 | model=model, 46 | group=group, 47 | horizon=horizon, 48 | times=fixed_t, 49 | na.action="na.fail", 50 | cif=cif, 51 | event_time=time, 52 | event_status=status, 53 | ...) 54 | 55 | # correct label 56 | if (cif & legend.title=="S(t)") { 57 | legend.title <- "F(t)" 58 | } 59 | 60 | # plot it 61 | p <- ggplot2::ggplot(plotdata, ggplot2::aes(x=.data$time, y=.data$cont, 62 | fill=.data$est)) + 63 | ggplot2::geom_raster(alpha=alpha, interpolate=interpolate) + 64 | ggplot2::labs(x=xlab, y=ylab, title=title, subtitle=subtitle, 65 | fill=legend.title) + 66 | gg_theme + 67 | ggplot2::theme(legend.position=legend.position) + 68 | ggplot2::scale_x_continuous(expand=c(axis_dist, axis_dist)) + 69 | ggplot2::scale_y_continuous(expand=c(axis_dist, axis_dist)) 70 | 71 | if (!is.null(start_color) & !is.null(end_color)) { 72 | p <- p + ggplot2::scale_fill_gradient(low=start_color, high=end_color) 73 | } 74 | if (!panel_border) { 75 | p <- p + ggplot2::theme(panel.border=ggplot2::element_blank()) 76 | } 77 | if (contour_lines) { 78 | p <- p + ggplot2::geom_contour(ggplot2::aes(x=.data$time, 79 | y=.data$cont, 80 | z=.data$est), 81 | colour=contour_color, 82 | linewidth=contour_size, 83 | linetype=contour_linetype, 84 | alpha=alpha, 85 | inherit.aes=FALSE) 86 | } 87 | # facet plot by factor variable 88 | if (!is.null(group)) { 89 | facet_args$facets <- stats::as.formula("~ group") 90 | facet_obj <- do.call(ggplot2::facet_wrap, facet_args) 91 | p <- p + facet_obj 92 | } 93 | return(p) 94 | } 95 | -------------------------------------------------------------------------------- /R/plot_surv_lines.r: -------------------------------------------------------------------------------- 1 | 2 | ## function to simply draw many survival curves or CIFs, one for each x-value 3 | #' @importFrom rlang .data 4 | #' @export 5 | plot_surv_lines <- function(time, status, variable, group=NULL, data, model, 6 | cif=FALSE, conf_int=FALSE, conf_level=0.95, 7 | n_boot=300, na.action=options()$na.action, 8 | horizon=NULL, fixed_t=NULL, max_t=Inf, 9 | discrete=TRUE, custom_colors=NULL, 10 | start_color="blue", end_color="red", 11 | size=1, linetype="solid", alpha=1, 12 | xlab="Time", ylab="Survival Probability", 13 | title=NULL, subtitle=NULL, 14 | legend.title=variable, legend.position="right", 15 | gg_theme=ggplot2::theme_bw(), facet_args=list(), 16 | ci_alpha=0.4, kaplan_meier=FALSE, km_size=0.5, 17 | km_linetype="solid", km_alpha=1, 18 | km_color="black", km_ci=FALSE, 19 | km_ci_type="plain", km_ci_level=0.95, 20 | km_ci_alpha=0.4, ...) { 21 | 22 | data <- use_data.frame(data) 23 | 24 | check_inputs_plots(time=time, status=status, variable=variable, 25 | data=data, model=model, na.action=na.action, 26 | horizon=horizon, fixed_t=fixed_t, max_t=max_t, 27 | discrete=discrete, panel_border=TRUE, t=1, tau=1, 28 | group=group) 29 | 30 | data <- prepare_inputdata(data=data, time=time, status=status, 31 | variable=variable, model=model, 32 | group=group, na.action=na.action) 33 | 34 | if (is.null(fixed_t)) { 35 | fixed_t <- c(0, sort(unique(data[, time][data[, status]==1]))) 36 | } 37 | if (is.null(horizon)) { 38 | horizon <- seq(min(data[, variable]), max(data[, variable]), length.out=12) 39 | } 40 | 41 | # only show up to max_t 42 | fixed_t <- fixed_t[fixed_t <= max_t] 43 | 44 | # get plotdata 45 | plotdata <- curve_cont(data=data, 46 | variable=variable, 47 | model=model, 48 | group=group, 49 | horizon=horizon, 50 | times=fixed_t, 51 | na.action="na.fail", 52 | cif=cif, 53 | event_time=time, 54 | event_status=status, 55 | conf_int=conf_int, 56 | conf_level=conf_level, 57 | n_boot=n_boot, 58 | ...) 59 | # plot it 60 | if (!discrete) { 61 | # create enough colors 62 | colgrad_fun <- grDevices::colorRampPalette(c(start_color, end_color)) 63 | colgrad <- colgrad_fun(length(horizon)) 64 | } else { 65 | plotdata$cont <- as.factor(plotdata$cont) 66 | } 67 | 68 | # correct label 69 | if (cif & ylab=="Survival Probability") { 70 | ylab <- "Cumulative Incidence" 71 | } 72 | 73 | # plot it 74 | p <- ggplot2::ggplot(plotdata, ggplot2::aes(x=.data$time, y=.data$est, 75 | color=.data$cont, 76 | group=as.factor(.data$cont))) 77 | # add confidence intervals 78 | if (conf_int) { 79 | requireNamespace("pammtools") 80 | 81 | p <- p + pammtools::geom_stepribbon(ggplot2::aes(x=.data$time, 82 | y=.data$est, 83 | ymin=.data$ci_lower, 84 | ymax=.data$ci_upper, 85 | fill=.data$cont), 86 | alpha=ci_alpha, inherit.aes=FALSE) 87 | } 88 | 89 | p <- p + 90 | ggplot2::geom_step(linewidth=size, linetype=linetype, alpha=alpha) + 91 | ggplot2::labs(x=xlab, y=ylab, title=title, subtitle=subtitle, 92 | fill=legend.title, color=legend.title) + 93 | gg_theme + 94 | ggplot2::theme(legend.position=legend.position) 95 | 96 | # add continuous of discrete color scale 97 | if (!discrete) { 98 | p <- p + ggplot2::scale_color_gradient(low=start_color, high=end_color) 99 | } else if (!is.null(custom_colors)) { 100 | p <- p + ggplot2::scale_colour_manual(values=custom_colors) 101 | if (conf_int) { 102 | p <- p + ggplot2::scale_fill_manual(values=custom_colors) 103 | } 104 | } 105 | # add kaplan-meier estimates 106 | if (kaplan_meier) { 107 | km_dat <- get_kaplan_meier(time=time, status=status, group=group, 108 | data=data, conf_int=km_ci, 109 | conf_type=km_ci_type, conf_level=km_ci_level, 110 | cif=cif) 111 | if (km_ci) { 112 | requireNamespace("pammtools") 113 | 114 | p <- p + pammtools::geom_stepribbon(data=km_dat, 115 | ggplot2::aes(x=.data$time, 116 | y=.data$est, 117 | ymin=.data$ci_lower, 118 | ymax=.data$ci_upper), 119 | fill=km_color, alpha=km_ci_alpha, 120 | inherit.aes=FALSE) 121 | } 122 | p <- p + ggplot2::geom_step(data=km_dat, ggplot2::aes(x=.data$time, 123 | y=.data$est), 124 | linewidth=km_size, color=km_color, 125 | alpha=km_alpha, linetype=km_linetype, 126 | inherit.aes=FALSE) 127 | } 128 | # facet plot by factor variable 129 | if (!is.null(group)) { 130 | facet_args$facets <- stats::as.formula("~ group") 131 | facet_obj <- do.call(ggplot2::facet_wrap, facet_args) 132 | p <- p + facet_obj 133 | } 134 | 135 | return(p) 136 | } 137 | -------------------------------------------------------------------------------- /R/plot_surv_quantiles.r: -------------------------------------------------------------------------------- 1 | 2 | ## small function to obtain the survival time quantiles from 3 | ## estimated plotdata 4 | cont_surv_quantiles <- function(plotdata, p) { 5 | 6 | plotdata$group <- as.factor(plotdata$cont) 7 | plotdata$surv <- plotdata$est 8 | 9 | levs <- unique(plotdata$group) 10 | 11 | out <- vector(mode="list", length=length(levs)) 12 | for (i in seq_len(length(levs))) { 13 | temp_dat <- plotdata[plotdata$group==levs[i],] 14 | temp_dat$group <- NULL 15 | 16 | val <- vapply(X=p, FUN=read_from_step_function, FUN.VALUE=numeric(1), 17 | data=temp_dat, est="time", time="surv") 18 | out[[i]] <- data.frame(p=p, group=levs[i], q_surv=val) 19 | } 20 | surv_q <- dplyr::bind_rows(out) 21 | 22 | surv_q$group <- as.numeric(as.character(surv_q$group)) 23 | surv_q$p <- as.factor(surv_q$p) 24 | 25 | return(surv_q) 26 | } 27 | 28 | ## function to plot survival time quantiles as they evolve over values of 29 | ## the continuous variable 30 | #' @importFrom rlang .data 31 | #' @export 32 | plot_surv_quantiles <- function(time, status, variable, group=NULL, data, model, 33 | na.action=options()$na.action, 34 | p=0.5, horizon=NULL, 35 | size=1, linetype="solid", alpha=1, 36 | custom_colors=NULL, single_color=NULL, 37 | xlab=variable, ylab="Survival Time Quantile", 38 | title=NULL, subtitle=NULL, 39 | legend.title=variable, legend.position="right", 40 | gg_theme=ggplot2::theme_bw(), facet_args=list(), 41 | ...) { 42 | 43 | data <- use_data.frame(data) 44 | 45 | check_inputs_plots(time=time, status=status, variable=variable, 46 | data=data, model=model, na.action=na.action, 47 | horizon=horizon, fixed_t=NULL, max_t=Inf, 48 | discrete=TRUE, panel_border=TRUE, t=1, tau=1, 49 | group=group) 50 | 51 | data <- prepare_inputdata(data=data, time=time, status=status, 52 | variable=variable, model=model, 53 | group=group, na.action=na.action) 54 | 55 | if (is.null(horizon)) { 56 | horizon <- seq(min(data[, variable]), max(data[, variable]), length.out=40) 57 | } 58 | 59 | # get plotdata 60 | fixed_t <- c(0, sort(unique(data[, time][data[, status]==1]))) 61 | plotdata <- curve_cont(data=data, 62 | variable=variable, 63 | model=model, 64 | group=group, 65 | horizon=horizon, 66 | times=fixed_t, 67 | na.action="na.fail", 68 | event_time=time, 69 | event_status=status, 70 | ...) 71 | 72 | # use the adjustedCurves package to calculate survival time quantiles 73 | if (is.null(group)) { 74 | surv_q <- cont_surv_quantiles(plotdata=plotdata, p=p) 75 | } else { 76 | group_levs <- levels(plotdata$group) 77 | surv_q <- vector(mode="list", length=length(group_levs)) 78 | for (i in seq_len(length(group_levs))) { 79 | temp <- plotdata[plotdata$group==group_levs[i], ] 80 | out_i <- cont_surv_quantiles(plotdata=temp, p=p) 81 | out_i$facet_var <- group_levs[i] 82 | surv_q[[i]] <- out_i 83 | } 84 | surv_q <- dplyr::bind_rows(surv_q) 85 | } 86 | 87 | # plot them 88 | plt <- ggplot2::ggplot(surv_q, ggplot2::aes(x=.data$group, y=.data$q_surv, 89 | color=.data$p, group=.data$p)) 90 | 91 | if (length(p)==1) { 92 | plt$mapping$colour <- NULL 93 | } 94 | 95 | if (!is.null(single_color)) { 96 | gg_lines <- ggplot2::geom_step(linewidth=size, linetype=linetype, 97 | alpha=alpha, color=single_color) 98 | } else { 99 | gg_lines <- ggplot2::geom_step(linewidth=size, linetype=linetype, 100 | alpha=alpha) 101 | } 102 | 103 | plt <- plt + gg_lines + 104 | ggplot2::labs(x=xlab, y=ylab, title=title, subtitle=subtitle, 105 | fill=legend.title) + 106 | gg_theme + 107 | ggplot2::theme(legend.position=legend.position) 108 | 109 | if (is.null(single_color) & !is.null(custom_colors)) { 110 | plt <- plt + ggplot2::scale_colour_manual(values=custom_colors) 111 | } 112 | # facet plot by factor variable 113 | if (!is.null(group)) { 114 | facet_args$facets <- stats::as.formula("~ facet_var") 115 | facet_obj <- do.call(ggplot2::facet_wrap, facet_args) 116 | plt <- plt + facet_obj 117 | } 118 | return(plt) 119 | } 120 | -------------------------------------------------------------------------------- /R/plot_surv_rmst.r: -------------------------------------------------------------------------------- 1 | 2 | ## function to plot restricted mean survival times as they evolve over values of 3 | ## the continuous variable 4 | #' @importFrom rlang .data 5 | #' @export 6 | plot_surv_rmst <- function(time, status, variable, group=NULL, 7 | data, model, na.action=options()$na.action, 8 | tau, horizon=NULL, custom_colors=NULL, 9 | size=1, linetype="solid", alpha=1, color="black", 10 | xlab=variable, ylab="Restricted Mean Survival Time", 11 | title=NULL, subtitle=NULL, 12 | legend.title=variable, legend.position="right", 13 | gg_theme=ggplot2::theme_bw(), 14 | facet_args=list(), ...) { 15 | requireNamespace("dplyr") 16 | 17 | data <- use_data.frame(data) 18 | 19 | check_inputs_plots(time=time, status=status, variable=variable, 20 | data=data, model=model, na.action=na.action, 21 | horizon=horizon, fixed_t=NULL, max_t=Inf, 22 | discrete=TRUE, panel_border=TRUE, t=1, tau=tau, 23 | group=group) 24 | 25 | data <- prepare_inputdata(data=data, time=time, status=status, 26 | variable=variable, model=model, 27 | group=group, na.action=na.action) 28 | 29 | if (is.null(horizon)) { 30 | horizon <- seq(min(data[, variable]), max(data[, variable]), 31 | length.out=100) 32 | } 33 | 34 | # get plotdata 35 | fixed_t <- c(0, sort(unique(data[, time][data[, status]==1]))) 36 | plotdata <- curve_cont(data=data, 37 | variable=variable, 38 | group=group, 39 | model=model, 40 | horizon=horizon, 41 | times=fixed_t, 42 | na.action="na.fail", 43 | event_time=time, 44 | event_status=status, 45 | ...) 46 | 47 | # calculate RMST values 48 | if (is.null(group)) { 49 | out <- cont_surv_auc(plotdata=plotdata, tau=tau) 50 | } else { 51 | group_levs <- levels(plotdata$group) 52 | out <- vector(mode="list", length=length(group_levs)) 53 | for (i in seq_len(length(group_levs))) { 54 | temp <- plotdata[plotdata$group==group_levs[i], ] 55 | out_i <- cont_surv_auc(plotdata=temp, tau=tau) 56 | out_i$group <- group_levs[i] 57 | out[[i]] <- out_i 58 | } 59 | out <- dplyr::bind_rows(out) 60 | } 61 | 62 | # plot them 63 | p <- ggplot2::ggplot(out, ggplot2::aes(x=.data$cont, y=.data$auc, 64 | color=.data$tau)) 65 | 66 | if (length(tau)==1) { 67 | p$mapping$colour <- NULL 68 | gg_line <- ggplot2::geom_line(linewidth=size, linetype=linetype, 69 | alpha=alpha, color=color) 70 | } else { 71 | gg_line <- ggplot2::geom_line(linewidth=size, linetype=linetype, 72 | alpha=alpha) 73 | } 74 | 75 | p <- p + gg_line + 76 | ggplot2::labs(x=xlab, y=ylab, title=title, subtitle=subtitle, 77 | fill=legend.title) + 78 | gg_theme + 79 | ggplot2::theme(legend.position=legend.position) 80 | 81 | if (length(tau) > 1 & !is.null(custom_colors)) { 82 | p <- p + ggplot2::scale_colour_manual(values=custom_colors) 83 | } 84 | # facet plot by factor variable 85 | if (!is.null(group)) { 86 | facet_args$facets <- stats::as.formula("~ group") 87 | facet_obj <- do.call(ggplot2::facet_wrap, facet_args) 88 | p <- p + facet_obj 89 | } 90 | 91 | return(p) 92 | } 93 | -------------------------------------------------------------------------------- /R/plot_surv_rmtl.r: -------------------------------------------------------------------------------- 1 | 2 | ## function to plot the restricted mean time lost as it evolves over values of 3 | ## the continuous variable 4 | #' @importFrom rlang .data 5 | #' @export 6 | plot_surv_rmtl <- function(time, status, variable, group=NULL, 7 | data, model, na.action=options()$na.action, 8 | tau, horizon=NULL, custom_colors=NULL, 9 | size=1, linetype="solid", alpha=1, color="black", 10 | xlab=variable, ylab="Restricted Mean Time Lost", 11 | title=NULL, subtitle=NULL, 12 | legend.title=variable, legend.position="right", 13 | gg_theme=ggplot2::theme_bw(), 14 | facet_args=list(), ...) { 15 | requireNamespace("dplyr") 16 | 17 | data <- use_data.frame(data) 18 | 19 | check_inputs_plots(time=time, status=status, variable=variable, 20 | data=data, model=model, na.action=na.action, 21 | horizon=horizon, fixed_t=NULL, max_t=Inf, 22 | discrete=TRUE, panel_border=TRUE, t=1, tau=tau, 23 | group=group) 24 | 25 | data <- prepare_inputdata(data=data, time=time, status=status, 26 | variable=variable, model=model, 27 | group=group, na.action=na.action) 28 | 29 | if (is.null(horizon)) { 30 | horizon <- seq(min(data[, variable]), max(data[, variable]), 31 | length.out=100) 32 | } 33 | 34 | # get plotdata 35 | fixed_t <- c(0, sort(unique(data[, time][data[, status] >= 1]))) 36 | plotdata <- curve_cont(data=data, 37 | variable=variable, 38 | group=group, 39 | model=model, 40 | horizon=horizon, 41 | times=fixed_t, 42 | na.action="na.fail", 43 | cif=TRUE, 44 | event_time=time, 45 | event_status=status, 46 | ...) 47 | 48 | # calculate RMTL values 49 | if (is.null(group)) { 50 | out <- cont_surv_auc(plotdata=plotdata, tau=tau) 51 | } else { 52 | group_levs <- levels(plotdata$group) 53 | out <- vector(mode="list", length=length(group_levs)) 54 | for (i in seq_len(length(group_levs))) { 55 | temp <- plotdata[plotdata$group==group_levs[i], ] 56 | out_i <- cont_surv_auc(plotdata=temp, tau=tau) 57 | out_i$group <- group_levs[i] 58 | out[[i]] <- out_i 59 | } 60 | out <- dplyr::bind_rows(out) 61 | } 62 | 63 | # plot them 64 | p <- ggplot2::ggplot(out, ggplot2::aes(x=.data$cont, y=.data$auc, 65 | color=.data$tau)) 66 | 67 | if (length(tau)==1) { 68 | p$mapping$colour <- NULL 69 | gg_line <- ggplot2::geom_line(linewidth=size, linetype=linetype, 70 | alpha=alpha, color=color) 71 | } else { 72 | gg_line <- ggplot2::geom_line(linewidth=size, linetype=linetype, 73 | alpha=alpha) 74 | } 75 | 76 | p <- p + gg_line + 77 | ggplot2::labs(x=xlab, y=ylab, title=title, subtitle=subtitle, 78 | fill=legend.title) + 79 | gg_theme + 80 | ggplot2::theme(legend.position=legend.position) 81 | 82 | if (length(tau) > 1 & !is.null(custom_colors)) { 83 | p <- p + ggplot2::scale_colour_manual(values=custom_colors) 84 | } 85 | # facet plot by factor variable 86 | if (!is.null(group)) { 87 | facet_args$facets <- stats::as.formula("~ group") 88 | facet_obj <- do.call(ggplot2::facet_wrap, facet_args) 89 | p <- p + facet_obj 90 | } 91 | 92 | return(p) 93 | } 94 | -------------------------------------------------------------------------------- /R/zzz.r: -------------------------------------------------------------------------------- 1 | 2 | .onAttach <- function(libname, pkgname) { 3 | cit <- paste0('Denz R, Timmesfeld N (2023). "Visualizing the', 4 | " (Causal) Effect of a Continuous Variable on a", 5 | ' Time-To-Event Outcome." Epidemiology, 34 (5).', 6 | " doi: 10.1097/EDE.0000000000001630.") 7 | packageStartupMessage("Please cite as: \n\n", cit) 8 | } 9 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | [![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) 3 | [![](https://www.r-pkg.org/badges/version/contsurvplot?color=green)](https://cran.r-project.org/package=contsurvplot) 4 | [![](http://cranlogs.r-pkg.org/badges/grand-total/contsurvplot?color=blue)](https://cran.r-project.org/package=contsurvplot) 5 | [![R-CMD-check](https://github.com/RobinDenz1/contsurvplot/workflows/R-CMD-check/badge.svg)](https://github.com/RobinDenz1/contsurvplot/actions) 6 | [![Codecov test coverage](https://codecov.io/gh/RobinDenz1/contsurvplot/branch/main/graph/badge.svg)](https://app.codecov.io/gh/RobinDenz1/contsurvplot?branch=main) 7 | [![](https://img.shields.io/badge/doi-10.1097/EDE.0000000000001630-green.svg)](https://doi.org/10.1097/EDE.0000000000001630) 8 | 9 | 10 | # contsurvplot 11 | 12 | Author: Robin Denz 13 | 14 | ## Description 15 | 16 | `contsurvplot` is an R-Package which can be used to visualize the causal effect of a continuous variable on a time-to-event outcome. 17 | It includes multiple different plot types, such as survival area plots, contour plots, heatmaps, survival quantile plots and more. 18 | All of them can be adjusted for confounders and all of them have a lot of build in options to customize them according to the users needs. 19 | Also, most of the plot functions are based on the `ggplot2` package, allowing the user to use the standard `ggplot2` syntax to customize the plots further. 20 | 21 | ## Installation 22 | 23 | The stable release version can be installed directly from CRAN using: 24 | 25 | ```R 26 | install.packages("contsurvplot") 27 | ``` 28 | 29 | Alternatively, the development version can be installed using the `devtools` R-Package: 30 | 31 | ```R 32 | library(devtools) 33 | 34 | devtools::install_github("RobinDenz1/contsurvplot") 35 | ``` 36 | 37 | or the `remotes` R-Package: 38 | 39 | ```R 40 | library(remotes) 41 | 42 | remotes::install_github("RobinDenz1/contsurvplot") 43 | ``` 44 | 45 | ## Bug Reports and Feature Requests 46 | 47 | If you encounter any bugs or have any specific feature requests, please file an [Issue](https://github.com/RobinDenz1/contsurvplot/issues). 48 | 49 | ## Examples 50 | 51 | Here are two quick examples using the `colon` dataset from the `survival` R-Package. 52 | Suppose we want to visualize the effect of the number of lymph nodes with detectable cancer (column `nodes`) on the survival time. 53 | A survival area plot can be produced using the following code: 54 | 55 | ```R 56 | library(contsurvplot) 57 | library(ggplot2) 58 | library(survival) 59 | library(riskRegression) 60 | 61 | # load colon data 62 | data(cancer) 63 | 64 | # fit cox model, adjusting for age and sex 65 | model <- coxph(Surv(time, status) ~ age + sex + nodes, data=colon, x=TRUE) 66 | 67 | # plot survival area 68 | plot_surv_area(time="time", 69 | status="status", 70 | variable="nodes", 71 | data=colon, 72 | model=model) 73 | 74 | ``` 75 | 76 | 77 | Alternatively, we can plot a contour plot to visualize the effect: 78 | 79 | ```R 80 | plot_surv_contour(time="time", 81 | status="status", 82 | variable="nodes", 83 | data=colon, 84 | model=model) 85 | ``` 86 | 87 | 88 | Or we can use a simple plot of the median survival time as a function of `nodes`: 89 | 90 | ```R 91 | plot_surv_quantiles(time="time", 92 | status="status", 93 | variable="nodes", 94 | data=colon, 95 | model=model, 96 | p=0.5) 97 | ``` 98 | 99 | 100 | More examples can be found in the documentation and the vignette. 101 | 102 | ## Citation 103 | 104 | The main paper associated with this R-Package is: 105 | 106 | Robin Denz, Nina Timmesfeld (2023). *Visualizing the (causal) effect of a continuous variable on a time-to-event outcome*. Epidemiology. 34.5 107 | 108 | In addition, some relevant literature can be found in the documentation pages. 109 | 110 | ## License 111 | 112 | © 2022 Robin Denz 113 | 114 | The contents of this repository are distributed under the GNU General Public License. You can find the full text of this License in this github repository. Alternatively, see . 115 | -------------------------------------------------------------------------------- /TODO.txt: -------------------------------------------------------------------------------- 1 | 2 | - add "plotdata" argument to allow the user to directly supply data himself 3 | - refactor auc / median survival calculation functions and export them (with man pages + tests) 4 | - add mid_color option 5 | -------------------------------------------------------------------------------- /_pkgdown.yml: -------------------------------------------------------------------------------- 1 | url: https://robindenz1.github.io/contsurvplot/ 2 | template: 3 | bootstrap: 5 4 | 5 | -------------------------------------------------------------------------------- /codecov.yml: -------------------------------------------------------------------------------- 1 | comment: false 2 | 3 | coverage: 4 | status: 5 | project: 6 | default: 7 | target: auto 8 | threshold: 1% 9 | informational: true 10 | patch: 11 | default: 12 | target: auto 13 | threshold: 1% 14 | informational: true 15 | -------------------------------------------------------------------------------- /contsurvplot.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | 18 | BuildType: Package 19 | PackageUseDevtools: Yes 20 | PackageInstallArgs: --no-multiarch --with-keep.source 21 | -------------------------------------------------------------------------------- /cran-comments.md: -------------------------------------------------------------------------------- 1 | ## R CMD check results 2 | 3 | 0 errors | 0 warnings | 0 notes 4 | 5 | * This is an update. 6 | 7 | The package was tested on Windows 10, macOS and ubuntu using github actions and rhub as well as local machines. 8 | There were no errors or warnings and no notes. 9 | -------------------------------------------------------------------------------- /docs/.nojekyll: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /docs/apple-touch-icon-120x120.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/docs/apple-touch-icon-120x120.png -------------------------------------------------------------------------------- /docs/apple-touch-icon-152x152.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/docs/apple-touch-icon-152x152.png -------------------------------------------------------------------------------- /docs/apple-touch-icon-180x180.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/docs/apple-touch-icon-180x180.png -------------------------------------------------------------------------------- /docs/apple-touch-icon-60x60.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/docs/apple-touch-icon-60x60.png -------------------------------------------------------------------------------- /docs/apple-touch-icon-76x76.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/docs/apple-touch-icon-76x76.png -------------------------------------------------------------------------------- /docs/apple-touch-icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/docs/apple-touch-icon.png -------------------------------------------------------------------------------- /docs/articles/index.html: -------------------------------------------------------------------------------- 1 | 2 | Articles • contsurvplot 6 | Skip to contents 7 | 8 | 9 |
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Visualizing the Causal Effect of a Continuous Variable on a Time-To-Event Outcome
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| 8 | 12 | 13 | -------------------------------------------------------------------------------- /docs/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/docs/logo.png -------------------------------------------------------------------------------- /docs/pkgdown.js: -------------------------------------------------------------------------------- 1 | /* http://gregfranko.com/blog/jquery-best-practices/ */ 2 | (function($) { 3 | $(function() { 4 | 5 | $('nav.navbar').headroom(); 6 | 7 | Toc.init({ 8 | $nav: $("#toc"), 9 | $scope: $("main h2, main h3, main h4, main h5, main h6") 10 | }); 11 | 12 | if ($('#toc').length) { 13 | $('body').scrollspy({ 14 | target: '#toc', 15 | offset: $("nav.navbar").outerHeight() + 1 16 | }); 17 | } 18 | 19 | // Activate popovers 20 | $('[data-bs-toggle="popover"]').popover({ 21 | container: 'body', 22 | html: true, 23 | trigger: 'focus', 24 | placement: "top", 25 | sanitize: false, 26 | }); 27 | 28 | $('[data-bs-toggle="tooltip"]').tooltip(); 29 | 30 | /* Clipboard --------------------------*/ 31 | 32 | function changeTooltipMessage(element, msg) { 33 | var tooltipOriginalTitle=element.getAttribute('data-original-title'); 34 | element.setAttribute('data-original-title', msg); 35 | $(element).tooltip('show'); 36 | element.setAttribute('data-original-title', tooltipOriginalTitle); 37 | } 38 | 39 | if(ClipboardJS.isSupported()) { 40 | $(document).ready(function() { 41 | var copyButton = ""; 42 | 43 | $("div.sourceCode").addClass("hasCopyButton"); 44 | 45 | // Insert copy buttons: 46 | $(copyButton).prependTo(".hasCopyButton"); 47 | 48 | // Initialize tooltips: 49 | $('.btn-copy-ex').tooltip({container: 'body'}); 50 | 51 | // Initialize clipboard: 52 | var clipboard = new ClipboardJS('[data-clipboard-copy]', { 53 | text: function(trigger) { 54 | return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); 55 | } 56 | }); 57 | 58 | clipboard.on('success', function(e) { 59 | changeTooltipMessage(e.trigger, 'Copied!'); 60 | e.clearSelection(); 61 | }); 62 | 63 | clipboard.on('error', function() { 64 | changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); 65 | }); 66 | 67 | }); 68 | } 69 | 70 | /* Search marking --------------------------*/ 71 | var url = new URL(window.location.href); 72 | var toMark = url.searchParams.get("q"); 73 | var mark = new Mark("main#main"); 74 | if (toMark) { 75 | mark.mark(toMark, { 76 | accuracy: { 77 | value: "complementary", 78 | limiters: [",", ".", ":", "/"], 79 | } 80 | }); 81 | } 82 | 83 | /* Search --------------------------*/ 84 | /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ 85 | // Initialise search index on focus 86 | var fuse; 87 | $("#search-input").focus(async function(e) { 88 | if (fuse) { 89 | return; 90 | } 91 | 92 | $(e.target).addClass("loading"); 93 | var response = await fetch($("#search-input").data("search-index")); 94 | var data = await response.json(); 95 | 96 | var options = { 97 | keys: ["what", "text", "code"], 98 | ignoreLocation: true, 99 | threshold: 0.1, 100 | includeMatches: true, 101 | includeScore: true, 102 | }; 103 | fuse = new Fuse(data, options); 104 | 105 | $(e.target).removeClass("loading"); 106 | }); 107 | 108 | // Use algolia autocomplete 109 | var options = { 110 | autoselect: true, 111 | debug: true, 112 | hint: false, 113 | minLength: 2, 114 | }; 115 | var q; 116 | async function searchFuse(query, callback) { 117 | await fuse; 118 | 119 | var items; 120 | if (!fuse) { 121 | items = []; 122 | } else { 123 | q = query; 124 | var results = fuse.search(query, { limit: 20 }); 125 | items = results 126 | .filter((x) => x.score <= 0.75) 127 | .map((x) => x.item); 128 | if (items.length === 0) { 129 | items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}]; 130 | } 131 | } 132 | callback(items); 133 | } 134 | $("#search-input").autocomplete(options, [ 135 | { 136 | name: "content", 137 | source: searchFuse, 138 | templates: { 139 | suggestion: (s) => { 140 | if (s.title == s.what) { 141 | return `${s.dir} >
${s.title}
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${s.title}
> ${s.what}`; 144 | } else { 145 | return `${s.dir} >
${s.title}
> ${s.previous_headings} > ${s.what}`; 146 | } 147 | }, 148 | }, 149 | }, 150 | ]).on('autocomplete:selected', function(event, s) { 151 | window.location.href = s.path + "?q=" + q + "#" + s.id; 152 | }); 153 | }); 154 | })(window.jQuery || window.$) 155 | 156 | 157 | -------------------------------------------------------------------------------- /docs/pkgdown.yml: -------------------------------------------------------------------------------- 1 | pandoc: '2.18' 2 | pkgdown: 2.0.6 3 | pkgdown_sha: ~ 4 | articles: 5 | introduction: introduction.html 6 | last_built: 2022-08-09T12:32Z 7 | urls: 8 | reference: https://robindenz1.github.io/contsurvplot/reference 9 | article: https://robindenz1.github.io/contsurvplot/articles 10 | 11 | -------------------------------------------------------------------------------- /docs/reference/Rplot001.png: -------------------------------------------------------------------------------- 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/inst/CITATION: -------------------------------------------------------------------------------- 1 | year <- sub("-.*", "", meta$Date) 2 | note <- sprintf("R package version %s", meta$Version) 3 | 4 | bibentry(bibtype = "Article", 5 | title = "Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome", 6 | author = c(person("Robin", "Denz"), 7 | person("Nina", "Timmesfeld")), 8 | journal = "Epidemiology", 9 | year = 2023, 10 | volume = 34, 11 | number = 5, 12 | url = "https://doi.org/10.1097/EDE.0000000000001630") -------------------------------------------------------------------------------- /inst/testdata/sim150.Rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/inst/testdata/sim150.Rds -------------------------------------------------------------------------------- /inst/testdata/sim500.Rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/inst/testdata/sim500.Rds -------------------------------------------------------------------------------- /man/contsurvplot-package.Rd: -------------------------------------------------------------------------------- 1 | \name{contsurvplot-package} 2 | \alias{contsurvplot-package} 3 | 4 | \title{ 5 | Visualize the Effect of a Continuous Variable on a Time-To-Event Outcome 6 | } 7 | \description{ 8 | 9 | \strong{\emph{What is this package about?}} 10 | 11 | This package provides different plotting routines to visualize the (causal) effect of a continuous variable on a time-to-event outcome using a previously fit model and g-computation. Unlike simpler alternatives, such as plotting survival curves for some categories, these plots always correspond to the results obtained by the time-to-event model and give an accurate depiction of the causal effect, if all assumptions are met. 12 | 13 | \strong{\emph{What features are included in this package?}} 14 | 15 | The package includes 11 different plot functions, most based on the \pkg{ggplot2} package. Those 11 plotting routines include \emph{value specific survival curves}, \emph{landmark survival probability plots}, \emph{survival time quantile plots}, \emph{survival probability heatmaps}, and \emph{survival area plots}, among others. A description and comparison of these plots can be found in the article associated with this R-package (Denz & Timmesfeld 2022). 16 | 17 | \strong{\emph{What does a typical workflow using this package look like?}} 18 | 19 | All the user has to do is fit a time-to-event model, such as the cox-model, including the continuous variable of interest (and possibly confounders) and plug it into one of the plot functions included in this package. Many different kind of models are supported. See \code{\link[contsurvplot]{curve_cont}} for more details. 20 | 21 | \strong{\emph{What type of plot should I use?}} 22 | 23 | There is no general answer to this question, but we would usually suggest using a plot method that is able to visualize the causal survival probability both as a function of time and as a function of the continuous variable. The \code{\link[contsurvplot]{plot_surv_area}}, \code{\link[contsurvplot]{plot_surv_heatmap}} and \code{\link[contsurvplot]{plot_surv_contour}} functions do just that. More discussion about this topic can be found in the vignette and the associated paper. 24 | 25 | \strong{\emph{What is the difference between displaying causal effects and associations?}} 26 | 27 | The plots generated by this package offer different ways to depict the association between a continuous variable and a time-to-event outcome. Under certain causal identifiability assumptions, which are described in detail in our article on this topic (see references), this association can be endowed with a causal interpretation. Under these assumptions, the estimates can be interpreted as the survival probability that would have been observed if all individuals in the target population had received a specific level of the continuous variable. If these assumptions are not met, this interpretation is invalid. 28 | 29 | \strong{\emph{Where can I get more information?}} 30 | 31 | The documentation pages contain a lot of information, relevant examples and literature references. Additional examples can be found in the vignette of this package, which can be accessed using \code{vignette(topic="introduction", package="contsurvplot")}. We also published a preprint of the article about this package on arXiv (see references), which contains an in-depth discussion about the plots and how to interpret them. 32 | 33 | \strong{\emph{I want to suggest a new feature / I want to report a bug. Where can I do this?}} 34 | 35 | Bug reports, suggestions and feature requests are highly welcome. Please file an issue on the official github page () or contact the author directly using the supplied e-mail address. 36 | 37 | } 38 | \references{ 39 | Robin Denz, Nina Timmesfeld (2023). "Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome". In: Epidemiology 34.5 40 | 41 | James Robins. A New Approach to Causal Inference in Mortality Studies with a Sustained Exposure Period: Application to Control of the Healthy Worker Survivor Effect. Mathematical Modelling (1986) 7, pages 1393-1512. 42 | } 43 | \author{ 44 | Robin Denz (robin.denz@rub.de) 45 | } 46 | -------------------------------------------------------------------------------- /man/figures/example_area.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/man/figures/example_area.png -------------------------------------------------------------------------------- /man/figures/example_contour.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobinDenz1/contsurvplot/d56dc53f0199049a0ac4595902c67020114c1241/man/figures/example_contour.png -------------------------------------------------------------------------------- /man/figures/example_quantiles.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 0.35 48 | 0.40 49 | 0.45 50 | 0.50 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 20 59 | 40 60 | 60 61 | x3 62 | Cumulative Incidence at t 63 | 64 | 65 | -------------------------------------------------------------------------------- /tests/testthat/_snaps/plot_surv_at_t/plot-lots-of-stuff.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 0.65 35 | 0.70 36 | 0.75 37 | 0.80 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 20 47 | 40 48 | 60 49 | x 50 | y 51 | Subtitle 52 | Title 53 | 54 | 55 | -------------------------------------------------------------------------------- /tests/testthat/_snaps/plot_surv_quantiles/plot-lots-of-stuff.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 20 35 | 22 36 | 24 37 | 26 38 | 28 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 20 49 | 40 50 | 60 51 | x 52 | y 53 | Subtitle 54 | Title 55 | 56 | 57 | -------------------------------------------------------------------------------- /tests/testthat/_snaps/plot_surv_rmtl/plot-lots-of-stuff.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 12 35 | 13 36 | 14 37 | 15 38 | 16 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 20 49 | 40 50 | 60 51 | x 52 | y 53 | Subtitle 54 | Title 55 | 56 | 57 | -------------------------------------------------------------------------------- /tests/testthat/test_.onAttach.r: -------------------------------------------------------------------------------- 1 | 2 | test_that("package startup message", { 3 | expect_message(.onAttach()) 4 | }) 5 | -------------------------------------------------------------------------------- /tests/testthat/test_check_horizon.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | 7 | test_that("wrong type", { 8 | expect_error(check_horizon(horizon="a", 9 | data=sim_dat, 10 | variable="x2"), 11 | "'horizon' must be a numeric vector with at least one value or NULL.") 12 | }) 13 | 14 | test_that("NA in horizon", { 15 | expect_error(check_horizon(horizon=c(1, 2, NA), 16 | data=sim_dat, 17 | variable="x2"), 18 | "Missing values in 'horizon' are not allowed.") 19 | }) 20 | 21 | test_that("too small", { 22 | expect_warning(check_horizon(horizon=c(-100, 2), 23 | data=sim_dat, 24 | variable="x2"), 25 | paste0("Some values in 'horizon' are smaller than the", 26 | " minimum observed in 'data', which might lead to problems.")) 27 | }) 28 | 29 | test_that("too big", { 30 | expect_warning(check_horizon(horizon=c(100, 2), 31 | data=sim_dat, 32 | variable="x2"), 33 | paste0("Some values in 'horizon' are bigger than the", 34 | " maximum observed in 'data', which might lead to problems.")) 35 | }) 36 | -------------------------------------------------------------------------------- /tests/testthat/test_get_kaplan_meier.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | test_that("standard case", { 12 | out <- get_kaplan_meier(time="time", status="event", data=sim_dat) 13 | expect_true(is.data.frame(out)) 14 | expect_true(ncol(out)==2 & nrow(out)==151) 15 | }) 16 | 17 | test_that("with conf_int", { 18 | out <- get_kaplan_meier(time="time", status="event", data=sim_dat, 19 | conf_int=TRUE) 20 | expect_true(is.data.frame(out)) 21 | expect_true(ncol(out)==4 & nrow(out)==151) 22 | }) 23 | 24 | test_that("with group", { 25 | out <- get_kaplan_meier(time="time", status="event", data=sim_dat, 26 | group="group") 27 | expect_true(is.data.frame(out)) 28 | expect_true(ncol(out)==3 & nrow(out)==152) 29 | }) 30 | 31 | test_that("with group and conf_int", { 32 | out <- get_kaplan_meier(time="time", status="event", data=sim_dat, 33 | conf_int=TRUE, group="group") 34 | expect_true(is.data.frame(out)) 35 | expect_true(ncol(out)==5 & nrow(out)==152) 36 | }) 37 | 38 | test_that("with conf_int and cif", { 39 | out <- get_kaplan_meier(time="time", status="event", data=sim_dat, 40 | conf_int=TRUE, cif=TRUE) 41 | expect_true(is.data.frame(out)) 42 | expect_true(ncol(out)==4 & nrow(out)==151) 43 | }) 44 | 45 | test_that("fixed_t", { 46 | out <- get_kaplan_meier(time="time", status="event", data=sim_dat, 47 | fixed_t=c(22, 40)) 48 | expect_true(is.data.frame(out)) 49 | expect_true(ncol(out)==2 & nrow(out)==2 & all(out$time==c(22, 40))) 50 | }) 51 | 52 | test_that("fixed_t, with group", { 53 | out <- get_kaplan_meier(time="time", status="event", data=sim_dat, 54 | group="group", fixed_t=c(22, 40)) 55 | expect_true(is.data.frame(out)) 56 | expect_true(ncol(out)==3 & nrow(out)==4 & all(out$time==c(22, 40, 22, 40))) 57 | }) 58 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_3Dsurface.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | 7 | model <- survival::coxph(survival::Surv(time, event) ~ x3, data=sim_dat, x=TRUE) 8 | 9 | test_that("plot, defaults", { 10 | plt <- plot_surv_3Dsurface(time="time", status="event", variable="x3", 11 | data=sim_dat, model=model) 12 | expect_true(is.matrix(plt)) 13 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 14 | }) 15 | 16 | test_that("plot, cif", { 17 | plt <- plot_surv_3Dsurface(time="time", status="event", variable="x3", 18 | data=sim_dat, model=model, cif=TRUE, 19 | na.action=na.omit) 20 | expect_true(is.matrix(plt)) 21 | vdiffr::expect_doppelganger("plot, cif", fig=plt) 22 | }) 23 | 24 | test_that("plot, change horizon", { 25 | plt <- plot_surv_3Dsurface(time="time", status="event", variable="x3", 26 | data=sim_dat, model=model, horizon=seq(30, 60, 1)) 27 | expect_true(is.matrix(plt)) 28 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 29 | }) 30 | 31 | test_that("plot, at t", { 32 | plt <- plot_surv_3Dsurface(time="time", status="event", variable="x3", 33 | data=sim_dat, model=model, fixed_t=seq(0, 30, 1)) 34 | expect_true(is.matrix(plt)) 35 | vdiffr::expect_doppelganger("plot, at t", fig=plt) 36 | }) 37 | 38 | test_that("plot, custom non-interactive", { 39 | plt <- plot_surv_3Dsurface(time="time", status="event", variable="x3", 40 | data=sim_dat, model=model, col="red", 41 | ticktype="simple", phi=30, theta=200, shade=0.3) 42 | expect_true(is.matrix(plt)) 43 | vdiffr::expect_doppelganger("plot, custom non-interactive", fig=plt) 44 | }) 45 | 46 | test_that("plot, interactive", { 47 | plt <- plot_surv_3Dsurface(time="time", status="event", variable="x3", 48 | data=sim_dat, model=model, color="red", 49 | interactive=TRUE) 50 | expect_s3_class(plt, "plotly") 51 | }) 52 | 53 | test_that("plot, interactive + args", { 54 | plt <- plot_surv_3Dsurface(time="time", status="event", variable="x3", 55 | data=sim_dat, model=model, color="red", 56 | interactive=TRUE, xlab="x", ylab="y", zlab="z", 57 | cif=TRUE) 58 | expect_s3_class(plt, "plotly") 59 | }) 60 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_animated.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | test_that("plot, defaults", { 12 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 13 | data=sim_dat, model=model) 14 | expect_s3_class(plt, "plotly") 15 | }) 16 | 17 | test_that("plot, kaplan_meier", { 18 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 19 | data=sim_dat, model=model, kaplan_meier=TRUE, 20 | km_size=1, km_alpha=0.8, km_linetype="dashed", 21 | km_color="red") 22 | expect_s3_class(plt, "plotly") 23 | }) 24 | 25 | test_that("plot, cif", { 26 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 27 | data=sim_dat, model=model, cif=TRUE) 28 | expect_s3_class(plt, "plotly") 29 | }) 30 | 31 | test_that("plot, change horizon", { 32 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 33 | data=sim_dat, model=model, horizon=seq(30, 60, 1)) 34 | expect_s3_class(plt, "plotly") 35 | }) 36 | 37 | test_that("plot, at t", { 38 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 39 | data=sim_dat, model=model, fixed_t=seq(0, 30, 1), 40 | na.action=na.omit) 41 | expect_s3_class(plt, "plotly") 42 | }) 43 | 44 | test_that("plot, custom color", { 45 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 46 | data=sim_dat, model=model, color="red") 47 | expect_s3_class(plt, "plotly") 48 | }) 49 | 50 | test_that("plot, no slider", { 51 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 52 | data=sim_dat, model=model, color="red", 53 | slider=FALSE) 54 | expect_s3_class(plt, "gganim") 55 | }) 56 | 57 | test_that("plot, no slider, kaplan_meier", { 58 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 59 | data=sim_dat, model=model, color="red", 60 | slider=FALSE, kaplan_meier=TRUE, 61 | km_size=1, km_alpha=0.8, km_linetype="dashed", 62 | km_color="red", km_ci=TRUE, km_ci_type="log", 63 | km_ci_level=0.9, km_ci_alpha=0.3) 64 | expect_s3_class(plt, "gganim") 65 | }) 66 | 67 | test_that("plot, no slider, with ci", { 68 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 69 | data=sim_dat, model=model, color="red", 70 | slider=FALSE, conf_int=TRUE, n_boot=3) 71 | expect_s3_class(plt, "gganim") 72 | }) 73 | 74 | test_that("plot, with group", { 75 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 76 | group="group", data=sim_dat, model=model) 77 | expect_s3_class(plt, "plotly") 78 | }) 79 | 80 | test_that("plot, lots of stuff", { 81 | plt <- plot_surv_animated(time="time", status="event", variable="x3", 82 | data=sim_dat, model=model, cif=TRUE, 83 | linetype="dashed", alpha=0.8, title="Title", 84 | subtitle="Subtitle", xlab="x", ylab="y", 85 | gg_theme=ggplot2::theme_classic()) 86 | expect_s3_class(plt, "plotly") 87 | }) 88 | 89 | test_that("error when trying conf_int with slider", { 90 | expect_error(plot_surv_animated(time="time", status="event", variable="x3", 91 | group="group", data=sim_dat, model=model, 92 | conf_int=TRUE, n_boot=2), 93 | paste0("Showing confidence intervals is currently not ", 94 | "supported when using slider=TRUE.")) 95 | }) 96 | 97 | test_that("error when trying km_ci with slider", { 98 | expect_error(plot_surv_animated(time="time", status="event", variable="x3", 99 | group="group", data=sim_dat, model=model, 100 | kaplan_meier=TRUE, km_ci=TRUE), 101 | paste0("Showing confidence intervals is currently not ", 102 | "supported when using slider=TRUE.")) 103 | }) 104 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_area.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | sim_dat$x4 <- sim_dat$x3 12 | sim_dat$x4[sim_dat$x4 > 30] <- sim_dat$x4[sim_dat$x4 > 30] - 100 13 | model2 <- survival::coxph(survival::Surv(time, event) ~ splines::bs(x4, df=3), 14 | data=sim_dat, x=TRUE) 15 | 16 | test_that("plot, defaults", { 17 | plt <- plot_surv_area(time="time", status="event", variable="x3", 18 | data=sim_dat, model=model) 19 | expect_s3_class(plt, "ggplot") 20 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 21 | }) 22 | 23 | test_that("plot, with group", { 24 | plt <- plot_surv_area(time="time", status="event", variable="x3", 25 | group="group", data=sim_dat, model=model) 26 | expect_s3_class(plt, "ggplot") 27 | vdiffr::expect_doppelganger("plot, with group", fig=plt) 28 | }) 29 | 30 | test_that("plot, with nonmonotonic", { 31 | plt <- plot_surv_area(time="time", status="event", variable="x4", 32 | monotonic=FALSE, data=sim_dat, model=model2) 33 | expect_s3_class(plt, "ggplot") 34 | vdiffr::expect_doppelganger("plot, with nonmonotonic", fig=plt) 35 | }) 36 | 37 | test_that("plot, cif", { 38 | plt <- plot_surv_area(time="time", status="event", variable="x3", 39 | data=sim_dat, model=model, cif=TRUE, 40 | na.action=na.omit) 41 | expect_s3_class(plt, "ggplot") 42 | vdiffr::expect_doppelganger("plot, cif", fig=plt) 43 | }) 44 | 45 | test_that("plot, change horizon", { 46 | plt <- plot_surv_area(time="time", status="event", variable="x3", 47 | data=sim_dat, model=model, horizon=seq(30, 60, 1)) 48 | expect_s3_class(plt, "ggplot") 49 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 50 | }) 51 | 52 | test_that("plot, at t", { 53 | plt <- plot_surv_area(time="time", status="event", variable="x3", 54 | data=sim_dat, model=model, fixed_t=seq(0, 30, 1)) 55 | expect_s3_class(plt, "ggplot") 56 | vdiffr::expect_doppelganger("plot, at t", fig=plt) 57 | }) 58 | 59 | test_that("plot, custom colors", { 60 | plt <- plot_surv_area(time="time", status="event", variable="x3", 61 | data=sim_dat, model=model, start_color="grey", 62 | end_color="black") 63 | expect_s3_class(plt, "ggplot") 64 | vdiffr::expect_doppelganger("plot, custom colors", fig=plt) 65 | }) 66 | 67 | test_that("plot, discrete + bins", { 68 | plt <- plot_surv_area(time="time", status="event", variable="x3", 69 | data=sim_dat, model=model, discrete=TRUE, bins=4) 70 | expect_s3_class(plt, "ggplot") 71 | vdiffr::expect_doppelganger("plot, discrete + bins", fig=plt) 72 | }) 73 | 74 | test_that("plot, discrete round", { 75 | plt <- plot_surv_area(time="time", status="event", variable="x3", 76 | data=sim_dat, model=model, discrete=TRUE, bins=4, 77 | label_digits=3) 78 | expect_s3_class(plt, "ggplot") 79 | vdiffr::expect_doppelganger("plot, discrete round", fig=plt) 80 | }) 81 | 82 | test_that("plot, sep_lines", { 83 | plt <- plot_surv_area(time="time", status="event", variable="x3", 84 | data=sim_dat, model=model, discrete=TRUE, 85 | sep_lines=TRUE, sep_size=0.2, sep_color="green", 86 | sep_linetype="dashed") 87 | expect_s3_class(plt, "ggplot") 88 | vdiffr::expect_doppelganger("plot, sep_lines", fig=plt) 89 | }) 90 | 91 | test_that("plot, with kaplan_meier", { 92 | plt <- plot_surv_area(time="time", status="event", variable="x3", 93 | data=sim_dat, model=model, discrete=TRUE, 94 | kaplan_meier=TRUE, km_size=1, km_color="white", 95 | km_alpha=0.8, km_linetype="dashed") 96 | expect_s3_class(plt, "ggplot") 97 | vdiffr::expect_doppelganger("plot, with kaplan_meier", fig=plt) 98 | }) 99 | 100 | test_that("plot, lots of stuff", { 101 | plt <- plot_surv_area(time="time", status="event", variable="x3", 102 | data=sim_dat, model=model, start_color="grey", 103 | end_color="black", cif=TRUE, discrete=TRUE, 104 | bins=4, sep_lines=TRUE, sep_size=0.2, title="Title", 105 | subtitle="Subtitle", xlab="x", ylab="y", 106 | legend.title="legend", legend.position="bottom", 107 | gg_theme=ggplot2::theme_classic()) 108 | expect_s3_class(plt, "ggplot") 109 | vdiffr::expect_doppelganger("plot, lots of stuff", fig=plt) 110 | }) 111 | 112 | test_that("using group with monotonic=FALSE", { 113 | expect_error(plot_surv_area(time="time", status="event", variable="x3", 114 | monotonic=FALSE, group="group", 115 | data=sim_dat, model=model), 116 | "The 'group' argument cannot be used with monotonic=FALSE.") 117 | }) 118 | 119 | test_that("using discrete with monotonic=FALSE", { 120 | expect_warning(plot_surv_area(time="time", status="event", variable="x3", 121 | monotonic=FALSE, discrete=TRUE, 122 | data=sim_dat, model=model), 123 | paste0("To obtain valid results when using monotonic=FALSE, the", 124 | " 'horizon' should contain at least 40 distinct values.")) 125 | }) 126 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_at_t.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | set.seed(34) 12 | 13 | test_that("plot, defaults", { 14 | plt <- plot_surv_at_t(time="time", status="event", variable="x3", 15 | data=sim_dat, model=model, t=20) 16 | expect_s3_class(plt, "ggplot") 17 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 18 | }) 19 | 20 | test_that("plot, with ci, one t", { 21 | plt <- plot_surv_at_t(time="time", status="event", variable="x3", 22 | data=sim_dat, model=model, t=20, conf_int=TRUE, 23 | n_boot=3) 24 | expect_s3_class(plt, "ggplot") 25 | vdiffr::expect_doppelganger("plot, with ci, one t", fig=plt) 26 | }) 27 | 28 | test_that("plot, with ci, multiple t", { 29 | plt <- plot_surv_at_t(time="time", status="event", variable="x3", 30 | data=sim_dat, model=model, t=c(20, 25), conf_int=TRUE, 31 | n_boot=3) 32 | expect_s3_class(plt, "ggplot") 33 | vdiffr::expect_doppelganger("plot, with ci, multiple t", fig=plt) 34 | }) 35 | 36 | test_that("plot, cif", { 37 | plt <- plot_surv_at_t(time="time", status="event", variable="x3", 38 | data=sim_dat, model=model, cif=TRUE, t=20, 39 | na.action=na.omit) 40 | expect_s3_class(plt, "ggplot") 41 | vdiffr::expect_doppelganger("plot, cif", fig=plt) 42 | }) 43 | 44 | test_that("plot, change horizon", { 45 | plt <- plot_surv_at_t(time="time", status="event", variable="x3", 46 | data=sim_dat, model=model, horizon=seq(30, 60, 1), 47 | t=20) 48 | expect_s3_class(plt, "ggplot") 49 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 50 | }) 51 | 52 | test_that("plot, custom colors", { 53 | plt <- plot_surv_at_t(time="time", status="event", variable="x3", 54 | data=sim_dat, model=model, t=c(7, 20, 45), 55 | custom_colors=c("black", "red", "green")) 56 | expect_s3_class(plt, "ggplot") 57 | vdiffr::expect_doppelganger("plot, custom colors", fig=plt) 58 | }) 59 | 60 | test_that("plot, with group", { 61 | plt <- plot_surv_at_t(time="time", status="event", variable="x3", 62 | group="group", data=sim_dat, model=model, t=20) 63 | expect_s3_class(plt, "ggplot") 64 | vdiffr::expect_doppelganger("plot, with group", fig=plt) 65 | }) 66 | 67 | test_that("plot, lots of stuff", { 68 | plt <- plot_surv_at_t(time="time", status="event", variable="x3", 69 | data=sim_dat, model=model, cif=TRUE, t=35, 70 | linetype="dashed", alpha=0.8, title="Title", 71 | subtitle="Subtitle", xlab="x", ylab="y", 72 | gg_theme=ggplot2::theme_classic()) 73 | expect_s3_class(plt, "ggplot") 74 | vdiffr::expect_doppelganger("plot, lots of stuff", fig=plt) 75 | }) 76 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_contour.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | # NOTE: The expect_doppelganger() tests sometimes do not work properly 12 | # for contour plots for unknown reasons. The output looks the same, 13 | # but gets flagged as different by ubuntu for example. Skipped. 14 | 15 | # to skip tests on github 16 | skip_on_actions <- function() { 17 | if (!identical(Sys.getenv("GITHUB_ACTIONS"), "true")) { 18 | return(invisible(TRUE)) 19 | } 20 | skip("On GitHub Actions") 21 | } 22 | 23 | test_that("plot, defaults", { 24 | plt <- plot_surv_contour(time="time", status="event", variable="x3", 25 | data=sim_dat, model=model) 26 | expect_s3_class(plt, "ggplot") 27 | skip_on_cran() 28 | skip_on_covr() 29 | skip_on_actions() 30 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 31 | }) 32 | 33 | test_that("plot, with group", { 34 | plt <- plot_surv_contour(time="time", status="event", variable="x3", 35 | group="group", data=sim_dat, model=model) 36 | expect_s3_class(plt, "ggplot") 37 | skip_on_cran() 38 | skip_on_covr() 39 | skip_on_actions() 40 | vdiffr::expect_doppelganger("plot, with group", fig=plt) 41 | }) 42 | 43 | test_that("plot, cif", { 44 | plt <- plot_surv_contour(time="time", status="event", variable="x3", 45 | data=sim_dat, model=model, cif=TRUE) 46 | expect_s3_class(plt, "ggplot") 47 | skip_on_cran() 48 | skip_on_covr() 49 | skip_on_actions() 50 | vdiffr::expect_doppelganger("plot, cif", fig=plt) 51 | }) 52 | 53 | test_that("plot, change horizon", { 54 | plt <- plot_surv_contour(time="time", status="event", variable="x3", 55 | data=sim_dat, model=model, horizon=seq(30, 60, 1)) 56 | expect_s3_class(plt, "ggplot") 57 | skip_on_cran() 58 | skip_on_covr() 59 | skip_on_actions() 60 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 61 | }) 62 | 63 | test_that("plot, at t", { 64 | plt <- plot_surv_contour(time="time", status="event", variable="x3", 65 | data=sim_dat, model=model, fixed_t=seq(0, 30, 1)) 66 | expect_s3_class(plt, "ggplot") 67 | skip_on_cran() 68 | skip_on_covr() 69 | skip_on_actions() 70 | vdiffr::expect_doppelganger("plot, at t", fig=plt) 71 | }) 72 | 73 | test_that("plot, custom colors", { 74 | plt <- plot_surv_contour(time="time", status="event", variable="x3", 75 | data=sim_dat, model=model, bins=5, 76 | custom_colors=c("black", "grey", "red", "yellow", 77 | "green")) 78 | expect_s3_class(plt, "ggplot") 79 | skip_on_cran() 80 | skip_on_covr() 81 | skip_on_actions() 82 | vdiffr::expect_doppelganger("plot, custom colors", fig=plt) 83 | }) 84 | 85 | test_that("plot, panel_border / axis_dist", { 86 | plt <- plot_surv_contour(time="time", status="event", variable="x3", 87 | data=sim_dat, model=model, panel_border=TRUE, 88 | axis_dist=0.1, na.action=na.omit) 89 | expect_s3_class(plt, "ggplot") 90 | skip_on_cran() 91 | skip_on_covr() 92 | skip_on_actions() 93 | vdiffr::expect_doppelganger("plot, panel_border / axis_dist", fig=plt) 94 | }) 95 | 96 | test_that("plot, lots of stuff", { 97 | plt <- plot_surv_contour(time="time", status="event", variable="x3", 98 | data=sim_dat, model=model, start_color="grey", 99 | end_color="black", cif=TRUE, contour_lines=TRUE, 100 | contour_size=1, contour_linetype="dotdash", 101 | title="Title", subtitle="Subtitle", 102 | xlab="x", ylab="y", 103 | legend.title="legend", legend.position="bottom", 104 | gg_theme=ggplot2::theme_classic()) 105 | expect_s3_class(plt, "ggplot") 106 | skip_on_cran() 107 | skip_on_covr() 108 | skip_on_actions() 109 | vdiffr::expect_doppelganger("plot, lots of stuff", fig=plt) 110 | }) 111 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_heatmap.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | # to skip tests on github 12 | skip_on_actions <- function() { 13 | if (!identical(Sys.getenv("GITHUB_ACTIONS"), "true")) { 14 | return(invisible(TRUE)) 15 | } 16 | skip("On GitHub Actions") 17 | } 18 | 19 | test_that("plot, defaults", { 20 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 21 | data=sim_dat, model=model) 22 | expect_s3_class(plt, "ggplot") 23 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 24 | }) 25 | 26 | test_that("plot, with group", { 27 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 28 | group="group", data=sim_dat, model=model) 29 | expect_s3_class(plt, "ggplot") 30 | vdiffr::expect_doppelganger("plot, with group", fig=plt) 31 | }) 32 | 33 | test_that("plot, cif", { 34 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 35 | data=sim_dat, model=model, cif=TRUE) 36 | expect_s3_class(plt, "ggplot") 37 | vdiffr::expect_doppelganger("plot, cif", fig=plt) 38 | }) 39 | 40 | test_that("plot, change horizon", { 41 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 42 | data=sim_dat, model=model, horizon=seq(30, 60, 1)) 43 | expect_s3_class(plt, "ggplot") 44 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 45 | }) 46 | 47 | test_that("plot, at t", { 48 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 49 | data=sim_dat, model=model, fixed_t=seq(0, 30, 1)) 50 | expect_s3_class(plt, "ggplot") 51 | vdiffr::expect_doppelganger("plot, at t", fig=plt) 52 | }) 53 | 54 | test_that("plot, custom colors", { 55 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 56 | data=sim_dat, model=model, start_color="grey", 57 | end_color="black") 58 | expect_s3_class(plt, "ggplot") 59 | vdiffr::expect_doppelganger("plot, custom colors", fig=plt) 60 | }) 61 | 62 | test_that("plot, panel_border / axis_dist", { 63 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 64 | data=sim_dat, model=model, panel_border=TRUE, 65 | axis_dist=0.1) 66 | expect_s3_class(plt, "ggplot") 67 | vdiffr::expect_doppelganger("plot, panel_border / axis_dist", fig=plt) 68 | }) 69 | 70 | test_that("plot, contour_lines", { 71 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 72 | data=sim_dat, model=model, contour_lines=TRUE, 73 | contour_size=1, contour_linetype="solid", 74 | na.action=na.omit) 75 | expect_s3_class(plt, "ggplot") 76 | skip_on_cran() 77 | skip_on_covr() 78 | skip_on_actions() 79 | vdiffr::expect_doppelganger("plot, contour_lines", fig=plt) 80 | }) 81 | 82 | test_that("plot, lots of stuff", { 83 | plt <- plot_surv_heatmap(time="time", status="event", variable="x3", 84 | data=sim_dat, model=model, start_color="grey", 85 | end_color="black", cif=TRUE, contour_lines=TRUE, 86 | contour_size=1, contour_linetype="dotdash", 87 | title="Title", subtitle="Subtitle", 88 | xlab="x", ylab="y", 89 | legend.title="legend", legend.position="bottom", 90 | gg_theme=ggplot2::theme_classic()) 91 | expect_s3_class(plt, "ggplot") 92 | vdiffr::expect_doppelganger("plot, lots of stuff", fig=plt) 93 | }) 94 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_lines.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | test_that("plot, defaults", { 12 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 13 | data=sim_dat, model=model) 14 | expect_s3_class(plt, "ggplot") 15 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 16 | }) 17 | 18 | test_that("plot, kaplan_meier", { 19 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 20 | data=sim_dat, model=model, kaplan_meier=TRUE, 21 | km_color="black", km_size=1, km_linetype="dashed", 22 | km_alpha=0.8, km_ci=TRUE, km_ci_type="log", 23 | km_ci_level=0.9, km_ci_alpha=0.4) 24 | expect_s3_class(plt, "ggplot") 25 | vdiffr::expect_doppelganger("plot, kaplan_meier", fig=plt) 26 | }) 27 | 28 | test_that("plot, with ci", { 29 | set.seed(455) 30 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 31 | data=sim_dat, model=model, conf_int=TRUE, 32 | n_boot=3, horizon=c(10, 30)) 33 | expect_s3_class(plt, "ggplot") 34 | vdiffr::expect_doppelganger("plot, with ci", fig=plt) 35 | }) 36 | 37 | test_that("plot, with group", { 38 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 39 | group="group", data=sim_dat, model=model) 40 | expect_s3_class(plt, "ggplot") 41 | vdiffr::expect_doppelganger("plot, with group", fig=plt) 42 | }) 43 | 44 | test_that("plot, cif", { 45 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 46 | data=sim_dat, model=model, cif=TRUE) 47 | expect_s3_class(plt, "ggplot") 48 | vdiffr::expect_doppelganger("plot, cif", fig=plt) 49 | }) 50 | 51 | test_that("plot, change horizon", { 52 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 53 | data=sim_dat, model=model, horizon=seq(30, 60, 1)) 54 | expect_s3_class(plt, "ggplot") 55 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 56 | }) 57 | 58 | test_that("plot, at t", { 59 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 60 | data=sim_dat, model=model, fixed_t=seq(0, 30, 1)) 61 | expect_s3_class(plt, "ggplot") 62 | vdiffr::expect_doppelganger("plot, at t", fig=plt) 63 | }) 64 | 65 | test_that("plot, custom colors", { 66 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 67 | data=sim_dat, model=model, horizon=c(7, 20, 45), 68 | custom_colors=c("black", "red", "green"), 69 | na.action=na.omit) 70 | expect_s3_class(plt, "ggplot") 71 | vdiffr::expect_doppelganger("plot, custom colors", fig=plt) 72 | }) 73 | 74 | test_that("plot, cont color", { 75 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 76 | data=sim_dat, model=model, discrete=FALSE, 77 | start_color="red", end_color="green") 78 | expect_s3_class(plt, "ggplot") 79 | vdiffr::expect_doppelganger("plot, cont color", fig=plt) 80 | }) 81 | 82 | test_that("plot, lots of stuff", { 83 | plt <- plot_surv_lines(time="time", status="event", variable="x3", 84 | data=sim_dat, model=model, cif=TRUE, discrete=TRUE, 85 | linetype="dashed", alpha=0.8, title="Title", 86 | subtitle="Subtitle", xlab="x", ylab="y", 87 | gg_theme=ggplot2::theme_classic()) 88 | expect_s3_class(plt, "ggplot") 89 | vdiffr::expect_doppelganger("plot, lots of stuff", fig=plt) 90 | }) 91 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_matrix.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | test_that("plot, defaults", { 12 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 13 | data=sim_dat, model=model) 14 | expect_s3_class(plt, "ggplot") 15 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 16 | }) 17 | 18 | test_that("plot, cif", { 19 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 20 | data=sim_dat, model=model, cif=TRUE) 21 | expect_s3_class(plt, "ggplot") 22 | vdiffr::expect_doppelganger("plot, cif", fig=plt) 23 | }) 24 | 25 | test_that("plot, change horizon", { 26 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 27 | data=sim_dat, model=model, horizon=seq(30, 60, 1)) 28 | expect_s3_class(plt, "ggplot") 29 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 30 | }) 31 | 32 | test_that("plot, change fixed_t", { 33 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 34 | data=sim_dat, model=model, fixed_t=seq(0, 30, 1)) 35 | expect_s3_class(plt, "ggplot") 36 | vdiffr::expect_doppelganger("plot, change fixed_t", fig=plt) 37 | }) 38 | 39 | test_that("plot, custom colors", { 40 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 41 | data=sim_dat, model=model, start_color="grey", 42 | end_color="black") 43 | expect_s3_class(plt, "ggplot") 44 | vdiffr::expect_doppelganger("plot, custom colors", fig=plt) 45 | }) 46 | 47 | test_that("plot, panel_border / axis_dist", { 48 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 49 | data=sim_dat, model=model, panel_border=TRUE, 50 | axis_dist=0.1) 51 | expect_s3_class(plt, "ggplot") 52 | vdiffr::expect_doppelganger("plot, panel_border / axis_dist", fig=plt) 53 | }) 54 | 55 | test_that("plot, change n_col and n_row", { 56 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 57 | data=sim_dat, model=model, n_row=5, n_col=5) 58 | expect_s3_class(plt, "ggplot") 59 | vdiffr::expect_doppelganger("plot, change n_col and n_row", fig=plt) 60 | }) 61 | 62 | test_that("plot, change border stuff", { 63 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 64 | data=sim_dat, model=model, border_color="black", 65 | border_size=2) 66 | expect_s3_class(plt, "ggplot") 67 | vdiffr::expect_doppelganger("plot, change border stuff", fig=plt) 68 | }) 69 | 70 | test_that("plot, change numbers stuff", { 71 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 72 | data=sim_dat, model=model, 73 | number_color="black", number_digits=1, 74 | number_size=5, number_fontface="italic", 75 | number_family="serif") 76 | expect_s3_class(plt, "ggplot") 77 | vdiffr::expect_doppelganger("plot, change numbers stuff", fig=plt) 78 | }) 79 | 80 | test_that("plot, using group", { 81 | plt <- plot_surv_matrix(time="time", status="event", variable="x3", 82 | data=sim_dat, model=model, group="group") 83 | expect_s3_class(plt, "ggplot") 84 | vdiffr::expect_doppelganger("plot, using group", fig=plt) 85 | }) 86 | 87 | test_that("n_col too big", { 88 | expect_error(plot_surv_matrix(time="time", status="event", variable="x3", 89 | data=sim_dat, model=model, n_col=105), 90 | paste0("'n_col' must be smaller than length(fixed_t). ", 91 | "Decrease n_col or increase the number of points ", 92 | "in time used in the estimation."), fixed=TRUE) 93 | }) 94 | 95 | test_that("n_row too big", { 96 | expect_error(plot_surv_matrix(time="time", status="event", variable="x3", 97 | data=sim_dat, model=model, n_row=105), 98 | paste0("'n_row' must be smaller than length(horizon). ", 99 | "Decrease n_row or increase the number of values ", 100 | "in horizon used in the estimation."), fixed=TRUE) 101 | }) 102 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_quantiles.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | test_that("plot, defaults", { 12 | plt <- plot_surv_quantiles(time="time", status="event", variable="x3", 13 | data=sim_dat, model=model) 14 | expect_s3_class(plt, "ggplot") 15 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 16 | }) 17 | 18 | test_that("plot, with group", { 19 | plt <- plot_surv_quantiles(time="time", status="event", variable="x3", 20 | group="group", data=sim_dat, model=model) 21 | expect_s3_class(plt, "ggplot") 22 | vdiffr::expect_doppelganger("plot, with group", fig=plt) 23 | }) 24 | 25 | test_that("plot, change horizon", { 26 | plt <- plot_surv_quantiles(time="time", status="event", variable="x3", 27 | data=sim_dat, model=model, horizon=seq(30, 60, 1)) 28 | expect_s3_class(plt, "ggplot") 29 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 30 | }) 31 | 32 | test_that("plot, custom colors", { 33 | plt <- plot_surv_quantiles(time="time", status="event", variable="x3", 34 | data=sim_dat, model=model, p=c(0.25, 0.5, 0.75), 35 | custom_colors=c("black", "red", "green")) 36 | expect_s3_class(plt, "ggplot") 37 | vdiffr::expect_doppelganger("plot, custom colors", fig=plt) 38 | }) 39 | 40 | test_that("plot, single color", { 41 | plt <- plot_surv_quantiles(time="time", status="event", variable="x3", 42 | data=sim_dat, model=model, p=c(0.25, 0.5, 0.75), 43 | single_color="blue", na.action=na.omit) 44 | expect_s3_class(plt, "ggplot") 45 | vdiffr::expect_doppelganger("plot, single colors", fig=plt) 46 | }) 47 | 48 | test_that("plot, lots of stuff", { 49 | plt <- plot_surv_quantiles(time="time", status="event", variable="x3", 50 | data=sim_dat, model=model, 51 | linetype="dashed", alpha=0.8, title="Title", 52 | subtitle="Subtitle", xlab="x", ylab="y", 53 | gg_theme=ggplot2::theme_classic()) 54 | expect_s3_class(plt, "ggplot") 55 | vdiffr::expect_doppelganger("plot, lots of stuff", fig=plt) 56 | }) 57 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_rmst.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | test_that("plot, defaults", { 12 | plt <- plot_surv_rmst(time="time", status="event", variable="x3", 13 | data=sim_dat, model=model, tau=35) 14 | expect_s3_class(plt, "ggplot") 15 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 16 | }) 17 | 18 | test_that("plot, with group", { 19 | plt <- plot_surv_rmst(time="time", status="event", variable="x3", 20 | group="group", data=sim_dat, model=model, tau=35) 21 | expect_s3_class(plt, "ggplot") 22 | vdiffr::expect_doppelganger("plot, with group", fig=plt) 23 | }) 24 | 25 | test_that("plot, change horizon", { 26 | plt <- plot_surv_rmst(time="time", status="event", variable="x3", 27 | data=sim_dat, model=model, horizon=seq(30, 60, 1), 28 | tau=35, na.action=na.omit) 29 | expect_s3_class(plt, "ggplot") 30 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 31 | }) 32 | 33 | test_that("plot, custom_colors", { 34 | plt <- plot_surv_rmst(time="time", status="event", variable="x3", 35 | data=sim_dat, model=model, tau=c(20, 35), 36 | custom_colors=c("green", "grey")) 37 | expect_s3_class(plt, "ggplot") 38 | vdiffr::expect_doppelganger("plot, custom_colors", fig=plt) 39 | }) 40 | 41 | test_that("plot, lots of stuff", { 42 | plt <- plot_surv_rmst(time="time", status="event", variable="x3", 43 | data=sim_dat, model=model, tau=c(20, 37, 50), 44 | linetype="dashed", alpha=0.8, title="Title", 45 | subtitle="Subtitle", xlab="x", ylab="y", 46 | gg_theme=ggplot2::theme_classic()) 47 | expect_s3_class(plt, "ggplot") 48 | vdiffr::expect_doppelganger("plot, lots of stuff", fig=plt) 49 | }) 50 | -------------------------------------------------------------------------------- /tests/testthat/test_plot_surv_rmtl.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | test_that("plot, defaults", { 12 | plt <- plot_surv_rmtl(time="time", status="event", variable="x3", 13 | data=sim_dat, model=model, tau=35) 14 | expect_s3_class(plt, "ggplot") 15 | vdiffr::expect_doppelganger("plot, defaults", fig=plt) 16 | }) 17 | 18 | test_that("plot, with group", { 19 | plt <- plot_surv_rmtl(time="time", status="event", variable="x3", 20 | group="group", data=sim_dat, model=model, tau=35) 21 | expect_s3_class(plt, "ggplot") 22 | vdiffr::expect_doppelganger("plot, with group", fig=plt) 23 | }) 24 | 25 | test_that("plot, change horizon", { 26 | plt <- plot_surv_rmtl(time="time", status="event", variable="x3", 27 | data=sim_dat, model=model, horizon=seq(30, 60, 1), 28 | tau=35, na.action=na.omit) 29 | expect_s3_class(plt, "ggplot") 30 | vdiffr::expect_doppelganger("plot, change horizon", fig=plt) 31 | }) 32 | 33 | test_that("plot, custom_colors", { 34 | plt <- plot_surv_rmtl(time="time", status="event", variable="x3", 35 | data=sim_dat, model=model, tau=c(20, 35), 36 | custom_colors=c("green", "grey")) 37 | expect_s3_class(plt, "ggplot") 38 | vdiffr::expect_doppelganger("plot, custom_colors", fig=plt) 39 | }) 40 | 41 | test_that("plot, lots of stuff", { 42 | plt <- plot_surv_rmtl(time="time", status="event", variable="x3", 43 | data=sim_dat, model=model, tau=37, 44 | linetype="dashed", alpha=0.8, title="Title", 45 | subtitle="Subtitle", xlab="x", ylab="y", 46 | gg_theme=ggplot2::theme_classic()) 47 | expect_s3_class(plt, "ggplot") 48 | vdiffr::expect_doppelganger("plot, lots of stuff", fig=plt) 49 | }) 50 | -------------------------------------------------------------------------------- /tests/testthat/test_prepare_inputdata.r: -------------------------------------------------------------------------------- 1 | 2 | suppressMessages(requireNamespace("survival")) 3 | 4 | sim_dat <- readRDS(system.file("testdata", "sim150.Rds", 5 | package="contsurvplot")) 6 | sim_dat$group <- as.factor(sim_dat$group) 7 | 8 | model <- survival::coxph(survival::Surv(time, event) ~ x3 + group, 9 | data=sim_dat, x=TRUE) 10 | 11 | model_glm <- glm(time ~ x3 + x1 + group, data=sim_dat) 12 | model_pecRpart <- list(rpart=list(terms=time ~ x3 + x1 + group)) 13 | class(model_pecRpart) <- "pecRpart" 14 | model_ranger <- list(call=list(A="something", B=time ~ x3 + x1 + group)) 15 | class(model_ranger) <- "ranger" 16 | 17 | test_that("glm model", { 18 | dat_clean <- prepare_inputdata(data=sim_dat, 19 | time="time", 20 | status="event", 21 | variable="x3", 22 | group=NULL, 23 | model=model_glm, 24 | na.action="na.omit") 25 | expect_true(nrow(sim_dat)==nrow(dat_clean)) 26 | expect_true(ncol(dat_clean)==5) 27 | }) 28 | 29 | test_that("pecRpart model", { 30 | dat_clean <- prepare_inputdata(data=sim_dat, 31 | time="time", 32 | status="event", 33 | variable="x3", 34 | group=NULL, 35 | model=model_pecRpart, 36 | na.action="na.omit") 37 | expect_true(nrow(sim_dat)==nrow(dat_clean)) 38 | expect_true(ncol(dat_clean)==5) 39 | }) 40 | 41 | test_that("ranger model", { 42 | dat_clean <- prepare_inputdata(data=sim_dat, 43 | time="time", 44 | status="event", 45 | variable="x3", 46 | group=NULL, 47 | model=model_ranger, 48 | na.action="na.omit") 49 | expect_true(nrow(sim_dat)==nrow(dat_clean)) 50 | expect_true(ncol(dat_clean)==5) 51 | }) 52 | 53 | test_that("NULL model", { 54 | dat_clean <- prepare_inputdata(data=sim_dat, 55 | time="time", 56 | status="event", 57 | variable="x3", 58 | group=NULL, 59 | model="A", 60 | na.action="na.omit") 61 | expect_true(nrow(sim_dat)==nrow(dat_clean)) 62 | expect_true(ncol(dat_clean)==3) 63 | }) 64 | 65 | test_that("wrong data", { 66 | expect_error(use_data.frame("A"), 67 | "'data' must be a data.frame object.") 68 | }) 69 | 70 | sim_dat$group <- NA 71 | 72 | test_that("no data left after na.action", { 73 | expect_error(prepare_inputdata(data=sim_dat, 74 | time="time", 75 | status="event", 76 | variable="x3", 77 | group=NULL, 78 | model=model, 79 | na.action="na.omit"), 80 | "There is no data left after removing the missing values.") 81 | }) 82 | -------------------------------------------------------------------------------- /tests/testthat/test_read_from_step_function.r: -------------------------------------------------------------------------------- 1 | 2 | test_dat <- data.frame(time=c(0.01, 0.1, 0.2, 0.3, 0.6, 0.63), 3 | surv=c(0.999, 0.99, 0.8, 0.76, 0.5, 0.3), 4 | cif=1-c(1, 0.99, 0.8, 0.76, 0.5, 0.3)) 5 | 6 | test_that("surv, t = 0, start", { 7 | out <- read_from_step_function(0, data=test_dat, est="surv") 8 | expect_equal(out, 1) 9 | }) 10 | 11 | test_that("cif, t = 0, start", { 12 | out <- read_from_step_function(0, data=test_dat, est="cif") 13 | expect_equal(out, 0) 14 | }) 15 | 16 | test_that("surv, t = 0.2, on jump", { 17 | out <- read_from_step_function(0.2, data=test_dat, est="surv") 18 | expect_equal(out, 0.8) 19 | }) 20 | 21 | test_that("cif, t = 0.2, on jump", { 22 | out <- read_from_step_function(0.2, data=test_dat, est="cif") 23 | expect_equal(out, 0.2) 24 | }) 25 | 26 | test_that("surv, t = 0.4, in slope = 0", { 27 | out <- read_from_step_function(0.4, data=test_dat, est="surv") 28 | expect_equal(out, 0.76) 29 | }) 30 | 31 | test_that("cif, t = 0.4, in slope = 0", { 32 | out <- read_from_step_function(0.4, data=test_dat, est="cif") 33 | expect_equal(out, 0.24) 34 | }) 35 | 36 | test_that("surv, t = 0.63, end", { 37 | out <- read_from_step_function(0.63, data=test_dat, est="surv") 38 | expect_equal(out, 0.3) 39 | }) 40 | 41 | test_that("cif, t = 0.63, end", { 42 | out <- read_from_step_function(0.63, data=test_dat, est="cif") 43 | expect_equal(out, 0.7) 44 | }) 45 | 46 | test_that("surv, t = 0.64, no extrapolation", { 47 | out <- read_from_step_function(0.64, data=test_dat, est="surv") 48 | expect_true(is.na(out)) 49 | }) 50 | 51 | test_that("cif, t = 0.64, no extrapolation", { 52 | out <- read_from_step_function(0.64, data=test_dat, est="cif") 53 | expect_true(is.na(out)) 54 | }) 55 | 56 | test_dat$est <- test_dat$surv 57 | 58 | test_that("est, t = 0, start", { 59 | out <- read_from_step_function(0, data=test_dat, est="est") 60 | expect_true(is.na(out)) 61 | }) 62 | -------------------------------------------------------------------------------- /vignettes/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | *.R 3 | --------------------------------------------------------------------------------