diff --git a/.Rbuildignore b/.Rbuildignore index 317d62e59..f714ad037 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -38,3 +38,5 @@ allpopsamples_hlye.csv$ ^vignettes/articles/\.quarto$ ^vignettes/articles/*_files$ ^man/check_strata\.Rd$ +^\.lintr\.R$ +^\.rscignore$ diff --git a/.github/MULTI_VERSION_DOCS.md b/.github/MULTI_VERSION_DOCS.md new file mode 100644 index 000000000..d72f8335e --- /dev/null +++ b/.github/MULTI_VERSION_DOCS.md @@ -0,0 +1,145 @@ +# Multi-Version Documentation Setup + +This repository uses multi-version pkgdown documentation with a version dropdown menu, based on the [insightsengineering/tern](https://github.com/insightsengineering/tern) setup. + +## Features + +### Version Dropdown Menu +Users can switch between different versions of the documentation using a dropdown menu in the navbar: +- **main**: Development version (latest commits on main branch) +- **latest-tag**: Most recent release version +- **v\*.\*.\***: Version-tagged releases (e.g., v1.0.0, v1.1.0) + +### Default Landing Page +The default page at https://ucd-serg.github.io/serocalculator/ shows the **latest-tag** (most recent release) documentation. + +### PR Previews +Pull requests automatically generate preview documentation at: +- `https://ucd-serg.github.io/serocalculator/preview/pr/` +- Preview link is posted as a comment on the PR +- Previews are automatically cleaned up when PRs are closed + +## How It Works + +### Workflow: `.github/workflows/docs.yaml` + +The workflow has four jobs: + +1. **docs**: Builds the pkgdown site + - Runs on: PRs, pushes to main, version tags + - Deploys to version-specific directories on gh-pages + +2. **multi-version-docs**: Creates version dropdown + - Runs on: pushes to main, workflow_dispatch + - Uses `insightsengineering/r-pkgdown-multiversion@v3` + - Generates navigation between versions + +3. **upload-release-assets**: Upload pkgdown.zip + - Runs on: version tags only + - Uploads documentation to GitHub Releases + +4. **cleanup-pr-preview**: Clean up PR previews + - Runs on: PR close events + - Removes preview directories from gh-pages + +### Configuration + +#### `pkgdown/_pkgdown.yml` +```yaml +template: + bootstrap: 5 # Required for multi-version action + +url: https://ucd-serg.github.io/serocalculator/ # Required + +search: + exclude: ['preview/'] # Exclude PR previews from search +``` + +#### `DESCRIPTION` +``` +URL: https://github.com/UCD-SERG/serocalculator, + https://ucd-serg.github.io/serocalculator/ +``` + +### Version Filtering + +Versions shown in the dropdown are controlled by: + +**refs-order**: `"main latest-tag"` +- Determines the order of versions in the dropdown + +**branches-or-tags-to-list**: `'^main$|^latest-tag$|^v([0-9]+\\.)?([0-9]+\\.)?([0-9]+)$'` +- Regex pattern matching versions to include +- Matches: `main`, `latest-tag`, and semver tags like `v1.0.0` + +## Creating a New Version + +To create a new versioned documentation: + +1. **Tag a release**: + ```bash + git tag v1.0.0 + git push origin v1.0.0 + ``` + +2. **Workflow runs automatically**: + - Builds pkgdown site for v1.0.0 + - Deploys to `gh-pages/v1.0.0/` + - Updates version dropdown to include v1.0.0 + - Uploads pkgdown.zip to the GitHub release + +3. **Version appears in dropdown**: + - Users can now select v1.0.0 from the versions menu + - If it's the latest tag, it becomes the default landing page + +## Triggering Builds + +### Automatic Triggers +- **PR opened/updated**: Builds preview documentation +- **Push to main**: Rebuilds main version docs +- **Version tag pushed**: Builds new version docs +- **PR closed**: Cleans up preview + +### Manual Trigger +Use the Actions tab to manually trigger the workflow via `workflow_dispatch`. + +## Troubleshooting + +### Workflow requires approval +First-time workflows using external actions need manual approval: +1. Go to repository Actions tab +2. Find the workflow run +3. Click "Approve and run" +4. Subsequent runs will execute automatically + +### Version not appearing in dropdown +Check that: +1. Documentation was successfully built for that version +2. Version name matches the regex pattern +3. Version is listed in `refs-order` or matches `branches-or-tags-to-list` + +### Default landing page not updating +Ensure: +1. `default-landing-page: "latest-tag"` is set in workflow +2. Latest tag has documentation built +3. Multi-version-docs job completed successfully + +## Comparison to Original Setup + +### Before (pkgdown.yaml) +- Single version (main branch only) +- PR previews only +- No version switching + +### After (docs.yaml) +- Multiple versions (main + all releases) +- Version dropdown menu +- PR previews maintained +- Default to latest release +- Version-specific documentation URLs + +## Resources + +- [insightsengineering/r-pkgdown-multiversion](https://github.com/insightsengineering/r-pkgdown-multiversion) +- [insightsengineering/tern](https://github.com/insightsengineering/tern) (reference implementation) +- [pkgdown documentation](https://pkgdown.r-lib.org/) diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 000000000..2da1d57f5 --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,506 @@ +# Copilot Instructions for serocalculator + +## Repository Overview + +**serocalculator** is an R package for estimating infection rates from serological data. It translates antibody levels measured in cross-sectional population samples into estimates of the frequency with which seroconversions (infections) occur in the sampled populations. This package replaces the previous `seroincidence` package. + +- **Type**: R package (statistical analysis) +- **Size**: ~43MB, ~393 files, ~84 R source files, ~5,178 lines of R code +- **Language**: R (>= 4.1.0) +- **Key Dependencies**: Rcpp, dplyr, ggplot2, tidyr, cli, foreach, doParallel +- **Lifecycle**: Stable + +## Critical Setup Requirements + +### Copilot Setup Workflow (Automatic Environment Configuration) + +The repository includes a **`.github/workflows/copilot-setup-steps.yml`** workflow that automatically configures the GitHub Copilot coding agent's environment with all required dependencies. This workflow runs automatically when Copilot starts working on a task, ensuring a consistent and properly configured development environment. + +#### What the Workflow Does + +The copilot-setup-steps.yml workflow: + +1. **Installs system dependencies**: All required Ubuntu packages for R package development (libcurl, libssl, libxml2, graphics libraries, etc.) +2. **Sets up R (>= 4.1.0)**: Installs the R release version that meets the package's minimum requirement +3. **Installs R package dependencies**: All Imports, Suggests, and development dependencies from DESCRIPTION +4. **Verifies installation**: Runs comprehensive checks to ensure R is properly configured + +#### When It Runs + +The workflow runs in the following scenarios: + +- **Automatically for Copilot**: When the GitHub Copilot coding agent starts working on a task, it uses this workflow to prepare the environment +- **On workflow changes**: When `.github/workflows/copilot-setup-steps.yml` is modified (via push or pull request) +- **Manual testing**: Can be triggered manually from the repository's "Actions" tab using workflow_dispatch + +#### Integration with CI Workflows + +The copilot-setup-steps.yml workflow complements but does not replace the CI workflows: + +- **Purpose**: Configures the Copilot agent's environment for development work, not for CI testing +- **Scope**: Runs on ubuntu-latest only, while CI workflows test on multiple platforms (Ubuntu, macOS, Windows) and R versions (release, devel, oldrel-1) +- **Alignment**: Uses the same R setup approach as the R-CMD-check.yaml workflow, ensuring consistency +- **Timeout**: Limited to 55 minutes (Copilot maximum is 59 minutes) + +#### Verification Steps + +The workflow includes detailed verification logging: + +- **R version check**: Ensures R >= 4.1.0 requirement is met +- **Package verification**: Lists key installed packages (devtools, rcmdcheck, lintr, spelling, testthat) + +#### Customization + +If you need to modify the Copilot environment setup: + +1. Edit `.github/workflows/copilot-setup-steps.yml` +2. Test changes by pushing to a branch or using workflow_dispatch +3. Ensure the job name remains `copilot-setup-steps` (required by Copilot) +4. Keep timeout under 59 minutes +5. Update this documentation to reflect any significant changes + +### Alternative: Quick Start with Docker + +**If you prefer manual Docker setup**, you can use the rocker/verse Docker image which includes R, RStudio, tidyverse, TeX, and many common R packages pre-installed. + +To use Docker: + +```bash +# Pull the rocker/verse image (includes R >= 4.1.0, tidyverse, devtools, and more) +docker pull rocker/verse:latest + +# Run container with repository mounted +docker run -d \ + -v /home/runner/work/serocalculator/serocalculator:/workspace \ + -w /workspace \ + --name serocalculator-dev \ + rocker/verse:latest + +# Execute commands in the container +docker exec serocalculator-dev R -e "devtools::install_dev_deps()" +docker exec serocalculator-dev R -e "devtools::check()" + +# Or start an interactive R session +docker exec -it serocalculator-dev R + +# Clean up when done +docker stop serocalculator-dev +docker rm serocalculator-dev +``` + +### Manual Installation (if not using devcontainer or Docker) + +If the devcontainer or Docker is not available or you prefer a native installation, follow the manual installation instructions below. + +### R Installation and Development Dependencies (REQUIRED) + +**ALWAYS install R and all development dependencies when starting work on a pull request.** This ensures you avoid issues caused by missing dependencies or environment misconfiguration during the development process. + +#### Installing R (>= 4.1.0) + +The package requires R version 4.1.0 or higher. Install R for your platform: + +- **Ubuntu/Linux**: + ```bash + # Add CRAN repository for latest R version + sudo apt-get update + sudo apt-get install -y software-properties-common dirmngr + wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/pubkey.gpg | \ + sudo tee /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc + sudo add-apt-repository \ + "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" + sudo apt-get update + sudo apt-get install -y r-base r-base-dev + + # Verify installation + R --version + ``` + +- **macOS**: + ```bash + # Install using Homebrew (recommended) + brew install r + + # Or download from CRAN: https://cran.r-project.org/bin/macosx/ + # Verify installation + R --version + ``` + +- **Windows**: + Download and install from https://cran.r-project.org/bin/windows/base/ + + Verify installation by opening R console and checking version: + ```r + R.version.string + ``` + +#### Installing Development Dependencies + +After installing R, install all required development dependencies: + +```r +# Install devtools (required for package development) +install.packages("devtools", repos = "https://cloud.r-project.org") + +# Install all package dependencies (Imports, Suggests, and development needs) +# This reads DESCRIPTION file and installs everything needed +devtools::install_dev_deps(dependencies = TRUE) +``` + +**Alternative approach** using pak (faster parallel installation): +```r +install.packages("pak", repos = "https://cloud.r-project.org") +pak::local_install_dev_deps(dependencies = TRUE) +``` + +#### Verify Development Environment + +After installation, verify your development environment is properly configured: + +```r +# Load devtools +library(devtools) + +# Check package dependencies +devtools::dev_sitrep() + +# Load the package in development mode +devtools::load_all() + +# Run a quick check +devtools::check_man() +``` + +**Note**: If you encounter issues with dependencies, particularly with system libraries, install the following system dependencies first: + +- **Ubuntu/Linux**: + ```bash + sudo apt-get install -y \ + libcurl4-openssl-dev \ + libssl-dev \ + libxml2-dev \ + libfontconfig1-dev \ + libharfbuzz-dev \ + libfribidi-dev \ + libfreetype6-dev \ + libpng-dev \ + libtiff5-dev \ + libjpeg-dev + ``` + +- **macOS**: Most system dependencies are handled by Homebrew, but you may need: + ```bash + brew install pkg-config cairo + ``` + +- **Windows**: Install Rtools from https://cran.r-project.org/bin/windows/Rtools/ (choose version matching your R version) + +## Build and Development Workflow + +### Initial Setup + +```r +# Install development dependencies +devtools::install_dev_deps() + +# Or using the package manager approach +install.packages("devtools") +``` + +### Documentation Generation + +**ALWAYS regenerate documentation after modifying roxygen2 comments in `.R` files.** + +```r +# Generate documentation from roxygen2 comments +devtools::document() +# or +roxygen2::roxygenise() +``` + +Documentation files in `man/` and `NAMESPACE` are auto-generated. Do NOT edit them directly. + +### README Updates + +README.md is generated from README.Rmd. **ALWAYS edit README.Rmd, never README.md directly.** + +To regenerate: +```r +rmarkdown::render("README.Rmd") +``` + +### Package Checking + +Run R CMD check to validate the package: + +```r +# Full package check (takes several minutes) +devtools::check() +# or +rcmdcheck::rcmdcheck(error_on = "note") +``` + +**Note**: This runs multiple validation steps including examples, tests, and documentation checks. Allow 5-10 minutes for completion. + +### Testing + +```r +# Run all tests +devtools::test() +# or +testthat::test_local(stop_on_warning = TRUE, stop_on_error = TRUE) +``` + +Tests are located in `tests/testthat/`. The package uses testthat 3.0+ with snapshot testing for validation. + +### Linting + +The package uses a custom lintr configuration (`.lintr`) with specific requirements: + +```r +# ALWAYS load the package first before linting +devtools::load_all() + +# Lint the entire package +lintr::lint_package() + +# Lint specific file +lintr::lint("R/filename.R") +``` + +**Important**: Always run `devtools::load_all()` before linting to avoid false positives about undefined functions. This loads the package in development mode, making internal and package functions available to the linter. + +**Key linting rules**: +- Use native pipe `|>` (configured in .lintr) +- Follow snake_case naming conventions +- Avoid trailing whitespace +- Ensure consistent code style + +Exclusions: Some vignettes may be exempt from specific linters (see `.lintr` configuration). + +### Spelling Check + +```r +# Check spelling +spelling::spell_check_package() +``` + +Custom words are in `inst/WORDLIST` (if it exists). + +## Continuous Integration (CI) Checks + +The following workflows run on every PR. **All must pass** for merge: + +1. **R-CMD-check.yaml**: Runs R CMD check on Ubuntu (release, devel, oldrel-1), macOS (release), and Windows (release). Fails on any NOTE. (~10-15 min) + +2. **lint-changed-files.yaml**: Lints only files changed in the PR using lintr with custom `.lintr` config. Fails if lints are found. (~2-3 min) + +3. **test-coverage.yaml**: Runs on macOS, generates code coverage via covr, uploads to Codecov. (~5-10 min) + +4. **check-spelling.yaml**: Spell checks using spelling package. (~1-2 min) + +5. **check-readme.yaml**: Renders README.Rmd and verifies it matches README.md. (~2-3 min) + +6. **R-check-docs.yml**: Runs `roxygen2::roxygenise()` and checks if `man/`, `NAMESPACE`, or `DESCRIPTION` changed. Fails if documentation is out of sync. (~2-3 min) + +7. **news.yaml**: Ensures NEWS.md is updated for every PR. Can be bypassed with `no-changelog` label. (~1 min) + +8. **version-check.yaml**: Verifies DESCRIPTION version number increased vs. main branch. Run `usethis::use_version()` to increment. (~1 min) + +9. **pkgdown.yaml**: Builds pkgdown website on PR (preview), tags, and main branch pushes. Requires Quarto setup. (~5-7 min) + +10. **copilot-setup-steps.yml**: Configures the GitHub Copilot coding agent's environment automatically. Runs when Copilot starts work, when the workflow file changes, or via manual dispatch. Not a required check for PR merges. See "Copilot Setup Workflow" section for details. (~5-10 min) + +### PR Commands + +Team members can trigger actions by commenting on PRs: +- `/document` - Runs `roxygen2::roxygenise()` and commits changes +- `/style` - Runs `styler::style_pkg()` and commits changes + +## Repository Structure + +### Key Directories + +- **R/**: Package source code (84 R files, ~5,178 lines) + - Main functions for serological calculations + - Statistical models and estimators + - Data processing and validation + - Plotting and visualization functions + - `serocalculator-package.R`: Package documentation + +- **tests/testthat/**: Unit tests + - Uses snapshot testing with `_snaps/` subdirectory + - Tests seed RNG for reproducibility + +- **man/**: Auto-generated documentation - **DO NOT EDIT** + +- **data/**: Package datasets + - Example serological datasets + +- **data-raw/**: Raw data processing scripts (not included in package build) + +- **inst/**: Installed files + - Additional package resources + - `inst/WORDLIST`: Custom spelling dictionary (if exists) + +- **vignettes/**: Package vignettes + - Documentation articles + - Usage examples + +- **pkgdown/**: pkgdown website configuration + - `_pkgdown.yml`: Site structure, reference organization + +- **src/**: C++ source code (Rcpp integration) + - Compiled code for performance-critical functions + +### Configuration Files + +- **DESCRIPTION**: Package metadata, dependencies, and version +- **NAMESPACE**: Auto-generated exports - **DO NOT EDIT** +- **.lintr**: Custom lintr configuration +- **.Rprofile**: Interactive session setup (if exists) +- **.Rbuildignore**: Files excluded from package build +- **serocalculator.Rproj**: RStudio project settings +- **_quarto.yml**: Quarto rendering configuration for vignettes +- **codecov.yml**: Code coverage thresholds +- **.gitignore**: Git exclusions + +## Common Issues and Workarounds + +### Documentation Out of Sync +**Symptom**: R-check-docs.yml workflow fails. +**Solution**: Run `devtools::document()` locally and commit the updated `man/` and `NAMESPACE` files. + +### Version Not Incremented +**Symptom**: version-check.yaml workflow fails. +**Solution**: Run `usethis::use_version()` to increment the version in DESCRIPTION. + +### NEWS.md Not Updated +**Symptom**: news.yaml workflow fails. +**Solution**: Add a bullet point to NEWS.md under the development version header, or add `no-changelog` label to PR if change doesn't warrant NEWS entry. + +### Linting Failures +**Symptom**: lint-changed-files.yaml fails. +**Solution**: Review `.lintr` for custom rules. Common issues: +- Wrong pipe operator (use `|>` not `%>%`) +- Trailing whitespace +- Code style inconsistencies + +### Compilation Issues (C++ code) +**Symptom**: Package build fails with Rcpp errors. +**Solution**: Ensure you have proper C++ compiler: +- **Linux**: Install `build-essential` and `r-base-dev` +- **macOS**: Install Xcode command line tools: `xcode-select --install` +- **Windows**: Install Rtools matching your R version + +## Testing Requirements Before Code Changes + +**ALWAYS establish value-based unit tests BEFORE modifying any functions.** This ensures that changes preserve existing behavior and new behavior is correctly validated. + +### Testing Strategy + +Choose the appropriate testing approach based on the context: + +#### When to Use Snapshot Tests +Use snapshot tests (`expect_snapshot()`, `expect_snapshot_value()`, or `expect_snapshot_data()`) when: +- Testing complex data structures (data.frames, lists, model outputs) +- Validating statistical results +- Output format stability is important +- The exact values are less important than structural consistency + +**Examples:** +```r +# For data frames with numeric precision control +dataset |> expect_snapshot_data(name = "test-data") + +# For R objects with serialization +results |> expect_snapshot_value(style = "serialize") + +# For simple output or error messages +output <- calculate_rates(data) |> expect_no_error() +testthat::expect_snapshot(output) +``` + +#### When to Use Explicit Value Tests +Use explicit value tests (`expect_equal()`, `expect_identical()`, etc.) when: +- Testing simple scalar outputs +- Validating specific numeric thresholds or boundaries +- Testing Boolean returns or categorical outputs +- Exact values are critical for correctness + +**Examples:** +```r +# Testing exact numeric values +expect_equal(calculate_mean(c(1, 2, 3)), 2) + +# Testing with tolerance for floating point +expect_equal(calculate_ratio(3, 7), 0.4285714, tolerance = 1e-6) + +# Testing logical conditions +expect_true(is_valid_input(data)) +expect_false(has_missing_values(complete_data)) +``` + +#### Testing Best Practices +- **Seed randomness**: Use `withr::local_seed()` or `withr::with_seed()` for reproducible tests involving random number generation +- **Use small test cases**: Keep tests fast by using minimal data +- **Platform-specific snapshots**: Use the `variant` parameter in snapshot functions when output differs by OS +- **Test fixtures**: Store complex test data in `tests/testthat/fixtures/` for reuse + +### Test-Driven Workflow +1. **Before modifying a function**: Write or verify existing tests capture the current behavior +2. **Add new tests**: Create tests for the new functionality you're adding +3. **Make changes**: Modify the function implementation +4. **Run tests**: Validate all tests pass, updating snapshots only when changes are intentional +5. **Review snapshots**: When snapshots change, review the diff to ensure changes are expected + +## Code Style Guidelines + +- **Follow tidyverse style guide**: https://style.tidyverse.org +- **Use native pipe**: `|>` not `%>%` +- **Naming**: snake_case, acronyms may be uppercase (e.g., `prep_IDs_data`) +- **Messaging**: Use `cli::cli_*()` functions for all user-facing messages +- **No `library()` in package code**: Use `::` or DESCRIPTION Imports +- **Document all exports**: Use roxygen2 (@title, @description, @param, @returns, @examples) +- **Test snapshot changes**: Use appropriate snapshot testing approaches +- **Seed tests**: Use `withr::local_seed()` for reproducible tests +- **Avoid code duplication**: Don't copy-paste substantial code chunks. Instead, decompose reusable logic into well-named helper functions +- **Quarto vignettes**: Use Quarto-style chunk options with `#|` prefix (e.g., `#| label: my-chunk`, `#| eval: false`) +- **Tidyverse replacements**: Use tidyverse/modern replacements for base R functions where available +- **Write tidy code**: Keep code clean, readable, and well-organized + +## Package Development Commands Summary + +```r +# Complete development workflow +devtools::load_all() # Load package for interactive testing +devtools::document() # Update documentation +devtools::test() # Run tests +devtools::check() # Full R CMD check (slow) +usethis::use_version() # Increment version +lintr::lint_package() # Check code style +spelling::spell_check_package() # Check spelling +rmarkdown::render("README.Rmd") # Update README +``` + +## Trust These Instructions + +These instructions have been validated against the actual repository structure, workflows, and configuration files. When making changes: + +1. **ALWAYS** install R (>= 4.1.0) and all development dependencies when starting work on a PR +2. **ALWAYS** establish value-based unit tests (snapshot or explicit value tests) BEFORE modifying functions +3. **ALWAYS** write tidy, clean, and well-organized code +4. **ALWAYS** run `devtools::document()` after modifying roxygen2 comments +5. **ALWAYS** edit README.Rmd (not README.md) for README changes +6. **ALWAYS** increment dev version number to be one ahead of main branch before requesting PR review +7. **ALWAYS** update NEWS.md for user-facing changes +8. **ALWAYS** run tests before committing (`devtools::test()`) +9. **ALWAYS** check and fix lintr issues in changed files in PRs before committing +10. **ALWAYS** run `devtools::document()` before requesting PR review +11. **ALWAYS** make sure `devtools::check()` passes before requesting PR review +12. **ALWAYS** make sure `devtools::spell_check()` passes before requesting PR review +13. **ALWAYS** run `pkgdown::build_site()` before requesting PR review to ensure the pkgdown site builds successfully +14. **ALWAYS** verify Quarto documents render successfully locally - don't rely on CI workflows +15. When `pkgdown::build_site()` has errors related to Quarto, use `quarto::quarto_render(input = "path/to/file.qmd", quiet = FALSE)` to debug + +Only search for additional information if these instructions are incomplete or incorrect for your specific task. diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 5001cbfb6..f9829354f 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -46,5 +46,5 @@ jobs: - uses: r-lib/actions/check-r-package@v2 with: upload-snapshots: true - error-on: '"warning"' + error-on: '"note"' build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")' diff --git a/.github/workflows/check-spelling.yaml b/.github/workflows/check-spelling.yaml index 746aba5ee..f9e1113d7 100644 --- a/.github/workflows/check-spelling.yaml +++ b/.github/workflows/check-spelling.yaml @@ -14,10 +14,18 @@ jobs: runs-on: ubuntu-latest name: Spellcheck container: - image: rocker/tidyverse:4.1.2 + image: rocker/tidyverse:latest steps: - name: Checkout repo uses: actions/checkout@v3 - - name: Run Spelling Check test + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + packages: any::spelling + + - name: Run Spelling Check Test uses: insightsengineering/r-spellcheck-action@v3.0.2 diff --git a/.github/workflows/copilot-setup-steps.yml b/.github/workflows/copilot-setup-steps.yml new file mode 100644 index 000000000..919438fd0 --- /dev/null +++ b/.github/workflows/copilot-setup-steps.yml @@ -0,0 +1,133 @@ +--- +# GitHub Copilot Setup Steps for serocalculator +# +# This workflow configures the GitHub Copilot coding agent's environment +# by preinstalling R and all required dependencies. +# +# See: https://docs.github.com/en/copilot/how-tos/use-copilot-agents/ +# coding-agent/customize-the-agent-environment +# For detailed setup instructions, see .github/copilot-instructions.md +# +# This workflow aligns with the setup requirements documented in +# .github/copilot-instructions.md: +# - R version >= 4.1.0 (as specified in DESCRIPTION) +# - All package dependencies (Imports, Suggests, and development needs) + +name: "Copilot Setup Steps" + +# Automatically run the setup steps when they are changed to allow +# for easy validation, and allow manual testing through the +# repository's "Actions" tab +on: + workflow_dispatch: + push: + paths: + - .github/workflows/copilot-setup-steps.yml + pull_request: + paths: + - .github/workflows/copilot-setup-steps.yml + +jobs: + # The job MUST be called `copilot-setup-steps` or it will not be + # picked up by Copilot. + copilot-setup-steps: + runs-on: ubuntu-latest + + # Set the permissions to the lowest permissions possible needed + # for your steps. Copilot will be given its own token for its + # operations. + permissions: + contents: read + + # Timeout after 55 minutes (max is 59 for copilot-setup-steps) + timeout-minutes: 55 + + steps: + # Checkout code - Copilot will do this automatically if we don't, + # but we need it to install dependencies from DESCRIPTION + - name: Checkout code + uses: actions/checkout@v4 + + # Install system dependencies required for R packages + # See .github/copilot-instructions.md + # "Verify Development Environment" section + - name: Install system dependencies + run: | + sudo apt-get update + sudo apt-get install -y \ + libcurl4-openssl-dev \ + libssl-dev \ + libxml2-dev \ + libfontconfig1-dev \ + libharfbuzz-dev \ + libfribidi-dev \ + libfreetype6-dev \ + libpng-dev \ + libtiff5-dev \ + libjpeg-dev + + # Set up pandoc for documentation + - name: Set up Pandoc + uses: r-lib/actions/setup-pandoc@v2 + + # Set up R using the standard GitHub Actions setup + # R version >= 4.1.0 required + # (see DESCRIPTION and .github/copilot-instructions.md) + - name: Set up R + uses: r-lib/actions/setup-r@v2 + with: + r-version: 'release' + use-public-rspm: true + + # Install R dependencies with caching for faster subsequent runs + - name: Install R dependencies + uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: | + any::devtools + any::rcmdcheck + any::lintr + any::spelling + any::rmarkdown + needs: check + + # Verify R environment is properly configured + # See .github/copilot-instructions.md + # "Verify Development Environment" section + - name: Verify development environment + run: | + echo "=== R Development Environment Status ===" + Rscript -e ' + cat("R version:", R.version.string, "\n\n") + + # Check R version meets minimum requirement (>= 4.1.0) + r_version <- paste(R.version$major, R.version$minor, sep = ".") + cat("Checking R version >= 4.1.0... ") + if (getRversion() >= "4.1.0") { + cat("PASSED (", r_version, ")\n\n", sep = "") + } else { + cat("FAILED (", r_version, ")\n\n", sep = "") + stop("R version must be >= 4.1.0") + } + + # Display key installed packages + cat("Key installed packages:\n") + key_packages <- c( + "devtools", "rcmdcheck", "lintr", + "spelling", "testthat", "Rcpp" + ) + for (pkg in key_packages) { + if (requireNamespace(pkg, quietly = TRUE)) { + cat( + " -", pkg, ":", + as.character(packageVersion(pkg)), "\n" + ) + } else { + cat(" -", pkg, ": NOT INSTALLED\n") + } + } + + cat("\nTotal packages installed:", + nrow(installed.packages()), "\n") + cat("\nDevelopment environment setup complete!\n") + ' diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 000000000..bf99a32e6 --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,363 @@ +--- +name: Docs 📚 + +on: + push: + branches: + - main + tags: + - "v*" + paths: + - "inst/templates/**" + - "pkgdown/**" + - "_pkgdown.*" + - DESCRIPTION + - "**.md" + - "**.Rmd" + - "man/**" + - "LICENSE.*" + - NAMESPACE + - ".github/workflows/docs.yaml" + release: + types: [published] + pull_request: + types: + - opened + - synchronize + - reopened + - ready_for_review + - closed + branches: + - main + paths: + - "inst/templates/**" + - "pkgdown/**" + - "_pkgdown.*" + - DESCRIPTION + - "**.md" + - "**.Rmd" + - "man/**" + - "LICENSE.*" + - NAMESPACE + - ".github/workflows/docs.yaml" + workflow_dispatch: + +concurrency: + group: docs-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + docs: + name: Generate 🐣 + runs-on: ubuntu-latest + if: > + (github.event_name != 'push' || !contains(github.event.head_commit.message, '[skip docs]')) + && !(github.event_name == 'pull_request' && github.event.action == 'closed') + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - name: Get branch names 🌿 + id: branch-name + uses: tj-actions/branch-names@v9.0.0 + + - name: Get current branch or tag 🏷️ + id: current-branch-or-tag + run: | + if [ "${{ github.event_name }}" == "release" ]; then + # For release events, use the tag from the release + echo "Release tag: ${{ github.event.release.tag_name }}" + echo "ref-name=${{ github.event.release.tag_name }}" >> $GITHUB_OUTPUT + elif [ "${{ steps.branch-name.outputs.is_tag }}" == "true" ]; then + echo "Current tag: ${{ steps.branch-name.outputs.tag }}" + echo "ref-name=${{ steps.branch-name.outputs.tag }}" >> $GITHUB_OUTPUT + else + echo "Current branch: ${{ steps.branch-name.outputs.current_branch }}" + echo "ref-name=${{ steps.branch-name.outputs.current_branch }}" >> $GITHUB_OUTPUT + fi + shell: bash + + - name: Checkout repo (PR) 🛎 + uses: actions/checkout@v4.3.1 + if: github.event_name == 'pull_request' + with: + ref: ${{ steps.branch-name.outputs.head_ref_branch }} + path: ${{ github.event.repository.name }} + repository: ${{ github.event.pull_request.head.repo.full_name }} + + - name: Checkout repo 🛎 + uses: actions/checkout@v4.3.1 + if: github.event_name != 'pull_request' + with: + ref: ${{ steps.current-branch-or-tag.outputs.ref-name }} + path: ${{ github.event.repository.name }} + + - name: Check commit message 💬 + run: | + git config --global --add safe.directory $(pwd) + export head_commit_message="$(git show -s --format=%B | tr '\r\n' ' ' | tr '\n' ' ')" + echo "head_commit_message = $head_commit_message" + if [[ $head_commit_message == *"$SKIP_INSTRUCTION"* ]]; then + echo "Skip instruction detected - cancelling the workflow." + exit 1 + fi + shell: bash + working-directory: ${{ github.event.repository.name }} + env: + SKIP_INSTRUCTION: "[skip docs]" + + - uses: r-lib/actions/setup-pandoc@v2 + + - name: Set up Quarto + uses: quarto-dev/quarto-actions/setup@v2 + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tinytex: true + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + working-directory: ${{ github.event.repository.name }} + needs: website + extra-packages: | + any::pkgdown + + - name: Install R package 🚧 + run: | + if (file.exists("renv.lock")) renv::restore() + install.packages(".", repos=NULL, type="source") + shell: Rscript {0} + working-directory: ${{ github.event.repository.name }} + + - name: Build docs 🏗 + if: > + github.event_name == 'pull_request' || startsWith(github.ref, 'refs/tags/v') + || github.event_name == 'push' + run: | + # Set devel = FALSE for version tags, TRUE for everything else + if [[ "${{ github.ref }}" == refs/tags/v* ]]; then + Rscript -e 'pkgdown::build_site(devel = FALSE)' + else + Rscript -e 'pkgdown::build_site(devel = TRUE)' + fi + shell: bash + working-directory: ${{ github.event.repository.name }} + + - name: Checkout gh-pages 🛎 + if: startsWith(github.ref, 'refs/tags/v') || github.event_name == 'push' + uses: actions/checkout@v4.3.1 + with: + path: "gh-pages" + fetch-depth: 0 + ref: "gh-pages" + + - name: Upload docs to gh-pages 📙 + if: startsWith(github.ref, 'refs/tags/v') || github.event_name == 'push' + run: | + GH_PAGES_DIR="gh-pages/${{ steps.current-branch-or-tag.outputs.ref-name }}" + mkdir -p $GH_PAGES_DIR + echo "Current contents of $GH_PAGES_DIR:" + ls -l $GH_PAGES_DIR || echo "Directory is empty or doesn't exist yet" + + # Remove any existing documentation for this version, but retain coverage and test reports + directories_to_retain="coverage-report,unit-test-report" + IFS=',' read -ra DIRECTORIES_TO_RETAIN <<< "$directories_to_retain" + echo "The following directories will be retained:" + for dir in "${DIRECTORIES_TO_RETAIN[@]}"; do + echo "$dir" + done + + # Remove all files from GH_PAGES_DIR, except any DIRECTORIES_TO_RETAIN + find $GH_PAGES_DIR -mindepth 1 -maxdepth 1 -print0 2>/dev/null | while IFS= read -r -d '' file; do + file_to_be_removed="true" + # Check if the file/directory matches any directory to be retained + for dir in "${DIRECTORIES_TO_RETAIN[@]}"; do + if [[ "$GH_PAGES_DIR/$dir" == "$file" ]]; then + echo "Not removing $file" + file_to_be_removed="false" + fi + done + if [[ "$file_to_be_removed" == "true" ]]; then + echo "Removing $file" + rm -rf "$file" + fi + done + + echo "::group::gh-pages contents" + echo "Current contents of $GH_PAGES_DIR:" + ls -l $GH_PAGES_DIR || echo "Directory is empty" + echo "::endgroup::" + + # Copy generated pkgdown documentation to gh-pages branch + cp -a ${{ github.event.repository.name }}/docs/. $GH_PAGES_DIR + + echo "::group::gh-pages contents after copy" + echo "Current contents of $GH_PAGES_DIR:" + ls -l $GH_PAGES_DIR + echo "::endgroup::" + + cd gh-pages + git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com" + git config --global user.name "github-actions[bot]" + git config pull.rebase false + git status + # Random delay to avoid conflicts + sleep $((RANDOM % 10)) + git pull origin gh-pages || true + git add -f . + git commit -m "Update pkgdown documentation ${{ github.sha }}" || true + git push origin gh-pages + shell: bash + + - name: Create documentation artifact 📂 + if: github.event_name == 'pull_request' || startsWith(github.ref, 'refs/tags/v') + run: | + pushd ${{ github.event.repository.name }}/docs/ + zip -r9 $OLDPWD/pkgdown.zip * + popd + shell: bash + + - name: Upload docs for review ⬆ + if: github.event_name == 'pull_request' || startsWith(github.ref, 'refs/tags/v') + uses: actions/upload-artifact@v4.6.2 + with: + name: pkgdown.zip + path: pkgdown.zip + + - name: Checkout gh-pages for PR preview 🛎 + if: github.event_name == 'pull_request' + uses: actions/checkout@v4.3.1 + with: + path: "gh-pages-preview" + fetch-depth: 0 + ref: "gh-pages" + + - name: Deploy PR preview 🚀 + if: github.event_name == 'pull_request' + id: deploy-pr-preview + run: | + cd gh-pages-preview + git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com" + git config --global user.name "github-actions[bot]" + git config pull.rebase false + + # Create preview directory + PREVIEW_DIR="preview/pr${{ github.event.pull_request.number }}" + mkdir -p $PREVIEW_DIR + + # Copy built docs to preview directory + cp -a ../${{ github.event.repository.name }}/docs/. $PREVIEW_DIR/ + + git add -f . + git commit -m "Update PR #${{ github.event.pull_request.number }} preview" || true + git pull origin gh-pages || true + git push origin gh-pages + + # Set output URL + REPO_NAME="${{ github.event.repository.name }}" + REPO_OWNER="${{ github.repository_owner }}" + echo "url=https://${REPO_OWNER}.github.io/${REPO_NAME}/${PREVIEW_DIR}/" >> $GITHUB_OUTPUT + shell: bash + + - name: Comment PR with preview link 💬 + if: github.event_name == 'pull_request' + uses: hasura/comment-progress@v2.2.0 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + repository: ${{ github.repository }} + number: ${{ github.event.pull_request.number }} + id: pkgdown-deploy + append: false + message: > + :book: ${{ steps.deploy-pr-preview.outputs.url }} + + Preview documentation for this PR (at commit ${{ github.event.pull_request.head.sha }}) + + multi-version-docs: + name: Multi-version docs 📑 + needs: docs + runs-on: ubuntu-latest + if: > + (github.event_name == 'push' || github.event_name == 'workflow_dispatch') + steps: + - name: Checkout repo 🛎 + uses: actions/checkout@v4.3.1 + with: + path: ${{ github.event.repository.name }} + ref: "gh-pages" + + - name: Create and publish docs ↗️ + uses: insightsengineering/r-pkgdown-multiversion@v3 + with: + path: ${{ github.event.repository.name }} + default-landing-page: "latest-tag" + refs-order: "main latest-tag" + branches-or-tags-to-list: '^main$|^latest-tag$|^(serocalculator |v)([0-9]+\\.)?([0-9]+\\.)?([0-9]+)$' + + upload-release-assets: + name: Upload documentation assets 🔼 + needs: docs + runs-on: ubuntu-latest + if: startsWith(github.ref, 'refs/tags/v') + steps: + - name: Checkout repo 🛎 + uses: actions/checkout@v4.3.1 + + - name: Download artifact ⏬ + uses: actions/download-artifact@v4.1.8 + with: + name: pkgdown.zip + + - name: Check if release exists ❓ + id: check-if-release-exists + uses: insightsengineering/release-existence-action@v1 + + - name: Upload binaries to release ⤴ + if: >- + steps.check-if-release-exists.outputs.release-exists == 'true' + uses: svenstaro/upload-release-action@v2 + with: + repo_token: ${{ secrets.GITHUB_TOKEN }} + file: pkgdown.zip + asset_name: pkgdown.zip + tag: ${{ github.ref }} + overwrite: true + + cleanup-pr-preview: + name: Clean up PR preview 🧹 + runs-on: ubuntu-latest + if: github.event_name == 'pull_request' && github.event.action == 'closed' + steps: + - name: Checkout gh-pages 🛎 + uses: actions/checkout@v4.3.1 + with: + ref: "gh-pages" + + - name: Remove PR preview 🗑️ + run: | + git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com" + git config --global user.name "github-actions[bot]" + + preview_dir="preview/pr${{ github.event.pull_request.number }}" + if [ -d "$preview_dir" ]; then + git rm -r $preview_dir + git commit -m "Remove $preview_dir (GitHub Actions)" || echo 'No preview to remove' + git push origin || echo 'No preview to remove' + else + echo 'No preview to remove' + fi + shell: bash + + - name: Notify cleanup 💬 + uses: hasura/comment-progress@v2.2.0 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + repository: ${{ github.repository }} + number: ${{ github.event.pull_request.number }} + id: pkgdown-deploy + message: | + _:closed_book: Preview documentation for this PR has been cleaned up._ diff --git a/.github/workflows/news.yaml b/.github/workflows/news.yaml index f64528223..e6a3e35ce 100644 --- a/.github/workflows/news.yaml +++ b/.github/workflows/news.yaml @@ -7,7 +7,7 @@ on: jobs: Check-Changelog: name: Check Changelog Action - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: UCD-SERG/changelog-check-action@v2 with: diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml deleted file mode 100644 index 57bfea1c0..000000000 --- a/.github/workflows/pkgdown.yaml +++ /dev/null @@ -1,159 +0,0 @@ -# Deploys pkgdown for Pull Requests, tags, and pushes to main branch -# PRs are deployed to /preview/pr/ -# Tags are deployed to // -# copied from https://github.com/rstudio/education-workflows/blob/main/examples/pkgdown.yaml -# referred from https://github.com/r-lib/actions/issues/865 -# more info: https://github.com/rstudio/education-workflows/tree/main/examples#deploy-pkgdown-to-github-pages-with-pr-previews-and-tagged-versions -on: - pull_request: - branches: - - main - types: - - opened - - reopened - - synchronize - - closed - paths: - - 'man/**' - - 'pkgdown/**' - - 'vignettes/**' - - '_quarto.yml' - - '.github/workflows/pkgdown.yaml' - - 'Readme.md' - push: - tags: - - 'v[0-9]+.[0-9]+.[0-9]+' # build on version tags - - '!v[0-9]+.[0-9]+.[0-9]+.[0-9]+' # but not if version involves a dev component - branches: - - main - workflow_dispatch: - inputs: - tag: - description: Tag to deploy - required: false - default: '' - -name: pkgdown - -jobs: - pkgdown-build: - runs-on: ubuntu-latest - if: ${{ !(github.event_name == 'pull_request' && github.event.action == 'closed') }} - # Only restrict concurrency for non-PR jobs - concurrency: - group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} - env: - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - steps: - - uses: actions/checkout@v2 - - - name: Configure git - run: | - git config --local user.name "$GITHUB_ACTOR" - git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com" - - - uses: r-lib/actions/pr-fetch@v2 - if: ${{ github.event_name == 'pull_request' }} - with: - repo-token: ${{ github.token }} - - - uses: r-lib/actions/setup-pandoc@v2 - - - uses: r-lib/actions/setup-r@v2 - with: - use-public-rspm: true - - - uses: r-lib/actions/setup-r-dependencies@v2 - with: - needs: | - connect - website - extra-packages: | - local::. - r-lib/pkgdown - - # If events is a PR, set subdir to 'preview/pr' - - name: "[PR] Set documentation subdirectory" - if: github.event_name == 'pull_request' - run: | - echo "PKGDOWN_DEV_MODE=unreleased" >> $GITHUB_ENV - echo "subdir=preview/pr${{ github.event.number }}" >> $GITHUB_ENV - - # If event is a tag, set subdir to '' - - name: "[tag] Set documentation subdirectory" - if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') - run: | - echo "PKGDOWN_DEV_MODE=release" >> $GITHUB_ENV - echo "subdir=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV - - # If event is workflow_dispatch, set subdir to 'inputs.tag' - - name: '[dispatch] Set documentation subdirectory' - if: github.event_name == 'workflow_dispatch' - run: | - echo "subdir=${{ github.event.inputs.tag }}" >> $GITHUB_ENV - - - name: Debug subdir - run: | - echo "Subdir is set to: ${{ env.subdir }}" - - - name: Deploy pkgdown site - id: deploy - shell: Rscript {0} - run: | - subdir <- "${{ env.subdir }}" - pkg <- pkgdown::as_pkgdown(".") - - # Deploy pkgdown site to branch - pkgdown::deploy_to_branch(subdir = if (nzchar(subdir)) subdir, clean = nzchar(subdir)) - - # Report deployed site URL - deployed_url <- file.path(pkg$meta$url, subdir) - cat(sprintf('url=%s', deployed_url), file = Sys.getenv("GITHUB_OUTPUT"), append = TRUE) - - - name: Notify pkgdown deployment - if: github.event_name == 'pull_request' - uses: hasura/comment-progress@v2.2.0 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - repository: ${{ github.repository }} - number: ${{ github.event.number }} - id: pkgdown-deploy - append: false - message: > - :book: ${{ steps.deploy.outputs.url }} - - Preview documentation for this PR (at commit ${{ github.event.pull_request.head.sha }}) - - pkgdown-clean: - if: ${{ github.event_name == 'pull_request' && github.event.action == 'closed' }} - runs-on: ubuntu-latest - env: - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - steps: - - uses: actions/checkout@v2 - with: - ref: "gh-pages" - - - name: Clean up PR Preview - run: | - git config --local user.name "$GITHUB_ACTOR" - git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com" - - preview_dir="preview/pr${{ github.event.pull_request.number }}" - if [ -d "$preview_dir" ]; then - git rm -r $preview_dir - git commit -m "Remove $preview_dir (GitHub Actions)" || echo 'No preview to remove' - git push origin || echo 'No preview to remove' - else - echo 'No preview to remove' - fi - - - name: Notify pkgdown cleanup - uses: hasura/comment-progress@v2.2.0 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - repository: ${{ github.repository }} - number: ${{ github.event.number }} - id: pkgdown-deploy - message: | - _:closed_book: Preview documentation for this PR has been cleaned up._ diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml index e050312ff..d28f5da04 100644 --- a/.github/workflows/test-coverage.yaml +++ b/.github/workflows/test-coverage.yaml @@ -24,7 +24,7 @@ jobs: - uses: r-lib/actions/setup-r-dependencies@v2 with: - extra-packages: any::covr, any::xml2 + extra-packages: any::covr, any::xml2, local::. needs: coverage - name: Test coverage @@ -37,6 +37,25 @@ jobs: covr::to_cobertura(cov) shell: Rscript {0} + # copied from https://github.com/dieghernan/nominatimlite/actions/runs/12116366823/workflow + - name: Create Junit Report + if: always() + run: | + test_out <- path.expand(file.path(getwd(), "junit.xml")) + testthat::test_local(reporter = testthat::JunitReporter$new(test_out)) + shell: Rscript {0} + + # following https://app.codecov.io/gh/UCD-SERG/serodynamics/tests/new + - name: Upload test results to Codecov + if: ${{ !cancelled() }} + uses: codecov/test-results-action@HEAD + with: + # Fail if error if not on PR, or if on PR and token is given + fail_ci_if_error: ${{ github.event_name != 'pull_request' || secrets.CODECOV_TOKEN }} + file: ./junit.xml + token: ${{ secrets.CODECOV_TOKEN }} + + - uses: codecov/codecov-action@v4 with: # Fail if error if not on PR, or if on PR and token is given diff --git a/.gitignore b/.gitignore index 8cc760f43..95e38a9a9 100644 --- a/.gitignore +++ b/.gitignore @@ -21,3 +21,7 @@ NEWS.html NEWS_files **/.quarto/ *.pdf +junit.xml + +**/*.quarto_ipynb +*.knit.md diff --git a/.lintr b/.lintr deleted file mode 100644 index b53fffaf4..000000000 --- a/.lintr +++ /dev/null @@ -1,5 +0,0 @@ -linters: linters_with_defaults( - return_linter = NULL, - pipe_consistency_linter(pipe = "|>")) -encoding: "UTF-8" - diff --git a/.lintr.R b/.lintr.R new file mode 100644 index 000000000..f64dbea26 --- /dev/null +++ b/.lintr.R @@ -0,0 +1,93 @@ + +library_info <- paste( + "\nuse `::`, `usethis::use_import_from()`, or `withr::local_package()`", + "instead of modifying the global search path.", + "\nSee:\n", + " and\n", + "", + "\nfor more details." +) + +undesirable_functions <- + lintr::default_undesirable_functions |> + lintr::modify_defaults( + + # following https://github.com/r-lib/devtools/blob/2aa51ef/.lintr.R: + # Base messaging + "message" = "use cli::cli_inform()", + "warning" = "use cli::cli_warn()", + "stop" = "use cli::cli_abort()", + # rlang messaging + "inform" = "use cli::cli_inform()", + "warn" = "use cli::cli_warn()", + "abort" = "use cli::cli_abort()", + # older cli + "cli_alert_danger" = "use cli::cli_inform()", + "cli_alert_info" = "use cli::cli_inform()", + "cli_alert_success" = "use cli::cli_inform()", + "cli_alert_warning" = "use cli::cli_inform()", + + "library" = library_info, + + structure = NULL, + browser = NULL + # see https://github.com/r-lib/lintr/pull/2227 and + # rebuttal https://github.com/r-lib/lintr/pull/2227#issuecomment-1800302675 + + ) + +# define snake_case with uppercase acronyms allowed; +# see https://github.com/r-lib/lintr/issues/2844 for details: +withr::local_package("rex") +snake_case_ACROs1 <- rex::rex( + start, + maybe("."), + list(some_of(upper), maybe("s"), zero_or_more(digit)) %or% + list(some_of(lower), zero_or_more(digit)), + zero_or_more( + "_", + list(some_of(upper), maybe("s"), zero_or_more(digit)) %or% + list(some_of(lower), zero_or_more(digit)) + ), + zero_or_more( + "_", + some_of(digit) + ), + end +) + +linters <- lintr::linters_with_defaults( + return_linter = NULL, + trailing_whitespace_linter = NULL, + lintr::redundant_equals_linter(), + lintr::pipe_consistency_linter(pipe = "|>"), + lintr::object_name_linter( + regexes = c(snake_case_ACROs1 = snake_case_ACROs1) + ), + lintr::undesirable_function_linter( + fun = undesirable_functions, + symbol_is_undesirable = TRUE + ) +) + +# prevent warnings from lintr::read_settings: +rm(undesirable_functions) +rm(snake_case_ACROs1) +rm(library_info) +exclusions <- list( + `data-raw` = list( + pipe_consistency_linter = Inf, + undesirable_function_linter = Inf + ), + vignettes = list( + undesirable_function_linter = Inf, + object_name_linter = Inf + ), + "inst/examples" = list( + undesirable_function_linter = Inf + ), + "tests/testthat.R" = list( + undesirable_function_linter = Inf + ) + +) diff --git a/DESCRIPTION b/DESCRIPTION index fc8b994be..d0d065d65 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,12 +1,13 @@ Type: Package Package: serocalculator Title: Estimating Infection Rates from Serological Data -Version: 1.3.0.9029 +Version: 1.4.0.9003 Authors@R: c( - person("Peter", "Teunis", , "p.teunis@emory.edu", role = c("aut", "cph"), - comment = "Author of the method and original code."), person("Kristina", "Lai", , "kwlai@ucdavis.edu", role = c("aut", "cre")), person("Chris", "Orwa", role = "aut"), + person("Kwan Ho", "Lee", , "ksjlee@ucdavis.edu", role = "ctb"), + person("Peter", "Teunis", , "p.teunis@emory.edu", role = c("aut", "cph"), + comment = "Author of the method and original code."), person("Kristen", "Aiemjoy", , "kaiemjoy@ucdavis.edu", role = "aut"), person("Douglas Ezra", "Morrison", , "demorrison@ucdavis.edu", role = "aut") ) @@ -22,6 +23,7 @@ Depends: R (>= 4.1.0) Imports: cli, + config, doParallel, dplyr, foreach, @@ -29,8 +31,6 @@ Imports: ggpubr, lifecycle, magrittr, - mixtools, - pkgload, Rcpp, rlang, rngtools, @@ -45,13 +45,15 @@ Imports: and, glue, stringr, - parallel + parallel, + labelled Suggests: bookdown, DT, fs, ggbeeswarm, knitr, + mixtools, pak, readr, quarto, @@ -62,7 +64,10 @@ Suggests: tidyverse, qrcode, vdiffr, - withr + withr, + forcats, + snapr (>= 0.0.0.9000), + rex LinkingTo: Rcpp Config/testthat/edition: 3 @@ -75,4 +80,6 @@ Language: en-US LazyData: true NeedsCompilation: no Roxygen: list(markdown = TRUE, roclets = c("collate", "rd", "namespace", "devtag::dev_roclet")) -RoxygenNote: 7.3.2 +RoxygenNote: 7.3.3 +Remotes: + d-morrison/snapr diff --git a/NAMESPACE b/NAMESPACE index 408f27bf9..692acdb8b 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,24 +5,35 @@ S3method(autoplot,curve_params) S3method(autoplot,pop_data) S3method(autoplot,seroincidence) S3method(autoplot,seroincidence.by) +S3method(autoplot,sim_results) S3method(autoplot,summary.seroincidence.by) +S3method(compare_seroincidence,default) +S3method(compare_seroincidence,seroincidence) +S3method(compare_seroincidence,seroincidence.by) S3method(print,seroincidence) S3method(print,seroincidence.by) S3method(print,summary.pop_data) S3method(print,summary.seroincidence.by) -S3method(strata,seroincidence.by) +S3method(strata,default) S3method(summary,pop_data) S3method(summary,seroincidence) S3method(summary,seroincidence.by) +export(analyze_sims) export(antibody_decay_curve) export(as_curve_params) export(as_noise_params) export(as_pop_data) +export(as_sr_params) export(autoplot) export(check_pop_data) +export(compare_seroincidence) +export(count_strata) export(curve_app) export(est.incidence) export(est.incidence.by) +export(est_seroincidence) +export(est_seroincidence_by) +export(expect_snapshot_data) export(f_dev) export(f_dev0) export(fdev) @@ -33,12 +44,14 @@ export(get_values) export(get_values_var) export(graph.curve.params) export(graph_loglik) +export(graph_seroresponse_model_1) export(ids) export(ids_varname) export(llik) export(load_curve_params) export(load_noise_params) export(load_pop_data) +export(load_sr_params) export(log_likelihood) export(pathogen_decay_curve) export(plot_decay_curve) @@ -48,6 +61,8 @@ export(sim.cs) export(sim.cs.multi) export(sim_pop_data) export(sim_pop_data_multi) +export(strat_ests_barplot) +export(strat_ests_scatterplot) export(strata) export(t1f) export(y1f) @@ -78,6 +93,7 @@ importFrom(dplyr,row_number) importFrom(dplyr,rowwise) importFrom(dplyr,select) importFrom(dplyr,semi_join) +importFrom(dplyr,slice_head) importFrom(dplyr,summarise) importFrom(dplyr,ungroup) importFrom(foreach,"%:%") @@ -90,15 +106,19 @@ importFrom(ggplot2,geom_line) importFrom(ggplot2,ggplot) importFrom(ggplot2,labs) importFrom(ggplot2,theme_bw) +importFrom(ggplot2,vars) importFrom(lifecycle,deprecated) importFrom(magrittr,"%>%") -importFrom(mixtools,normalmixEM) importFrom(rlang,.data) importFrom(rlang,.env) importFrom(rngtools,RNGseq) importFrom(rngtools,setRNG) +importFrom(shiny,h2) importFrom(shiny,reactive) +importFrom(shiny,renderPlot) +importFrom(shiny,renderTable) importFrom(shiny,renderText) +importFrom(shiny,sliderInput) importFrom(stats,dlnorm) importFrom(stats,formula) importFrom(stats,lm) @@ -121,6 +141,7 @@ importFrom(tidyselect,contains) importFrom(tidyselect,ends_with) importFrom(utils,capture.output) importFrom(utils,download.file) +importFrom(utils,head) importFrom(utils,tail) importFrom(utils,unzip) useDynLib(serocalculator, .registration = TRUE) diff --git a/NEWS.md b/NEWS.md index 809929c63..20e004582 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,13 +1,44 @@ # serocalculator (development version) +# serocalculator 1.4.0 + ## New features +* Added `compare_seroincidence()` function for statistical comparison of seroincidence rates + - Performs two-sample z-tests to compare seroincidence estimates + - Returns `htest` format when comparing two single estimates + - Returns formatted table with all pairwise comparisons for stratified estimates + - Added examples to tutorial vignette and comprehensive unit tests +* Implemented multi-version pkgdown documentation with version dropdown menu + - Users can now switch between main, latest-tag, and versioned releases + - Default landing page shows latest-tag (most recent release) + - Based on insightsengineering/r-pkgdown-multiversion setup +* Added `chain_color` option to `graph.curve.params()` to control MCMC line color (#455) +* Made `graph.curve.params()` the default sub-method for `autoplot.curve_params()` (#450) +* Added `log_x` and `log_y` options to `graph.curve.params()` sub-method for +`autoplot.curve_params()` (#453) +* Extended `sim_pop_data_multi()` to loop over multiple sample sizes (#444) +* Added new functions `analyze_sims()` and `autoplot.sim_results()` (#444) +* Rename `estimate_scr()` to `est_seroincidence_by()` (#439) +* Rename `estimate_scr()` to `est_seroincidence()` (#432) +* Rename argument `curve_params` to `sr_params` for estimation functions (#424) +* added documentation for `count_strata()` (#431) +* Rename `as_curve_params()` to `as_sr_params()` (#421) +* Rename `load_curve_params()` to `load_sr_params()` (#421) +* added default for `xvar` in `"scatter"` option for `autoplot.seroincidence.by()` (#417) +* Extended `autoplot.summary.seroincidence.by()` to include types for either scatter or bar plots of stratified results (#397) +* added option to add lines using `group_var` input to `autoplot.summary.seroincidence.by()` (#410) +* `autoplot.pop_data(type = "age-scatter")` now shows legend at bottom (#407) +* `autoplot.pop_data(type = "age-scatter")` now facets by antigen isotype (#406) +* Rename `est.incidence.by()` to `estimate_scr_by()` (#389) +* Rename `est.incidence()` to `estimate_scr()` (#389) * Improved warning messages for `get_biomarker_names_var()` * Added `get_*()` extractor functions to API (#380) * Added optional CI error bars to `autoplot.summary.seroincidence.by()` (#372) * Improved y-limit calculation in `graph.curve.params()` (#368) * Added option for `graph.curve.params()` to show all curves (#368) * Added color-coding for `graph.curve.params()` (#383) +* Added `quantiles` parameter to `graph.curve.params()` and corresponding test in `test-graph.curve.params.R` (#434) * Removed `warn.missing.strata()` from API (#366) * Added more details about contributing PRs in `Contributing.md` (#280) @@ -34,12 +65,19 @@ ## Bug fixes +* Fixed CRAN errors (#464) +* Fixed stratification issue in enteric fever vignette (#418) * Fixed issue in `graph.curve.params()` where MCMC samples with the same iteration number from different MCMC chains would get merged by `ggplot2::aes(group = iter)` (#382) ## Internal changes +* switched `expect_snapshot_data()` to an internal function due to CRAN errors (#464) +* generalized `ab1()` +* added codecov/test-results-action to test-coverage.yaml workflow +* added test for censored data in f_dev() (#399) +* added test for `autoplot.curve_params()` * added test for `graph.curve.params()` (#368) * reverted Readme source file from qmd to Rmd. * switched pkgdown GHA from `any::pkgdown` to `r-lib/pkgdown` (i.e., dev version) (#359) diff --git a/R/ab1.R b/R/ab1.R new file mode 100644 index 000000000..ac2b46ef0 --- /dev/null +++ b/R/ab1.R @@ -0,0 +1,10 @@ +# uses r > 1 scale for shape +ab1 <- function(t, y0, y1, t1, alpha, shape) { + beta <- bt(y0, y1, t1) + yt <- ifelse( + t <= t1, + y0 * exp(beta * t), + (y1^(1 - shape) - (1 - shape) * alpha * (t - t1))^(1 / (1 - shape)) + ) + return(yt) +} diff --git a/R/age_scatter.R b/R/age_scatter.R new file mode 100644 index 000000000..56c943eb4 --- /dev/null +++ b/R/age_scatter.R @@ -0,0 +1,56 @@ +age_scatter <- function( + object, + strata = NULL, + age_var = object |> get_age_var(), + value_var = object |> get_values_var()) { + # create default plotting + + biomarker_var <- object |> get_biomarker_names_var() + + if (is.null(strata)) { + plot1 <- + object |> + ggplot2::ggplot() + + ggplot2::aes( + x = .data[[age_var]], + y = .data[[value_var]] + ) + } else { + plot1 <- + object |> + ggplot2::ggplot() + + ggplot2::aes( + col = .data[[strata]], + x = .data[[age_var]], + y = .data[[value_var]] + ) + + ggplot2::labs(colour = strata) + } + + plot1 <- plot1 + + ggplot2::theme_linedraw() + + # ggplot2::scale_y_log10() + + + # avoid log 0 (https://bit.ly/4eqDkT4) + ggplot2::scale_y_continuous( + trans = scales::pseudo_log_trans(sigma = 0.01), + breaks = c(-1, -0.1, 0, 0.1, 1, 10), + minor_breaks = NULL + ) + + ggplot2::geom_point(size = .6, alpha = .7) + + ggplot2::geom_smooth( + method = "lm", + se = FALSE, + formula = y ~ x, + na.rm = TRUE + ) + + ggplot2::facet_wrap(biomarker_var) + + ggplot2::labs( + title = "Quantitative Antibody Responses by Age", + x = "Age", + y = "Antibody Response Value" + ) + + ggplot2::theme(legend.position = "bottom") + + return(plot1) +} diff --git a/R/analyze_sims.R b/R/analyze_sims.R new file mode 100644 index 000000000..3615f8661 --- /dev/null +++ b/R/analyze_sims.R @@ -0,0 +1,69 @@ +#' Analyze simulation results +#' +#' @param data a [tibble::tbl_df] with columns: +#' * `lambda.sim`, +#' * `incidence.rate`, +#' * `SE`, +#' * `CI.lwr`, +#' * `CI.upr` +#' for example, as produced by [summary.seroincidence.by()] with +#' `lambda.sim` as a stratifying variable +#' +#' @returns a `sim_results` object (extends [tibble::tbl_df]) +#' @export +#' +#' @example inst/examples/exm-analyze_sims.R +#' +analyze_sims <- function( + data) { + + to_return <- + data |> + split( + f = ~ sample_size + lambda.sim + ) |> + lapply(FUN = analyze_sims_one_stratum) |> + bind_rows() + + class(to_return) <- union("sim_results", class(to_return)) + + return(to_return) +} + +analyze_sims_one_stratum <- function( + data, + true_lambda = data$lambda.sim, + sample_size = data$sample_size) { + + # Filter out rows where CI.lwr or CI.upr is Inf or NaN + data <- data |> + filter(is.finite(.data$CI.lwr) & is.finite(.data$CI.upr)) + + # Compute Bias + bias <- mean(data$incidence.rate - true_lambda, na.rm = TRUE) + + # Standard Error (Mean of reported standard errors) + standard_error <- mean(data$SE, na.rm = TRUE) + + # RMSE (Root Mean Square Error) + rmse <- mean((data$incidence.rate - true_lambda)^2, na.rm = TRUE) |> sqrt() + + # Confidence Interval Width (Mean of Upper - Lower bounds, without Inf values) + ci_width <- mean(data$CI.upr - data$CI.lwr, na.rm = TRUE) + + coverage_prop <- + mean(data$CI.lwr <= true_lambda & data$CI.upr >= true_lambda, na.rm = TRUE) + + to_return <- tibble( + lambda.sim = mean(true_lambda), + sample_size = mean(sample_size), + Bias = bias, + Mean_Est_SE = standard_error, + Empirical_SE = stats::sd(data$incidence.rate, na.rm = TRUE), + RMSE = rmse, + Mean_CI_Width = ci_width, + CI_Coverage = coverage_prop + ) + # Return computed statistics as a list + return(to_return) +} diff --git a/R/as_curve_params.R b/R/as_sr_params.R similarity index 65% rename from R/as_curve_params.R rename to R/as_sr_params.R index 4d2d4c899..a9effab73 100644 --- a/R/as_curve_params.R +++ b/R/as_sr_params.R @@ -1,4 +1,4 @@ -#' Load antibody decay curve parameter +#' Load longitudinal seroresponse parameters #' #' @param data a [data.frame()] or [tibble::tbl_df] #' @param antigen_isos a [character()] vector of antigen isotypes @@ -14,7 +14,7 @@ #' as_curve_params() #' #' print(curve_data) -as_curve_params <- function(data, antigen_isos = NULL) { +as_sr_params <- function(data, antigen_isos = NULL) { if (!is.data.frame(data)) { cli::cli_abort( @@ -29,7 +29,7 @@ as_curve_params <- function(data, antigen_isos = NULL) { } curve_data <- - data %>% + data |> tibble::as_tibble() # check if object has expected columns: @@ -38,7 +38,7 @@ as_curve_params <- function(data, antigen_isos = NULL) { curve_cols <- c("antigen_iso", "y0", "y1", "t1", "alpha", "r") # get columns from provided data - data_cols <- data %>% names() + data_cols <- data |> names() # get any missing column(s) missing_cols <- setdiff(x = curve_cols, y = data_cols) @@ -67,11 +67,44 @@ as_curve_params <- function(data, antigen_isos = NULL) { )) } + # if `object` lacks an `iter` column, add it: + if (!is.element("iter", names(curve_data))) { + cli::cli_warn( + c( + "`data` is missing `iter` column", + "It will be inferred from row ordering." + ) + ) + curve_data <- + curve_data |> + mutate( + .by = any_of(c("antigen_iso", "chain")), + iter = row_number() + ) + } + # assign antigen attribute attr(curve_data, "antigen_isos") <- antigen_isos - curve_data <- curve_data %>% + curve_data <- curve_data |> set_biomarker_var(biomarker = "antigen_iso", standardize = FALSE) return(curve_data) } + +#' @title Load antibody decay curve parameter +#' +#' @description +#' `r lifecycle::badge("deprecated")` +#' +#' `as_curve_params()` was renamed to [as_sr_params()] to create a more +#' consistent API. +#' @keywords internal +#' @export +as_curve_params <- function( # nolint: object_name_linter + ...) { + lifecycle::deprecate_soft("1.3.1", "as_curve_params()", "as_sr_params()") + as_sr_params( + ... + ) +} diff --git a/R/autoplot.curve_params.R b/R/autoplot.curve_params.R index f77925812..4fe6be6a1 100644 --- a/R/autoplot.curve_params.R +++ b/R/autoplot.curve_params.R @@ -1,22 +1,18 @@ -#' graph antibody decay curves by antigen isotype +#' Graph antibody decay curves by antigen isotype +#' @param object +#' a `curve_params` object (constructed using [as_sr_params()]), which is +#' a [data.frame()] containing MCMC samples of antibody decay curve parameters +#' @param method a [character] string indicating whether to use +#' - [graph.curve.params()] (default) or +#' - [graph_seroresponse_model_1()] (previous default) +#' as the graphing method. #' -#' @inheritParams plot_curve_params_one_ab -#' @inheritDotParams plot_curve_params_one_ab -#' @param antigen_isos antigen isotypes to analyze (can subset `curve_params`) -#' @param ncol how many columns of subfigures to use in panel plot +#' @param ... additional arguments passed to the sub-function +#' indicated by the `method` argument. #' @details -#' ## `rows_to_graph` -#' If you directly specify `rows_to_graph` when calling this function, -#' the row numbers are enumerated separately for each antigen isotype; -#' in other words, for the purposes of this argument, -#' row numbers start over at 1 for each antigen isotype. -#' There is currently no way to specify different row numbers for different antigen isotypes; -#' if you want to do that, -#' you will could call [plot_curve_params_one_ab()] directly for each antigen isotype -#' and combine the resulting panels yourself. -#' Or you could subset `curve_params` manually, -#' before passing it to this function, -#' and set the `n_curves` argument to `Inf`. +#' Currently, the backend for this method is [graph.curve.params()]. +#' Previously, the backend for this method was [graph_seroresponse_model_1()]. +#' That function is still available if preferred. #' @return a [ggplot2::ggplot()] object #' @export #' @examples @@ -26,44 +22,22 @@ #' library(magrittr) #' #' curve <- -#' serocalculator_example("example_curve_params.csv") %>% -#' read.csv() %>% -#' as_curve_params() %>% -#' filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) %>% +#' serocalculator_example("example_curve_params.csv") |> +#' read.csv() |> +#' as_sr_params() |> +#' filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) |> #' autoplot() #' #' curve #' } autoplot.curve_params <- function( object, - antigen_isos = unique(object$antigen_iso), - ncol = min(3, length(antigen_isos)), + method = c("graph.curve.params", "graph_seroresponse_model_1"), ...) { - split_data <- object %>% - filter(.data$antigen_iso %in% antigen_isos) %>% - droplevels() %>% - split(~antigen_iso) - labels <- names(split_data) - figs <- split_data %>% - lapply(FUN = plot_curve_params_one_ab, ...) - - for (i in seq_along(figs)) { - figs[[i]] <- figs[[i]] + ggplot2::ggtitle(labels[i]) - } - - - nrow <- ceiling(length(figs) / ncol) - figure <- do.call( - what = function(...) { - ggpubr::ggarrange( - ..., - ncol = ncol, - nrow = nrow - ) - }, - args = figs - ) - - return(figure) + # spaghettified in order to swap out implementations with minimal + # disruption to API + method <- match.arg(method) + cur_function <- match.fun(method) + object |> cur_function(...) } diff --git a/R/autoplot.pop_data.R b/R/autoplot.pop_data.R index d64e19b5c..899af1c2f 100644 --- a/R/autoplot.pop_data.R +++ b/R/autoplot.pop_data.R @@ -19,12 +19,12 @@ #' library(magrittr) #' #' xs_data <- -#' serocalculator_example("example_pop_data.csv") %>% -#' read.csv() %>% +#' serocalculator_example("example_pop_data.csv") |> +#' read.csv() |> #' as_pop_data() #' -#' xs_data %>% autoplot(strata = "catchment", type = "density") -#' xs_data %>% autoplot(strata = "catchment", type = "age-scatter") +#' xs_data |> autoplot(strata = "catchment", type = "density") +#' xs_data |> autoplot(strata = "catchment", type = "age-scatter") #' } #' @export autoplot.pop_data <- function( @@ -33,7 +33,6 @@ autoplot.pop_data <- function( type = "density", strata = NULL, ...) { - if (!is.null(strata) && !is.element(strata, names(object))) { cli::cli_abort( class = "unavailable_strata", @@ -61,127 +60,3 @@ autoplot.pop_data <- function( ) } } - -age_scatter <- function( - object, - strata = NULL, - age_var = object %>% get_age_var(), - value_var = object %>% get_values_var()) { - # create default plotting - - if (is.null(strata)) { - plot1 <- - object %>% - ggplot2::ggplot() + - ggplot2::aes( - x = .data[[age_var]], - y = .data[[value_var]] - ) - } else { - plot1 <- - object %>% - ggplot2::ggplot() + - ggplot2::aes( - col = .data[[strata]], - x = .data[[age_var]], - y = .data[[value_var]] - ) + - ggplot2::labs(colour = strata) - } - - plot1 <- plot1 + - ggplot2::theme_linedraw() + - # ggplot2::scale_y_log10() + - - # avoid log 0 (https://bit.ly/4eqDkT4) - ggplot2::scale_y_continuous( - trans = scales::pseudo_log_trans(sigma = 0.01), - breaks = c(-1, -0.1, 0, 0.1, 1, 10), - minor_breaks = NULL - ) + - ggplot2::geom_point(size = .6, alpha = .7) + - ggplot2::geom_smooth( - method = "lm", - se = FALSE, - formula = y ~ x, - na.rm = TRUE - ) + - ggplot2::labs( - title = "Quantitative Antibody Responses by Age", - x = "Age", - y = "Antibody Response Value" - ) - - return(plot1) -} - -# density plotting function -density_plot <- function( - object, - strata = NULL, - log = FALSE, - value_var = object %>% get_values_var()) { - plot1 <- - object %>% - ggplot2::ggplot() + - ggplot2::aes(x = .data[[value_var]]) + - ggplot2::theme_linedraw() + - ggplot2::facet_wrap(~antigen_iso, nrow = 3) - - if (is.null(strata)) { - plot1 <- plot1 + - ggplot2::geom_density( - alpha = .6, - color = "black" - ) - } else { - plot1 <- plot1 + - ggplot2::geom_density( - alpha = .6, - color = "black", - aes(fill = get(strata)) - ) + - ggplot2::labs(fill = strata) - } - if (log) { - - min_nonzero_val <- - object %>% - get_values() %>% - purrr::keep(~ . > 0) %>% - min() - - max_val <- - object %>% - get_values() %>% - max() - - breaks1 <- c(0, 10^seq( - min_nonzero_val %>% log10() %>% floor(), - max_val %>% log10() %>% ceiling() - )) - - plot1 <- plot1 + - ggplot2::scale_x_continuous( - labels = scales::label_comma(), - transform = scales::pseudo_log_trans( - sigma = min_nonzero_val / 10, - base = 10 - ), - breaks = breaks1 - ) + - ggplot2::labs( - title = "Distribution of Cross-sectional Antibody Responses", - x = "Quantitative antibody response", - y = "Frequency" - ) - } else { - plot1 <- plot1 + - ggplot2::labs( - title = "Distribution of Cross-sectional Antibody Responses", - x = "Antibody Response Value", - y = "Frequency" - ) - } - return(plot1) -} diff --git a/R/autoplot.seroincidence.R b/R/autoplot.seroincidence.R index 11deab3cc..3bc311fca 100644 --- a/R/autoplot.seroincidence.R +++ b/R/autoplot.seroincidence.R @@ -1,7 +1,8 @@ #' Plot the log-likelihood curve for the incidence rate estimate #' -#' @param object a `seroincidence` object (from [est.incidence()]) -#' @param log_x should the x-axis be on a logarithmic scale (`TRUE`) or linear scale (`FALSE`, default)? +#' @param object a `seroincidence` object (from [est_seroincidence()]) +#' @param log_x should the x-axis be on a logarithmic scale (`TRUE`) +#' or linear scale (`FALSE`, default)? #' @param ... unused #' #' @return a [ggplot2::ggplot()] @@ -21,9 +22,9 @@ #' noise <- #' example_noise_params_pk #' -#' est1 <- est.incidence( +#' est1 <- est_seroincidence( #' pop_data = xs_data, -#' curve_param = curve, +#' sr_param = curve, #' noise_param = noise, #' antigen_isos = c("HlyE_IgG", "HlyE_IgA"), #' build_graph = TRUE @@ -32,17 +33,15 @@ #' # Plot the log-likelihood curve #' autoplot(est1) #'} -autoplot.seroincidence = - function(object, log_x = FALSE, ...) -{ - to_return = attr(object, "ll_graph") +autoplot.seroincidence <- + function(object, log_x = FALSE, ...) { + to_return <- attr(object, "ll_graph") if (is.null(to_return)) { - stop( + cli::cli_abort(c( "Graphs cannot be extracted; ", - "`build_graph` was not `TRUE` in the call to `est.incidence()`" - ) - figure <- NULL + "`build_graph` was not `TRUE` in the call to `est_seroincidence()`" + )) } if (log_x) { diff --git a/R/autoplot.seroincidence.by.R b/R/autoplot.seroincidence.by.R index 1e653a586..df0773052 100644 --- a/R/autoplot.seroincidence.by.R +++ b/R/autoplot.seroincidence.by.R @@ -1,10 +1,10 @@ #' Plot `seroincidence.by` log-likelihoods #' @description #' Plots log-likelihood curves by stratum, for `seroincidence.by` objects -#' @param object a '"seroincidence.by"' object (from [est.incidence.by()]) +#' @param object a '"seroincidence.by"' object (from [est_seroincidence_by()]) #' @param ncol number of columns to use for panel of plots #' @inheritDotParams autoplot.seroincidence -#' @return an object of class `"ggarrange"`, which is a [ggplot2::ggplot()] or a [list()] of [ggplot2::ggplot()]s. +#' @return a `"ggarrange"` object: a single or [list()] of [ggplot2::ggplot()]s #' @export #' @examples #'\donttest{ @@ -21,10 +21,10 @@ #' noise <- #' example_noise_params_pk #' -#' est2 <- est.incidence.by( +#' est2 <- est_seroincidence_by( #' strata = c("catchment"), #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = curve, #' curve_strata_varnames= NULL, #' noise_strata_varnames = NULL, #' noise_params = noise, @@ -36,27 +36,28 @@ #' # Plot the log-likelihood curve #' autoplot(est2) #'} -autoplot.seroincidence.by = function( +autoplot.seroincidence.by <- function( object, ncol = min(3, length(object)), ...) { if (length(object) == 0) { - stop("The input doesn't contain any fits. Did subsetting go wrong?") + cli::cli_abort( + "The input doesn't contain any fits. Did subsetting go wrong?" + ) } if (!attr(object, "graphs_included")) { - stop( + cli::cli_abort(c( "Graphs cannot be extracted; ", - "`build_graph` was not `TRUE` in the call to `est.incidence.by()`" - ) + "`build_graph` was not `TRUE` in the call to `est_seroincidence_by()`" + )) figure <- NULL } labels <- names(object) figs <- lapply(object, FUN = autoplot.seroincidence, ...) - for (i in 1:length(figs)) - { + for (i in seq_along(figs)){ figs[[i]] <- figs[[i]] + ggplot2::ggtitle(labels[i]) } diff --git a/R/autoplot.sim_results.R b/R/autoplot.sim_results.R new file mode 100644 index 000000000..e4a12eaab --- /dev/null +++ b/R/autoplot.sim_results.R @@ -0,0 +1,27 @@ +#' Plot simulation results +#' `autoplot()` method for `sim_results` objects +#' +#' @param object a `sim_results` object (from [analyze_sims()]) +#' @param statistic which column of `object` should be the y-axis? +#' @param ... unused +#' @returns a [ggplot2::ggplot] +#' @export +#' +#' @example inst/examples/exm-autoplot.sim_results.R +autoplot.sim_results <- function( + object, + statistic = "Empirical_SE", + ...) { + object |> + dplyr::mutate(lambda.sim = factor(.data$lambda.sim)) |> + ggplot2::ggplot() + + ggplot2::aes( + x = .data$sample_size, + group = .data$lambda.sim, + col = .data$lambda.sim, + y = .data[[statistic]] + ) + + ggplot2::geom_point() + + ggplot2::geom_line() + + ggplot2::theme(legend.position = "bottom") +} diff --git a/R/autoplot.summary.seroincidence.by.R b/R/autoplot.summary.seroincidence.by.R index f4c220104..7238c4e0f 100644 --- a/R/autoplot.summary.seroincidence.by.R +++ b/R/autoplot.summary.seroincidence.by.R @@ -3,14 +3,14 @@ #' @param object #' a `summary.seroincidence.by` object #' (generated by applying the `summary()` -#' method to the output of [est.incidence.by()]). -#' @param xvar the name of a stratifying variable in `object` -#' @param alpha transparency for the points in the graph -#' (1 = no transparency, 0 = fully transparent) -#' @param shape shape argument for `geom_point()` -#' @param dodge_width width for jitter -#' @param CIs [logical], if `TRUE`, add CI error bars -#' @param ... unused +#' method to the output of [est_seroincidence_by()]). +#' @param type +#' [character] string indicating which type of plot to generate. +#' The implemented options are: +#' - `"scatter"`: calls [strat_ests_scatterplot()] to generate a scatterplot +#' - `"bar"`: calls `strat_ests_barplot()` to generate a barplot +#' @inheritDotParams strat_ests_scatterplot +#' @inheritDotParams strat_ests_barplot #' #' @return a [ggplot2::ggplot()] object #' @export @@ -29,10 +29,10 @@ #' noise <- #' example_noise_params_pk #' -#' est2 <- est.incidence.by( -#' strata = c("catchment"), +#' est2 <- est_seroincidence_by( +#' strata = c("catchment", "ageCat"), #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = curve, #' noise_params = noise, #' curve_strata_varnames= NULL, #' noise_strata_varnames = NULL, @@ -42,53 +42,38 @@ #' #' est2sum <- summary(est2) #' -#' autoplot(est2sum, "catchment") +#' est2sum |> autoplot( +#' type ="scatter", +#' xvar = "ageCat", +#' color_var = "catchment", +#' CIs = TRUE, +#' group_var = "catchment") +#' +#' est2sum |> autoplot( +#' type = "bar", +#' yvar = "ageCat", +#' color_var = "catchment", +#' CIs = TRUE) #' autoplot.summary.seroincidence.by <- function( object, - xvar, - alpha = .7, - shape = 1, - dodge_width = 0.001, - CIs = FALSE, + type, ...) { - plot1 <- - object |> - ggplot2::ggplot() + - ggplot2::aes( - x = get(xvar), - y = .data$incidence.rate, - col = .data$nlm.convergence.code - ) + - ggplot2::xlab(xvar) + - ggplot2::ylab("Estimated incidence rate") + - ggplot2::theme_linedraw() + - ggplot2::theme( - panel.grid.minor.x = ggplot2::element_blank(), - panel.grid.minor.y = ggplot2::element_blank()) + - ggplot2::expand_limits(y = 0) + - ggplot2::labs(col = "`nlm()` convergence code") + - ggplot2::theme(legend.position = "bottom") + if (type == "scatter") { + plot1 <- strat_ests_scatterplot(object, ...) - if(CIs) { - plot1 <- plot1 + - ggplot2::geom_pointrange( - alpha = alpha, - position = ggplot2::position_dodge2(width = dodge_width), - aes(ymin = .data$CI.lwr, ymax = .data$CI.upr) - ) + } else if (type == "bar") { + plot1 <- strat_ests_barplot(object, ...) } else { - plot1 <- plot1 + - ggplot2::geom_point( - position = ggplot2::position_dodge2(width = dodge_width), - shape = shape, - alpha = alpha + cli::cli_abort( + c( + "Invalid plot `type` specified: {.str {type}}.", + "i" = "Please choose either 'scatter' or 'bar'." + ) ) - } return(plot1) - } diff --git a/R/check_parallel_cores.R b/R/check_parallel_cores.R index d2bb870d4..dae37b4ff 100644 --- a/R/check_parallel_cores.R +++ b/R/check_parallel_cores.R @@ -11,7 +11,7 @@ check_parallel_cores <- function(num_cores) { c( "This computer appears to have {parallel::detectCores()} cores available. - `est.incidence.by()` has reduced its + `est_seroincidence_by()` has reduced its `num_cores` argument to {num_cores} to avoid destabilizing the computer." ) diff --git a/R/check_pop_data.R b/R/check_pop_data.R index fa15bc501..01ab5c1ec 100644 --- a/R/check_pop_data.R +++ b/R/check_pop_data.R @@ -8,8 +8,8 @@ #' library(magrittr) #' #' xs_data <- -#' serocalculator_example("example_pop_data.csv") %>% -#' read.csv() %>% +#' serocalculator_example("example_pop_data.csv") |> +#' read.csv() |> #' as_pop_data() #' #' check_pop_data(xs_data, verbose = TRUE) @@ -27,24 +27,24 @@ check_pop_data <- function(pop_data, verbose = FALSE) { } missing_age <- is.element( - pop_data %>% get_age_var(), - pop_data %>% names() + pop_data |> get_age_var(), + pop_data |> names() ) if (!missing_age) { "Argument {.arg pop_data} is missing column - {.var {pop_data %>% get_age_var()}}(age, in years)" %>% + {.var {pop_data |> get_age_var()}} (age, in years)" |> cli::cli_abort(class = "missing-var") } missing_value <- is.element( - pop_data %>% get_values_var(), - pop_data %>% names() + pop_data |> get_values_var(), + pop_data |> names() ) if (!missing_value) { "Argument {.arg pop_data} is missing column - {.var {pop_data %>% get_values_var()}} (antibody measurement)" %>% + {.var {pop_data |> get_values_var()}} (antibody measurement)" |> cli::cli_abort(class = "missing-var") } diff --git a/R/check_strata.R b/R/check_strata.R index 612dda283..0fdfe60ff 100644 --- a/R/check_strata.R +++ b/R/check_strata.R @@ -10,7 +10,7 @@ #' sees_pop_data_pk_100 |> #' check_strata(strata = c("ag", "catch", "Count")) |> #' try() -#' @dev +#' @keywords internal check_strata <- function(pop_data, strata, biomarker_names_var = @@ -32,16 +32,26 @@ check_strata <- function(pop_data, message0 <- c( "Can't stratify provided {.arg pop_data} with the provided {.arg strata}:", - "i" = "variable {.var {missing_strata_vars}} + "x" = "variable {.var {missing_strata_vars}} {?is/are} missing in {.arg pop_data}." ) - partial_matches <- - purrr::map(missing_strata_vars, function(x) { - stringr::str_subset(string = names(pop_data), pattern = x) |> + f1 <- function(x) { + temp <- stringr::str_subset(string = names(pop_data), pattern = x) + + if (length(temp) > 0) { + temp <- temp |> glue::backtick() |> and::or() - }) |> + } else { + temp = character() + } + + return(temp) + } + + partial_matches <- + purrr::map(missing_strata_vars, f1) |> rlang::set_names(missing_strata_vars) |> purrr::keep(~ length(.x) > 0) diff --git a/R/compare_seroincidence.R b/R/compare_seroincidence.R new file mode 100644 index 000000000..8e226d8dc --- /dev/null +++ b/R/compare_seroincidence.R @@ -0,0 +1,265 @@ +#' Compare seroincidence rates between two groups +#' +#' @description +#' Perform a two-sample z-test to compare seroincidence rates between +#' two groups. Since we use maximum likelihood estimation (MLE) for each +#' seroincidence estimate and estimates from different strata or data sets +#' are uncorrelated, we can use a simple two-sample z-test using the +#' Gaussian distribution. The standard error for the difference is computed +#' by adding the estimated variances and taking the square root. +#' +#' @param x A `"seroincidence"` object from [est_seroincidence()] or +#' a `"seroincidence.by"` object from [est_seroincidence_by()] +#' @param y A `"seroincidence"` object from [est_seroincidence()] +#' (optional if `x` is a `"seroincidence.by"` object) +#' @param coverage Desired confidence interval coverage probability +#' (default = 0.95) +#' @param verbose Logical indicating whether to print verbose messages +#' (default = FALSE) +#' @param ... Additional arguments (currently unused) +#' +#' @details +#' When comparing two single `"seroincidence"` objects, this function performs a +#' two-sample z-test and returns results in the standard `htest` format. +#' +#' When applied to a `"seroincidence.by"` object (stratified estimates), +#' the function compares all pairs of strata and returns a nicely formatted +#' table with point estimates for the difference in seroincidence, p-values, +#' and confidence intervals. +#' +#' The test statistic is computed as: +#' \deqn{z = \frac{\lambda_1 - \lambda_2}{\sqrt{SE_1^2 + SE_2^2}}} +#' +#' where \eqn{\lambda_1} and \eqn{\lambda_2} are the estimated incidence rates, +#' and \eqn{SE_1} and \eqn{SE_2} are their standard errors. +#' +#' @return +#' * When comparing two `"seroincidence"` objects: An object of class +#' `"htest"` containing the test statistic, p-value, confidence interval, +#' and estimates. +#' * When applied to a `"seroincidence.by"` object: A [tibble::tibble()] +#' with columns for each pair of strata, the difference in incidence rates, +#' standard error, z-statistic, p-value, and confidence interval bounds. +#' +#' @export +#' @examples +#' \dontrun{ +#' # See inst/examples/exm-compare_seroincidence.R for complete examples +#' } +compare_seroincidence <- function( + x, + y = NULL, + coverage = 0.95, + verbose = FALSE, + ...) { + UseMethod("compare_seroincidence") +} + +#' @export +compare_seroincidence.default <- function( + x, + y = NULL, + coverage = 0.95, + verbose = FALSE, + ...) { + cli::cli_abort( + c( + paste0( + "{.arg x} must be a {.cls seroincidence} or ", + "{.cls seroincidence.by} object." + ), + "x" = "You supplied an object of class {.cls {class(x)}}." + ) + ) +} + +#' @export +#' @describeIn compare_seroincidence Compare two single seroincidence +#' estimates +compare_seroincidence.seroincidence <- function( + x, + y = NULL, + coverage = 0.95, + verbose = FALSE, + ...) { + if (is.null(y)) { + cli::cli_abort( + c( + paste0( + "When {.arg x} is a {.cls seroincidence} object, ", + "{.arg y} must also be provided." + ), + "x" = "{.arg y} is {.val NULL}." + ) + ) + } + + if (!inherits(y, "seroincidence")) { + cli::cli_abort( + c( + "{.arg y} must be a {.cls seroincidence} object.", + "x" = "You supplied an object of class {.cls {class(y)}}." + ) + ) + } + + # Get summaries for both estimates + sum_x <- summary(x, coverage = coverage, verbose = FALSE) + sum_y <- summary(y, coverage = coverage, verbose = FALSE) + + # Extract estimates and standard errors + lambda_1 <- sum_x$incidence.rate + lambda_2 <- sum_y$incidence.rate + se_1 <- sum_x$SE + se_2 <- sum_y$SE + + # Compute difference and its standard error + diff <- lambda_1 - lambda_2 + se_diff <- sqrt(se_1^2 + se_2^2) + + # Compute z-statistic and p-value + z_stat <- diff / se_diff + p_value <- 2 * stats::pnorm(-abs(z_stat)) + + # Compute confidence interval for the difference + alpha <- 1 - coverage + z_crit <- stats::qnorm(1 - alpha / 2) + ci_lower <- diff - z_crit * se_diff + ci_upper <- diff + z_crit * se_diff + + # Create htest object + result <- list( + statistic = c(z = z_stat), + p.value = p_value, + estimate = c( + "incidence rate 1" = lambda_1, + "incidence rate 2" = lambda_2, + "difference" = diff + ), + conf.int = c(ci_lower, ci_upper), + null.value = c("difference in incidence rates" = 0), + alternative = "two.sided", + method = "Two-sample z-test for difference in seroincidence rates", + data.name = paste("seroincidence estimates") + ) + + attr(result$conf.int, "conf.level") <- coverage + class(result) <- "htest" + + return(result) +} + +#' @export +#' @describeIn compare_seroincidence Compare all pairs of stratified +#' seroincidence estimates +compare_seroincidence.seroincidence.by <- function( + x, + y = NULL, + coverage = 0.95, + verbose = FALSE, + ...) { + if (!is.null(y)) { + cli::cli_warn( + c( + paste0( + "When {.arg x} is a {.cls seroincidence.by} object, ", + "{.arg y} is ignored." + ), + "i" = "Comparisons will be made among all strata in {.arg x}." + ) + ) + } + + # Get summary of stratified estimates + sum_x <- summary(x, coverage = coverage, verbose = FALSE) + + # Extract strata information + strata_vars <- attr(sum_x, "Strata") + n_strata <- nrow(sum_x) + + if (n_strata < 2) { + cli::cli_abort( + c( + "At least 2 strata are required for comparison.", + "x" = "Only {n_strata} stratum found in {.arg x}." + ) + ) + } + + # Create all pairwise comparisons + comparisons <- list() + idx <- 1 + + # Pre-compute string patterns for column relocation + strata_patterns_1 <- paste0(strata_vars, ".1") + strata_patterns_2 <- paste0(strata_vars, ".2") + + for (i in 1:(n_strata - 1)) { + for (j in (i + 1):n_strata) { + # Extract data for this pair + lambda_1 <- sum_x$incidence.rate[i] + lambda_2 <- sum_x$incidence.rate[j] + se_1 <- sum_x$SE[i] + se_2 <- sum_x$SE[j] + + # Compute difference and its standard error + diff <- lambda_1 - lambda_2 + se_diff <- sqrt(se_1^2 + se_2^2) + + # Compute z-statistic and p-value + z_stat <- diff / se_diff + p_value <- 2 * stats::pnorm(-abs(z_stat)) + + # Compute confidence interval for the difference + alpha <- 1 - coverage + z_crit <- stats::qnorm(1 - alpha / 2) + ci_lower <- diff - z_crit * se_diff + ci_upper <- diff + z_crit * se_diff + + # Store comparison results + comparison <- tibble::tibble( + Stratum_1 = sum_x$Stratum[i], + Stratum_2 = sum_x$Stratum[j], + incidence.rate.1 = lambda_1, + incidence.rate.2 = lambda_2, + difference = diff, + SE = se_diff, + z.statistic = z_stat, + p.value = p_value, + CI.lwr = ci_lower, + CI.upr = ci_upper + ) + + # Add stratum variables + for (var in strata_vars) { + comparison[[paste0(var, ".1")]] <- sum_x[[var]][i] + comparison[[paste0(var, ".2")]] <- sum_x[[var]][j] + } + + # Reorder columns to put stratum variables first + comparison <- comparison |> + dplyr::relocate( + tidyselect::starts_with(strata_patterns_1), + tidyselect::starts_with(strata_patterns_2), + .before = "incidence.rate.1" + ) + + comparisons[[idx]] <- comparison + idx <- idx + 1 + } + } + + # Combine all comparisons + result <- dplyr::bind_rows(comparisons) + + # Add metadata + result <- result |> + structure( + coverage = coverage, + strata_vars = strata_vars, + antigen_isos = attr(sum_x, "antigen_isos"), + class = c("comparison.seroincidence.by", class(result)) + ) + + return(result) +} diff --git a/R/count_strata.R b/R/count_strata.R index 095bf8947..6dcdb9ca9 100644 --- a/R/count_strata.R +++ b/R/count_strata.R @@ -1,13 +1,27 @@ -count_strata <- function(data, - strata_varnames, - biomarker_names_var = get_biomarker_names_var(data) - ) { +#' Count observations by stratum +#' +#' @param data a `"pop_data"` object (e.g., from [as_pop_data()]) +#' @param strata_varnames a [vector] of [character] strings matching +#' colnames to stratify on from `data` +#' @param biomarker_names_var a [character] string indicating the column +#' of `data` indicating which biomarker is being measured +#' +#' @returns a [tibble::tbl_df] counting observations by stratum +#' @export +#' @keywords internal +#' @examples +#' sees_pop_data_pk_100 |> count_strata(strata_varnames = "catchment") +count_strata <- function( + data, + strata_varnames, + biomarker_names_var = get_biomarker_names_var(data) +) { to_return <- - data %>% + data |> count(across(any_of(c(strata_varnames, biomarker_names_var)))) uneven_counts <- - to_return %>% + to_return |> dplyr::filter( .by = all_of(strata_varnames), n_distinct(n) > 1 @@ -21,12 +35,12 @@ count_strata <- function(data, "Sample size for each stratum will be calculated as the minimum number of observations across all antigen isotypes." ) |> - cli::cli_warn(class = "incomplete-obs", - body = capture.output(uneven_counts)) + cli::cli_warn(class = "incomplete-obs", + body = capture.output(uneven_counts)) } to_return <- - to_return %>% + to_return |> dplyr::summarize( .by = all_of(strata_varnames), n = min(n) @@ -34,8 +48,8 @@ count_strata <- function(data, if (!("Stratum" %in% strata_varnames)) { to_return <- - to_return %>% - mutate(Stratum = paste("Stratum", row_number())) %>% + to_return |> + mutate(Stratum = paste("Stratum", row_number())) |> dplyr::relocate("Stratum", .before = everything()) } @@ -44,8 +58,8 @@ count_strata <- function(data, "The data contain multiple strata with the same value of the {.var Stratum} variable.", "Please disambiguate." - ) %>% - cli::cli_abort() + ) |> + cli::cli_abort() } attr(to_return, "strata_vars") <- strata_varnames diff --git a/R/curve_app_server.R b/R/curve_app_server.R index 0ffc11cf6..e173530bc 100644 --- a/R/curve_app_server.R +++ b/R/curve_app_server.R @@ -12,15 +12,15 @@ curve_app_server = function(input, output, session) alpha = input$alpha |> exp10(), rho = input$rho, t1 = t1f( - mu_y = mu_y, - mu_b = mu_b, - gamma = gamma, - y0 = y0, - b0 = b0), + mu_y = .data$mu_y, + mu_b = .data$mu_b, + gamma = .data$gamma, + y0 = .data$y0, + b0 = .data$b0), y1 = y1f( - y0 = y0, - mu_y = mu_y, - t1 = t1) + y0 = .data$y0, + mu_y = .data$mu_y, + t1 = .data$t1) ) }|> reactive() diff --git a/R/curve_app_ui.R b/R/curve_app_ui.R index 0ef6797e1..a67400b86 100644 --- a/R/curve_app_ui.R +++ b/R/curve_app_ui.R @@ -10,7 +10,7 @@ curve_app_ui = function(request) min = -2, max = 2, step = .1, - val = 0), + value = 0), sliderInput( @@ -19,7 +19,7 @@ curve_app_ui = function(request) min = -2, max = 2, step = .1, - val = 0), + value = 0), sliderInput( inputId = "mu_b", @@ -27,21 +27,21 @@ curve_app_ui = function(request) min = -2, max = 2, step = .1, - val = log10(0.18432798)), + value = log10(0.18432798)), sliderInput( inputId = "mu_y", label = "log10(mu_y)", min = -2, max = 2, step = .1, - val = log10(0.36853621)), + value = log10(0.36853621)), sliderInput( inputId = "gamma", label = "log10(gamma)", min = -10, max = 10, step = .1, - val = log10(0.0013040664)), + value = log10(0.0013040664)), sliderInput( inputId = "rho", @@ -49,7 +49,7 @@ curve_app_ui = function(request) min = 1, max = 3, step = .1, - val = 2), + value = 2), sliderInput( inputId = "alpha", @@ -57,7 +57,7 @@ curve_app_ui = function(request) min = -8, max = -1, step = .1, - val = log10(0.00002192627)) + value = log10(0.00002192627)) ) @@ -81,7 +81,7 @@ curve_app_ui = function(request) min = 0, max = 5, step = .1, - val = 1), + value = 1), # shiny::column( # width = 6, h2("antibodies"), @@ -93,7 +93,7 @@ curve_app_ui = function(request) min = 2, max = 10, step = .1, - val = 4.5) + value = 4.5) ) diff --git a/R/density_plot.R b/R/density_plot.R new file mode 100644 index 000000000..ff23455a7 --- /dev/null +++ b/R/density_plot.R @@ -0,0 +1,69 @@ +# density plotting function +density_plot <- function( + object, + strata = NULL, + log = FALSE, + value_var = object |> get_values_var()) { + plot1 <- + object |> + ggplot2::ggplot() + + ggplot2::aes(x = .data[[value_var]]) + + ggplot2::theme_linedraw() + + ggplot2::facet_wrap(~antigen_iso, nrow = 3) + + if (is.null(strata)) { + plot1 <- plot1 + + ggplot2::geom_density( + alpha = .6, + color = "black" + ) + } else { + plot1 <- plot1 + + ggplot2::geom_density( + alpha = .6, + color = "black", + aes(fill = get(strata)) + ) + + ggplot2::labs(fill = strata) + } + if (log) { + min_nonzero_val <- + object |> + get_values() |> + purrr::keep(~ . > 0) |> + min() + + max_val <- + object |> + get_values() |> + max() + + breaks1 <- c(0, 10^seq( + min_nonzero_val |> log10() |> floor(), + max_val |> log10() |> ceiling() + )) + + plot1 <- plot1 + + ggplot2::scale_x_continuous( + labels = scales::label_comma(), + transform = scales::pseudo_log_trans( + sigma = min_nonzero_val / 10, + base = 10 + ), + breaks = breaks1 + ) + + ggplot2::labs( + title = "Distribution of Cross-sectional Antibody Responses", + x = "Quantitative antibody response", + y = "Frequency" + ) + } else { + plot1 <- plot1 + + ggplot2::labs( + title = "Distribution of Cross-sectional Antibody Responses", + x = "Antibody Response Value", + y = "Frequency" + ) + } + return(plot1) +} diff --git a/R/est.incidence.R b/R/est_seroincidence.R similarity index 58% rename from R/est.incidence.R rename to R/est_seroincidence.R index 5f88b46ba..b143604cd 100644 --- a/R/est.incidence.R +++ b/R/est_seroincidence.R @@ -1,17 +1,37 @@ #' Find the maximum likelihood estimate of the incidence rate parameter #' -#' This function models seroincidence using maximum likelihood estimation; that is, it finds the value of the seroincidence parameter which maximizes the likelihood (i.e., joint probability) of the data. +#' This function models seroincidence using maximum likelihood estimation; +#' that is, it finds the value of the seroincidence parameter which +#' maximizes the likelihood (i.e., joint probability) of the data. #' @inheritParams log_likelihood #' @inheritParams stats::nlm -#' @param pop_data a [data.frame] with cross-sectional serology data per antibody and age, and additional columns -#' @param lambda_start starting guess for incidence rate, in years/event. -#' @param antigen_isos Character vector with one or more antibody names. Values must match `pop_data` -#' @param build_graph whether to graph the log-likelihood function across a range of incidence rates (lambda values) -#' @param print_graph whether to display the log-likelihood curve graph in the course of running `est.incidence()` -#' @param stepmin A positive scalar providing the minimum allowable relative step length. +#' @param pop_data a [data.frame] with cross-sectional serology data per +#' antibody and age, and additional columns +#' @param lambda_start starting guess for incidence rate, in events/year. +#' @param antigen_isos Character vector with one or more antibody names. +#' Must match `pop_data` +#' @param build_graph whether to graph the log-likelihood function across +#' a range of incidence rates (lambda values) +#' @param print_graph whether to display the log-likelihood curve graph +#' in the course of running `est_seroincidence()` +#' @param stepmin A positive scalar providing the minimum allowable +#' relative step length. +#' @param sr_params a [data.frame()] containing MCMC samples of parameters +#' from the Bayesian posterior distribution of a longitudinal decay curve model. +#' The parameter columns must be named: +#' - `antigen_iso`: a [character()] vector indicating antigen-isotype +#' combinations +#' - `iter`: an [integer()] vector indicating MCMC sampling iterations +#' - `y0`: baseline antibody level at $t=0$ ($y(t=0)$) +#' - `y1`: antibody peak level (ELISA units) +#' - `t1`: duration of infection +#' - `alpha`: antibody decay rate +#' (1/days for the current longitudinal parameter sets) +#' - `r`: shape factor of antibody decay #' @inheritDotParams stats::nlm -f -p -hessian -print.level -steptol -#' @returns a `"seroincidence"` object, which is a [stats::nlm()] fit object with extra meta-data attributes `lambda_start`, `antigen_isos`, and `ll_graph` +#' @returns a `"seroincidence"` object, which is a [stats::nlm()] fit object +#' with extra metadata attributes `lambda_start`, `antigen_isos`, and `ll_graph` #' @export #' @examples #' @@ -20,24 +40,24 @@ #' xs_data <- #' sees_pop_data_pk_100 #' -#' curve <- -#' typhoid_curves_nostrat_100 %>% +#' sr_curve <- +#' typhoid_curves_nostrat_100 |> #' filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) #' #' noise <- #' example_noise_params_pk #' -#' est1 <- est.incidence( +#' est1 <- est_seroincidence( #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = sr_curve, #' noise_params = noise, #' antigen_isos = c("HlyE_IgG", "HlyE_IgA"), #' ) #' #' summary(est1) -est.incidence <- function( +est_seroincidence <- function( pop_data, - curve_params, + sr_params, noise_params, antigen_isos = get_biomarker_names(pop_data), lambda_start = 0.1, @@ -48,37 +68,37 @@ est.incidence <- function( print_graph = build_graph & verbose, ...) { if (verbose > 1) { - message("inputs to `est.incidence()`:") - print(environment() %>% as.list()) + message("inputs to `est_seroincidence()`:") + print(environment() |> as.list()) } - .errorCheck( + .error_check( data = pop_data, antigen_isos = antigen_isos, - curve_params = curve_params + curve_params = sr_params ) - pop_data <- pop_data %>% - dplyr::filter(.data$antigen_iso %in% antigen_isos) %>% + pop_data <- pop_data |> + dplyr::filter(.data$antigen_iso %in% antigen_isos) |> dplyr::select( - pop_data %>% get_values_var(), - pop_data %>% get_age_var(), + pop_data |> get_values_var(), + pop_data |> get_age_var(), "antigen_iso" - ) %>% + ) |> filter(if_all(everything(), ~!is.na(.x))) - curve_params <- curve_params %>% - ungroup() %>% + sr_params <- sr_params |> + ungroup() |> dplyr::mutate( alpha = .data$alpha * 365.25, d = .data$r - 1 - ) %>% - dplyr::filter(.data$antigen_iso %in% antigen_isos) %>% - dplyr::select("y1", "alpha", "d", "antigen_iso") %>% + ) |> + dplyr::filter(.data$antigen_iso %in% antigen_isos) |> + dplyr::select("y1", "alpha", "d", "antigen_iso") |> droplevels() - noise_params <- noise_params %>% - dplyr::filter(.data$antigen_iso %in% antigen_isos) %>% + noise_params <- noise_params |> + dplyr::filter(.data$antigen_iso %in% antigen_isos) |> droplevels() # incidence can not be calculated if there are zero observations. @@ -87,30 +107,30 @@ est.incidence <- function( } if (verbose) { - message("nrow(curve_params) = ", nrow(curve_params)) + cli::cli_inform(c(i = "nrow(sr_params) = {nrow(sr_params)}")) } if (nrow(noise_params) != length(antigen_isos)) { stop("too many rows of noise parameters.") } - pop_data <- pop_data %>% split(~antigen_iso) - curve_params <- curve_params %>% split(~antigen_iso) - noise_params <- noise_params %>% split(~antigen_iso) + pop_data <- pop_data |> split(~antigen_iso) + sr_params <- sr_params |> split(~antigen_iso) + noise_params <- noise_params |> split(~antigen_iso) # First, check if we find numeric results... res <- .nll( pop_data = pop_data, log.lambda = log(lambda_start), antigen_isos = antigen_isos, - curve_params = curve_params, + curve_params = sr_params, noise_params = noise_params, verbose = verbose, ... ) if (is.na(res)) { - warning("Could not calculate the log-likelihood with starting parameter value.") + warning("Could not calculate log-likelihood with starting parameter value.") return(NULL) } @@ -125,7 +145,7 @@ est.incidence <- function( highlight_point_names = "lambda_start", pop_data = pop_data, antigen_isos = antigen_isos, - curve_params = curve_params, + curve_params = sr_params, noise_params = noise_params ) if (print_graph) { @@ -141,20 +161,21 @@ est.incidence <- function( } - # [stats::nlm()] expects an objective function `f` "returning a single numeric value", + # [stats::nlm()] expects an objective function `f` + # "returning a single numeric value", # but [.nll()] is vectorized via its subfunction [f_dev()]. # The vectorization doesn't appear to cause a problem for [nlm()]. if (verbose) message("about to call `nlm()`") # Estimate lambda - time <- + time <- system.time( { fit <- nlm( f = .nll, p = log(lambda_start), pop_data = pop_data, antigen_isos = antigen_isos, - curve_params = curve_params, + curve_params = sr_params, noise_params = noise_params, hessian = TRUE, stepmax = stepmax, @@ -163,8 +184,8 @@ est.incidence <- function( print.level = ifelse(verbose, 2, 0), ... ) - } %>% - system.time() + } + ) code_text <- nlm_exit_codes[fit$code] message1 <- "\n`nlm()` completed with the following convergence code:\n" @@ -176,19 +197,19 @@ est.incidence <- function( ) } - if (verbose) { + if (verbose >= 2) { message("\nElapsed time: ") print(time) } if (build_graph) { graph <- - graph %>% + graph |> add_point_to_graph( fit = fit, pop_data = pop_data, antigen_isos = antigen_isos, - curve_params = curve_params, + curve_params = sr_params, noise_params = noise_params ) @@ -202,7 +223,7 @@ est.incidence <- function( } } - fit <- fit %>% + fit <- fit |> structure( class = union("seroincidence", class(fit)), lambda_start = lambda_start, @@ -212,3 +233,20 @@ est.incidence <- function( return(fit) } + +#' @title Estimate Seroincidence +#' +#' @description +#' `r lifecycle::badge("deprecated")` +#' +#' `est.incidence()` was renamed to [est_seroincidence()] to create a more +#' consistent API. +#' @keywords internal +#' @export +est.incidence <- function( # nolint: object_name_linter + ...) { + lifecycle::deprecate_soft("1.3.1", "est.incidence()", "est_seroincidence()") + est_seroincidence( + ... + ) +} diff --git a/R/est.incidence.by.R b/R/est_seroincidence_by.R similarity index 77% rename from R/est.incidence.by.R rename to R/est_seroincidence_by.R index 5ecb4837a..bcb6d84ce 100644 --- a/R/est.incidence.by.R +++ b/R/est_seroincidence_by.R @@ -21,22 +21,23 @@ #' @details #' #' If `strata` is left empty, a warning will be produced, -#' recommending that you use [est.incidence()] for unstratified analyses, -#' and then the data will be passed to [est.incidence()]. -#' If for some reason you want to use [est.incidence.by()] -#' with no strata instead of calling [est.incidence()], +#' recommending that you use [est_seroincidence()] for unstratified analyses, +#' and then the data will be passed to [est_seroincidence()]. +#' If for some reason you want to use [est_seroincidence_by()] +#' with no strata instead of calling [est_seroincidence()], #' you may use `NA`, `NULL`, or `""` as the `strata` #' argument to avoid that warning. #' #' -#' @inheritParams est.incidence -#' @inheritDotParams est.incidence +#' @inheritParams est_seroincidence +#' @inheritParams log_likelihood +#' @inheritDotParams est_seroincidence #' @inheritDotParams stats::nlm -f -p -hessian -print.level -steptol #' #' @return #' * if `strata` has meaningful inputs: #' An object of class `"seroincidence.by"`; i.e., a list of -#' `"seroincidence"` objects from [est.incidence()], one for each stratum, +#' `"seroincidence"` objects from [est_seroincidence()], one for each stratum, #' with some meta-data attributes. #' * if `strata` is missing, `NULL`, `NA`, or `""`: #' An object of class `"seroincidence"`. @@ -50,16 +51,16 @@ #' sees_pop_data_pk_100 #' #' curve <- -#' typhoid_curves_nostrat_100 %>% +#' typhoid_curves_nostrat_100 |> #' filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) #' #' noise <- #' example_noise_params_pk #' -#' est2 <- est.incidence.by( +#' est2 <- est_seroincidence_by( #' strata = "catchment", #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = curve, #' noise_params = noise, #' antigen_isos = c("HlyE_IgG", "HlyE_IgA"), #' # num_cores = 8 # Allow for parallel processing to decrease run time @@ -68,15 +69,15 @@ #' print(est2) #' summary(est2) #' -est.incidence.by <- function( +est_seroincidence_by <- function( pop_data, - curve_params, + sr_params, noise_params, strata, curve_strata_varnames = strata, noise_strata_varnames = strata, - antigen_isos = pop_data %>% - pull("antigen_iso") %>% + antigen_isos = pop_data |> + pull("antigen_iso") |> unique(), lambda_start = 0.1, build_graph = FALSE, @@ -97,7 +98,7 @@ est.incidence.by <- function( c( "The {.arg strata} argument to {.fn est.incidence.by} is missing.", "i" = "If you do not want to stratify your data, - consider using the {.fn est.incidence} function to + consider using the {.fn est_seroincidence} function to simplify your code and avoid this warning.", "i" = "Since the {.arg strata} argument is empty, {.fn est.incidence.by} will return a {.cls seroincidence} object, @@ -106,9 +107,9 @@ est.incidence.by <- function( ) to_return <- - est.incidence( + est_seroincidence( pop_data = pop_data, - curve_params = curve_params, + sr_params = sr_params, noise_params = noise_params, lambda_start = lambda_start, antigen_isos = antigen_isos, @@ -121,24 +122,24 @@ est.incidence.by <- function( check_strata(pop_data, strata = strata) - .errorCheck( + .error_check( data = pop_data, antigen_isos = antigen_isos, - curve_params = curve_params + curve_params = sr_params ) # Split data per stratum stratum_data_list <- stratify_data( antigen_isos = antigen_isos, - data = pop_data %>% filter(.data$antigen_iso %in% antigen_isos), - curve_params = curve_params %>% filter(.data$antigen_iso %in% antigen_isos), - noise_params = noise_params %>% filter(.data$antigen_iso %in% antigen_isos), + data = pop_data |> filter(.data$antigen_iso %in% antigen_isos), + curve_params = sr_params |> filter(.data$antigen_iso %in% antigen_isos), + noise_params = noise_params |> filter(.data$antigen_iso %in% antigen_isos), strata_varnames = strata, curve_strata_varnames = curve_strata_varnames, noise_strata_varnames = noise_strata_varnames ) - strata_table <- stratum_data_list %>% attr("strata") + strata_table <- stratum_data_list |> attr("strata") if (verbose) { cli::cli_inform( @@ -164,7 +165,7 @@ est.incidence.by <- function( if (num_cores > 1L) { requireNamespace("parallel", quietly = FALSE) - num_cores <- num_cores %>% check_parallel_cores() + num_cores <- num_cores |> check_parallel_cores() if (verbose) { cli::cli_inform("Setting up parallel processing with @@ -173,8 +174,8 @@ est.incidence.by <- function( lib_paths <- .libPaths() cl <- - num_cores %>% - parallel::makeCluster() %>% + num_cores |> + parallel::makeCluster() |> suppressMessages() on.exit({ parallel::stopCluster(cl) @@ -198,7 +199,7 @@ est.incidence.by <- function( X = stratum_data_list, fun = function(x) { do.call( - what = est.incidence, + what = est_seroincidence, args = c( x, list( @@ -226,7 +227,7 @@ est.incidence.by <- function( for (cur_stratum in names(stratum_data_list)) { cur_stratum_vars <- - strata_table %>% + strata_table |> dplyr::filter(.data$Stratum == cur_stratum) if (verbose) { @@ -235,7 +236,7 @@ est.incidence.by <- function( } fits[[cur_stratum]] <- do.call( - what = est.incidence, + what = est_seroincidence, args = c( stratum_data_list[[cur_stratum]], list( @@ -263,8 +264,26 @@ est.incidence.by <- function( antigen_isos = antigen_isos, Strata = strata_table, graphs_included = build_graph, - class = "seroincidence.by" %>% union(class(fits)) + class = "seroincidence.by" |> union(class(fits)) ) return(incidence_data) } + +#' @title Estimate Seroincidence +#' +#' @description +#' `r lifecycle::badge("deprecated")` +#' +#' `est.incidence.by()` was renamed to [est_seroincidence_by()] to create a more +#' consistent API. +#' @keywords internal +#' @export +est.incidence.by <- function( # nolint: object_name_linter + ...) { + lifecycle::deprecate_soft("1.4.0", "est.incidence.by()", + "est_seroincidence_by()") + est_seroincidence_by( + ... + ) +} diff --git a/R/example_noise_params_pk.R b/R/example_noise_params_pk.R index a238f0b3c..f0a577f9c 100644 --- a/R/example_noise_params_pk.R +++ b/R/example_noise_params_pk.R @@ -4,7 +4,7 @@ #' for examples and testing, for Pakistan #' #' @format ## `example_noise_params_pk` -#' A `curve_params` object (from [as_curve_params()]) with 4 rows and 7 columns: +#' A `curve_params` object (from [as_sr_params()]) with 4 rows and 7 columns: #' \describe{ #' \item{antigen_iso}{which antigen and isotype are being measured #' (data is in long format)} @@ -31,7 +31,7 @@ #' for examples and testing. #' #' @format ## `example_noise_params_pk` -#' A `curve_params` object (from [as_curve_params()]) with 4 rows and 7 columns: +#' A `curve_params` object (from [as_sr_params()]) with 4 rows and 7 columns: #' \describe{ #' \item{antigen_iso}{which antigen and isotype are being measured #' (data is in long format)} diff --git a/R/example_typhoid_curves_nostrat.R b/R/example_typhoid_curves_nostrat.R index 3d77103a4..2966127a6 100644 --- a/R/example_typhoid_curves_nostrat.R +++ b/R/example_typhoid_curves_nostrat.R @@ -3,7 +3,7 @@ #' A subset of data from the SEES study, for examples and testing. #' #' @format ## `typhoid_curves_nostrat_100` -#' A `curve_params` object (from [as_curve_params()]) with 500 rows and 7 +#' A `curve_params` object (from [as_sr_params()]) with 500 rows and 7 #' columns: #' \describe{ #' \item{antigen_iso}{which antigen and isotype are being measured diff --git a/R/expect_snapshot_data.R b/R/expect_snapshot_data.R new file mode 100644 index 000000000..fde73d20b --- /dev/null +++ b/R/expect_snapshot_data.R @@ -0,0 +1,35 @@ +#' Snapshot testing for [data.frame]s +#' @description +#' copied from +#' with permission () +#' +#' @param x a [data.frame] to snapshot +#' @param name [character] snapshot name +#' @param digits [integer] passed to [signif()] for numeric variables +#' +#' @returns [NULL] (from [testthat::expect_snapshot_file()]) +#' @export +#' @keywords internal +#' @examples +#' \dontrun{ +#' expect_snapshot_data(iris, name = "iris") +#' } +expect_snapshot_data <- function(x, name, digits = 6) { + fun <- function(x) signif(x, digits = digits) + lapply_fun <- function(x) I(lapply(x, fun)) + x <- dplyr::mutate(x, dplyr::across(tidyselect::where(is.numeric), fun)) + x <- dplyr::mutate(x, dplyr::across(tidyselect::where(is.list), lapply_fun)) + path <- save_csv(x) + testthat::expect_snapshot_file( + path, + paste0(name, ".csv"), + compare = testthat::compare_file_text + ) +} + + +save_csv <- function(x) { + path <- tempfile(fileext = ".csv") + readr::write_csv(x, path) + path +} diff --git a/R/graph.curve.params.R b/R/graph.curve.params.R index ef3e4c6b7..4672ec5e8 100644 --- a/R/graph.curve.params.R +++ b/R/graph.curve.params.R @@ -1,36 +1,45 @@ -#' Graph estimated antibody decay curve +#' Graph estimated antibody decay curves #' -#' @param curve_params +#' @param object #' a [data.frame()] containing MCMC samples of antibody decay curve parameters #' @param verbose verbose output -#' @param show_all_curves whether to show individual curves under quantiles -#' @param antigen_isos antigen isotypes +#' @param antigen_isos antigen isotypes to analyze +#' (can subset `object`) #' @param alpha_samples `alpha` parameter passed to [ggplot2::geom_line] -#' (has no effect if `show_all_curves = FALSE`) -#' @param show_quantiles whether to show point-wise (over time) quantiles -#' -#' @returns a [ggplot2::ggplot()] object +#' (has no effect if `iters_to_graph` is empty) +#' @param quantiles Optional [numeric] [vector] of point-wise (over time) +#' quantiles to plot (e.g., 10%, 50%, and 90% = `c(0.1, 0.5, 0.9)`). +#' If `NULL`, no quantile lines are shown. +#' @param log_x should the x-axis be on a logarithmic scale (`TRUE`) +#' or linear scale (`FALSE`, default)? +#' @param log_y should the Y-axis be on a logarithmic scale +#' (default, `TRUE`) or linear scale (`FALSE`)? +#' @param chain_color [logical]: if [TRUE] (default), MCMC chain lines +#' are colored by chain. +#' If [FALSE], all MCMC chain lines are black. +#' @inheritParams plot_curve_params_one_ab +#' @param ... not currently used +#' @returns a [ggplot2::ggplot()] object showing the antibody dynamic +#' kinetics of selected antigen/isotype combinations, with optional posterior +#' distribution quantile curves. #' @export +#' @inherit plot_curve_params_one_ab details #' -#' @examples -#' curve <- -#' typhoid_curves_nostrat_100 |> -#' dplyr::filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) -#' -#' plot1 <- graph.curve.params(curve) -#' -#' print(plot1) -#' -#' plot2 <- graph.curve.params(curve, show_all_curves = TRUE) -#' show(plot2) +#' @example inst/examples/exm-graph.curve.params.R #' + graph.curve.params <- function( # nolint: object_name_linter - curve_params, - antigen_isos = unique(curve_params$antigen_iso), + object, + antigen_isos = unique(object$antigen_iso), verbose = FALSE, - show_quantiles = TRUE, - show_all_curves = FALSE, - alpha_samples = 0.3 + quantiles = c(0.1, 0.5, 0.9), + alpha_samples = 0.3, + chain_color = TRUE, + log_x = FALSE, + log_y = TRUE, + n_curves = 100, + iters_to_graph = object$iter |> unique() |> head(n_curves), + ... ) { if (verbose) { message( @@ -39,34 +48,12 @@ graph.curve.params <- function( # nolint: object_name_linter ) } - curve_params <- curve_params |> + object <- object |> dplyr::filter(.data$antigen_iso %in% antigen_isos) tx2 <- 10^seq(-1, 3.1, 0.025) - bt <- function(y0, y1, t1) { - log(y1 / y0) / t1 - } - - # uses r > 1 scale for shape - ab <- function(t, y0, y1, t1, alpha, shape) { - beta <- bt(y0, y1, t1) - - yt <- 0 - - if (t <= t1) { - yt <- y0 * exp(beta * t) - } - - if (t > t1) { - yt <- (y1^(1 - shape) - (1 - shape) * alpha * (t - t1))^(1 / (1 - shape)) - } - - return(yt) - } - - - d <- curve_params + d <- object dT <- # nolint: object_linter data.frame(t = tx2) |> @@ -90,142 +77,141 @@ graph.curve.params <- function( # nolint: object_name_linter cols = dplyr::starts_with("time"), values_to = "t" ) |> - select(-"name") |> - rowwise() |> - mutate(res = ab( - .data$t, - .data$y0, - .data$y1, - .data$t1, - .data$alpha, - .data$r - )) |> - ungroup() - - if (verbose) message("starting to compute quantiles") - serocourse_sum <- serocourse_all |> - summarise( - .by = c("antigen_iso", "t"), - res.med = quantile(.data$res, 0.5), - res.low = quantile(.data$res, 0.025), - res.high = quantile(.data$res, 0.975), - res.p75 = quantile(.data$res, 0.75), - res.p25 = quantile(.data$res, 0.25), - res.p10 = quantile(.data$res, 0.10), - res.p90 = quantile(.data$res, 0.90) - ) |> - pivot_longer( - names_to = "quantile", - cols = c( - "res.med", - "res.low", - "res.high", - "res.p25", - "res.p75", - "res.p10", - "res.p90" - ), - names_prefix = "res.", - values_to = "res" + dplyr::select(-"name") |> + dplyr::mutate( + res = ab1( + .data$t, + .data$y0, + .data$y1, + .data$t1, + .data$alpha, + .data$r + ) ) + if (!is.null(quantiles)) { + serocourse_sum <- serocourse_all |> + dplyr::group_by(.data$antigen_iso, .data$t) |> + dplyr::summarise( + res_vals = list(.data$res), + .groups = "drop" + ) |> + dplyr::mutate( + quantiles_df = purrr::map( + .data$res_vals, + ~ tibble::tibble( + quantile = quantiles, + res = stats::quantile(.x, probs = quantiles, na.rm = TRUE) + ) + ) + ) |> + tidyr::unnest(c("quantiles_df")) + } range <- - serocourse_sum |> - dplyr::filter(.data$quantile %in% c("med", "p10", "p90")) |> + serocourse_all |> dplyr::summarize( - min = min(.data$res, 0.9), - max = max(.data$res, 2000) + min = min(.data$res, na.rm = TRUE), + max = max(.data$res, na.rm = TRUE) ) - - plot1 <- - serocourse_sum |> - ggplot2::ggplot() + - ggplot2::aes(x = .data$t, - y = .data$res) + - ggplot2::facet_wrap( - ~ .data$antigen_iso, - ncol = 2 - ) + + plot1 <- ggplot2::ggplot() + + ggplot2::aes(x = .data$t, y = .data$res) + + ggplot2::facet_wrap(~ antigen_iso, ncol = 2) + ggplot2::theme_minimal() + ggplot2::theme(axis.line = ggplot2::element_line()) + - ggplot2::labs(x = "Days since fever onset", - y = "ELISA units", - col = if_else(show_all_curves, "MCMC chain", "")) + + ggplot2::labs( + x = "Days since fever onset", + y = "ELISA units", + col = if_else( + length(iters_to_graph) > 0, + "MCMC chain", + "" + ) + ) + ggplot2::theme(legend.position = "bottom") - if (show_all_curves) { + if (length(iters_to_graph) > 0) { - range <- + sc_to_graph <- serocourse_all |> + filter(.data$iter %in% iters_to_graph) + + range <- + sc_to_graph |> dplyr::summarize( - min = min(.data$res, 0.9), - max = max(.data$res, 2000) + min = min(.data$res), + max = max(.data$res) ) group_vars <- c("iter", "chain") |> - intersect(names(serocourse_all)) + intersect(names(sc_to_graph)) if (length(group_vars) > 1) { - serocourse_all <- - serocourse_all |> + sc_to_graph <- + sc_to_graph |> mutate( - iter = interaction(across(all_of(group_vars))) + group = interaction(across(all_of(group_vars))) ) plot1 <- plot1 + geom_line( - data = serocourse_all, + data = sc_to_graph, alpha = alpha_samples, aes( - color = .data$chain |> factor(), - group = .data$iter + color = if (chain_color) .data$chain |> factor(), + group = .data$group ) - ) + - ggplot2::expand_limits(y = range) + ) } else { - plot1 <- plot1 + - geom_line(data = serocourse_all, + geom_line(data = sc_to_graph, alpha = alpha_samples, aes(group = .data$iter)) + ggplot2::expand_limits(y = range) } - + plot1 <- + plot1 + ggplot2::expand_limits(y = unlist(range)) } - plot1 <- - plot1 + - ggplot2::scale_y_log10( - limits = unlist(range), - labels = scales::label_comma(), - minor_breaks = NULL - ) - if (show_quantiles) { + if (log_y) { plot1 <- plot1 + + ggplot2::scale_y_log10( + limits = unlist(range), + labels = scales::label_comma(), + minor_breaks = NULL + ) + } + + if (log_x) { + plot1 <- plot1 + + ggplot2::scale_x_log10(labels = scales::label_comma()) + } + + if (!is.null(quantiles)) { + plot1 <- plot1 + ggplot2::geom_line( - ggplot2::aes(col = "median"), - data = serocourse_sum |> filter(.data$quantile == "med"), - linewidth = 1 - ) + - ggplot2::geom_line( - ggplot2::aes(col = "10% quantile"), - data = serocourse_sum |> filter(quantile == "p10"), - linewidth = .5 - ) + - ggplot2::geom_line( - ggplot2::aes(col = "90% quantile"), - data = serocourse_sum |> filter(quantile == "p90"), - linewidth = .5 + data = serocourse_sum, + aes( + color = paste0(.data$quantile * 100, "% quantile"), + group = .data$quantile + ), + linewidth = 0.75 ) + + label <- if (length(iters_to_graph) > 0 && chain_color) { + "MCMC chain" + } else { + "" + } + plot1 <- plot1 + ggplot2::labs(col = label) } - return(plot1) + return(plot1) } diff --git a/R/graph_seroresponse_model_1.R b/R/graph_seroresponse_model_1.R new file mode 100644 index 000000000..e81049b70 --- /dev/null +++ b/R/graph_seroresponse_model_1.R @@ -0,0 +1,72 @@ +#' graph antibody decay curves by antigen isotype +#' +#' @inheritParams plot_curve_params_one_ab +#' @inheritDotParams plot_curve_params_one_ab +#' @param antigen_isos antigen isotypes to analyze (can subset `curve_params`) +#' @param ncol how many columns of subfigures to use in panel plot +#' @details +#' ## `iters_to_graph` +#' If you directly specify `iters_to_graph` when calling this function, +#' the row numbers are enumerated separately for each antigen isotype; +#' in other words, for the purposes of this argument, +#' row numbers start over at 1 for each antigen isotype. +#' There is currently no way to specify different row numbers +#' for different antigen isotypes; +#' if you want to do that, +#' you will could call [plot_curve_params_one_ab()] directly +#' for each antigen isotype +#' and combine the resulting panels yourself. +#' Or you could subset `curve_params` manually, +#' before passing it to this function, +#' and set the `n_curves` argument to `Inf`. +#' @return a [ggplot2::ggplot()] object +#' @keywords internal +#' @export +#' @examples +#' \donttest{ +#' library(dplyr) +#' library(ggplot2) +#' library(magrittr) +#' +#' curve <- +#' serocalculator_example("example_curve_params.csv") |> +#' read.csv() |> +#' as_sr_params() |> +#' filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) |> +#' graph_seroresponse_model_1() +#' +#' curve +#' } +graph_seroresponse_model_1 <- function( + object, + antigen_isos = unique(object$antigen_iso), + ncol = min(3, length(antigen_isos)), + ...) { + split_data <- object |> + filter(.data$antigen_iso %in% antigen_isos) |> + droplevels() |> + split(~antigen_iso) + + labels <- names(split_data) + figs <- split_data |> + lapply(FUN = plot_curve_params_one_ab, ...) + + for (i in seq_along(figs)) { + figs[[i]] <- figs[[i]] + ggplot2::ggtitle(labels[i]) + } + + + nrow <- ceiling(length(figs) / ncol) + figure <- do.call( + what = function(...) { + ggpubr::ggarrange( + ..., + ncol = ncol, + nrow = nrow + ) + }, + args = figs + ) + + return(figure) +} diff --git a/R/load_curve_params.R b/R/load_curve_params.R deleted file mode 100644 index ed9302d5f..000000000 --- a/R/load_curve_params.R +++ /dev/null @@ -1,24 +0,0 @@ -#' Load antibody decay curve parameter samples -#' -#' @param file_path path to an RDS file containing MCMC samples of antibody decay curve parameters `y0`, `y1`, `t1`, `alpha`, and `r`, stored as a [data.frame()] or [tibble::tbl_df] -#' @param antigen_isos [character()] vector of antigen isotypes to be used in analyses -#' -#' @returns a `curve_params` object (a [tibble::tbl_df] with extra attribute `antigen_isos`) -#' @export -#' @examples -#' curve <- load_curve_params(serocalculator_example("example_curve_params.rds")) -#' -#' print(curve) -#' -load_curve_params <- function(file_path, antigen_isos = NULL) { - if (file_path %>% substr(1, 4) == "http") { - file_path <- url(file_path) - } - - curve_params <- - file_path %>% - readRDS() %>% - as_curve_params() - - return(curve_params) -} diff --git a/R/load_sr_params.R b/R/load_sr_params.R new file mode 100644 index 000000000..0d6439bf3 --- /dev/null +++ b/R/load_sr_params.R @@ -0,0 +1,44 @@ +#' Load longitudinal seroresponse parameter samples +#' +#' @param file_path path to an RDS file containing MCMC samples of antibody +#' seroresponse parameters `y0`, `y1`, `t1`, `alpha`, and `r`, +#' stored as a [data.frame()] or [tibble::tbl_df] +#' @param antigen_isos [character()] vector of antigen isotypes used in analyses +#' +#' @returns a `curve_params` object (a [tibble::tbl_df] +#' with extra attribute `antigen_isos`) +#' @export +#' @examples +#' curve <- load_sr_params(serocalculator_example("example_curve_params.rds")) +#' +#' print(curve) +#' +load_sr_params <- function(file_path, antigen_isos = NULL) { + if (file_path |> substr(1, 4) == "http") { + file_path <- url(file_path) + } + + curve_params <- + file_path |> + readRDS() |> + as_sr_params() + + return(curve_params) +} + +#' @title Load antibody decay curve parameter samples +#' +#' @description +#' `r lifecycle::badge("deprecated")` +#' +#' `load_curve_params()` was renamed to [load_sr_params()] to create a more +#' consistent API. +#' @keywords internal +#' @export +load_curve_params <- function( + ...) { + lifecycle::deprecate_soft("1.3.1", "load_curve_params()", "load_sr_params()") + load_sr_params( + ... + ) +} diff --git a/R/nlm_exit_codes.R b/R/nlm_exit_codes.R index 827115391..111dd9c0c 100644 --- a/R/nlm_exit_codes.R +++ b/R/nlm_exit_codes.R @@ -1,8 +1,15 @@ nlm_exit_codes <- c( - "1" = "1: relative gradient is close to zero, current iterate is probably solution.", - "2" = "2: successive iterates within tolerance, current iterate is probably solution.", - "3" = "3: Last global step failed to locate a point lower than x. Either x is an approximate local minimum of the function, the function is too non-linear for this algorithm, or `stepmin` in `est.incidence()` (a.k.a. `steptol` in `nlm()`) is too large.", + "1" = "1: relative gradient is close to zero, + current iterate is probably solution.", + "2" = "2: successive iterates within tolerance, + current iterate is probably solution.", + "3" = "3: Last global step failed to locate a point lower than x. + Either x is an approximate local minimum of the function, + the function is too non-linear for this algorithm, + or `stepmin` in `est_seroincidence()` + (a.k.a. `steptol` in `nlm()`) is too large.", "4" = "4: iteration limit exceeded; increase `iterlim`.", - "5" = "5: maximum step size `stepmax` exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or `stepmax` is too small." + "5" = "5: maximum step size `stepmax` exceeded five consecutive times. + Either the function is unbounded below, becomes asymptotic to a finite value + from above in some direction or `stepmax` is too small." ) - diff --git a/R/graph.decay.curves.R b/R/plot_curve_params_one_ab.R similarity index 82% rename from R/graph.decay.curves.R rename to R/plot_curve_params_one_ab.R index d624781f6..6e49fd525 100644 --- a/R/graph.decay.curves.R +++ b/R/plot_curve_params_one_ab.R @@ -6,7 +6,7 @@ #' @param n_curves how many curves to plot (see details). #' @param n_points Number of points to interpolate along the x axis #' (passed to [ggplot2::geom_function()]) -#' @param rows_to_graph which rows of `curve_params` to plot +#' @param iters_to_graph which MCMC iterations in `curve_params` to plot #' (overrides `n_curves`). #' @param alpha (passed to [ggplot2::geom_function()]) #' how transparent the curves should be: @@ -20,15 +20,15 @@ #' @inheritDotParams ggplot2::geom_function #' @returns a [ggplot2::ggplot()] object #' @details -#' ## `n_curves` and `rows_to_graph` -#' In most cases, `curve_params` will contain too many rows of MCMC +#' ## `n_curves` and `iters_to_graph` +#' In most cases, `object` will contain too many rows of MCMC #' samples for all of these samples to be plotted at once. #' * Setting the `n_curves` argument to a value smaller than the #' number of rows in `curve_params` will cause this function to select #' the first `n_curves` rows to graph. #' * Setting `n_curves` larger than the number of rows in ` will #' result all curves being plotted. -#' * If the user directly specifies the `rows_to_graph` argument, +#' * If the user directly specifies the `iters_to_graph` argument, #' then `n_curves` has no effect. #' @examples #' \donttest{ @@ -50,7 +50,7 @@ plot_curve_params_one_ab <- function( n_points = 1000, log_x = FALSE, log_y = TRUE, - rows_to_graph = seq_len(min(n_curves, nrow(object))), + iters_to_graph = seq_len(min(n_curves, nrow(object))), xlim = c(10 ^ -1, 10 ^ 3.1), ...) { plot1 <- @@ -60,8 +60,9 @@ plot_curve_params_one_ab <- function( ggplot2::theme(axis.line = ggplot2::element_line()) + ggplot2::labs(x = "Days since fever onset", y = "Antibody concentration") + ggplot2::ggtitle("Antibody Response Curve") + - ggplot2::theme(plot.title = - ggplot2::element_text(size = 20, face = "bold")) + ggplot2::theme( + plot.title = ggplot2::element_text(size = 20, face = "bold") + ) if (log_y) { plot1 <- @@ -70,18 +71,19 @@ plot_curve_params_one_ab <- function( minor_breaks = NULL) } - layer_function <- function(cur_row) { - cur_params <- object[cur_row, ] + layer_function <- function(cur_iter) { + cur_params <- object |> filter(.data$iter == cur_iter) ggplot2::geom_function( alpha = alpha, fun = ab0, args = list(curve_params = cur_params), n = n_points, - ...) + ... + ) } layers <- - lapply(X = rows_to_graph, FUN = layer_function) + lapply(X = iters_to_graph, FUN = layer_function) plot1 <- plot1 + layers diff --git a/R/print.seroincidence.R b/R/print.seroincidence.R index fd587bec4..78e82f0b3 100644 --- a/R/print.seroincidence.R +++ b/R/print.seroincidence.R @@ -2,9 +2,9 @@ #' Print Method for `seroincidence` Class #' #' @description -#' Custom [print()] function for `seroincidence` objects from [est.incidence()] +#' [print()] function for `seroincidence` objects from [est_seroincidence()] #' -#' @param x A list containing output of function [est.incidence()]. +#' @param x A list containing output of function [est_seroincidence()]. #' @param ... Additional arguments affecting the summary produced. #' @returns an [invisible] copy of input parameter `x` #' @examples @@ -20,9 +20,9 @@ #' noise <- #' example_noise_params_pk #' -#' est1 <- est.incidence( +#' est1 <- est_seroincidence( #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = curve, #' noise_params = noise, #' antigen_isos = c("HlyE_IgG", "HlyE_IgA"), #' ) diff --git a/R/print.seroincidence.by.R b/R/print.seroincidence.by.R index 382ac2660..d581d992e 100644 --- a/R/print.seroincidence.by.R +++ b/R/print.seroincidence.by.R @@ -3,9 +3,9 @@ #' #' @description #' Custom [print()] function for `seroincidence.by` objects -#' (from [est.incidence.by()]) +#' (from [est_seroincidence_by()]) #' -#' @param x A list containing output of function [est.incidence.by()]. +#' @param x A list containing output of function [est_seroincidence_by()]. #' @param ... Additional arguments affecting the summary produced. #' @inherit print.seroincidence return #' @examples @@ -22,10 +22,10 @@ #' example_noise_params_pk #' #' # estimate seroincidence -#' est2 <- est.incidence.by( +#' est2 <- est_seroincidence_by( #' strata = c("catchment"), #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = curve, #' noise_params = noise, #' antigen_isos = c("HlyE_IgG", "HlyE_IgA"), #' # num_cores = 8 # Allow for parallel processing to decrease run time @@ -51,7 +51,7 @@ print.seroincidence.by <- function(x, ...) { cat("`antigen_isos` -", "Character vector of antigen isotypes used in analysis.\n") cat("`Strata` -", - "Input parameter strata of function `est.incidence.by()`\n") + "Input parameter strata of function `est_seroincidence_by()`\n") cat("\n") cat("Call the `summary()` function to obtain output results.\n") invisible(x) diff --git a/R/print.summary.seroincidence.by.R b/R/print.summary.seroincidence.by.R index c901a064b..0ad15daa4 100644 --- a/R/print.summary.seroincidence.by.R +++ b/R/print.summary.seroincidence.by.R @@ -23,10 +23,10 @@ #' example_noise_params_pk #' #' # estimate seroincidence -#' est2 <- est.incidence.by( +#' est2 <- est_seroincidence_by( #' strata = c("catchment"), #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = curve, #' noise_params = noise, #' antigen_isos = c("HlyE_IgG", "HlyE_IgA"), #' # num_cores = 8 # Allow for parallel processing to decrease run time diff --git a/R/rho-funcs.R b/R/rho-funcs.R deleted file mode 100644 index 710490b50..000000000 --- a/R/rho-funcs.R +++ /dev/null @@ -1,365 +0,0 @@ -.rhoCdf <- function(y, par) { - return(plnorm(y, meanlog = par[1], sdlog = par[2])) -} - -.rhoCdf1 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - nMc <- length(y1) - rho <- rep(0, nMc) - if (y <= 0) { - return(rho) - } - index <- .tau1(y, age, param) - for (j in 0:m) { - rho[index] <- rho[index] + - pgamma( - q = log(y1[index] / y) * lambda / alpha[index], - shape = j + 1, - rate = 1, - lower.tail = FALSE - ) / - factorial(j) - } - rho[index] <- rho[index] / (m + 1) - rho[y > y1] <- 1 - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoCdf(y, par0) - } - return(rho[rho != 0]) -} - -.rhoCdf2 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - r <- param$r - nMc <- length(y1) - rho <- rep(0, nMc) - if (y <= 0) { - return(rho) - } - - index <- .tau2(y, age, param) - for (j in 0:m) { - rho[index] <- rho[index] + - pgamma( - q = lambda * (y1[index]^(1 - r[index]) - y^(1 - r[index])) / - (alpha[index] * (1 - r[index])), - shape = j + 1, - rate = 1, - lower.tail = FALSE - ) / - factorial(j) - } - rho[index] <- rho[index] / (m + 1) - rho[y > y1] <- 1 - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoCdf(y, par0) - } - return(rho[rho != 0]) -} - -.rhoCdf3 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - y0 <- param$y0 - mu1 <- param$mu1 - nMc <- length(y1) - # Rising branch - rho1 <- rep(0, nMc) - # Decaying branch - rho2 <- rep(0, nMc) - if (y <= 0) { - return(rho1) - } - - # Then y = y0 in rho1 - index0 <- .tau30(y, age, param) - # Then y = y in rho1 - index1 <- .tau31(y, age, param) - # Then y = y in rho2 - index2 <- .tau32(y, age, param) - # Then y = y1 in rho1 and in rho2 - index3 <- .tau33(y, age, param) - for (j in 0:m) { - rho1[index0] <- rho1[index0] + 1 / factorial(j) - rho1[index1] <- rho1[index1] + - pgamma( - q = lambda * log(y / y0[index1]) / mu1[index1], - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - rho1[index3] <- rho1[index3] + - pgamma( - q = lambda * log(y1[index3] / y0[index3]) / mu1[index3], - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - rho2[index2] <- rho2[index2] + - pgamma( - q = (log(y1[index2] / y0[index2]) / mu1[index2] + - log(y1[index2] / y) / alpha[index2]) * lambda, - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - rho2[index3] <- rho2[index3] + - pgamma( - q = (log(y1[index3] / y0[index3]) / mu1[index3]) * lambda, - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - } - rho1 <- 1 - rho1 / (m + 1) - rho2 <- rho2 / (m + 1) - rho <- rho1 + rho2 - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoCdf(y, par0) - } - return(rho[rho != 0]) -} - -.rhoCdf4 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - r <- param$r - y0 <- param$y0 - mu1 <- param$mu1 - nMc <- length(y1) - # Rising branch - rho1 <- rep(0, nMc) - # Decaying branch - rho2 <- rep(0, nMc) - if (y <= 0) { - return(rho1) - } - - # Then y = y0 in rho1 - index0 <- .tau40(y, age, param) - # Then y = y in rho1 - index1 <- .tau41(y, age, param) - # Then y = y in rho2 - index2 <- .tau42(y, age, param) - # Then y = y1 in rho1 and in rho2 - index3 <- .tau43(y, age, param) - for (j in 0:m) { - rho1[index0] <- rho1[index0] + 1 / factorial(j) - rho1[index1] <- rho1[index1] + - pgamma( - q = lambda * log(y / y0[index1]) / mu1[index1], - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - rho1[index3] <- rho1[index3] + - pgamma( - q = lambda * log(y1[index3] / y0[index3]) / mu1[index3], - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - rho2[index2] <- rho2[index2] + - pgamma( - q = lambda * (log(y1[index2] / y0[index2]) / mu1[index2] + - (y1[index2]^(1 - r[index2]) - y^(1 - r[index2])) / - (alpha[index2] * (1 - r[index2]))), - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - rho2[index3] <- rho2[index3] + - pgamma( - q = lambda * (log(y1[index3] / y0[index3]) / mu1[index3]), - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - } - rho1 <- 1 - rho1 / (m + 1) - rho2 <- rho2 / (m + 1) - rho <- rho1 + rho2 - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoCdf(y, par0) - } - return(rho[rho != 0]) -} - -.rhoCdf5 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - yb <- param$yb - nMc <- length(y1) - rho <- rep(0, nMc) - if (y <= 0) { - return(rho) - } - - index <- .tau5(y, age, param) - for (j in 0:m) { - rho[index] <- rho[index] + - pgamma( - q = log(y1[index] / (y - yb[index])) * lambda / alpha[index], - shape = j + 1, rate = 1, lower.tail = FALSE - ) / - factorial(j) - } - rho[index] <- rho[index] / (m + 1) - rho[y > y1] <- 1 - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoCdf(y, par0) - } - return(rho[rho != 0]) -} - - -.rhoPdf <- function(y, par) { - return(dlnorm(y, meanlog = par[1], sdlog = par[2])) -} - -.rhoPdf1 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - nMc <- length(y1) - rho <- rep(0, nMc) - if (y <= 0) { - return(rho) - } - - index <- .tau1(y, age, param) - deltaTP <- (m + 1) / lambda - rho[index] <- - pgamma( - q = log(y1[index] / y) / alpha[index], - shape = m + 1, rate = lambda, lower.tail = FALSE - ) / - (alpha[index] * deltaTP * y) - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoPdf(y, par0) - } - return(rho[rho != 0]) -} - -.rhoPdf2 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - r <- param$r - nMc <- length(y1) - rho <- rep(0, nMc) - if (y <= 0) { - return(rho) - } - - index <- .tau2(y, age, param) - deltaTP <- (m + 1) / lambda - rho[index] <- - pgamma( - q = (y1[index]^(1 - r[index]) - y^(1 - r[index])) / (alpha[index] * (1 - r[index])), - shape = m + 1, rate = lambda, lower.tail = FALSE - ) / - (alpha[index] * deltaTP * y^(r[index])) - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoPdf(y, par0) - } - return(rho[rho != 0]) -} - -.rhoPdf3 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - y0 <- param$y0 - mu1 <- param$mu1 - nMc <- length(y1) - # Rising branch - rho1 <- rep(0, nMc) - # Decaying branch - rho2 <- rep(0, nMc) - if (y <= 0) { - return(rho1) - } - index1 <- .tau31(y, age, param) - index2 <- .tau32(y, age, param) - deltaTP <- (m + 1) / lambda - rho1[index1] <- - pgamma( - q = log(y / y0[index1]) / mu1[index1], - shape = m + 1, rate = lambda, lower.tail = FALSE - ) / - (mu1[index1] * deltaTP * y) - rho2[index2] <- - pgamma( - q = log(y1[index2] / y0[index2]) / mu1[index2] + log(y1[index2] / y) / alpha[index2], - shape = m + 1, rate = lambda, lower.tail = FALSE - ) / - (alpha[index2] * deltaTP * y) - rho <- rho1 + rho2 - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoPdf(y, par0) - } - return(rho[rho != 0]) -} - -.rhoPdf4 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - r <- param$r - y0 <- param$y0 - mu1 <- param$mu1 - nMc <- length(y1) - # Rising branch - rho1 <- rep(0, nMc) - # Decaying branch - rho2 <- rep(0, nMc) - if (y < 0) { - return(rho1) - } - index1 <- .tau41(y, age, param) - index2 <- .tau42(y, age, param) - deltaTP <- (m + 1) / lambda - rho1[index1] <- - pgamma( - q = log(y / y0[index1]) / mu1[index1], - shape = m + 1, rate = lambda, lower.tail = FALSE - ) / - (mu1[index1] * deltaTP * y) - rho2[index2] <- - pgamma( - q = log(y1[index2] / y0[index2]) / mu1[index2] + - (y1[index2]^(1 - r[index2]) - y^(1 - r[index2])) / - (alpha[index2] * (1 - r[index2])), - shape = m + 1, rate = lambda, lower.tail = FALSE - ) / - (alpha[index2] * deltaTP * y^(r[index2])) - rho <- rho1 + rho2 - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoPdf(y, par0) - } - return(rho[rho != 0]) -} - -.rhoPdf5 <- function(y, age, lambda, m, param, par0) { - y1 <- param$y1 - alpha <- param$alpha - yb <- param$yb - nMc <- length(y1) - rho <- rep(0, nMc) - if (y <= 0) { - return(rho) - } - index <- .tau5(y, age, param) - deltaTP <- (m + 1) / lambda - rho[index] <- - pgamma( - q = log(y1[index] / (y - yb[index])) / alpha[index], - shape = m + 1, rate = lambda, lower.tail = FALSE - ) / - (alpha[index] * deltaTP * (y - yb[index])) - if (!is.na(age)) { - sProb <- .surv(age, lambda, m) - rho <- rho + sProb * .rhoPdf(y, par0) - } - return(rho[rho != 0]) -} diff --git a/R/sees_typhoid_ests_strat.R b/R/sees_typhoid_ests_strat.R new file mode 100644 index 000000000..0299bf37a --- /dev/null +++ b/R/sees_typhoid_ests_strat.R @@ -0,0 +1,7 @@ +#' Example `"seroincidence.by"` object +#' @description +#' Typhoid seroconversion rate estimates by country and age category +#' from the SEES study. +#' @source +#'`serocalculator/data-raw/sees_typhoid_ests_strat.R` +"sees_typhoid_ests_strat" diff --git a/R/serocalculator-package.R b/R/serocalculator-package.R index ad2ee2b7b..b9d1ce3a6 100644 --- a/R/serocalculator-package.R +++ b/R/serocalculator-package.R @@ -28,6 +28,7 @@ #' @importFrom dplyr rowwise #' @importFrom dplyr select #' @importFrom dplyr semi_join +#' @importFrom dplyr slice_head #' @importFrom dplyr summarise #' @importFrom dplyr ungroup #' @importFrom foreach %:% @@ -40,16 +41,20 @@ #' @importFrom ggplot2 ggplot #' @importFrom ggplot2 labs #' @importFrom ggplot2 theme_bw +#' @importFrom ggplot2 vars #' @importFrom lifecycle deprecated #' @importFrom magrittr %>% -#' @importFrom mixtools normalmixEM #' @importFrom Rcpp sourceCpp #' @importFrom rlang .data #' @importFrom rlang .env #' @importFrom rngtools RNGseq #' @importFrom rngtools setRNG +#' @importFrom shiny h2 #' @importFrom shiny reactive +#' @importFrom shiny renderPlot +#' @importFrom shiny renderTable #' @importFrom shiny renderText +#' @importFrom shiny sliderInput #' @importFrom stats dlnorm optim pgamma plnorm #' @importFrom stats formula #' @importFrom stats lm @@ -69,6 +74,7 @@ #' @importFrom tidyselect ends_with #' @importFrom utils capture.output #' @importFrom utils download.file unzip +#' @importFrom utils head #' @importFrom utils tail #' @useDynLib serocalculator, .registration = TRUE ## usethis namespace: end diff --git a/R/sim_pop_data.R b/R/sim_pop_data.R index 255b0ac3a..14bb59153 100644 --- a/R/sim_pop_data.R +++ b/R/sim_pop_data.R @@ -29,6 +29,9 @@ #' * `"long"` (one measurement per row) or #' * `"wide"` (one serum sample per row) #' @inheritDotParams simcs.tinf +#' @inheritDotParams ldpar +#' @inheritDotParams ab +#' @inheritDotParams mk_baseline #' @inheritParams log_likelihood # verbose #' @return a [tibble::tbl_df] containing simulated cross-sectional serosurvey #' data, with columns: @@ -106,7 +109,7 @@ sim_pop_data <- function( predpar <- curve_params |> - filter(.data$antigen_iso %in% antigen_isos) |> + dplyr::filter(.data$antigen_iso %in% antigen_isos) |> droplevels() |> prep_curve_params_for_array() |> df_to_array(dim_var_names = c("antigen_iso", "parameter")) @@ -196,6 +199,7 @@ sim_pop_data <- function( #' consistent API. #' @keywords internal #' @export + sim.cs <- function( # nolint: object_name_linter n.smpl, # nolint: object_name_linter age.rng, # nolint: object_name_linter diff --git a/R/sim_pop_data_multi.R b/R/sim_pop_data_multi.R index 4dc90cbf4..95d10a405 100644 --- a/R/sim_pop_data_multi.R +++ b/R/sim_pop_data_multi.R @@ -3,53 +3,18 @@ #' @param nclus number of clusters #' @param rng_seed starting seed for random number generator, #' passed to [rngtools::RNGseq()] -#' @param lambdas #incidence rate, in events/person*year +#' @param sample_sizes sample sizes to simulate +#' @param lambdas incidence rate, in events/person*year #' @param num_cores number of cores to use for parallel computations #' @param verbose whether to report verbose information #' @param ... arguments passed to [sim.cs()] #' @inheritDotParams sim_pop_data #' @return a [tibble::tibble()] #' @export -#' @examples -#' # Load curve parameters -#' dmcmc <- typhoid_curves_nostrat_100 -#' -#' # Specify the antibody-isotype responses to include in analyses -#' antibodies <- c("HlyE_IgA", "HlyE_IgG") -#' -#' # Set seed to reproduce results -#' set.seed(54321) -#' -#' # Simulated incidence rate per person-year -#' lambdas = c(.05, .1, .15, .2, .3) -#' -#' # Range covered in simulations -#' lifespan <- c(0, 10); -#' -#' # Cross-sectional sample size -#' nrep <- 100 -#' -#' # Biologic noise distribution -#' dlims <- rbind( -#' "HlyE_IgA" = c(min = 0, max = 0.5), -#' "HlyE_IgG" = c(min = 0, max = 0.5) -#' ) -#' -#' sim_pop_data_multi( -#' curve_params = dmcmc, -#' lambdas = lambdas, -#' n_samples = nrep, -#' age_range = lifespan, -#' antigen_isos = antibodies, -#' n_mcmc_samples = 0, -#' renew_params = TRUE, -#' add_noise = TRUE, -#' noise_limits = dlims, -#' format = "long", -#' nclus = 10) -#' +#' @example inst/examples/exm-sim_pop_data_multi.R sim_pop_data_multi <- function( nclus = 10, + sample_sizes = 100, lambdas = c(.05, .1, .15, .2, .3), num_cores = max(1, parallel::detectCores() - 1), rng_seed = 1234, @@ -81,31 +46,61 @@ sim_pop_data_multi <- function( } doParallel::registerDoParallel(cores = num_cores) - + n_sample_sizes <- length(sample_sizes) n_lambda <- length(lambdas) # trying to reproduce results using parallel - rng <- rngtools::RNGseq(n_lambda * nclus, rng_seed) + rng <- rngtools::RNGseq(n_sample_sizes * n_lambda * nclus, rng_seed) + + dimnames1 <- + list( + "iteration" = 1:nclus, + "lambda" = lambdas, + "sample size" = sample_sizes + ) + + dims1 <- + sapply(FUN = length, dimnames1) + + rng <- rng |> + array( + dimnames = dimnames1, + dim = dims1 + ) i <- NA + j <- NA r <- NA sim_df <- + foreach::foreach( + .combine = bind_rows, + j = seq_along(sample_sizes) + + ) %:% foreach::foreach( .combine = bind_rows, i = seq_along(lambdas) + ) %:% foreach::foreach( .combine = bind_rows, n = 1:nclus, - r = rng[(i - 1) * nclus + 1:nclus] + r = rng[1:nclus, i, j] ) %dopar% { l <- lambdas[i] + ns <- sample_sizes[j] rngtools::setRNG(r) sim_pop_data( lambda = l, + n_samples = ns, ... ) |> - mutate(lambda.sim = l, cluster = n) + mutate( + lambda.sim = l, + sample_size = ns, + cluster = n + ) |> + structure(r = r) } doParallel::stopImplicitCluster() sim_df <- sim_df |> set_biomarker_var(biomarker = "antigen_iso") diff --git a/R/strat_ests_barplot.R b/R/strat_ests_barplot.R new file mode 100644 index 000000000..fc7d19daa --- /dev/null +++ b/R/strat_ests_barplot.R @@ -0,0 +1,87 @@ +#' Barplot method for `summary.seroincidence.by` objects +#' +#' @param object a `summary.seroincidence.by` object (generated by applying the +#' `summary()` method to the output of [est_seroincidence_by()]). +#' @param yvar the name of a stratifying variable in `object`. +#' @param color_var +#' [character] the name of the fill color variable (e.g., "Country"). +#' @param alpha +#' transparency for the bars (1 = no transparency, 0 = fully transparent). +#' @param CIs [logical], if `TRUE`, add CI error bars. +#' @param title a title for the final plot. +#' @param xlab a label for the x-axis of the final plot. +#' @param ylab a label for the y-axis of the final plot. +#' @param fill_lab fill label. +#' @param color_palette optional color palette for bar color. +#' @param ... unused. +#' +#' @return a [ggplot2::ggplot()] object. +#' @export +#' @keywords internal +#' +strat_ests_barplot <- function( + object, + yvar, + color_var = NULL, + alpha = 0.7, + CIs = FALSE, # nolint: object_name_linter + title = NULL, + xlab = "Seroconversion rate per 1000 person-years", + ylab = yvar, + fill_lab = NULL, + color_palette = NULL, + ...) { + + # Check if yvar exists in the dataset + if (!is.element(yvar, names(object))) { + cli::cli_abort( + class = "unavailable_yvar", + message = c( + "The variable `{yvar}` specified by argument `yvar` + does not exist in `object`.", + "Please choose a column that exists in `object`." + ) + ) + } + + plot1 <- ggplot2::ggplot(object) + + ggplot2::aes( + y = forcats::fct_rev(.data[[yvar]]), + x = .data$incidence.rate * 1000, + fill = if (!is.null(color_var)) .data[[color_var]] else NULL + ) + + ggplot2::geom_bar( + stat = "identity", + position = ggplot2::position_dodge(), + show.legend = !is.null(color_var), + alpha = alpha + ) + + ggplot2::labs( + title = title, + x = xlab, + y = ylab, + fill = fill_lab + ) + + ggplot2::theme_linedraw() + + ggplot2::theme( + axis.text.y = ggplot2::element_text(size = 11), + axis.text.x = ggplot2::element_text(size = 11) + ) + + ggplot2::scale_x_continuous(expand = c(0, 10)) + + # Add error bars if CIs are requested + if (CIs && ("CI.lwr" %in% names(object)) && ("CI.upr" %in% names(object))) { + plot1 <- plot1 + + ggplot2::geom_errorbar(ggplot2::aes( + xmin = .data$CI.lwr * 1000, + xmax = .data$CI.upr * 1000 + ), width = 0.2, position = ggplot2::position_dodge(width = 0.9)) + } + + # Apply custom color palette if provided + if (!is.null(color_palette) && !is.null(color_var)) { + plot1 <- plot1 + ggplot2::scale_fill_manual(values = color_palette) + } + + return(plot1) +} diff --git a/R/strat_ests_scatterplot.R b/R/strat_ests_scatterplot.R new file mode 100644 index 000000000..6fd1fffa9 --- /dev/null +++ b/R/strat_ests_scatterplot.R @@ -0,0 +1,99 @@ +#' Scatterplot method for `summary.seroincidence.by` objects +#' +#' @param object +#' a `summary.seroincidence.by` object +#' (generated by applying the `summary()` +#' method to the output of [est_seroincidence_by()]). +#' @param xvar the name of a stratifying variable in `object` +#' @param alpha transparency for the points in the graph +#' (1 = no transparency, 0 = fully transparent) +#' @param shape shape argument for `geom_point()` +#' @param dodge_width width for jitter +#' @param CIs [logical], if `TRUE`, add CI error bars +#' @param color_var [character] which variable in `object` to use +#' to determine point color +#' @param group_var [character] which variable in `object` to use +#' to connect points with lines (`NULL` for no lines) +#' @param ... unused +#' +#' @return a [ggplot2::ggplot()] object +#' @export +#' @keywords internal +#' @example inst/examples/exm-strat_ests_scatterplot.R +#' +strat_ests_scatterplot <- function( + object, + xvar = strata(object)[1], + alpha = .7, + shape = 1, + dodge_width = 0.001, + CIs = FALSE, # nolint: object_name_linter + color_var = "nlm.convergence.code", + group_var = NULL, + ...) { + + # Check if xvar exists in the dataset + if (!is.element(xvar, names(object))) { + cli::cli_abort( + class = "unavailable_xvar", + message = c( + "The variable `{xvar}` specified by argument `xvar` + does not exist in `object`.", + "Please choose a column that exists in `object`." + ) + ) + } + + color_label <- object[[color_var]] |> labelled::get_label_attribute() + if (is.null(color_label)) color_label <- color_var + + plot1 <- + object |> + ggplot2::ggplot() + + ggplot2::aes( + x = get(xvar), + y = .data$incidence.rate, + group = if (!is.null(group_var)) .data[[group_var]], + col = .data[[color_var]] + ) + + ggplot2::xlab(xvar) + + ggplot2::ylab("Estimated incidence rate") + + ggplot2::theme_linedraw() + + ggplot2::theme( + panel.grid.minor.x = ggplot2::element_blank(), + panel.grid.minor.y = ggplot2::element_blank() + ) + + ggplot2::expand_limits(y = 0) + + ggplot2::labs(col = color_label) + + ggplot2::theme(legend.position = "bottom") + + if (CIs) { + plot1 <- plot1 + + ggplot2::geom_pointrange( + alpha = alpha, + position = ggplot2::position_dodge2(width = dodge_width), + aes(ymin = .data$CI.lwr, ymax = .data$CI.upr) + ) + + } else { + plot1 <- plot1 + + ggplot2::geom_point( + position = ggplot2::position_dodge2(width = dodge_width), + shape = shape, + alpha = alpha + ) + + } + + if (!is.null(group_var)) { + plot1 <- plot1 + + ggplot2::geom_line( + position = ggplot2::position_dodge2(width = dodge_width), + alpha = alpha + ) + } + + + return(plot1) + +} diff --git a/R/strata.seroincidence.ests.R b/R/strata.R similarity index 63% rename from R/strata.seroincidence.ests.R rename to R/strata.R index d6c378c99..bc2fdb6f5 100644 --- a/R/strata.seroincidence.ests.R +++ b/R/strata.R @@ -1,10 +1,10 @@ -#' Extract strata from an object +#' Extract `Strata` metadata from an object #' -#' Generic method for extracting strata from objects. -#' See [strata.seroincidence.by()] +#' Generic method for extracting strata metadata from objects. +#' See [strata.default()] #' @param x an object #' @export -#' @return the strata of `x` +#' @return the strata metadata of `x` #' strata <- function(x) { UseMethod("strata") @@ -19,6 +19,6 @@ strata <- function(x) { #' * `NULL` if `x` does not have a `"strata"` attribute #' @export #' @keywords internal -strata.seroincidence.by <- function(x) { +strata.default <- function(x) { attr(x, "Strata") } diff --git a/R/stratify_data.R b/R/stratify_data.R index dbf376193..a111b684f 100644 --- a/R/stratify_data.R +++ b/R/stratify_data.R @@ -65,13 +65,13 @@ stratify_data <- function(data, all_data <- list( pop_data = pop_data, - curve_params = curve_params |> select(all_of(curve_param_names)), + sr_params = curve_params |> select(all_of(curve_param_names)), noise_params = noise_params |> select(all_of(noise_param_names)), antigen_isos = antigen_isos |> intersect(data |> get_biomarker_names()) ) |> structure(class = union("biomarker_data_and_params", "list")) - # est.incidence.by() expects a list: + # est_seroincidence_by() expects a list: stratum_data_list <- list(`all data` = all_data) |> structure(antigen_isos = antigen_isos, # might be able to remove @@ -123,10 +123,10 @@ stratify_data <- function(data, antigen_isos = antigen_isos_cur_stratum) if (length(strata_vars_curve_params) == 0) { - data_and_params_cur_stratum$curve_params <- + data_and_params_cur_stratum$sr_params <- curve_params |> select(all_of(curve_param_names)) } else { - data_and_params_cur_stratum$curve_params <- + data_and_params_cur_stratum$sr_params <- curve_params |> semi_join(cur_stratum_vals, by = strata_vars_curve_params) |> select(all_of(curve_param_names)) diff --git a/R/summary.seroincidence.R b/R/summary.seroincidence.R index e164b89aa..65c5f8d31 100644 --- a/R/summary.seroincidence.R +++ b/R/summary.seroincidence.R @@ -2,7 +2,7 @@ #' @description #' This function is a `summary()` method for `seroincidence` objects. #' -#' @param object a [list()], outputted by [stats::nlm()] or [est.incidence()] +#' @param object a [list()] outputted by [stats::nlm()] or [est_seroincidence()] #' @param coverage desired confidence interval coverage probability #' @param verbose whether to produce verbose messaging #' @param ... unused @@ -15,7 +15,7 @@ #' * `CI.upr`: upper limit of confidence interval for incidence rate #' * `coverage`: coverage probability #' * `log.lik`: -#' log-likelihood of the data used in the call to `est.incidence()`, +#' log-likelihood of the data used in the call to `est_seroincidence()`, #' evaluated at the maximum-likelihood estimate of lambda #' (i.e., at `incidence.rate`) #' * `iterations`: the number of iterations used @@ -32,7 +32,7 @@ #' * 3: Last global step failed to locate a point lower than x. #' Either x is an approximate local minimum of the function, #' the function is too non-linear for this algorithm, -#' or `stepmin` in [est.incidence()] +#' or `stepmin` in [est_seroincidence()] #' (a.k.a., `steptol` in [stats::nlm()]) is too large. #' * 4: iteration limit exceeded; increase `iterlim`. #' * 5: maximum step size `stepmax` exceeded five consecutive times. @@ -54,9 +54,9 @@ #' noise <- #' example_noise_params_pk #' -#' est1 <- est.incidence( +#' est1 <- est_seroincidence( #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = curve, #' noise_params = noise, #' antigen_isos = c("HlyE_IgG", "HlyE_IgA") #' ) @@ -97,7 +97,10 @@ summary.seroincidence <- function( log.lik = -object$minimum, iterations = object$iterations, antigen.isos = antigen_isos |> paste(collapse = "+"), - nlm.convergence.code = object$code |> factor(levels = 1:5, ordered = TRUE) + nlm.convergence.code = + object$code |> + factor(levels = 1:5, ordered = TRUE) |> + labelled::set_label_attribute("`nlm()` convergence code") # |> factor(levels = 1:5, labels = nlm_exit_codes) ) diff --git a/R/summary.seroincidence.by.R b/R/summary.seroincidence.by.R index 09f8c028e..a35c44941 100644 --- a/R/summary.seroincidence.by.R +++ b/R/summary.seroincidence.by.R @@ -3,9 +3,9 @@ #' #' @description #' Calculate seroincidence from output of the seroincidence calculator -#' [est.incidence.by()]. +#' [est_seroincidence_by()]. #' -#' @param object A dataframe containing output of function [est.incidence.by()]. +#' @param object A dataframe containing output of [est_seroincidence_by()]. #' @param verbose a [logical] #' scalar indicating whether to print verbose messages to the console #' @param ... Additional arguments affecting the summary produced. @@ -32,8 +32,8 @@ #' (accessible through [base::attr()]): #' * `antigen_isos` #' Character vector with names of input antigen isotypes -#' used in [est.incidence.by()] -#' * `Strata` Character with names of strata used in [est.incidence.by()] +#' used in [est_seroincidence_by()] +#' * `Strata` Character with names of strata used in [est_seroincidence_by()] #' #' #' @examples @@ -50,10 +50,10 @@ #' example_noise_params_pk #' #' # estimate seroincidence -#' est2 <- est.incidence.by( +#' est2 <- est_seroincidence_by( #' strata = c("catchment"), #' pop_data = xs_data, -#' curve_params = curve, +#' sr_params = curve, #' noise_params = noise, #' antigen_isos = c("HlyE_IgG", "HlyE_IgA"), #' # num_cores = 8 # Allow for parallel processing to decrease run time diff --git a/R/tau-funcs.R b/R/tau-funcs.R deleted file mode 100644 index 648138b9d..000000000 --- a/R/tau-funcs.R +++ /dev/null @@ -1,115 +0,0 @@ -.tau1 <- function(y, age, param) { - y1 <- param$y1 - alpha <- param$alpha - if (is.na(age)) { - return(y <= y1) - } - - tm <- log(y1 / y) / alpha - return(tm < age & y <= y1) -} - -.tau2 <- function(y, age, param) { - y1 <- param$y1 - alpha <- param$alpha - r <- param$r - if (is.na(age)) { - return(y <= y1) - } - - tm <- (y1^(1 - r) - y^(1 - r)) / (alpha * (1 - r)) - return(tm < age & y <= y1) -} - -.tau30 <- function(y, age, param) { - y0 <- param$y0 - return(y < y0) -} - -.tau31 <- function(y, age, param) { - y0 <- param$y0 - mu1 <- param$mu1 - y1 <- param$y1 - if (is.na(age)) { - return(y >= y0 & y < y1) - } - - tm <- log(y / y0) / mu1 - return(tm < age & y >= y0 & y < y1) -} - -.tau32 <- function(y, age, param) { - y1 <- param$y1 - alpha <- param$alpha - t1 <- param$t1 - if (is.na(age)) { - return(y <= y1) - } - - tm <- t1 + log(y1 / y) / alpha - return(tm < age & y <= y1) -} - -.tau33 <- function(y, age, param) { - y1 <- param$y1 - t1 <- param$t1 - if (is.na(age)) { - return(y > y1) - } - - return(t1 > age & y > y1) -} - -.tau40 <- function(y, age, param) { - y0 <- param$y0 - return(y < y0) -} - -.tau41 <- function(y, age, param) { - y0 <- param$y0 - mu1 <- param$mu1 - y1 <- param$y1 - if (is.na(age)) { - return(y >= y0 & y < y1) - } - - tm <- log(y / y0) / mu1 - return(tm < age & y >= y0 & y < y1) -} - -.tau42 <- function(y, age, param) { - y1 <- param$y1 - alpha <- param$alpha - r <- param$r - t1 <- param$t1 - if (is.na(age)) { - return(y <= y1) - } - - tm <- t1 + (y1^(1 - r) - y^(1 - r)) / (alpha * (1 - r)) - return(tm < age & y <= y1) -} - -.tau43 <- function(y, age, param) { - y1 <- param$y1 - t1 <- param$t1 - if (is.na(age)) { - return(y > y1) - } - - return(t1 > age & y > y1) -} - -.tau5 <- function(y, age, param) { - y1 <- param$y1 - alpha <- param$alpha - yb <- param$yb - if (is.na(age)) { - return(y > yb & y <= y1) - } - - tm <- y1 / (y - yb) - tm[y <= yb] <- 1 - tm <- log(tm) / alpha - return(tm < age & y > yb & y <= y1) -} diff --git a/R/utils.R b/R/utils.R index a04d79ad7..eaf8fffc1 100644 --- a/R/utils.R +++ b/R/utils.R @@ -1,85 +1,87 @@ -.pasteN <- function(...) { +.paste_n <- function(...) { paste(..., sep = "\n") } -.appendNames <- function(abNames) { +.append_names <- function(ab_names) { res <- c() - for (k in seq_len(length(abNames))) { + for (k in seq_along(ab_names)) { res <- c( res, - paste0(abNames[k], ".lo"), - paste0(abNames[k], ".hi") + paste0(ab_names[k], ".lo"), + paste0(ab_names[k], ".hi") ) } return(res) } -.stripNames <- function(abNames) { - if (grepl(".", abNames, fixed = TRUE)) { - return(substr(abNames, 1, nchar(abNames) - 3)) +.strip_names <- function(ab_names) { + if (grepl(".", ab_names, fixed = TRUE)) { + return(substr(ab_names, 1, nchar(ab_names) - 3)) } - return(abNames) + return(ab_names) } -.errorCheck <- function(data, antigen_isos, curve_params) { - .checkAntibodies(pop_data = data, antigen_isos = antigen_isos) +.error_check <- function(data, antigen_isos, curve_params) { + .check_antibodies(pop_data = data, antigen_isos = antigen_isos) check_pop_data(pop_data = data) - .checkParams(antigen_isos = antigen_isos, params = curve_params) + .check_params(antigen_isos = antigen_isos, params = curve_params) invisible(NULL) } -.checkAntibodies <- function(pop_data, - antigen_isos = pop_data %>% attr("antigen_isos")) -{ +.check_antibodies <- function( + pop_data, + antigen_isos = pop_data |> attr("antigen_isos")) { if (!is.character(antigen_isos) && !is.factor(antigen_isos)) { - stop( - paste0( - "In `est.incidence()`, the argument `antigen_isos` should be a ", + cli::cli_abort( + c( + "In `est_seroincidence()`, the argument `antigen_isos` should be a ", "`character()` or `factor()` variable, but ", - 'currently, `class(antigen_isos) == "', - class(antigen_isos), - '"`.', - "\nPlease provide a character vector with at least one antibody name." + 'currently, `class(antigen_isos) == "{class(antigen_isos)}"`.', + "Please provide a character vector with at least one antibody name." ) ) } if (setequal(antigen_isos, "")) { - stop( - .pasteN( + cli::cli_abort( + c( "Argument `antigen_isos` is empty.", "Provide a character vector with at least one antibody name." ) ) } - missing_AIs = - antigen_isos %>% - setdiff(pop_data %>% get_biomarker_names()) - - if (length(missing_AIs) != 0) - { - message = "`pop_data` has no observations for the following {pop_data %>% get_biomarker_names_var()}s: {missing_AIs" - - cli::cli_warn(message = message, class = "missing_biomarker") + missing_ais <- + antigen_isos |> + setdiff(pop_data |> get_biomarker_names()) + + if (length(missing_ais) != 0) { + cli::cli_inform( + c( + "`pop_data` has no observations for the following ", + "{pop_data |> get_biomarker_names_var()}s: ", + "{paste(missing_ais, collapse = ', ')}" + ), + class = "missing_biomarker" + ) } invisible(NULL) } -.checkParams <- function(antigen_isos, params) { +.check_params <- function(antigen_isos, params) { message1 <- paste( - "Please provide a `data.frame()` containing Monte Carlo samples of the longitudinal parameters", - "`y1`, `alpha`, and `r`", + "Please provide a `data.frame()` containing Monte Carlo samples", + "of the longitudinal parameters `y1`, `alpha`, and `r`", "for each value of `antigen_iso` in `pop_data`" ) if (!is.data.frame(params)) { - stop( - .pasteN( + cli::cli_abort( + c( "Argument `params` is not a `data.frame()`.", message1 ) @@ -87,8 +89,8 @@ } if (!all(c("y1", "alpha", "r") %in% names(params))) { - stop( - .pasteN( + cli::cli_abort( + c( "The parameter names do not match.", message1 ) @@ -96,7 +98,7 @@ } if (!all(antigen_isos %in% params$antigen_iso)) { - stop("Some `antigen_iso` values are missing.") + cli::cli_abort("Some `antigen_iso` values are missing.") } invisible(NULL) diff --git a/R/warn_missing_strata.R b/R/warn_missing_strata.R index 4b94ba1c1..ad8e9d096 100644 --- a/R/warn_missing_strata.R +++ b/R/warn_missing_strata.R @@ -43,7 +43,7 @@ warn_missing_strata <- function( "To avoid this warning,", "specify the desired set of stratifying variables", "in the `curve_strata_varnames` and", - "`noise_strata_varnames` arguments to `est.incidence.by()`." + "`noise_strata_varnames` arguments to `est_seroincidence_by()`." ) @@ -54,12 +54,10 @@ warn_missing_strata <- function( } if (length(present_strata_vars) > 0) { - strata2 <- data |> count_strata(present_strata_vars) - missing_strata <- anti_join( strata, - strata2, + data, by = present_strata_vars ) |> distinct(across(all_of(present_strata_vars))) diff --git a/README.Rmd b/README.Rmd index 494150638..59d2bedca 100644 --- a/README.Rmd +++ b/README.Rmd @@ -141,7 +141,7 @@ on [GitHub](https://github.com/UCD-SERG/serocalculator/issues). Another great resource is **The Epidemiologist R Handbook**, which includes an introductory page on asking for help with R packages via GitHub: -https://epirhandbook.com/en/getting-help.html +https://epirhandbook.com/en/new_pages/help.html ## Contributing to this project @@ -156,7 +156,7 @@ for more information. This QR code is a direct link to the latest-release version of the package website: -```{r svg} +```{r} #| echo: false #| label: fig-qr-code library(qrcode) diff --git a/README.md b/README.md index 3c37157da..278e5eb1c 100644 --- a/README.md +++ b/README.md @@ -156,7 +156,7 @@ reproducible example](https://reprex.tidyverse.org/) on Another great resource is **The Epidemiologist R Handbook**, which includes an introductory page on asking for help with R packages via -GitHub: +GitHub: ## Contributing to this project @@ -170,6 +170,8 @@ for more information. This QR code is a direct link to the latest-release version of the package website: + #> Warning: package 'qrcode' was built under R version 4.4.2 +
QR code for serocalculator website diff --git a/_quarto.yml b/_quarto.yml index d584ac199..a49dd14a9 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -1,5 +1,7 @@ project: - render: ['*.qmd'] + render: + - "*.qmd" # Render all Quarto markdown files + - "!data-raw/" # Exclude anything in the data-raw directory author: "UC Davis Seroepidemiology Research Group (UCD-SERG)" date: '`r Sys.Date()`' format: diff --git a/cran-comments.md b/cran-comments.md index f4aa9e392..d08b27d7a 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,15 +1,13 @@ ## Resubmission -* Package had been archived from CRAN on 26-11-2024 following policy violation (Internet Access) -* In response, we removed urls with unstable servers and added internal datasets for examples and testing +* Package was flagged for errors on 11-14-2025 due to errors in `expect_snapshot_data()` and an out of date URL +* In response, we copied `expect_snapshot_data()` into an internal function (with permission from creators) + and replaced the out of date URL * Includes additional developments consistent with a minor version increase. * Full updates are described in NEWS.md. * There are currently no downstream dependencies for this package ## R CMD check results -0 errors | 0 warnings | 1 note +0 errors | 0 warnings | 0 notes -* There are some words flagged as misspellings in DESCRIPTION, however these are spelled correctly. - -* Package was previously archived on CRAN diff --git a/data-raw/.gitignore b/data-raw/.gitignore index 24b61650d..31f8b5710 100644 --- a/data-raw/.gitignore +++ b/data-raw/.gitignore @@ -1 +1,2 @@ -test_sim_020425_2.pdf +*.pdf +*.rmarkdown diff --git a/data-raw/coverage-CI.R b/data-raw/coverage-CI.R new file mode 100644 index 000000000..761f9d28c --- /dev/null +++ b/data-raw/coverage-CI.R @@ -0,0 +1,22 @@ +# Compute Coverage and its Confidence Interval +compute_coverage_ci <- function(coverage_count, total_count) { + test_result <- stats::binom.test( + coverage_count, + total_count, + conf.level = 0.95 + ) + # 95% CI + coverage_proportion <- coverage_count / total_count + ci_lower <- test_result$conf.int[1] + ci_upper <- test_result$conf.int[2] + + return( + list( + coverage = coverage_proportion, + ci_lower = ci_lower, + ci_upper = ci_upper + ) + ) +} + +# coverage_result <- compute_coverage_ci(coverage_count, nrow(data)) # nolint: object_usage_linter diff --git a/data-raw/generate_sim_table.R b/data-raw/generate_sim_table.R new file mode 100644 index 000000000..486d341d8 --- /dev/null +++ b/data-raw/generate_sim_table.R @@ -0,0 +1,42 @@ +#' Generate table of simulation results +#' +#' @param results_list output from [simulate_seroincidence()] +#' @param sample_size sample size of simulated data sets +#' @noRd +#' @returns a [tibble::tbl_df] +generate_sim_table <- function( + results_list, + sample_size = results_list |> attr("sample_size")) { + # Initialize an empty list to store the results + summary_results <- list() + + # Loop through each of the 100 results and extract the required columns + for (i in 1:300) { + # Extract the summary for each result + result_summary <- summary(results_list[[i]]$est1) + + # Select the required columns + extracted_columns <- result_summary %>% + select(incidence.rate, SE, CI.lwr, CI.upr) + + # Add a column for the index (optional, for tracking) + extracted_columns <- extracted_columns %>% + mutate(index = i) + + # Append to the list + summary_results[[i]] <- extracted_columns + } + + # Combine all results into a single data frame + final_table <- bind_rows(summary_results) %>% + mutate(sample_size = sample_size) # Add sample size column for clarity + + final_table <- + final_table |> + structure( + sample_size = sample_size, + true_lambda = results_list |> attr("lambda_true") + ) + + return(final_table) +} diff --git a/data-raw/profile-fdev.R b/data-raw/profile-fdev.R index 2b8cfb161..0ec39f6a3 100644 --- a/data-raw/profile-fdev.R +++ b/data-raw/profile-fdev.R @@ -4,7 +4,7 @@ library(dplyr) library(tibble) # load in longitudinal parameters -curve_params = load_curve_params("https://osf.io/download/rtw5k") +curve_params = load_sr_params("https://osf.io/download/rtw5k") xs_data <- "https://osf.io/download//n6cp3/" %>% load_pop_data() #Load noise params diff --git a/data-raw/sees_typhoid_ests_strat.R b/data-raw/sees_typhoid_ests_strat.R new file mode 100644 index 000000000..c06ed0e50 --- /dev/null +++ b/data-raw/sees_typhoid_ests_strat.R @@ -0,0 +1,24 @@ +library(dplyr) + +xs_data <- readr::read_rds("https://osf.io/download//n6cp3/") |> + as_pop_data() + +curves <- + "https://osf.io/download/rtw5k/" |> + load_sr_params() + +noise <- "https://osf.io/download//hqy4v/" |> readr::read_rds() + +sees_typhoid_ests_strat <- est_seroincidence_by( + strata = c("ageCat", "Country"), + pop_data = xs_data, + sr_params = curve, + curve_strata_varnames = NULL, + noise_params = noise, + noise_strata_varnames = "Country", + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + verbose = TRUE, + num_cores = 8 # Allow for parallel processing to decrease run time +) + +usethis::use_data(sees_typhoid_ests_strat, overwrite = TRUE) diff --git a/data-raw/simulate_seroincidence.R b/data-raw/simulate_seroincidence.R new file mode 100644 index 000000000..99ccbe27c --- /dev/null +++ b/data-raw/simulate_seroincidence.R @@ -0,0 +1,89 @@ +#' Simulate seroincidence +#' +#' @param nrep number of samples per simulated dataset +#' @param n_sim number of simulations to run +#' @param renew.params whether to sample new parameters for each infection +#' in a simulated individual's longitudinal data +#' @param sr_params Curve parameters +#' @param antibodies which antibodies to simulate +#' @param lambda Simulated incidence rate per person-year +#' @param lifespan range of ages to simulate +#' @param dlims biologic noise distribution +#' @param cond Noise parameters +#' +#' @returns a [list] of simulation results +#' @noRd +#' +#' @examples +#' simulate_seroincidence(sr_params = typhoid_curves_nostrat_100) +simulate_seroincidence <- function( + nrep = 100, + n_sim = 10, + sr_params, + renew.params = TRUE, + antibodies = c("HlyE_IgA", "HlyE_IgG"), + lambda = 0.01, + lifespan = c(0, 10), # Age range + + # + dlims = rbind( + "HlyE_IgA" = c(min = 0, max = 0.5), + "HlyE_IgG" = c(min = 0, max = 0.5) + ), + + # + cond = tibble( + antigen_iso = c("HlyE_IgG", "HlyE_IgA"), + nu = c(0.5, 0.5), # Biologic noise (nu) + eps = c(0, 0), # Measurement noise (eps) + y.low = c(1, 1), # Low cutoff (llod) + y.high = c(5e6, 5e6) # High cutoff (y.high) + ) +) { + # Parameters + dmcmc <- sr_params # + + + # Perform simulations in parallel + results <- future_map(1:n_sim, function(i) { + # Generate cross-sectional data + csdata <- sim.cs( + curve_params = dmcmc, + lambda = lambda, + n.smpl = nrep, + age.rng = lifespan, + antigen_isos = antibodies, + n.mc = 0, + renew.params = renew.params, # Use different parameters for each simulation + add.noise = TRUE, + noise_limits = dlims, + format = "long" + ) + + # Estimate seroincidence + est <- est_seroincidence( + pop_data = csdata, + sr_params = dmcmc, + noise_params = cond, + lambda_start = 0.005, + build_graph = TRUE, + verbose = FALSE, + print_graph = FALSE, + antigen_isos = antibodies + ) + + # Return results for this simulation + list( + csdata = csdata, + est1 = est + ) + }, .options = furrr_options(seed = TRUE)) + + results <- + results |> + structure( + lambda_true = lambda, + sample_size = nrep) + + return(results) +} diff --git a/data-raw/test_sim_020425.qmd b/data-raw/test_sim_020425.qmd new file mode 100644 index 000000000..6ce94c533 --- /dev/null +++ b/data-raw/test_sim_020425.qmd @@ -0,0 +1,314 @@ +--- +title: "Simulation 02.04.2025 renew.params" +format: + pdf: + number-sections: true + number-depth: 2 + number-offset: [0, 0] +output: + pdf_document: + orientation: landscape +editor_options: + chunk_output_type: console +--- + +```{r setup, include=FALSE,echo=FALSE} +library(knitr) +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r, echo=FALSE, message=FALSE} +library(gridExtra) +library(mgcv) # For advanced GAM smoothing +library(haven) +library(knitr) +library(plotly) +library(kableExtra) +library(tidyr) +library(arsenal) +library(dplyr) +library(forcats) +library(huxtable) +library(magrittr) +library(parameters) +library(kableExtra) +library(ggplot2) +library(ggeasy) +library(scales) +library(plotly) +library(patchwork) +library(tidyverse) +library(gtsummary) +library(readxl) +library(purrr) +library(serocalculator) +library(runjags) +library(coda) +library(ggmcmc) +library(here) +library(bayesplot) +library(table1) +library(tibble) +library(furrr) +library(dplyr) +``` + +# Simulation (renew.params=TRUE) + +The true lambda is 0.01 and lambda_start is 0.005. + +# Get parameters from longitudinal data + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +test_sim<-"https://osf.io/download/rtw5k" |> + load_sr_params() |> + dplyr::filter(iter < 500) + +``` + +# Approach 2 (Simulate Data Fresh for Each Sample) + +## Function for approach2 + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +## Run simulation +plan(multisession) # Use multiple sessions for parallelism (local machine) + +# Run the simulations in parallel +set.seed(129251) +results_50 <- simulate_seroincidence(nrep = 50, n_sim = 300) + +set.seed(129252) +results_100 <- simulate_seroincidence(nrep = 100, n_sim = 300) + +set.seed(129253) +results_150 <- simulate_seroincidence(nrep = 150, n_sim = 300) + +set.seed(129254) +results_200 <- simulate_seroincidence(nrep = 200, n_sim = 300) + +# Stop parallel processing +plan(sequential) # Return to sequential processing + +``` + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +# Store each sample size's summary as table +final_table_50 <- generate_sim_table(results_50, 50) +final_table_100 <- generate_sim_table(results_100, 100) +final_table_150 <- generate_sim_table(results_150, 100) +final_table_200 <- generate_sim_table(results_200, 100) + +# Define the true lambda +true_lambda <- 0.01 + +``` + +## Bias, SE, RMSE, CI Width, and CI Coverage + +```{r, echo=FALSE, message=FALSE} +# Define the function to compute statistics + +statistics_50<-analyze_sims(final_table_50, true_lambda) +statistics_100<-analyze_sims(final_table_100, true_lambda) +statistics_150<-analyze_sims(final_table_150, true_lambda) +statistics_200<-analyze_sims(final_table_200, true_lambda) + + +# Convert the statistics lists to data frames +df_50 <- as_tibble(statistics_50) |> mutate(Sample_Size = 50) +df_100 <- as_tibble(statistics_100) |> mutate(Sample_Size = 100) +df_150 <- as_tibble(statistics_150) |> mutate(Sample_Size = 150) +df_200 <- as_tibble(statistics_200) |> mutate(Sample_Size = 200) + +# Combine all statistics into a single table +final_statistics_table <- bind_rows(df_50, df_100, df_150, df_200) |> + select(Sample_Size, everything()) # Reorder to show Sample Size first + +# Convert Bias to scientific notation for better visibility +final_statistics_table <- final_statistics_table |> + mutate(Bias = formatC(Bias, format = "e", digits = 4)) # Scientific notation + +``` + +Since some values in CI.upr and CI.lwr are Inf or extremely large due to outliers, we should filter them out before computing the statistics. + +# Table (renew.params=TRUE) + +Make a table with columns bias, standard error, root mean squared error, confidence interval width, confidence interval coverage, sample size, and renew_params, for various sample sizes and each value of renew_params + +```{r, echo=FALSE, message=FALSE} +# Display the table in a simple format for PDF output +kable(final_statistics_table, + format = "latex", # Use "latex" for PDF output + digits = 4, + caption = "Statistics Results for Different Sample Sizes") + +``` + +\newpage + +# Simulation (renew.params=FALSE) + +The true lambda is 0.01 and lambda_start is 0.005. + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +# Define the simulation function +simulate_seroincidence2 <- function(nrep, n_sim) { + # Parameters + dmcmc <- test_sim # Curve parameters + antibodies <-c("HlyE_IgA", "HlyE_IgG") + lambda <- 0.01 # Simulated incidence rate per person-year + lifespan <- c(0, 10) # Age range + + # biologic noise distribution + dlims <- rbind( + "HlyE_IgA" = c(min = 0, max = 0.5), + "HlyE_IgG" = c(min = 0, max = 0.5) + ) + + # Noise parameters + cond <- tibble( + antigen_iso = c("HlyE_IgG", "HlyE_IgA"), + nu = c(0.5, 0.5), # Biologic noise (nu) + eps = c(0, 0), # Measurement noise (eps) + y.low = c(1, 1), # Low cutoff (llod) + y.high = c(5e6, 5e6) # High cutoff (y.high) + ) + + # Perform simulations in parallel + results <- future_map(1:n_sim, function(i) { + # Generate cross-sectional data + csdata <- sim.cs( + curve_params = dmcmc, + lambda = lambda, + n.smpl = nrep, + age.rng = lifespan, + antigen_isos = antibodies, + n.mc = 0, + renew.params = FALSE, # Use different parameters for each simulation + add.noise = TRUE, + noise_limits = dlims, + format = "long" + ) + + # Estimate seroincidence + est <- est.incidence( + pop_data = csdata, + curve_params = dmcmc, + noise_params = cond, + lambda_start = 0.005, + build_graph = TRUE, + verbose = FALSE, + print_graph = FALSE, + antigen_isos = antibodies + ) + + # Return results for this simulation + list( + csdata = csdata, + est1 = est + ) + }, .options = furrr_options(seed = TRUE)) + + return(results) +} + +``` + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +## Run simulation +plan(multisession) # Use multiple sessions for parallelism (local machine) + +# Run the simulations in parallel +set.seed(129255) +results_50_2 <- simulate_seroincidence2(nrep = 50, n_sim = 300) + +set.seed(129256) +results_100_2 <- simulate_seroincidence2(nrep = 100, n_sim = 300) + +set.seed(129257) +results_150_2 <- simulate_seroincidence2(nrep = 150, n_sim = 300) + +set.seed(129258) +results_200_2 <- simulate_seroincidence2(nrep = 200, n_sim = 300) + +# Stop parallel processing +plan(sequential) # Return to sequential processing + +``` + +```{r, echo=FALSE, message=FALSE} +# Store each sample size's summary as table +final_table_50_2 <- generate_sim_table(results_50_2, 50) +final_table_100_2 <- generate_sim_table(results_100_2, 100) +final_table_150_2 <- generate_sim_table(results_150_2, 100) +final_table_200_2 <- generate_sim_table(results_200_2, 100) + +``` + +# Table (renew.params=FALSE) + +```{r} +statistics_50_2<-analyze_sims(final_table_50_2, true_lambda) +statistics_100_2<-analyze_sims(final_table_100_2, true_lambda) +statistics_150_2<-analyze_sims(final_table_150_2, true_lambda) +statistics_200_2<-analyze_sims(final_table_200_2, true_lambda) + + +# Convert the statistics lists to data frames +df_50_2 <- as_tibble(statistics_50_2) |> mutate(Sample_Size = 50) +df_100_2 <- as_tibble(statistics_100_2) |> mutate(Sample_Size = 100) +df_150_2 <- as_tibble(statistics_150_2) |> mutate(Sample_Size = 150) +df_200_2 <- as_tibble(statistics_200_2) |> mutate(Sample_Size = 200) + +# Combine all statistics into a single table +final_statistics_table2 <- bind_rows(df_50_2, df_100_2, df_150_2, df_200_2) |> + select(Sample_Size, everything()) # Reorder to show Sample Size first + +# Convert Bias to scientific notation for better visibility +final_statistics_table2 <- final_statistics_table2 |> + mutate(Bias = formatC(Bias, format = "e", digits = 4)) # Scientific notation + +# Display the table in a simple format for PDF output +kable(final_statistics_table2, + format = "latex", # Use "latex" for PDF output + digits = 4, + caption = "Statistics Results for Different Sample Sizes") + +``` + +\newpage + +# Table (renew.params:TRUE vs. FALSE) + +```{r, echo=FALSE, message=FALSE} +# Convert the statistics lists to data frames and add 'Renew_Params' column +df_50_true <- as_tibble(statistics_50) |> mutate(Sample_Size = 50, renew.params = "TRUE") +df_100_true <- as_tibble(statistics_100) |> mutate(Sample_Size = 100, renew.params = "TRUE") +df_150_true <- as_tibble(statistics_150) |> mutate(Sample_Size = 150, renew.params = "TRUE") +df_200_true <- as_tibble(statistics_200) |> mutate(Sample_Size = 200, renew.params = "TRUE") + +df_50_false <- as_tibble(statistics_50_2) |> mutate(Sample_Size = 50, renew.params = "FALSE") +df_100_false <- as_tibble(statistics_100_2) |> mutate(Sample_Size = 100, renew.params = "FALSE") +df_150_false <- as_tibble(statistics_150_2) |> mutate(Sample_Size = 150, renew.params = "FALSE") +df_200_false <- as_tibble(statistics_200_2) |> mutate(Sample_Size = 200, renew.params = "FALSE") + +# Combine all statistics into a single table +final_statistics_table_merged <- bind_rows( + df_50_true, df_100_true, df_150_true, df_200_true, + df_50_false, df_100_false, df_150_false, df_200_false +) |> + select(Sample_Size, renew.params, everything()) # Reorder columns + +# Convert Bias to scientific notation for better visibility +final_statistics_table_merged <- final_statistics_table_merged |> + mutate(Bias = formatC(Bias, format = "e", digits = 4)) + +# Display the table in a simple format for PDF output +kable(final_statistics_table_merged, + format = "latex", # Use "latex" for PDF output + digits = 4, + caption = "Statistics Results for Different Sample Sizes") + +``` diff --git a/data-raw/test_sim_020425.r b/data-raw/test_sim_020425.r new file mode 100644 index 000000000..d44ae75f0 --- /dev/null +++ b/data-raw/test_sim_020425.r @@ -0,0 +1,206 @@ +### For sample size 50's graph distribution of confidence intervals +## When renew.params=TRUE + +library(dplyr) +library(tibble) +library(serocalculator) + +############################################################################### +## Load longitudinal parameters + +test_sim<-"https://osf.io/download/rtw5k" %>% + load_sr_params() %>% + dplyr::filter(iter < 500) +############################################################################### + +# Define the simulation function +simulate_seroincidence <- function(nrep, n_sim, true_lambda, lambda.start) { + # Parameters + dmcmc <- test_sim # Curve parameters + antibodies <-c("HlyE_IgA", "HlyE_IgG") + lambda <- true_lambda # Simulated incidence rate per person-year + lifespan <- c(0, 10) # Age range + + # biologic noise distribution + dlims <- rbind( + "HlyE_IgA" = c(min = 0, max = 0.5), + "HlyE_IgG" = c(min = 0, max = 0.5) + ) + + # Noise parameters + cond <- tibble( + antigen_iso = c("HlyE_IgG", "HlyE_IgA"), + nu = c(0.5, 0.5), # Biologic noise (nu) + eps = c(0, 0), # Measurement noise (eps) + y.low = c(1, 1), # Low cutoff (llod) + y.high = c(5e6, 5e6) # High cutoff (y.high) + ) + + # Perform simulations in parallel + results <- future_map(1:n_sim, function(i) { + # Generate cross-sectional data + csdata <- sim.cs( + curve_params = dmcmc, + lambda = lambda, + n.smpl = nrep, + age.rng = lifespan, + antigen_isos = antibodies, + n.mc = 0, + renew.params = TRUE, # Use different parameters for each simulation + add.noise = TRUE, + noise_limits = dlims, + format = "long" + ) + + # Estimate seroincidence + est <- est.incidence( + pop_data = csdata, + curve_params = dmcmc, + noise_params = cond, + lambda_start = lambda.start, + build_graph = TRUE, + verbose = FALSE, + print_graph = FALSE, + antigen_isos = antibodies + ) + + # Return results for this simulation + list( + csdata = csdata, + est1 = est + ) + }, .options = furrr_options(seed = TRUE)) + + return(results) +} + +############################################################################### +## Run simulation +plan(multisession) # Use multiple sessions for parallelism (local machine) + +# Run the simulations in parallel +set.seed(204251) +results_50_11 <- simulate_seroincidence(nrep = 50, n_sim = 300, + true_lambda=0.01, lambda.start=0.005) + +set.seed(204252) +results_50_22 <- simulate_seroincidence(nrep = 50, n_sim = 300, + true_lambda=0.05, lambda.start=0.02) + +set.seed(204253) +results_50_33 <- simulate_seroincidence(nrep = 50, n_sim = 300, + true_lambda=0.1, lambda.start=0.05) + +set.seed(204254) +results_50_44 <- simulate_seroincidence(nrep = 50, n_sim = 300, + true_lambda=0.2, lambda.start=0.1) + + +# Stop parallel processing +plan(sequential) # Return to sequential processing + + +############################################################################### +## First approach + +# Define a function to generate final tables with all summary columns +generate_sim_table <- function(results_list, sample_size, lambda_sim) { + # Initialize an empty list to store the results + summary_results <- list() + + # Loop through each of the 300 results and extract the full summary + for (i in 1:300) { + # Extract the full summary for each result + result_summary <- summary(results_list[[i]]$est1) + + # Add an index column for tracking + result_summary <- result_summary %>% + mutate(index = i) + + # Append to the list + summary_results[[i]] <- result_summary + } + + # Combine all results into a single data frame and add sample_size and lambda.sim columns + final_table <- bind_rows(summary_results) %>% + mutate(sample_size = sample_size, + lambda.sim = lambda_sim) # Add lambda.sim column + + return(final_table) +} + + +# Store each sample size's summary as table +final_table_50_11 <- generate_sim_table(results_50_11, 50, 0.01) +final_table_50_22 <- generate_sim_table(results_50_22, 50, 0.05) +final_table_50_33 <- generate_sim_table(results_50_33, 50, 0.1) +final_table_50_44 <- generate_sim_table(results_50_44, 50, 0.2) + +# Merge the four tables into one data frame +table_merged_50 <- bind_rows( + final_table_50_11, + final_table_50_22, + final_table_50_33, + final_table_50_44 +) + + + + +table_merged_50 |> + autoplot(xvar = "lambda.sim", + CI = TRUE, + dodge_width = .05) + + ggplot2::geom_function( + fun = function(x) x, + col = "red", + aes(linetype = "data-generating incidence rate") + ) + + labs(linetype = "") + + scale_x_log10() + +############################################################################## +## Second approach + +# List of result objects +results_list <- list( + results_50_11 = 0.01, + results_50_22 = 0.05, + results_50_33 = 0.1, + results_50_44 = 0.2 +) + + +# Extract summaries and combine into a tibble, iterating over `i = 1:300` +ests_summary <- map_dfr(names(results_list), function(res_name) { + res_obj <- get(res_name) # Retrieve the actual list object + + # Loop through all `i` indices from 1 to 300 + map_dfr(1:300, function(i) { + if (!is.null(res_obj[[i]]$est1)) { # Ensure the object exists + res_summary <- summary(res_obj[[i]]$est1) # Extract summary + res_summary <- res_summary %>% + mutate(lambda.sim = results_list[[res_name]], # Assign corresponding lambda.sim + index = i) # Track index value + return(res_summary) + } else { + return(NULL) # Skip if the element is NULL + } + }) +}) + +# Reorder columns to place lambda.sim and index first +ests_summary <- ests_summary %>% relocate(lambda.sim, index) + + +ests_summary |> + autoplot(xvar = "lambda.sim", + CI = TRUE, + dodge_width = .05) + + ggplot2::geom_function( + fun = function(x) x, + col = "red", + aes(linetype = "data-generating incidence rate") + ) + + labs(linetype = "") + + scale_x_log10() diff --git a/data-raw/test_sim_020425_2.qmd b/data-raw/test_sim_020425_2.qmd new file mode 100644 index 000000000..15127bd24 --- /dev/null +++ b/data-raw/test_sim_020425_2.qmd @@ -0,0 +1,262 @@ +--- +title: "Simulation 02.04.2025 renew_params" +format: + pdf: + number-sections: true + number-depth: 2 + number-offset: [0, 0] +output: + pdf_document: + orientation: landscape +editor_options: + chunk_output_type: console +--- + +```{r setup, include=FALSE,echo=FALSE} +library(knitr) +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r, echo=FALSE, message=FALSE} +library(gridExtra) +library(mgcv) # For advanced GAM smoothing +library(haven) +library(knitr) +library(plotly) +library(kableExtra) +library(tidyr) +library(arsenal) +library(dplyr) +library(forcats) +library(huxtable) +library(magrittr) +library(parameters) +library(kableExtra) +library(ggplot2) +library(ggeasy) +library(scales) +library(plotly) +library(patchwork) +library(tidyverse) +library(gtsummary) +library(readxl) +library(purrr) +# library(serocalculator) +devtools::load_all() +library(runjags) +library(coda) +library(ggmcmc) +library(here) +library(bayesplot) +library(table1) +library(tibble) +library(furrr) +library(dplyr) +``` + +# Simulation (renew_params=TRUE) + +The true lambda is 0.01 and lambda_start is 0.005. + +# Get parameters from longitudinal data + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +test_sim<-"https://osf.io/download/rtw5k" |> + load_sr_params() |> + dplyr::filter(iter < 500) + +``` + +# Approach 2 (Simulate Data Fresh for Each Sample) + +## Function for approach2 + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +## Run simulation +plan(multisession) # Use multiple sessions for parallelism (local machine) + +# Define the true lambda +true_lambda <- 0.01 + +# Run the simulations in parallel +set.seed(129251) +results_50 <- simulate_seroincidence(sr_params = test_sim, nrep = 50, n_sim = 300, lambda = true_lambda) + +set.seed(129252) +results_100 <- simulate_seroincidence(sr_params = test_sim, nrep = 100, n_sim = 300, lambda = true_lambda) + +set.seed(129253) +results_150 <- simulate_seroincidence(sr_params = test_sim, nrep = 150, n_sim = 300, lambda = true_lambda) + +set.seed(129254) +results_200 <- simulate_seroincidence(sr_params = test_sim, nrep = 200, n_sim = 300, lambda = true_lambda) + +# Stop parallel processing +plan(sequential) # Return to sequential processing + +``` + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +# Store each sample size's summary as table +final_table_50 <- generate_sim_table(results_50) +final_table_100 <- generate_sim_table(results_100) +final_table_150 <- generate_sim_table(results_150) +final_table_200 <- generate_sim_table(results_200) + + + +``` + +## Bias, SE, RMSE, CI Width, and CI Coverage + +```{r, echo=FALSE, message=FALSE} +# Define the function to compute statistics + + +statistics_50 <- analyze_sims(final_table_50) +statistics_100 <- analyze_sims(final_table_100) +statistics_150 <- analyze_sims(final_table_150) +statistics_200 <- analyze_sims(final_table_200) + + +# Convert the statistics lists to data frames +df_50 <- as_tibble(statistics_50) +df_100 <- as_tibble(statistics_100) +df_150 <- as_tibble(statistics_150) +df_200 <- as_tibble(statistics_200) +# Combine all statistics into a single table +final_statistics_table <- bind_rows(df_50, df_100, df_150, df_200) |> + select(Sample_Size, everything()) # Reorder to show Sample Size first + +## Convert Bias to scientific notation for better visibility +# final_statistics_table <- final_statistics_table |> +# mutate(Bias = formatC(Bias, format = "e", digits = 4)) # Scientific notation + +``` + +```{r} +final_statistics_table |> + autoplot(statistic = "Standard_Error") + +``` + + +Since some values in CI.upr and CI.lwr are Inf or extremely large due to outliers, +we should filter them out before computing the statistics. + +# Table (renew_params=TRUE) + +Make a table with columns bias, standard error, root mean squared error, confidence interval width, confidence interval coverage, sample size, and renew_params, for various sample sizes and each value of renew_params + +```{r, echo=FALSE, message=FALSE} +# Display the table in a simple format for PDF output +kable(final_statistics_table, + format = "latex", # Use "latex" for PDF output + digits = 4, + caption = "Statistics Results for Different Sample Sizes") + +``` + +\newpage + +# Simulation (renew_params=FALSE) + +The true lambda is 0.01 and lambda_start is 0.005. + +```{r,echo=FALSE, message=FALSE, warning=FALSE,results='hide'} +## Run simulation +plan(multisession) # Use multiple sessions for parallelism (local machine) + +# Run the simulations in parallel +set.seed(129255) +results_50_2 <- simulate_seroincidence(sr_params = test_sim, renew.params = FALSE, nrep = 50, n_sim = 300) + +set.seed(129256) +results_100_2 <- simulate_seroincidence(sr_params = test_sim, renew.params = FALSE, nrep = 100, n_sim = 300) + +set.seed(129257) +results_150_2 <- simulate_seroincidence(sr_params = test_sim, renew.params = FALSE, nrep = 150, n_sim = 300) + +set.seed(129258) +results_200_2 <- simulate_seroincidence(sr_params = test_sim, renew.params = FALSE, nrep = 200, n_sim = 300) + +# Stop parallel processing +plan(sequential) # Return to sequential processing + +``` + +```{r, echo=FALSE, message=FALSE} +# Store each sample size's summary as table +final_table_50_2 <- generate_sim_table(results_50_2, 50) +final_table_100_2 <- generate_sim_table(results_100_2, 100) +final_table_150_2 <- generate_sim_table(results_150_2, 100) +final_table_200_2 <- generate_sim_table(results_200_2, 100) + +``` + +# Table (renew_params=FALSE) + +```{r} +statistics_50_2<-analyze_sims(final_table_50_2, true_lambda) +statistics_100_2<-analyze_sims(final_table_100_2, true_lambda) +statistics_150_2<-analyze_sims(final_table_150_2, true_lambda) +statistics_200_2<-analyze_sims(final_table_200_2, true_lambda) + + +# Convert the statistics lists to data frames +df_50_2 <- as_tibble(statistics_50_2) |> mutate(Sample_Size = 50) +df_100_2 <- as_tibble(statistics_100_2) |> mutate(Sample_Size = 100) +df_150_2 <- as_tibble(statistics_150_2) |> mutate(Sample_Size = 150) +df_200_2 <- as_tibble(statistics_200_2) |> mutate(Sample_Size = 200) + +# Combine all statistics into a single table +final_statistics_table2 <- bind_rows(df_50_2, df_100_2, df_150_2, df_200_2) |> + select(Sample_Size, everything()) # Reorder to show Sample Size first + +# Convert Bias to scientific notation for better visibility +final_statistics_table2 <- final_statistics_table2 |> + mutate(Bias = formatC(Bias, format = "e", digits = 4)) # Scientific notation + +# Display the table in a simple format for PDF output +kable(final_statistics_table2, + format = "latex", # Use "latex" for PDF output + digits = 4, + caption = "Statistics Results for Different Sample Sizes") + +``` + +{{< pagebreak >}} + +# Table (renew_params:TRUE vs. FALSE) + +```{r, echo=FALSE, message=FALSE} +# Convert the statistics lists to data frames and add 'renew_params' column +df_50_true <- as_tibble(statistics_50) |> mutate(Sample_Size = 50, renew_params = "TRUE") +df_100_true <- as_tibble(statistics_100) |> mutate(Sample_Size = 100, renew_params = "TRUE") +df_150_true <- as_tibble(statistics_150) |> mutate(Sample_Size = 150, renew_params = "TRUE") +df_200_true <- as_tibble(statistics_200) |> mutate(Sample_Size = 200, renew_params = "TRUE") + +df_50_false <- as_tibble(statistics_50_2) |> mutate(Sample_Size = 50, renew_params = "FALSE") +df_100_false <- as_tibble(statistics_100_2) |> mutate(Sample_Size = 100, renew_params = "FALSE") +df_150_false <- as_tibble(statistics_150_2) |> mutate(Sample_Size = 150, renew_params = "FALSE") +df_200_false <- as_tibble(statistics_200_2) |> mutate(Sample_Size = 200, renew_params = "FALSE") + +# Combine all statistics into a single table +final_statistics_table_merged <- bind_rows( + df_50_true, df_100_true, df_150_true, df_200_true, + df_50_false, df_100_false, df_150_false, df_200_false +) |> + select(Sample_Size, renew_params, everything()) # Reorder columns + +# Convert Bias to scientific notation for better visibility +final_statistics_table_merged <- final_statistics_table_merged |> + mutate(Bias = formatC(Bias, format = "e", digits = 4)) + +# Display the table in a simple format for PDF output +kable(final_statistics_table_merged, + format = "latex", # Use "latex" for PDF output + digits = 4, + caption = "Statistics Results for Different Sample Sizes") + + +``` diff --git a/data-raw/typhoid_curves_nostrat_100.R b/data-raw/typhoid_curves_nostrat_100.R index 74e58f475..e7c5cbecb 100644 --- a/data-raw/typhoid_curves_nostrat_100.R +++ b/data-raw/typhoid_curves_nostrat_100.R @@ -1,5 +1,5 @@ typhoid_curves_nostrat_100 <- - load_curve_params("https://osf.io/download/rtw5k/") %>% + load_sr_params("https://osf.io/download/rtw5k/") %>% dplyr::filter(iter %in% 1:100) usethis::use_data(typhoid_curves_nostrat_100, overwrite = TRUE) diff --git a/data-raw/typhoid_results.R b/data-raw/typhoid_results.R index 56838c0cc..f3a377045 100644 --- a/data-raw/typhoid_results.R +++ b/data-raw/typhoid_results.R @@ -5,36 +5,37 @@ xs_data <- load_pop_data( value = "result", id = "index_id", standardize = TRUE -) %>% +) |> filter(Country == "Pakistan") # get noise data -noise <- load_noise_params("https://osf.io/download//hqy4v/") %>% +noise <- load_noise_params("https://osf.io/download//hqy4v/") |> filter(Country == "Pakistan") # get curve data -curve <- load_curve_params("https://osf.io/download/rtw5k/") +curve <- load_sr_params("https://osf.io/download/rtw5k/") # Initial estimates for lambda start <- .05 # Estimate incidence -fit <- est.incidence( +fit <- est_seroincidence( pop_data = xs_data, - curve_param = curve, + sr_params = curve, noise_param = noise, antigen_isos = c("HlyE_IgG", "HlyE_IgA") ) -typhoid_results <- fit %>% +typhoid_results <- fit |> summary.seroincidence( coverage = .95, start = start - ) %>% + ) |> mutate( ageCat = NULL, antigen.iso = paste(collapse = "+", "HlyE_IgG") - ) %>% + ) |> structure(noise.parameters = noise) -saveRDS(object = typhoid_results,file = "tests/testthat/fixtures/typhoid_results.rds") +saveRDS(object = typhoid_results, + file = "tests/testthat/fixtures/typhoid_results.rds") diff --git a/data/sees_typhoid_ests_strat.rda b/data/sees_typhoid_ests_strat.rda new file mode 100644 index 000000000..c1ff736ce Binary files /dev/null and b/data/sees_typhoid_ests_strat.rda differ diff --git a/inst/WORDLIST b/inst/WORDLIST index ecebe4a70..8f6476afd 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -33,7 +33,6 @@ Serological TW UC Unif -Vectorized Vellore Volterra al @@ -44,11 +43,15 @@ biomarker biomarkers boldsymbol cdot +codecov +colour csv dev devtools displaystyle +dropdown dt +eps estimands et expf @@ -58,11 +61,11 @@ geoms ggproto hemolysin infty +insightsengineering invf isos isotype isotypes -jitter kDa le leq @@ -74,10 +77,13 @@ mathbb mathbf mathcal mcmc +mitre modelled multicohort +multiversion nd nr +olds overcount overcounts overline @@ -93,7 +99,6 @@ qmd qquad recombinant renewcommand -rescale sectionally sera seroconversion @@ -119,3 +124,4 @@ unstratified varepsilon vec vee +yaml diff --git a/inst/examples/exm-analyze_sims.R b/inst/examples/exm-analyze_sims.R new file mode 100644 index 000000000..6dcae69a0 --- /dev/null +++ b/inst/examples/exm-analyze_sims.R @@ -0,0 +1,57 @@ +\donttest{ +dmcmc <- typhoid_curves_nostrat_100 + +n_cores <- 2 + +nclus <- 20 +# cross-sectional sample size +nrep <- c(50, 200) + +# incidence rate in e +lambdas <- c(.05, .8) + +antibodies <- c("HlyE_IgA", "HlyE_IgG") +lifespan <- c(0, 10) +dlims = rbind( +"HlyE_IgA" = c(min = 0, max = 0.5), +"HlyE_IgG" = c(min = 0, max = 0.5) +) +sim_df <- sim_pop_data_multi( +n_cores = n_cores, +lambdas = lambdas, +nclus = nclus, +sample_sizes = nrep, +age_range = lifespan, +antigen_isos = antibodies, +renew_params = FALSE, +add_noise = TRUE, +curve_params = dmcmc, +noise_limits = dlims, +format = "long" +) +cond <- tibble::tibble( +antigen_iso = c("HlyE_IgG", "HlyE_IgA"), +nu = c(0.5, 0.5), # Biologic noise (nu) +eps = c(0, 0), # M noise (eps) +y.low = c(1, 1), # low cutoff (llod) +y.high = c(5e6, 5e6) +) +ests <- +est_seroincidence_by( +pop_data = sim_df, +sr_params = dmcmc, +noise_params = cond, +num_cores = n_cores, +strata = c("lambda.sim", "sample_size", "cluster"), +curve_strata_varnames = NULL, +noise_strata_varnames = NULL, +verbose = FALSE, +build_graph = FALSE, # slows down the function substantially +antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +ests |> +summary() |> +analyze_sims() + +} diff --git a/inst/examples/exm-autoplot.sim_results.R b/inst/examples/exm-autoplot.sim_results.R new file mode 100644 index 000000000..9d3e76e68 --- /dev/null +++ b/inst/examples/exm-autoplot.sim_results.R @@ -0,0 +1,58 @@ +\donttest{ +dmcmc <- typhoid_curves_nostrat_100 + +n_cores <- 2 + +nclus <- 20 +# cross-sectional sample size +nrep <- c(50, 200) + +# incidence rate in e +lambdas <- c(.05, .8) +lifespan <- c(0, 10) +antibodies <- c("HlyE_IgA", "HlyE_IgG") +dlims <- rbind( +"HlyE_IgA" = c(min = 0, max = 0.5), +"HlyE_IgG" = c(min = 0, max = 0.5) +) +sim_df <- +sim_pop_data_multi( +n_cores = n_cores, +lambdas = lambdas, +nclus = nclus, +sample_sizes = nrep, +age_range = lifespan, +antigen_isos = antibodies, +renew_params = FALSE, +add_noise = TRUE, +curve_params = dmcmc, +noise_limits = dlims, +format = "long" +) +cond <- tibble::tibble( +antigen_iso = c("HlyE_IgG", "HlyE_IgA"), +nu = c(0.5, 0.5), # Biologic noise (nu) +eps = c(0, 0), # M noise (eps) +y.low = c(1, 1), # low cutoff (llod) +y.high = c(5e6, 5e6) +) +ests <- +est_seroincidence_by( +pop_data = sim_df, +sr_params = dmcmc, +noise_params = cond, +num_cores = n_cores, +strata = c("lambda.sim", "sample_size", "cluster"), +curve_strata_varnames = NULL, +noise_strata_varnames = NULL, +verbose = FALSE, +build_graph = FALSE, # slows down the function substantially +antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +ests |> +summary() |> +analyze_sims() |> +autoplot() + +} diff --git a/inst/examples/exm-compare_seroincidence.R b/inst/examples/exm-compare_seroincidence.R new file mode 100644 index 000000000..bd4c2eafa --- /dev/null +++ b/inst/examples/exm-compare_seroincidence.R @@ -0,0 +1,51 @@ +library(dplyr) + +# Example 1: Compare two single seroincidence estimates +# Create estimates for two different catchments +xs_data_c1 <- sees_pop_data_pk_100 |> filter(catchment == "kgh") +xs_data_c2 <- sees_pop_data_pk_100 |> filter(catchment == "aku") + +est_c1 <- est_seroincidence( + pop_data = xs_data_c1, + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +est_c2 <- est_seroincidence( + pop_data = xs_data_c2, + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +# Compare the two estimates - returns htest format +comparison <- compare_seroincidence(est_c1, est_c2) +print(comparison) + +# Example 2: Compare stratified seroincidence estimates +# Estimate seroincidence by catchment +est_by_catchment <- est_seroincidence_by( + strata = c("catchment"), + pop_data = sees_pop_data_pk_100, + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +# Compare all pairs of catchments - returns a table +comparisons_table <- compare_seroincidence(est_by_catchment) +print(comparisons_table) + +# Example 3: Compare stratified estimates by multiple variables +est_by_multiple <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = sees_pop_data_pk_100, + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +# Compare all pairs +comparisons_multi <- compare_seroincidence(est_by_multiple) +print(comparisons_multi) diff --git a/inst/examples/exm-graph.curve.params.R b/inst/examples/exm-graph.curve.params.R new file mode 100644 index 000000000..da880c6e8 --- /dev/null +++ b/inst/examples/exm-graph.curve.params.R @@ -0,0 +1,24 @@ +# Load example dataset +curve <- typhoid_curves_nostrat_100 |> + dplyr::filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) + +# Plot quantiles without showing all curves +plot1 <- graph.curve.params(curve, n_curves = 0) +print(plot1) + +# Plot with additional quantiles and show all curves +plot2 <- graph.curve.params( + curve, + n_curves = Inf, + quantiles = c(0.1, 0.5, 0.9) +) +print(plot2) + +# Plot with MCMC chains in black +plot3 <- graph.curve.params( + curve, + n_curves = Inf, + quantiles = c(0.1, 0.5, 0.9), + chain_color = FALSE +) +print(plot3) diff --git a/inst/examples/exm-sim_pop_data_multi.R b/inst/examples/exm-sim_pop_data_multi.R new file mode 100644 index 000000000..87bdd87cb --- /dev/null +++ b/inst/examples/exm-sim_pop_data_multi.R @@ -0,0 +1,41 @@ +\donttest{ +# Load curve parameters +dmcmc <- typhoid_curves_nostrat_100 + +# Specify the antibody-isotype responses to include in analyses +antibodies <- c("HlyE_IgA", "HlyE_IgG") + +# Set seed to reproduce results +set.seed(54321) + +# Simulated incidence rate per person-year +lambdas = c(.05, .1, .15, .2, .3) + +# Range covered in simulations +lifespan <- c(0, 10); + +# Cross-sectional sample size +nrep <- 100 + +# Biologic noise distribution +dlims <- rbind( + "HlyE_IgA" = c(min = 0, max = 0.5), + "HlyE_IgG" = c(min = 0, max = 0.5) +) + +sim_data <- sim_pop_data_multi( + curve_params = dmcmc, + lambdas = lambdas, + sample_sizes = nrep, + age_range = lifespan, + antigen_isos = antibodies, + n_mcmc_samples = 0, + renew_params = TRUE, + add_noise = TRUE, + noise_limits = dlims, + format = "long", + nclus = 10) + +sim_data + +} diff --git a/inst/examples/exm-strat_ests_barplot.R b/inst/examples/exm-strat_ests_barplot.R new file mode 100644 index 000000000..15291cc23 --- /dev/null +++ b/inst/examples/exm-strat_ests_barplot.R @@ -0,0 +1,32 @@ +library(dplyr) +library(ggplot2) + +xs_data <- + sees_pop_data_pk_100 + +curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) + +noise <- + example_noise_params_pk + +est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + num_cores = 2 # Allow for parallel processing to decrease run time +) + +est2sum <- summary(est2) + +est2sum |> autoplot( + type = "bar", + yvar = "ageCat", + color_var = "catchment", + CIs = TRUE +) diff --git a/inst/examples/exm-strat_ests_scatterplot.R b/inst/examples/exm-strat_ests_scatterplot.R new file mode 100644 index 000000000..06c739a2e --- /dev/null +++ b/inst/examples/exm-strat_ests_scatterplot.R @@ -0,0 +1,31 @@ +library(dplyr) +library(ggplot2) + +xs_data <- + sees_pop_data_pk_100 + +curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) + +noise <- + example_noise_params_pk + +est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + num_cores = 2 # Allow for parallel processing to decrease run time +) + +est2sum <- summary(est2) + +strat_ests_scatterplot(est2sum, + xvar = "ageCat", + color_var = "catchment", + CIs = TRUE, + group_var = "catchment") diff --git a/man/analyze_sims.Rd b/man/analyze_sims.Rd new file mode 100644 index 000000000..4d7bdaea3 --- /dev/null +++ b/man/analyze_sims.Rd @@ -0,0 +1,85 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/analyze_sims.R +\name{analyze_sims} +\alias{analyze_sims} +\title{Analyze simulation results} +\usage{ +analyze_sims(data) +} +\arguments{ +\item{data}{a \link[tibble:tbl_df-class]{tibble::tbl_df} with columns: +\itemize{ +\item \code{lambda.sim}, +\item \code{incidence.rate}, +\item \code{SE}, +\item \code{CI.lwr}, +\item \code{CI.upr} +for example, as produced by \code{\link[=summary.seroincidence.by]{summary.seroincidence.by()}} with +\code{lambda.sim} as a stratifying variable +}} +} +\value{ +a \code{sim_results} object (extends \link[tibble:tbl_df-class]{tibble::tbl_df}) +} +\description{ +Analyze simulation results +} +\examples{ +\donttest{ +dmcmc <- typhoid_curves_nostrat_100 + +n_cores <- 2 + +nclus <- 20 +# cross-sectional sample size +nrep <- c(50, 200) + +# incidence rate in e +lambdas <- c(.05, .8) + +antibodies <- c("HlyE_IgA", "HlyE_IgG") +lifespan <- c(0, 10) +dlims = rbind( +"HlyE_IgA" = c(min = 0, max = 0.5), +"HlyE_IgG" = c(min = 0, max = 0.5) +) +sim_df <- sim_pop_data_multi( +n_cores = n_cores, +lambdas = lambdas, +nclus = nclus, +sample_sizes = nrep, +age_range = lifespan, +antigen_isos = antibodies, +renew_params = FALSE, +add_noise = TRUE, +curve_params = dmcmc, +noise_limits = dlims, +format = "long" +) +cond <- tibble::tibble( +antigen_iso = c("HlyE_IgG", "HlyE_IgA"), +nu = c(0.5, 0.5), # Biologic noise (nu) +eps = c(0, 0), # M noise (eps) +y.low = c(1, 1), # low cutoff (llod) +y.high = c(5e6, 5e6) +) +ests <- +est_seroincidence_by( +pop_data = sim_df, +sr_params = dmcmc, +noise_params = cond, +num_cores = n_cores, +strata = c("lambda.sim", "sample_size", "cluster"), +curve_strata_varnames = NULL, +noise_strata_varnames = NULL, +verbose = FALSE, +build_graph = FALSE, # slows down the function substantially +antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +ests |> +summary() |> +analyze_sims() + +} +} diff --git a/man/as_curve_params.Rd b/man/as_curve_params.Rd index ca2127a36..d3178f778 100644 --- a/man/as_curve_params.Rd +++ b/man/as_curve_params.Rd @@ -1,30 +1,15 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/as_curve_params.R +% Please edit documentation in R/as_sr_params.R \name{as_curve_params} \alias{as_curve_params} \title{Load antibody decay curve parameter} \usage{ -as_curve_params(data, antigen_isos = NULL) -} -\arguments{ -\item{data}{a \code{\link[=data.frame]{data.frame()}} or \link[tibble:tbl_df-class]{tibble::tbl_df}} - -\item{antigen_isos}{a \code{\link[=character]{character()}} vector of antigen isotypes -to be used in analyses} -} -\value{ -a \code{curve_data} object -(a \link[tibble:tbl_df-class]{tibble::tbl_df} with extra attribute \code{antigen_isos}) +as_curve_params(...) } \description{ -Load antibody decay curve parameter -} -\examples{ -library(magrittr) -curve_data <- - serocalculator_example("example_curve_params.csv") \%>\% - read.csv() \%>\% - as_curve_params() +\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} -print(curve_data) +\code{as_curve_params()} was renamed to \code{\link[=as_sr_params]{as_sr_params()}} to create a more +consistent API. } +\keyword{internal} diff --git a/man/as_sr_params.Rd b/man/as_sr_params.Rd new file mode 100644 index 000000000..c818c5775 --- /dev/null +++ b/man/as_sr_params.Rd @@ -0,0 +1,30 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/as_sr_params.R +\name{as_sr_params} +\alias{as_sr_params} +\title{Load longitudinal seroresponse parameters} +\usage{ +as_sr_params(data, antigen_isos = NULL) +} +\arguments{ +\item{data}{a \code{\link[=data.frame]{data.frame()}} or \link[tibble:tbl_df-class]{tibble::tbl_df}} + +\item{antigen_isos}{a \code{\link[=character]{character()}} vector of antigen isotypes +to be used in analyses} +} +\value{ +a \code{curve_data} object +(a \link[tibble:tbl_df-class]{tibble::tbl_df} with extra attribute \code{antigen_isos}) +} +\description{ +Load longitudinal seroresponse parameters +} +\examples{ +library(magrittr) +curve_data <- + serocalculator_example("example_curve_params.csv") \%>\% + read.csv() \%>\% + as_curve_params() + +print(curve_data) +} diff --git a/man/autoplot.curve_params.Rd b/man/autoplot.curve_params.Rd index 7436c0fb3..cbde79705 100644 --- a/man/autoplot.curve_params.Rd +++ b/man/autoplot.curve_params.Rd @@ -2,65 +2,38 @@ % Please edit documentation in R/autoplot.curve_params.R \name{autoplot.curve_params} \alias{autoplot.curve_params} -\title{graph antibody decay curves by antigen isotype} +\title{Graph antibody decay curves by antigen isotype} \usage{ \method{autoplot}{curve_params}( object, - antigen_isos = unique(object$antigen_iso), - ncol = min(3, length(antigen_isos)), + method = c("graph.curve.params", "graph_seroresponse_model_1"), ... ) } \arguments{ -\item{object}{a \code{\link[=data.frame]{data.frame()}} of curve parameters (one or more MCMC samples)} +\item{object}{a \code{curve_params} object (constructed using \code{\link[=as_sr_params]{as_sr_params()}}), which is +a \code{\link[=data.frame]{data.frame()}} containing MCMC samples of antibody decay curve parameters} -\item{antigen_isos}{antigen isotypes to analyze (can subset \code{curve_params})} - -\item{ncol}{how many columns of subfigures to use in panel plot} - -\item{...}{ - Arguments passed on to \code{\link[=plot_curve_params_one_ab]{plot_curve_params_one_ab}} - \describe{ - \item{\code{verbose}}{verbose output} - \item{\code{xlim}}{range of x values to graph} - \item{\code{n_curves}}{how many curves to plot (see details).} - \item{\code{n_points}}{Number of points to interpolate along the x axis -(passed to \code{\link[ggplot2:geom_function]{ggplot2::geom_function()}})} - \item{\code{rows_to_graph}}{which rows of \code{curve_params} to plot -(overrides \code{n_curves}).} - \item{\code{alpha}}{(passed to \code{\link[ggplot2:geom_function]{ggplot2::geom_function()}}) -how transparent the curves should be: +\item{method}{a \link{character} string indicating whether to use \itemize{ -\item 0 = fully transparent (invisible) -\item 1 = fully opaque +\item \code{\link[=graph.curve.params]{graph.curve.params()}} (default) or +\item \code{\link[=graph_seroresponse_model_1]{graph_seroresponse_model_1()}} (previous default) +as the graphing method. }} - \item{\code{log_x}}{should the x-axis be on a logarithmic scale (\code{TRUE}) -or linear scale (\code{FALSE}, default)?} - \item{\code{log_y}}{should the Y-axis be on a logarithmic scale -(default, \code{TRUE}) or linear scale (\code{FALSE})?} - }} + +\item{...}{additional arguments passed to the sub-function +indicated by the \code{method} argument.} } \value{ a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} object } \description{ -graph antibody decay curves by antigen isotype +Graph antibody decay curves by antigen isotype } \details{ -\subsection{\code{rows_to_graph}}{ - -If you directly specify \code{rows_to_graph} when calling this function, -the row numbers are enumerated separately for each antigen isotype; -in other words, for the purposes of this argument, -row numbers start over at 1 for each antigen isotype. -There is currently no way to specify different row numbers for different antigen isotypes; -if you want to do that, -you will could call \code{\link[=plot_curve_params_one_ab]{plot_curve_params_one_ab()}} directly for each antigen isotype -and combine the resulting panels yourself. -Or you could subset \code{curve_params} manually, -before passing it to this function, -and set the \code{n_curves} argument to \code{Inf}. -} +Currently, the backend for this method is \code{\link[=graph.curve.params]{graph.curve.params()}}. +Previously, the backend for this method was \code{\link[=graph_seroresponse_model_1]{graph_seroresponse_model_1()}}. +That function is still available if preferred. } \examples{ \donttest{ @@ -69,10 +42,10 @@ library(ggplot2) library(magrittr) curve <- - serocalculator_example("example_curve_params.csv") \%>\% - read.csv() \%>\% - as_curve_params() \%>\% - filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) \%>\% + serocalculator_example("example_curve_params.csv") |> + read.csv() |> + as_sr_params() |> + filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) |> autoplot() curve diff --git a/man/autoplot.pop_data.Rd b/man/autoplot.pop_data.Rd index e6fa6407b..085b7fe35 100644 --- a/man/autoplot.pop_data.Rd +++ b/man/autoplot.pop_data.Rd @@ -32,11 +32,11 @@ library(ggplot2) library(magrittr) xs_data <- - serocalculator_example("example_pop_data.csv") \%>\% - read.csv() \%>\% + serocalculator_example("example_pop_data.csv") |> + read.csv() |> as_pop_data() -xs_data \%>\% autoplot(strata = "catchment", type = "density") -xs_data \%>\% autoplot(strata = "catchment", type = "age-scatter") +xs_data |> autoplot(strata = "catchment", type = "density") +xs_data |> autoplot(strata = "catchment", type = "age-scatter") } } diff --git a/man/autoplot.seroincidence.Rd b/man/autoplot.seroincidence.Rd index 4678f748a..dc6173bf5 100644 --- a/man/autoplot.seroincidence.Rd +++ b/man/autoplot.seroincidence.Rd @@ -7,9 +7,10 @@ \method{autoplot}{seroincidence}(object, log_x = FALSE, ...) } \arguments{ -\item{object}{a \code{seroincidence} object (from \code{\link[=est.incidence]{est.incidence()}})} +\item{object}{a \code{seroincidence} object (from \code{\link[=est_seroincidence]{est_seroincidence()}})} -\item{log_x}{should the x-axis be on a logarithmic scale (\code{TRUE}) or linear scale (\code{FALSE}, default)?} +\item{log_x}{should the x-axis be on a logarithmic scale (\code{TRUE}) +or linear scale (\code{FALSE}, default)?} \item{...}{unused} } @@ -34,9 +35,9 @@ curve <- noise <- example_noise_params_pk -est1 <- est.incidence( +est1 <- est_seroincidence( pop_data = xs_data, - curve_param = curve, + sr_param = curve, noise_param = noise, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), build_graph = TRUE diff --git a/man/autoplot.seroincidence.by.Rd b/man/autoplot.seroincidence.by.Rd index 0af7dbda1..a4540441a 100644 --- a/man/autoplot.seroincidence.by.Rd +++ b/man/autoplot.seroincidence.by.Rd @@ -7,18 +7,19 @@ \method{autoplot}{seroincidence.by}(object, ncol = min(3, length(object)), ...) } \arguments{ -\item{object}{a '"seroincidence.by"' object (from \code{\link[=est.incidence.by]{est.incidence.by()}})} +\item{object}{a '"seroincidence.by"' object (from \code{\link[=est_seroincidence_by]{est_seroincidence_by()}})} \item{ncol}{number of columns to use for panel of plots} \item{...}{ Arguments passed on to \code{\link[=autoplot.seroincidence]{autoplot.seroincidence}} \describe{ - \item{\code{log_x}}{should the x-axis be on a logarithmic scale (\code{TRUE}) or linear scale (\code{FALSE}, default)?} + \item{\code{log_x}}{should the x-axis be on a logarithmic scale (\code{TRUE}) +or linear scale (\code{FALSE}, default)?} }} } \value{ -an object of class \code{"ggarrange"}, which is a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} or a \code{\link[=list]{list()}} of \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}}s. +a \code{"ggarrange"} object: a single or \code{\link[=list]{list()}} of \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}}s } \description{ Plots log-likelihood curves by stratum, for \code{seroincidence.by} objects @@ -38,10 +39,10 @@ curve <- noise <- example_noise_params_pk -est2 <- est.incidence.by( +est2 <- est_seroincidence_by( strata = c("catchment"), pop_data = xs_data, - curve_params = curve, + sr_params = curve, curve_strata_varnames= NULL, noise_strata_varnames = NULL, noise_params = noise, diff --git a/man/autoplot.sim_results.Rd b/man/autoplot.sim_results.Rd new file mode 100644 index 000000000..813227fb0 --- /dev/null +++ b/man/autoplot.sim_results.Rd @@ -0,0 +1,83 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/autoplot.sim_results.R +\name{autoplot.sim_results} +\alias{autoplot.sim_results} +\title{Plot simulation results +\code{autoplot()} method for \code{sim_results} objects} +\usage{ +\method{autoplot}{sim_results}(object, statistic = "Empirical_SE", ...) +} +\arguments{ +\item{object}{a \code{sim_results} object (from \code{\link[=analyze_sims]{analyze_sims()}})} + +\item{statistic}{which column of \code{object} should be the y-axis?} + +\item{...}{unused} +} +\value{ +a \link[ggplot2:ggplot]{ggplot2::ggplot} +} +\description{ +Plot simulation results +\code{autoplot()} method for \code{sim_results} objects +} +\examples{ +\donttest{ +dmcmc <- typhoid_curves_nostrat_100 + +n_cores <- 2 + +nclus <- 20 +# cross-sectional sample size +nrep <- c(50, 200) + +# incidence rate in e +lambdas <- c(.05, .8) +lifespan <- c(0, 10) +antibodies <- c("HlyE_IgA", "HlyE_IgG") +dlims <- rbind( +"HlyE_IgA" = c(min = 0, max = 0.5), +"HlyE_IgG" = c(min = 0, max = 0.5) +) +sim_df <- +sim_pop_data_multi( +n_cores = n_cores, +lambdas = lambdas, +nclus = nclus, +sample_sizes = nrep, +age_range = lifespan, +antigen_isos = antibodies, +renew_params = FALSE, +add_noise = TRUE, +curve_params = dmcmc, +noise_limits = dlims, +format = "long" +) +cond <- tibble::tibble( +antigen_iso = c("HlyE_IgG", "HlyE_IgA"), +nu = c(0.5, 0.5), # Biologic noise (nu) +eps = c(0, 0), # M noise (eps) +y.low = c(1, 1), # low cutoff (llod) +y.high = c(5e6, 5e6) +) +ests <- +est_seroincidence_by( +pop_data = sim_df, +sr_params = dmcmc, +noise_params = cond, +num_cores = n_cores, +strata = c("lambda.sim", "sample_size", "cluster"), +curve_strata_varnames = NULL, +noise_strata_varnames = NULL, +verbose = FALSE, +build_graph = FALSE, # slows down the function substantially +antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +ests |> +summary() |> +analyze_sims() |> +autoplot() + +} +} diff --git a/man/autoplot.summary.seroincidence.by.Rd b/man/autoplot.summary.seroincidence.by.Rd index c85562746..21d0e69c0 100644 --- a/man/autoplot.summary.seroincidence.by.Rd +++ b/man/autoplot.summary.seroincidence.by.Rd @@ -4,33 +4,40 @@ \alias{autoplot.summary.seroincidence.by} \title{Plot method for \code{summary.seroincidence.by} objects} \usage{ -\method{autoplot}{summary.seroincidence.by}( - object, - xvar, - alpha = 0.7, - shape = 1, - dodge_width = 0.001, - CIs = FALSE, - ... -) +\method{autoplot}{summary.seroincidence.by}(object, type, ...) } \arguments{ \item{object}{a \code{summary.seroincidence.by} object (generated by applying the \code{summary()} -method to the output of \code{\link[=est.incidence.by]{est.incidence.by()}}).} +method to the output of \code{\link[=est_seroincidence_by]{est_seroincidence_by()}}).} -\item{xvar}{the name of a stratifying variable in \code{object}} +\item{type}{\link{character} string indicating which type of plot to generate. +The implemented options are: +\itemize{ +\item \code{"scatter"}: calls \code{\link[=strat_ests_scatterplot]{strat_ests_scatterplot()}} to generate a scatterplot +\item \code{"bar"}: calls \code{strat_ests_barplot()} to generate a barplot +}} -\item{alpha}{transparency for the points in the graph +\item{...}{ + Arguments passed on to \code{\link[=strat_ests_scatterplot]{strat_ests_scatterplot}}, \code{\link[=strat_ests_barplot]{strat_ests_barplot}} + \describe{ + \item{\code{xvar}}{the name of a stratifying variable in \code{object}} + \item{\code{alpha}}{transparency for the points in the graph (1 = no transparency, 0 = fully transparent)} - -\item{shape}{shape argument for \code{geom_point()}} - -\item{dodge_width}{width for jitter} - -\item{CIs}{\link{logical}, if \code{TRUE}, add CI error bars} - -\item{...}{unused} + \item{\code{shape}}{shape argument for \code{geom_point()}} + \item{\code{dodge_width}}{width for jitter} + \item{\code{CIs}}{\link{logical}, if \code{TRUE}, add CI error bars} + \item{\code{color_var}}{\link{character} which variable in \code{object} to use +to determine point color} + \item{\code{group_var}}{\link{character} which variable in \code{object} to use +to connect points with lines (\code{NULL} for no lines)} + \item{\code{yvar}}{the name of a stratifying variable in \code{object}.} + \item{\code{title}}{a title for the final plot.} + \item{\code{xlab}}{a label for the x-axis of the final plot.} + \item{\code{ylab}}{a label for the y-axis of the final plot.} + \item{\code{fill_lab}}{fill label.} + \item{\code{color_palette}}{optional color palette for bar color.} + }} } \value{ a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} object @@ -53,10 +60,10 @@ curve <- noise <- example_noise_params_pk -est2 <- est.incidence.by( - strata = c("catchment"), +est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), pop_data = xs_data, - curve_params = curve, + sr_params = curve, noise_params = noise, curve_strata_varnames= NULL, noise_strata_varnames = NULL, @@ -66,6 +73,17 @@ est2 <- est.incidence.by( est2sum <- summary(est2) -autoplot(est2sum, "catchment") +est2sum |> autoplot( + type ="scatter", + xvar = "ageCat", + color_var = "catchment", + CIs = TRUE, + group_var = "catchment") + +est2sum |> autoplot( + type = "bar", + yvar = "ageCat", + color_var = "catchment", + CIs = TRUE) } diff --git a/man/check_pop_data.Rd b/man/check_pop_data.Rd index 9cbdf0083..4c223493f 100644 --- a/man/check_pop_data.Rd +++ b/man/check_pop_data.Rd @@ -21,8 +21,8 @@ Check the formatting of a cross-sectional antibody survey dataset. library(magrittr) xs_data <- - serocalculator_example("example_pop_data.csv") \%>\% - read.csv() \%>\% + serocalculator_example("example_pop_data.csv") |> + read.csv() |> as_pop_data() check_pop_data(xs_data, verbose = TRUE) diff --git a/man/compare_seroincidence.Rd b/man/compare_seroincidence.Rd new file mode 100644 index 000000000..6fa95d9b5 --- /dev/null +++ b/man/compare_seroincidence.Rd @@ -0,0 +1,76 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/compare_seroincidence.R +\name{compare_seroincidence} +\alias{compare_seroincidence} +\alias{compare_seroincidence.seroincidence} +\alias{compare_seroincidence.seroincidence.by} +\title{Compare seroincidence rates between two groups} +\usage{ +compare_seroincidence(x, y = NULL, coverage = 0.95, verbose = FALSE, ...) + +\method{compare_seroincidence}{seroincidence}(x, y = NULL, coverage = 0.95, verbose = FALSE, ...) + +\method{compare_seroincidence}{seroincidence.by}(x, y = NULL, coverage = 0.95, verbose = FALSE, ...) +} +\arguments{ +\item{x}{A \code{"seroincidence"} object from \code{\link[=est_seroincidence]{est_seroincidence()}} or +a \code{"seroincidence.by"} object from \code{\link[=est_seroincidence_by]{est_seroincidence_by()}}} + +\item{y}{A \code{"seroincidence"} object from \code{\link[=est_seroincidence]{est_seroincidence()}} +(optional if \code{x} is a \code{"seroincidence.by"} object)} + +\item{coverage}{Desired confidence interval coverage probability +(default = 0.95)} + +\item{verbose}{Logical indicating whether to print verbose messages +(default = FALSE)} + +\item{...}{Additional arguments (currently unused)} +} +\value{ +\itemize{ +\item When comparing two \code{"seroincidence"} objects: An object of class +\code{"htest"} containing the test statistic, p-value, confidence interval, +and estimates. +\item When applied to a \code{"seroincidence.by"} object: A \code{\link[tibble:tibble]{tibble::tibble()}} +with columns for each pair of strata, the difference in incidence rates, +standard error, z-statistic, p-value, and confidence interval bounds. +} +} +\description{ +Perform a two-sample z-test to compare seroincidence rates between +two groups. Since we use maximum likelihood estimation (MLE) for each +seroincidence estimate and estimates from different strata or data sets +are uncorrelated, we can use a simple two-sample z-test using the +Gaussian distribution. The standard error for the difference is computed +by adding the estimated variances and taking the square root. +} +\details{ +When comparing two single \code{"seroincidence"} objects, this function performs a +two-sample z-test and returns results in the standard \code{htest} format. + +When applied to a \code{"seroincidence.by"} object (stratified estimates), +the function compares all pairs of strata and returns a nicely formatted +table with point estimates for the difference in seroincidence, p-values, +and confidence intervals. + +The test statistic is computed as: +\deqn{z = \frac{\lambda_1 - \lambda_2}{\sqrt{SE_1^2 + SE_2^2}}} + +where \eqn{\lambda_1} and \eqn{\lambda_2} are the estimated incidence rates, +and \eqn{SE_1} and \eqn{SE_2} are their standard errors. +} +\section{Methods (by class)}{ +\itemize{ +\item \code{compare_seroincidence(seroincidence)}: Compare two single seroincidence +estimates + +\item \code{compare_seroincidence(seroincidence.by)}: Compare all pairs of stratified +seroincidence estimates + +}} +\examples{ +\dontrun{ +# See inst/examples/exm-compare_seroincidence.R for complete examples +} +} diff --git a/man/count_strata.Rd b/man/count_strata.Rd new file mode 100644 index 000000000..688b09b75 --- /dev/null +++ b/man/count_strata.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/count_strata.R +\name{count_strata} +\alias{count_strata} +\title{Count observations by stratum} +\usage{ +count_strata( + data, + strata_varnames, + biomarker_names_var = get_biomarker_names_var(data) +) +} +\arguments{ +\item{data}{a \code{"pop_data"} object (e.g., from \code{\link[=as_pop_data]{as_pop_data()}})} + +\item{strata_varnames}{a \link{vector} of \link{character} strings matching +colnames to stratify on from \code{data}} + +\item{biomarker_names_var}{a \link{character} string indicating the column +of \code{data} indicating which biomarker is being measured} +} +\value{ +a \link[tibble:tbl_df-class]{tibble::tbl_df} counting observations by stratum +} +\description{ +Count observations by stratum +} +\examples{ +sees_pop_data_pk_100 |> count_strata(strata_varnames = "catchment") +} +\keyword{internal} diff --git a/man/est.incidence.Rd b/man/est.incidence.Rd index 848353aa9..2500c0993 100644 --- a/man/est.incidence.Rd +++ b/man/est.incidence.Rd @@ -1,120 +1,15 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/est.incidence.R +% Please edit documentation in R/est_seroincidence.R \name{est.incidence} \alias{est.incidence} -\title{Find the maximum likelihood estimate of the incidence rate parameter} +\title{Estimate Seroincidence} \usage{ -est.incidence( - pop_data, - curve_params, - noise_params, - antigen_isos = get_biomarker_names(pop_data), - lambda_start = 0.1, - stepmin = 1e-08, - stepmax = 3, - verbose = FALSE, - build_graph = FALSE, - print_graph = build_graph & verbose, - ... -) -} -\arguments{ -\item{pop_data}{a \link{data.frame} with cross-sectional serology data per antibody and age, and additional columns} - -\item{curve_params}{a \code{\link[=data.frame]{data.frame()}} containing MCMC samples of parameters -from the Bayesian posterior distribution of a longitudinal decay curve model. -The parameter columns must be named: -\itemize{ -\item \code{antigen_iso}: a \code{\link[=character]{character()}} vector indicating antigen-isotype -combinations -\item \code{iter}: an \code{\link[=integer]{integer()}} vector indicating MCMC sampling iterations -\item \code{y0}: baseline antibody level at $t=0$ ($y(t=0)$) -\item \code{y1}: antibody peak level (ELISA units) -\item \code{t1}: duration of infection -\item \code{alpha}: antibody decay rate -(1/days for the current longitudinal parameter sets) -\item \code{r}: shape factor of antibody decay -}} - -\item{noise_params}{a \code{\link[=data.frame]{data.frame()}} (or \code{\link[tibble:tibble]{tibble::tibble()}}) -containing the following variables, -specifying noise parameters for each antigen isotype: -\itemize{ -\item \code{antigen_iso}: antigen isotype whose noise parameters are being specified -on each row -\item \code{nu}: biological noise -\item \code{eps}: measurement noise -\item \code{y.low}: lower limit of detection for the current antigen isotype -\item \code{y.high}: upper limit of detection for the current antigen isotype -}} - -\item{antigen_isos}{Character vector with one or more antibody names. Values must match \code{pop_data}} - -\item{lambda_start}{starting guess for incidence rate, in years/event.} - -\item{stepmin}{A positive scalar providing the minimum allowable relative step length.} - -\item{stepmax}{a positive scalar which gives the maximum allowable - scaled step length. \code{stepmax} is used to prevent steps which - would cause the optimization function to overflow, to prevent the - algorithm from leaving the area of interest in parameter space, or to - detect divergence in the algorithm. \code{stepmax} would be chosen - small enough to prevent the first two of these occurrences, but should - be larger than any anticipated reasonable step.} - -\item{verbose}{logical: if TRUE, print verbose log information to console} - -\item{build_graph}{whether to graph the log-likelihood function across a range of incidence rates (lambda values)} - -\item{print_graph}{whether to display the log-likelihood curve graph in the course of running \code{est.incidence()}} - -\item{...}{ - Arguments passed on to \code{\link[stats:nlm]{stats::nlm}} - \describe{ - \item{\code{typsize}}{an estimate of the size of each parameter - at the minimum.} - \item{\code{fscale}}{an estimate of the size of \code{f} at the minimum.} - \item{\code{ndigit}}{the number of significant digits in the function \code{f}.} - \item{\code{gradtol}}{a positive scalar giving the tolerance at which the - scaled gradient is considered close enough to zero to - terminate the algorithm. The scaled gradient is a - measure of the relative change in \code{f} in each direction - \code{p[i]} divided by the relative change in \code{p[i]}.} - \item{\code{iterlim}}{a positive integer specifying the maximum number of - iterations to be performed before the program is terminated.} - \item{\code{check.analyticals}}{a logical scalar specifying whether the - analytic gradients and Hessians, if they are supplied, should be - checked against numerical derivatives at the initial parameter - values. This can help detect incorrectly formulated gradients or - Hessians.} - }} -} -\value{ -a \code{"seroincidence"} object, which is a \code{\link[stats:nlm]{stats::nlm()}} fit object with extra meta-data attributes \code{lambda_start}, \code{antigen_isos}, and \code{ll_graph} +est.incidence(...) } \description{ -This function models seroincidence using maximum likelihood estimation; that is, it finds the value of the seroincidence parameter which maximizes the likelihood (i.e., joint probability) of the data. -} -\examples{ - -library(dplyr) - -xs_data <- - sees_pop_data_pk_100 - -curve <- - typhoid_curves_nostrat_100 \%>\% - filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) - -noise <- - example_noise_params_pk - -est1 <- est.incidence( - pop_data = xs_data, - curve_params = curve, - noise_params = noise, - antigen_isos = c("HlyE_IgG", "HlyE_IgA"), -) +\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} -summary(est1) +\code{est.incidence()} was renamed to \code{\link[=est_seroincidence]{est_seroincidence()}} to create a more +consistent API. } +\keyword{internal} diff --git a/man/est.incidence.by.Rd b/man/est.incidence.by.Rd index 94bc8b415..cc8ead132 100644 --- a/man/est.incidence.by.Rd +++ b/man/est.incidence.by.Rd @@ -1,158 +1,15 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/est.incidence.by.R +% Please edit documentation in R/est_seroincidence_by.R \name{est.incidence.by} \alias{est.incidence.by} \title{Estimate Seroincidence} \usage{ -est.incidence.by( - pop_data, - curve_params, - noise_params, - strata, - curve_strata_varnames = strata, - noise_strata_varnames = strata, - antigen_isos = pop_data \%>\% pull("antigen_iso") \%>\% unique(), - lambda_start = 0.1, - build_graph = FALSE, - num_cores = 1L, - verbose = FALSE, - print_graph = FALSE, - ... -) -} -\arguments{ -\item{pop_data}{a \link{data.frame} with cross-sectional serology data per -antibody and age, and additional columns corresponding to -each element of the \code{strata} input} - -\item{curve_params}{a \code{\link[=data.frame]{data.frame()}} containing MCMC samples of parameters -from the Bayesian posterior distribution of a longitudinal decay curve model. -The parameter columns must be named: -\itemize{ -\item \code{antigen_iso}: a \code{\link[=character]{character()}} vector indicating antigen-isotype -combinations -\item \code{iter}: an \code{\link[=integer]{integer()}} vector indicating MCMC sampling iterations -\item \code{y0}: baseline antibody level at $t=0$ ($y(t=0)$) -\item \code{y1}: antibody peak level (ELISA units) -\item \code{t1}: duration of infection -\item \code{alpha}: antibody decay rate -(1/days for the current longitudinal parameter sets) -\item \code{r}: shape factor of antibody decay -}} - -\item{noise_params}{a \code{\link[=data.frame]{data.frame()}} (or \code{\link[tibble:tibble]{tibble::tibble()}}) -containing the following variables, -specifying noise parameters for each antigen isotype: -\itemize{ -\item \code{antigen_iso}: antigen isotype whose noise parameters are being specified -on each row -\item \code{nu}: biological noise -\item \code{eps}: measurement noise -\item \code{y.low}: lower limit of detection for the current antigen isotype -\item \code{y.high}: upper limit of detection for the current antigen isotype -}} - -\item{strata}{a \link{character} vector of stratum-defining variables. -Values must be variable names in \code{pop_data}.} - -\item{curve_strata_varnames}{A subset of \code{strata}. -Values must be variable names in \code{curve_params}. Default = "".} - -\item{noise_strata_varnames}{A subset of \code{strata}. -Values must be variable names in \code{noise_params}. Default = "".} - -\item{antigen_isos}{Character vector with one or more antibody names. Values must match \code{pop_data}} - -\item{lambda_start}{starting guess for incidence rate, in years/event.} - -\item{build_graph}{whether to graph the log-likelihood function across a range of incidence rates (lambda values)} - -\item{num_cores}{Number of processor cores to use for -calculations when computing by strata. If set to -more than 1 and package \pkg{parallel} is available, -then the computations are executed in parallel. Default = 1L.} - -\item{verbose}{logical: if TRUE, print verbose log information to console} - -\item{print_graph}{whether to display the log-likelihood curve graph in the course of running \code{est.incidence()}} - -\item{...}{ - Arguments passed on to \code{\link[=est.incidence]{est.incidence}}, \code{\link[stats:nlm]{stats::nlm}} - \describe{ - \item{\code{stepmin}}{A positive scalar providing the minimum allowable relative step length.} - \item{\code{stepmax}}{a positive scalar which gives the maximum allowable - scaled step length. \code{stepmax} is used to prevent steps which - would cause the optimization function to overflow, to prevent the - algorithm from leaving the area of interest in parameter space, or to - detect divergence in the algorithm. \code{stepmax} would be chosen - small enough to prevent the first two of these occurrences, but should - be larger than any anticipated reasonable step.} - \item{\code{typsize}}{an estimate of the size of each parameter - at the minimum.} - \item{\code{fscale}}{an estimate of the size of \code{f} at the minimum.} - \item{\code{ndigit}}{the number of significant digits in the function \code{f}.} - \item{\code{gradtol}}{a positive scalar giving the tolerance at which the - scaled gradient is considered close enough to zero to - terminate the algorithm. The scaled gradient is a - measure of the relative change in \code{f} in each direction - \code{p[i]} divided by the relative change in \code{p[i]}.} - \item{\code{iterlim}}{a positive integer specifying the maximum number of - iterations to be performed before the program is terminated.} - \item{\code{check.analyticals}}{a logical scalar specifying whether the - analytic gradients and Hessians, if they are supplied, should be - checked against numerical derivatives at the initial parameter - values. This can help detect incorrectly formulated gradients or - Hessians.} - }} -} -\value{ -\itemize{ -\item if \code{strata} has meaningful inputs: -An object of class \code{"seroincidence.by"}; i.e., a list of -\code{"seroincidence"} objects from \code{\link[=est.incidence]{est.incidence()}}, one for each stratum, -with some meta-data attributes. -\item if \code{strata} is missing, \code{NULL}, \code{NA}, or \code{""}: -An object of class \code{"seroincidence"}. -} +est.incidence.by(...) } \description{ -Function to estimate seroincidences based on cross-sectional -serology data and longitudinal -response model. -} -\details{ -If \code{strata} is left empty, a warning will be produced, -recommending that you use \code{\link[=est.incidence]{est.incidence()}} for unstratified analyses, -and then the data will be passed to \code{\link[=est.incidence]{est.incidence()}}. -If for some reason you want to use \code{\link[=est.incidence.by]{est.incidence.by()}} -with no strata instead of calling \code{\link[=est.incidence]{est.incidence()}}, -you may use \code{NA}, \code{NULL}, or \code{""} as the \code{strata} -argument to avoid that warning. -} -\examples{ - -library(dplyr) - -xs_data <- - sees_pop_data_pk_100 - -curve <- - typhoid_curves_nostrat_100 \%>\% - filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) - -noise <- - example_noise_params_pk - -est2 <- est.incidence.by( - strata = "catchment", - pop_data = xs_data, - curve_params = curve, - noise_params = noise, - antigen_isos = c("HlyE_IgG", "HlyE_IgA"), - # num_cores = 8 # Allow for parallel processing to decrease run time - iterlim = 5 # limit iterations for the purpose of this example -) -print(est2) -summary(est2) +\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} +\code{est.incidence.by()} was renamed to \code{\link[=est_seroincidence_by]{est_seroincidence_by()}} to create a more +consistent API. } +\keyword{internal} diff --git a/man/est_seroincidence.Rd b/man/est_seroincidence.Rd new file mode 100644 index 000000000..25cc2bbfb --- /dev/null +++ b/man/est_seroincidence.Rd @@ -0,0 +1,128 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/est_seroincidence.R +\name{est_seroincidence} +\alias{est_seroincidence} +\title{Find the maximum likelihood estimate of the incidence rate parameter} +\usage{ +est_seroincidence( + pop_data, + sr_params, + noise_params, + antigen_isos = get_biomarker_names(pop_data), + lambda_start = 0.1, + stepmin = 1e-08, + stepmax = 3, + verbose = FALSE, + build_graph = FALSE, + print_graph = build_graph & verbose, + ... +) +} +\arguments{ +\item{pop_data}{a \link{data.frame} with cross-sectional serology data per +antibody and age, and additional columns} + +\item{sr_params}{a \code{\link[=data.frame]{data.frame()}} containing MCMC samples of parameters +from the Bayesian posterior distribution of a longitudinal decay curve model. +The parameter columns must be named: +\itemize{ +\item \code{antigen_iso}: a \code{\link[=character]{character()}} vector indicating antigen-isotype +combinations +\item \code{iter}: an \code{\link[=integer]{integer()}} vector indicating MCMC sampling iterations +\item \code{y0}: baseline antibody level at $t=0$ ($y(t=0)$) +\item \code{y1}: antibody peak level (ELISA units) +\item \code{t1}: duration of infection +\item \code{alpha}: antibody decay rate +(1/days for the current longitudinal parameter sets) +\item \code{r}: shape factor of antibody decay +}} + +\item{noise_params}{a \code{\link[=data.frame]{data.frame()}} (or \code{\link[tibble:tibble]{tibble::tibble()}}) +containing the following variables, +specifying noise parameters for each antigen isotype: +\itemize{ +\item \code{antigen_iso}: antigen isotype whose noise parameters are being specified +on each row +\item \code{nu}: biological noise +\item \code{eps}: measurement noise +\item \code{y.low}: lower limit of detection for the current antigen isotype +\item \code{y.high}: upper limit of detection for the current antigen isotype +}} + +\item{antigen_isos}{Character vector with one or more antibody names. +Must match \code{pop_data}} + +\item{lambda_start}{starting guess for incidence rate, in events/year.} + +\item{stepmin}{A positive scalar providing the minimum allowable +relative step length.} + +\item{stepmax}{a positive scalar which gives the maximum allowable + scaled step length. \code{stepmax} is used to prevent steps which + would cause the optimization function to overflow, to prevent the + algorithm from leaving the area of interest in parameter space, or to + detect divergence in the algorithm. \code{stepmax} would be chosen + small enough to prevent the first two of these occurrences, but should + be larger than any anticipated reasonable step.} + +\item{verbose}{logical: if TRUE, print verbose log information to console} + +\item{build_graph}{whether to graph the log-likelihood function across +a range of incidence rates (lambda values)} + +\item{print_graph}{whether to display the log-likelihood curve graph +in the course of running \code{est_seroincidence()}} + +\item{...}{ + Arguments passed on to \code{\link[stats:nlm]{stats::nlm}} + \describe{ + \item{\code{typsize}}{an estimate of the size of each parameter + at the minimum.} + \item{\code{fscale}}{an estimate of the size of \code{f} at the minimum.} + \item{\code{ndigit}}{the number of significant digits in the function \code{f}.} + \item{\code{gradtol}}{a positive scalar giving the tolerance at which the + scaled gradient is considered close enough to zero to + terminate the algorithm. The scaled gradient is a + measure of the relative change in \code{f} in each direction + \code{p[i]} divided by the relative change in \code{p[i]}.} + \item{\code{iterlim}}{a positive integer specifying the maximum number of + iterations to be performed before the program is terminated.} + \item{\code{check.analyticals}}{a logical scalar specifying whether the + analytic gradients and Hessians, if they are supplied, should be + checked against numerical derivatives at the initial parameter + values. This can help detect incorrectly formulated gradients or + Hessians.} + }} +} +\value{ +a \code{"seroincidence"} object, which is a \code{\link[stats:nlm]{stats::nlm()}} fit object +with extra metadata attributes \code{lambda_start}, \code{antigen_isos}, and \code{ll_graph} +} +\description{ +This function models seroincidence using maximum likelihood estimation; +that is, it finds the value of the seroincidence parameter which +maximizes the likelihood (i.e., joint probability) of the data. +} +\examples{ + +library(dplyr) + +xs_data <- + sees_pop_data_pk_100 + +sr_curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) + +noise <- + example_noise_params_pk + +est1 <- est_seroincidence( + pop_data = xs_data, + sr_params = sr_curve, + noise_params = noise, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), +) + +summary(est1) +} diff --git a/man/est_seroincidence_by.Rd b/man/est_seroincidence_by.Rd new file mode 100644 index 000000000..2eba32086 --- /dev/null +++ b/man/est_seroincidence_by.Rd @@ -0,0 +1,162 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/est_seroincidence_by.R +\name{est_seroincidence_by} +\alias{est_seroincidence_by} +\title{Estimate Seroincidence} +\usage{ +est_seroincidence_by( + pop_data, + sr_params, + noise_params, + strata, + curve_strata_varnames = strata, + noise_strata_varnames = strata, + antigen_isos = unique(pull(pop_data, "antigen_iso")), + lambda_start = 0.1, + build_graph = FALSE, + num_cores = 1L, + verbose = FALSE, + print_graph = FALSE, + ... +) +} +\arguments{ +\item{pop_data}{a \link{data.frame} with cross-sectional serology data per +antibody and age, and additional columns corresponding to +each element of the \code{strata} input} + +\item{sr_params}{a \code{\link[=data.frame]{data.frame()}} containing MCMC samples of parameters +from the Bayesian posterior distribution of a longitudinal decay curve model. +The parameter columns must be named: +\itemize{ +\item \code{antigen_iso}: a \code{\link[=character]{character()}} vector indicating antigen-isotype +combinations +\item \code{iter}: an \code{\link[=integer]{integer()}} vector indicating MCMC sampling iterations +\item \code{y0}: baseline antibody level at $t=0$ ($y(t=0)$) +\item \code{y1}: antibody peak level (ELISA units) +\item \code{t1}: duration of infection +\item \code{alpha}: antibody decay rate +(1/days for the current longitudinal parameter sets) +\item \code{r}: shape factor of antibody decay +}} + +\item{noise_params}{a \code{\link[=data.frame]{data.frame()}} (or \code{\link[tibble:tibble]{tibble::tibble()}}) +containing the following variables, +specifying noise parameters for each antigen isotype: +\itemize{ +\item \code{antigen_iso}: antigen isotype whose noise parameters are being specified +on each row +\item \code{nu}: biological noise +\item \code{eps}: measurement noise +\item \code{y.low}: lower limit of detection for the current antigen isotype +\item \code{y.high}: upper limit of detection for the current antigen isotype +}} + +\item{strata}{a \link{character} vector of stratum-defining variables. +Values must be variable names in \code{pop_data}.} + +\item{curve_strata_varnames}{A subset of \code{strata}. +Values must be variable names in \code{curve_params}. Default = "".} + +\item{noise_strata_varnames}{A subset of \code{strata}. +Values must be variable names in \code{noise_params}. Default = "".} + +\item{antigen_isos}{Character vector with one or more antibody names. +Must match \code{pop_data}} + +\item{lambda_start}{starting guess for incidence rate, in events/year.} + +\item{build_graph}{whether to graph the log-likelihood function across +a range of incidence rates (lambda values)} + +\item{num_cores}{Number of processor cores to use for +calculations when computing by strata. If set to +more than 1 and package \pkg{parallel} is available, +then the computations are executed in parallel. Default = 1L.} + +\item{verbose}{logical: if TRUE, print verbose log information to console} + +\item{print_graph}{whether to display the log-likelihood curve graph +in the course of running \code{est_seroincidence()}} + +\item{...}{ + Arguments passed on to \code{\link[=est_seroincidence]{est_seroincidence}}, \code{\link[stats:nlm]{stats::nlm}} + \describe{ + \item{\code{stepmin}}{A positive scalar providing the minimum allowable +relative step length.} + \item{\code{stepmax}}{a positive scalar which gives the maximum allowable + scaled step length. \code{stepmax} is used to prevent steps which + would cause the optimization function to overflow, to prevent the + algorithm from leaving the area of interest in parameter space, or to + detect divergence in the algorithm. \code{stepmax} would be chosen + small enough to prevent the first two of these occurrences, but should + be larger than any anticipated reasonable step.} + \item{\code{typsize}}{an estimate of the size of each parameter + at the minimum.} + \item{\code{fscale}}{an estimate of the size of \code{f} at the minimum.} + \item{\code{ndigit}}{the number of significant digits in the function \code{f}.} + \item{\code{gradtol}}{a positive scalar giving the tolerance at which the + scaled gradient is considered close enough to zero to + terminate the algorithm. The scaled gradient is a + measure of the relative change in \code{f} in each direction + \code{p[i]} divided by the relative change in \code{p[i]}.} + \item{\code{iterlim}}{a positive integer specifying the maximum number of + iterations to be performed before the program is terminated.} + \item{\code{check.analyticals}}{a logical scalar specifying whether the + analytic gradients and Hessians, if they are supplied, should be + checked against numerical derivatives at the initial parameter + values. This can help detect incorrectly formulated gradients or + Hessians.} + }} +} +\value{ +\itemize{ +\item if \code{strata} has meaningful inputs: +An object of class \code{"seroincidence.by"}; i.e., a list of +\code{"seroincidence"} objects from \code{\link[=est_seroincidence]{est_seroincidence()}}, one for each stratum, +with some meta-data attributes. +\item if \code{strata} is missing, \code{NULL}, \code{NA}, or \code{""}: +An object of class \code{"seroincidence"}. +} +} +\description{ +Function to estimate seroincidences based on cross-sectional +serology data and longitudinal +response model. +} +\details{ +If \code{strata} is left empty, a warning will be produced, +recommending that you use \code{\link[=est_seroincidence]{est_seroincidence()}} for unstratified analyses, +and then the data will be passed to \code{\link[=est_seroincidence]{est_seroincidence()}}. +If for some reason you want to use \code{\link[=est_seroincidence_by]{est_seroincidence_by()}} +with no strata instead of calling \code{\link[=est_seroincidence]{est_seroincidence()}}, +you may use \code{NA}, \code{NULL}, or \code{""} as the \code{strata} +argument to avoid that warning. +} +\examples{ + +library(dplyr) + +xs_data <- + sees_pop_data_pk_100 + +curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) + +noise <- + example_noise_params_pk + +est2 <- est_seroincidence_by( + strata = "catchment", + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + # num_cores = 8 # Allow for parallel processing to decrease run time + iterlim = 5 # limit iterations for the purpose of this example +) +print(est2) +summary(est2) + +} diff --git a/man/example_noise_params_pk.Rd b/man/example_noise_params_pk.Rd index dc98597e0..f3af82d80 100644 --- a/man/example_noise_params_pk.Rd +++ b/man/example_noise_params_pk.Rd @@ -7,7 +7,7 @@ \format{ \subsection{\code{example_noise_params_pk}}{ -A \code{curve_params} object (from \code{\link[=as_curve_params]{as_curve_params()}}) with 4 rows and 7 columns: +A \code{curve_params} object (from \code{\link[=as_sr_params]{as_sr_params()}}) with 4 rows and 7 columns: \describe{ \item{antigen_iso}{which antigen and isotype are being measured (data is in long format)} diff --git a/man/example_noise_params_sees.Rd b/man/example_noise_params_sees.Rd index 0f5b9b289..8bd60a2d4 100644 --- a/man/example_noise_params_sees.Rd +++ b/man/example_noise_params_sees.Rd @@ -7,7 +7,7 @@ \format{ \subsection{\code{example_noise_params_pk}}{ -A \code{curve_params} object (from \code{\link[=as_curve_params]{as_curve_params()}}) with 4 rows and 7 columns: +A \code{curve_params} object (from \code{\link[=as_sr_params]{as_sr_params()}}) with 4 rows and 7 columns: \describe{ \item{antigen_iso}{which antigen and isotype are being measured (data is in long format)} diff --git a/man/expect_snapshot_data.Rd b/man/expect_snapshot_data.Rd new file mode 100644 index 000000000..7e86c9562 --- /dev/null +++ b/man/expect_snapshot_data.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/expect_snapshot_data.R +\name{expect_snapshot_data} +\alias{expect_snapshot_data} +\title{Snapshot testing for \link{data.frame}s} +\usage{ +expect_snapshot_data(x, name, digits = 6) +} +\arguments{ +\item{x}{a \link{data.frame} to snapshot} + +\item{name}{\link{character} snapshot name} + +\item{digits}{\link{integer} passed to \code{\link[=signif]{signif()}} for numeric variables} +} +\value{ +\link{NULL} (from \code{\link[testthat:expect_snapshot_file]{testthat::expect_snapshot_file()}}) +} +\description{ +copied from \url{https://github.com/bcgov/ssdtools} +with permission (\url{https://github.com/bcgov/ssdtools/issues/379}) +} +\examples{ +\dontrun{ +expect_snapshot_data(iris, name = "iris") +} +} +\keyword{internal} diff --git a/man/graph.curve.params.Rd b/man/graph.curve.params.Rd index 7bd3f137b..7c0f55578 100644 --- a/man/graph.curve.params.Rd +++ b/man/graph.curve.params.Rd @@ -2,47 +2,101 @@ % Please edit documentation in R/graph.curve.params.R \name{graph.curve.params} \alias{graph.curve.params} -\title{Graph estimated antibody decay curve} +\title{Graph estimated antibody decay curves} \usage{ graph.curve.params( - curve_params, - antigen_isos = unique(curve_params$antigen_iso), + object, + antigen_isos = unique(object$antigen_iso), verbose = FALSE, - show_quantiles = TRUE, - show_all_curves = FALSE, - alpha_samples = 0.3 + quantiles = c(0.1, 0.5, 0.9), + alpha_samples = 0.3, + chain_color = TRUE, + log_x = FALSE, + log_y = TRUE, + n_curves = 100, + iters_to_graph = head(unique(object$iter), n_curves), + ... ) } \arguments{ -\item{curve_params}{a \code{\link[=data.frame]{data.frame()}} containing MCMC samples of antibody decay curve parameters} +\item{object}{a \code{\link[=data.frame]{data.frame()}} containing MCMC samples of antibody decay curve parameters} -\item{antigen_isos}{antigen isotypes} +\item{antigen_isos}{antigen isotypes to analyze +(can subset \code{object})} \item{verbose}{verbose output} -\item{show_quantiles}{whether to show point-wise (over time) quantiles} - -\item{show_all_curves}{whether to show individual curves under quantiles} +\item{quantiles}{Optional \link{numeric} \link{vector} of point-wise (over time) +quantiles to plot (e.g., 10\%, 50\%, and 90\% = \code{c(0.1, 0.5, 0.9)}). +If \code{NULL}, no quantile lines are shown.} \item{alpha_samples}{\code{alpha} parameter passed to \link[ggplot2:geom_path]{ggplot2::geom_line} -(has no effect if \code{show_all_curves = FALSE})} +(has no effect if \code{iters_to_graph} is empty)} + +\item{chain_color}{\link{logical}: if \link{TRUE} (default), MCMC chain lines +are colored by chain. +If \link{FALSE}, all MCMC chain lines are black.} + +\item{log_x}{should the x-axis be on a logarithmic scale (\code{TRUE}) +or linear scale (\code{FALSE}, default)?} + +\item{log_y}{should the Y-axis be on a logarithmic scale +(default, \code{TRUE}) or linear scale (\code{FALSE})?} + +\item{n_curves}{how many curves to plot (see details).} + +\item{iters_to_graph}{which MCMC iterations in \code{curve_params} to plot +(overrides \code{n_curves}).} + +\item{...}{not currently used} } \value{ -a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} object +a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} object showing the antibody dynamic +kinetics of selected antigen/isotype combinations, with optional posterior +distribution quantile curves. } \description{ -Graph estimated antibody decay curve +Graph estimated antibody decay curves +} +\details{ +\subsection{\code{n_curves} and \code{iters_to_graph}}{ + +In most cases, \code{object} will contain too many rows of MCMC +samples for all of these samples to be plotted at once. +\itemize{ +\item Setting the \code{n_curves} argument to a value smaller than the +number of rows in \code{curve_params} will cause this function to select +the first \code{n_curves} rows to graph. +\item Setting \code{n_curves} larger than the number of rows in ` will +result all curves being plotted. +\item If the user directly specifies the \code{iters_to_graph} argument, +then \code{n_curves} has no effect. +} +} } \examples{ -curve <- - typhoid_curves_nostrat_100 |> +# Load example dataset +curve <- typhoid_curves_nostrat_100 |> dplyr::filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) -plot1 <- graph.curve.params(curve) - +# Plot quantiles without showing all curves +plot1 <- graph.curve.params(curve, n_curves = 0) print(plot1) -plot2 <- graph.curve.params(curve, show_all_curves = TRUE) -show(plot2) +# Plot with additional quantiles and show all curves +plot2 <- graph.curve.params( + curve, + n_curves = Inf, + quantiles = c(0.1, 0.5, 0.9) +) +print(plot2) +# Plot with MCMC chains in black +plot3 <- graph.curve.params( + curve, + n_curves = Inf, + quantiles = c(0.1, 0.5, 0.9), + chain_color = FALSE +) +print(plot3) } diff --git a/man/graph_seroresponse_model_1.Rd b/man/graph_seroresponse_model_1.Rd new file mode 100644 index 000000000..74f93789f --- /dev/null +++ b/man/graph_seroresponse_model_1.Rd @@ -0,0 +1,83 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/graph_seroresponse_model_1.R +\name{graph_seroresponse_model_1} +\alias{graph_seroresponse_model_1} +\title{graph antibody decay curves by antigen isotype} +\usage{ +graph_seroresponse_model_1( + object, + antigen_isos = unique(object$antigen_iso), + ncol = min(3, length(antigen_isos)), + ... +) +} +\arguments{ +\item{object}{a \code{\link[=data.frame]{data.frame()}} of curve parameters (one or more MCMC samples)} + +\item{antigen_isos}{antigen isotypes to analyze (can subset \code{curve_params})} + +\item{ncol}{how many columns of subfigures to use in panel plot} + +\item{...}{ + Arguments passed on to \code{\link[=plot_curve_params_one_ab]{plot_curve_params_one_ab}} + \describe{ + \item{\code{verbose}}{verbose output} + \item{\code{xlim}}{range of x values to graph} + \item{\code{n_curves}}{how many curves to plot (see details).} + \item{\code{n_points}}{Number of points to interpolate along the x axis +(passed to \code{\link[ggplot2:geom_function]{ggplot2::geom_function()}})} + \item{\code{iters_to_graph}}{which MCMC iterations in \code{curve_params} to plot +(overrides \code{n_curves}).} + \item{\code{alpha}}{(passed to \code{\link[ggplot2:geom_function]{ggplot2::geom_function()}}) +how transparent the curves should be: +\itemize{ +\item 0 = fully transparent (invisible) +\item 1 = fully opaque +}} + \item{\code{log_x}}{should the x-axis be on a logarithmic scale (\code{TRUE}) +or linear scale (\code{FALSE}, default)?} + \item{\code{log_y}}{should the Y-axis be on a logarithmic scale +(default, \code{TRUE}) or linear scale (\code{FALSE})?} + }} +} +\value{ +a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} object +} +\description{ +graph antibody decay curves by antigen isotype +} +\details{ +\subsection{\code{iters_to_graph}}{ + +If you directly specify \code{iters_to_graph} when calling this function, +the row numbers are enumerated separately for each antigen isotype; +in other words, for the purposes of this argument, +row numbers start over at 1 for each antigen isotype. +There is currently no way to specify different row numbers +for different antigen isotypes; +if you want to do that, +you will could call \code{\link[=plot_curve_params_one_ab]{plot_curve_params_one_ab()}} directly +for each antigen isotype +and combine the resulting panels yourself. +Or you could subset \code{curve_params} manually, +before passing it to this function, +and set the \code{n_curves} argument to \code{Inf}. +} +} +\examples{ +\donttest{ +library(dplyr) +library(ggplot2) +library(magrittr) + +curve <- + serocalculator_example("example_curve_params.csv") |> + read.csv() |> + as_sr_params() |> + filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) |> + graph_seroresponse_model_1() + +curve +} +} +\keyword{internal} diff --git a/man/load_curve_params.Rd b/man/load_curve_params.Rd index baf49c4cb..60683bc04 100644 --- a/man/load_curve_params.Rd +++ b/man/load_curve_params.Rd @@ -1,25 +1,15 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/load_curve_params.R +% Please edit documentation in R/load_sr_params.R \name{load_curve_params} \alias{load_curve_params} \title{Load antibody decay curve parameter samples} \usage{ -load_curve_params(file_path, antigen_isos = NULL) -} -\arguments{ -\item{file_path}{path to an RDS file containing MCMC samples of antibody decay curve parameters \code{y0}, \code{y1}, \code{t1}, \code{alpha}, and \code{r}, stored as a \code{\link[=data.frame]{data.frame()}} or \link[tibble:tbl_df-class]{tibble::tbl_df}} - -\item{antigen_isos}{\code{\link[=character]{character()}} vector of antigen isotypes to be used in analyses} -} -\value{ -a \code{curve_params} object (a \link[tibble:tbl_df-class]{tibble::tbl_df} with extra attribute \code{antigen_isos}) +load_curve_params(...) } \description{ -Load antibody decay curve parameter samples -} -\examples{ -curve <- load_curve_params(serocalculator_example("example_curve_params.rds")) - -print(curve) +\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} +\code{load_curve_params()} was renamed to \code{\link[=load_sr_params]{load_sr_params()}} to create a more +consistent API. } +\keyword{internal} diff --git a/man/load_sr_params.Rd b/man/load_sr_params.Rd new file mode 100644 index 000000000..f3b459c30 --- /dev/null +++ b/man/load_sr_params.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/load_sr_params.R +\name{load_sr_params} +\alias{load_sr_params} +\title{Load longitudinal seroresponse parameter samples} +\usage{ +load_sr_params(file_path, antigen_isos = NULL) +} +\arguments{ +\item{file_path}{path to an RDS file containing MCMC samples of antibody +seroresponse parameters \code{y0}, \code{y1}, \code{t1}, \code{alpha}, and \code{r}, +stored as a \code{\link[=data.frame]{data.frame()}} or \link[tibble:tbl_df-class]{tibble::tbl_df}} + +\item{antigen_isos}{\code{\link[=character]{character()}} vector of antigen isotypes used in analyses} +} +\value{ +a \code{curve_params} object (a \link[tibble:tbl_df-class]{tibble::tbl_df} +with extra attribute \code{antigen_isos}) +} +\description{ +Load longitudinal seroresponse parameter samples +} +\examples{ +curve <- load_sr_params(serocalculator_example("example_curve_params.rds")) + +print(curve) + +} diff --git a/man/plot_curve_params_one_ab.Rd b/man/plot_curve_params_one_ab.Rd index dff8b911d..b5c9dfecc 100644 --- a/man/plot_curve_params_one_ab.Rd +++ b/man/plot_curve_params_one_ab.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/graph.decay.curves.R +% Please edit documentation in R/plot_curve_params_one_ab.R \name{plot_curve_params_one_ab} \alias{plot_curve_params_one_ab} \title{Graph an antibody decay curve model} @@ -12,7 +12,7 @@ plot_curve_params_one_ab( n_points = 1000, log_x = FALSE, log_y = TRUE, - rows_to_graph = seq_len(min(n_curves, nrow(object))), + iters_to_graph = seq_len(min(n_curves, nrow(object))), xlim = c(10^-1, 10^3.1), ... ) @@ -40,7 +40,7 @@ or linear scale (\code{FALSE}, default)?} \item{log_y}{should the Y-axis be on a logarithmic scale (default, \code{TRUE}) or linear scale (\code{FALSE})?} -\item{rows_to_graph}{which rows of \code{curve_params} to plot +\item{iters_to_graph}{which MCMC iterations in \code{curve_params} to plot (overrides \code{n_curves}).} \item{xlim}{range of x values to graph} @@ -55,7 +55,7 @@ mapping.} \item{\code{data}}{Ignored by \code{stat_function()}, do not use.} \item{\code{stat}}{The statistical transformation to use on the data for this layer. When using a \verb{geom_*()} function to construct a layer, the \code{stat} -argument can be used the override the default coupling between geoms and +argument can be used to override the default coupling between geoms and stats. The \code{stat} argument accepts the following: \itemize{ \item A \code{Stat} ggproto subclass, for example \code{StatCount}. @@ -77,17 +77,25 @@ to use \code{position_jitter()}, give the position as \code{"jitter"}. \item For more information and other ways to specify the position, see the \link[ggplot2:layer_positions]{layer position} documentation. }} + \item{\code{arrow}}{Arrow specification, as created by \code{\link[grid:arrow]{grid::arrow()}}.} + \item{\code{arrow.fill}}{fill colour to use for the arrow head (if closed). \code{NULL} +means use \code{colour} aesthetic.} + \item{\code{lineend}}{Line end style (round, butt, square).} + \item{\code{linejoin}}{Line join style (round, mitre, bevel).} + \item{\code{linemitre}}{Line mitre limit (number greater than 1).} \item{\code{na.rm}}{If \code{FALSE}, the default, missing values are removed with a warning. If \code{TRUE}, missing values are silently removed.} \item{\code{show.legend}}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to -display.} +display. To include legend keys for all levels, even +when no data exists, use \code{TRUE}. If \code{NA}, all levels are shown in legend, +but unobserved levels are omitted.} \item{\code{inherit.aes}}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from -the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} +the default plot specification, e.g. \code{\link[ggplot2:annotation_borders]{annotation_borders()}}.} }} } \value{ @@ -97,9 +105,9 @@ a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} object Graph an antibody decay curve model } \details{ -\subsection{\code{n_curves} and \code{rows_to_graph}}{ +\subsection{\code{n_curves} and \code{iters_to_graph}}{ -In most cases, \code{curve_params} will contain too many rows of MCMC +In most cases, \code{object} will contain too many rows of MCMC samples for all of these samples to be plotted at once. \itemize{ \item Setting the \code{n_curves} argument to a value smaller than the @@ -107,7 +115,7 @@ number of rows in \code{curve_params} will cause this function to select the first \code{n_curves} rows to graph. \item Setting \code{n_curves} larger than the number of rows in ` will result all curves being plotted. -\item If the user directly specifies the \code{rows_to_graph} argument, +\item If the user directly specifies the \code{iters_to_graph} argument, then \code{n_curves} has no effect. } } diff --git a/man/print.seroincidence.Rd b/man/print.seroincidence.Rd index 33575d719..b7124101c 100644 --- a/man/print.seroincidence.Rd +++ b/man/print.seroincidence.Rd @@ -7,7 +7,7 @@ \method{print}{seroincidence}(x, ...) } \arguments{ -\item{x}{A list containing output of function \code{\link[=est.incidence]{est.incidence()}}.} +\item{x}{A list containing output of function \code{\link[=est_seroincidence]{est_seroincidence()}}.} \item{...}{Additional arguments affecting the summary produced.} } @@ -15,7 +15,7 @@ an \link{invisible} copy of input parameter \code{x} } \description{ -Custom \code{\link[=print]{print()}} function for \code{seroincidence} objects from \code{\link[=est.incidence]{est.incidence()}} +\code{\link[=print]{print()}} function for \code{seroincidence} objects from \code{\link[=est_seroincidence]{est_seroincidence()}} } \examples{ library(dplyr) @@ -30,9 +30,9 @@ curve <- noise <- example_noise_params_pk -est1 <- est.incidence( +est1 <- est_seroincidence( pop_data = xs_data, - curve_params = curve, + sr_params = curve, noise_params = noise, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), ) diff --git a/man/print.seroincidence.by.Rd b/man/print.seroincidence.by.Rd index a0a9b963a..0a78eb80d 100644 --- a/man/print.seroincidence.by.Rd +++ b/man/print.seroincidence.by.Rd @@ -7,7 +7,7 @@ \method{print}{seroincidence.by}(x, ...) } \arguments{ -\item{x}{A list containing output of function \code{\link[=est.incidence.by]{est.incidence.by()}}.} +\item{x}{A list containing output of function \code{\link[=est_seroincidence_by]{est_seroincidence_by()}}.} \item{...}{Additional arguments affecting the summary produced.} } @@ -16,7 +16,7 @@ an \link{invisible} copy of input parameter \code{x} } \description{ Custom \code{\link[=print]{print()}} function for \code{seroincidence.by} objects -(from \code{\link[=est.incidence.by]{est.incidence.by()}}) +(from \code{\link[=est_seroincidence_by]{est_seroincidence_by()}}) } \examples{ library(dplyr) @@ -32,10 +32,10 @@ noise <- example_noise_params_pk # estimate seroincidence -est2 <- est.incidence.by( +est2 <- est_seroincidence_by( strata = c("catchment"), pop_data = xs_data, - curve_params = curve, + sr_params = curve, noise_params = noise, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), # num_cores = 8 # Allow for parallel processing to decrease run time diff --git a/man/print.summary.seroincidence.by.Rd b/man/print.summary.seroincidence.by.Rd index 2b48c3ae5..7da804926 100644 --- a/man/print.summary.seroincidence.by.Rd +++ b/man/print.summary.seroincidence.by.Rd @@ -33,10 +33,10 @@ noise <- example_noise_params_pk # estimate seroincidence -est2 <- est.incidence.by( +est2 <- est_seroincidence_by( strata = c("catchment"), pop_data = xs_data, - curve_params = curve, + sr_params = curve, noise_params = noise, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), # num_cores = 8 # Allow for parallel processing to decrease run time diff --git a/man/sees_typhoid_ests_strat.Rd b/man/sees_typhoid_ests_strat.Rd new file mode 100644 index 000000000..3efd00afe --- /dev/null +++ b/man/sees_typhoid_ests_strat.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sees_typhoid_ests_strat.R +\docType{data} +\name{sees_typhoid_ests_strat} +\alias{sees_typhoid_ests_strat} +\title{Example \code{"seroincidence.by"} object} +\format{ +An object of class \code{seroincidence.by} (inherits from \code{list}) of length 9. +} +\source{ +\code{serocalculator/data-raw/sees_typhoid_ests_strat.R} +} +\usage{ +sees_typhoid_ests_strat +} +\description{ +Typhoid seroconversion rate estimates by country and age category +from the SEES study. +} +\keyword{datasets} diff --git a/man/serocalculator-package.Rd b/man/serocalculator-package.Rd index 94d44096a..bddb6eeb6 100644 --- a/man/serocalculator-package.Rd +++ b/man/serocalculator-package.Rd @@ -22,11 +22,16 @@ Useful links: Authors: \itemize{ - \item Peter Teunis \email{p.teunis@emory.edu} (Author of the method and original code.) [copyright holder] \item Chris Orwa + \item Peter Teunis \email{p.teunis@emory.edu} (Author of the method and original code.) [copyright holder] \item Kristen Aiemjoy \email{kaiemjoy@ucdavis.edu} \item Douglas Ezra Morrison \email{demorrison@ucdavis.edu} } +Other contributors: +\itemize{ + \item Kwan Ho Lee \email{ksjlee@ucdavis.edu} [contributor] +} + } \keyword{internal} diff --git a/man/sim_pop_data.Rd b/man/sim_pop_data.Rd index bacc56b24..7645d1885 100644 --- a/man/sim_pop_data.Rd +++ b/man/sim_pop_data.Rd @@ -77,9 +77,20 @@ combinations \item{verbose}{logical: if TRUE, print verbose log information to console} \item{...}{ - Arguments passed on to \code{\link[=simcs.tinf]{simcs.tinf}} + Arguments passed on to \code{\link[=simcs.tinf]{simcs.tinf}}, \code{\link[=ldpar]{ldpar}}, \code{\link[=ab]{ab}}, \code{\link[=mk_baseline]{mk_baseline}} \describe{ - \item{\code{}}{} + \item{\code{age}}{age at infection} + \item{\code{nmc}}{mcmc sample to use} + \item{\code{npar}}{number of parameters} + \item{\code{t}}{\link{numeric} \link{vector} of elapsed times since start of infection} + \item{\code{par}}{\link{numeric} \link{matrix} of model parameters: +\itemize{ +\item rows are parameters +\item columns are biomarkers +}} + \item{\code{kab}}{\link{integer} indicating which row to read from \code{blims}} + \item{\code{n}}{number of observations} + \item{\code{blims}}{range of possible baseline antibody levels} }} } \value{ diff --git a/man/sim_pop_data_multi.Rd b/man/sim_pop_data_multi.Rd index 36c395596..b5ca75f8a 100644 --- a/man/sim_pop_data_multi.Rd +++ b/man/sim_pop_data_multi.Rd @@ -6,6 +6,7 @@ \usage{ sim_pop_data_multi( nclus = 10, + sample_sizes = 100, lambdas = c(0.05, 0.1, 0.15, 0.2, 0.3), num_cores = max(1, parallel::detectCores() - 1), rng_seed = 1234, @@ -16,7 +17,9 @@ sim_pop_data_multi( \arguments{ \item{nclus}{number of clusters} -\item{lambdas}{#incidence rate, in events/person*year} +\item{sample_sizes}{sample sizes to simulate} + +\item{lambdas}{incidence rate, in events/person*year} \item{num_cores}{number of cores to use for parallel computations} @@ -79,6 +82,7 @@ a \code{\link[tibble:tibble]{tibble::tibble()}} Simulate multiple data sets } \examples{ +\donttest{ # Load curve parameters dmcmc <- typhoid_curves_nostrat_100 @@ -103,10 +107,10 @@ dlims <- rbind( "HlyE_IgG" = c(min = 0, max = 0.5) ) -sim_pop_data_multi( +sim_data <- sim_pop_data_multi( curve_params = dmcmc, lambdas = lambdas, - n_samples = nrep, + sample_sizes = nrep, age_range = lifespan, antigen_isos = antibodies, n_mcmc_samples = 0, @@ -116,4 +120,7 @@ sim_pop_data_multi( format = "long", nclus = 10) +sim_data + +} } diff --git a/man/strat_ests_barplot.Rd b/man/strat_ests_barplot.Rd new file mode 100644 index 000000000..de0468c7d --- /dev/null +++ b/man/strat_ests_barplot.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/strat_ests_barplot.R +\name{strat_ests_barplot} +\alias{strat_ests_barplot} +\title{Barplot method for \code{summary.seroincidence.by} objects} +\usage{ +strat_ests_barplot( + object, + yvar, + color_var = NULL, + alpha = 0.7, + CIs = FALSE, + title = NULL, + xlab = "Seroconversion rate per 1000 person-years", + ylab = yvar, + fill_lab = NULL, + color_palette = NULL, + ... +) +} +\arguments{ +\item{object}{a \code{summary.seroincidence.by} object (generated by applying the +\code{summary()} method to the output of \code{\link[=est_seroincidence_by]{est_seroincidence_by()}}).} + +\item{yvar}{the name of a stratifying variable in \code{object}.} + +\item{color_var}{\link{character} the name of the fill color variable (e.g., "Country").} + +\item{alpha}{transparency for the bars (1 = no transparency, 0 = fully transparent).} + +\item{CIs}{\link{logical}, if \code{TRUE}, add CI error bars.} + +\item{title}{a title for the final plot.} + +\item{xlab}{a label for the x-axis of the final plot.} + +\item{ylab}{a label for the y-axis of the final plot.} + +\item{fill_lab}{fill label.} + +\item{color_palette}{optional color palette for bar color.} + +\item{...}{unused.} +} +\value{ +a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} object. +} +\description{ +Barplot method for \code{summary.seroincidence.by} objects +} +\keyword{internal} diff --git a/man/strat_ests_scatterplot.Rd b/man/strat_ests_scatterplot.Rd new file mode 100644 index 000000000..e3acd4e25 --- /dev/null +++ b/man/strat_ests_scatterplot.Rd @@ -0,0 +1,82 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/strat_ests_scatterplot.R +\name{strat_ests_scatterplot} +\alias{strat_ests_scatterplot} +\title{Scatterplot method for \code{summary.seroincidence.by} objects} +\usage{ +strat_ests_scatterplot( + object, + xvar = strata(object)[1], + alpha = 0.7, + shape = 1, + dodge_width = 0.001, + CIs = FALSE, + color_var = "nlm.convergence.code", + group_var = NULL, + ... +) +} +\arguments{ +\item{object}{a \code{summary.seroincidence.by} object +(generated by applying the \code{summary()} +method to the output of \code{\link[=est_seroincidence_by]{est_seroincidence_by()}}).} + +\item{xvar}{the name of a stratifying variable in \code{object}} + +\item{alpha}{transparency for the points in the graph +(1 = no transparency, 0 = fully transparent)} + +\item{shape}{shape argument for \code{geom_point()}} + +\item{dodge_width}{width for jitter} + +\item{CIs}{\link{logical}, if \code{TRUE}, add CI error bars} + +\item{color_var}{\link{character} which variable in \code{object} to use +to determine point color} + +\item{group_var}{\link{character} which variable in \code{object} to use +to connect points with lines (\code{NULL} for no lines)} + +\item{...}{unused} +} +\value{ +a \code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} object +} +\description{ +Scatterplot method for \code{summary.seroincidence.by} objects +} +\examples{ +library(dplyr) +library(ggplot2) + +xs_data <- + sees_pop_data_pk_100 + +curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso \%in\% c("HlyE_IgA", "HlyE_IgG")) + +noise <- + example_noise_params_pk + +est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + num_cores = 2 # Allow for parallel processing to decrease run time +) + +est2sum <- summary(est2) + +strat_ests_scatterplot(est2sum, + xvar = "ageCat", + color_var = "catchment", + CIs = TRUE, + group_var = "catchment") +} +\keyword{internal} diff --git a/man/strata.Rd b/man/strata.Rd index d2544834c..53eca7f7b 100644 --- a/man/strata.Rd +++ b/man/strata.Rd @@ -1,8 +1,8 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/strata.seroincidence.ests.R +% Please edit documentation in R/strata.R \name{strata} \alias{strata} -\title{Extract strata from an object} +\title{Extract \code{Strata} metadata from an object} \usage{ strata(x) } @@ -10,9 +10,9 @@ strata(x) \item{x}{an object} } \value{ -the strata of \code{x} +the strata metadata of \code{x} } \description{ -Generic method for extracting strata from objects. -See \code{\link[=strata.seroincidence.by]{strata.seroincidence.by()}} +Generic method for extracting strata metadata from objects. +See \code{\link[=strata.default]{strata.default()}} } diff --git a/man/strata.seroincidence.by.Rd b/man/strata.default.Rd similarity index 72% rename from man/strata.seroincidence.by.Rd rename to man/strata.default.Rd index 25c4a47ab..782fb451b 100644 --- a/man/strata.seroincidence.by.Rd +++ b/man/strata.default.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/strata.seroincidence.ests.R -\name{strata.seroincidence.by} -\alias{strata.seroincidence.by} +% Please edit documentation in R/strata.R +\name{strata.default} +\alias{strata.default} \title{Extract the \code{Strata} attribute from an object, if present} \usage{ -\method{strata}{seroincidence.by}(x) +\method{strata}{default}(x) } \arguments{ \item{x}{any R object} diff --git a/man/stratify_data.Rd b/man/stratify_data.Rd index 7273db863..b543d2bdf 100644 --- a/man/stratify_data.Rd +++ b/man/stratify_data.Rd @@ -15,43 +15,8 @@ stratify_data( ) } \arguments{ -\item{curve_params}{a \code{\link[=data.frame]{data.frame()}} containing MCMC samples of parameters -from the Bayesian posterior distribution of a longitudinal decay curve model. -The parameter columns must be named: -\itemize{ -\item \code{antigen_iso}: a \code{\link[=character]{character()}} vector indicating antigen-isotype -combinations -\item \code{iter}: an \code{\link[=integer]{integer()}} vector indicating MCMC sampling iterations -\item \code{y0}: baseline antibody level at $t=0$ ($y(t=0)$) -\item \code{y1}: antibody peak level (ELISA units) -\item \code{t1}: duration of infection -\item \code{alpha}: antibody decay rate -(1/days for the current longitudinal parameter sets) -\item \code{r}: shape factor of antibody decay -}} - -\item{noise_params}{a \code{\link[=data.frame]{data.frame()}} (or \code{\link[tibble:tibble]{tibble::tibble()}}) -containing the following variables, -specifying noise parameters for each antigen isotype: -\itemize{ -\item \code{antigen_iso}: antigen isotype whose noise parameters are being specified -on each row -\item \code{nu}: biological noise -\item \code{eps}: measurement noise -\item \code{y.low}: lower limit of detection for the current antigen isotype -\item \code{y.high}: upper limit of detection for the current antigen isotype -}} - \item{strata_varnames}{\code{\link[=character]{character()}} vector of names of variables in \code{data} to stratify by} - -\item{curve_strata_varnames}{A subset of \code{strata}. -Values must be variable names in \code{curve_params}. Default = "".} - -\item{noise_strata_varnames}{A subset of \code{strata}. -Values must be variable names in \code{noise_params}. Default = "".} - -\item{antigen_isos}{Character vector with one or more antibody names. Values must match \code{pop_data}} } \value{ a \code{"biomarker_data_and_params.list"} object diff --git a/man/summary.seroincidence.Rd b/man/summary.seroincidence.Rd index 24fd6a3ba..45d4d7897 100644 --- a/man/summary.seroincidence.Rd +++ b/man/summary.seroincidence.Rd @@ -7,7 +7,7 @@ \method{summary}{seroincidence}(object, coverage = 0.95, verbose = TRUE, ...) } \arguments{ -\item{object}{a \code{\link[=list]{list()}}, outputted by \code{\link[stats:nlm]{stats::nlm()}} or \code{\link[=est.incidence]{est.incidence()}}} +\item{object}{a \code{\link[=list]{list()}} outputted by \code{\link[stats:nlm]{stats::nlm()}} or \code{\link[=est_seroincidence]{est_seroincidence()}}} \item{coverage}{desired confidence interval coverage probability} @@ -25,7 +25,7 @@ a \code{\link[tibble:tibble]{tibble::tibble()}} containing the following: \item \code{CI.upr}: upper limit of confidence interval for incidence rate \item \code{coverage}: coverage probability \item \code{log.lik}: -log-likelihood of the data used in the call to \code{est.incidence()}, +log-likelihood of the data used in the call to \code{est_seroincidence()}, evaluated at the maximum-likelihood estimate of lambda (i.e., at \code{incidence.rate}) \item \code{iterations}: the number of iterations used @@ -43,7 +43,7 @@ current iterate is probably solution. \item 3: Last global step failed to locate a point lower than x. Either x is an approximate local minimum of the function, the function is too non-linear for this algorithm, -or \code{stepmin} in \code{\link[=est.incidence]{est.incidence()}} +or \code{stepmin} in \code{\link[=est_seroincidence]{est_seroincidence()}} (a.k.a., \code{steptol} in \code{\link[stats:nlm]{stats::nlm()}}) is too large. \item 4: iteration limit exceeded; increase \code{iterlim}. \item 5: maximum step size \code{stepmax} exceeded five consecutive times. @@ -70,9 +70,9 @@ curve <- noise <- example_noise_params_pk -est1 <- est.incidence( +est1 <- est_seroincidence( pop_data = xs_data, - curve_params = curve, + sr_params = curve, noise_params = noise, antigen_isos = c("HlyE_IgG", "HlyE_IgA") ) diff --git a/man/summary.seroincidence.by.Rd b/man/summary.seroincidence.by.Rd index c2ba3283b..54d5e1bee 100644 --- a/man/summary.seroincidence.by.Rd +++ b/man/summary.seroincidence.by.Rd @@ -14,7 +14,7 @@ ) } \arguments{ -\item{object}{A dataframe containing output of function \code{\link[=est.incidence.by]{est.incidence.by()}}.} +\item{object}{A dataframe containing output of \code{\link[=est_seroincidence_by]{est_seroincidence_by()}}.} \item{confidence_level}{desired confidence interval coverage probability} @@ -49,13 +49,13 @@ The object also has the following metadata \itemize{ \item \code{antigen_isos} Character vector with names of input antigen isotypes -used in \code{\link[=est.incidence.by]{est.incidence.by()}} -\item \code{Strata} Character with names of strata used in \code{\link[=est.incidence.by]{est.incidence.by()}} +used in \code{\link[=est_seroincidence_by]{est_seroincidence_by()}} +\item \code{Strata} Character with names of strata used in \code{\link[=est_seroincidence_by]{est_seroincidence_by()}} } } \description{ Calculate seroincidence from output of the seroincidence calculator -\code{\link[=est.incidence.by]{est.incidence.by()}}. +\code{\link[=est_seroincidence_by]{est_seroincidence_by()}}. } \examples{ library(dplyr) @@ -71,10 +71,10 @@ noise <- example_noise_params_pk # estimate seroincidence -est2 <- est.incidence.by( +est2 <- est_seroincidence_by( strata = c("catchment"), pop_data = xs_data, - curve_params = curve, + sr_params = curve, noise_params = noise, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), # num_cores = 8 # Allow for parallel processing to decrease run time diff --git a/man/typhoid_curves_nostrat_100.Rd b/man/typhoid_curves_nostrat_100.Rd index f333a706a..1775c1545 100644 --- a/man/typhoid_curves_nostrat_100.Rd +++ b/man/typhoid_curves_nostrat_100.Rd @@ -7,7 +7,7 @@ \format{ \subsection{\code{typhoid_curves_nostrat_100}}{ -A \code{curve_params} object (from \code{\link[=as_curve_params]{as_curve_params()}}) with 500 rows and 7 +A \code{curve_params} object (from \code{\link[=as_sr_params]{as_sr_params()}}) with 500 rows and 7 columns: \describe{ \item{antigen_iso}{which antigen and isotype are being measured diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index 9e00eb2f5..3643d3760 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -5,9 +5,6 @@ template: bibliography: vignettes/references.bib -development: - mode: auto - search: exclude: ['preview/'] @@ -16,8 +13,8 @@ reference: contents: - as_pop_data - load_pop_data - - as_curve_params - - load_curve_params + - as_sr_params + - load_sr_params - as_noise_params - load_noise_params - check_pop_data @@ -40,8 +37,8 @@ reference: - log_likelihood - title: Estimate seroconversion incidence rates contents: - - est.incidence - - est.incidence.by + - est_seroincidence + - est_seroincidence_by - title: Summarize seroconversion incidence rate estimates contents: - summary.seroincidence @@ -51,6 +48,9 @@ reference: - autoplot.seroincidence - autoplot.seroincidence.by - autoplot.summary.seroincidence.by +- title: Compare seroconversion incidence rates + contents: + - compare_seroincidence - title: Example data sets contents: - sees_pop_data_100 @@ -58,11 +58,14 @@ reference: - typhoid_curves_nostrat_100 - example_noise_params_sees - example_noise_params_pk + - sees_typhoid_ests_strat - serocalculator_example - title: Simulate data sets contents: - sim_pop_data - sim_pop_data_multi + - analyze_sims + - autoplot.sim_results articles: - title: Get started diff --git a/tests/testthat/_snaps/.gitignore b/tests/testthat/_snaps/.gitignore new file mode 100644 index 000000000..7c4efca54 --- /dev/null +++ b/tests/testthat/_snaps/.gitignore @@ -0,0 +1 @@ +*.new.* diff --git a/tests/testthat/_snaps/ab1.md b/tests/testthat/_snaps/ab1.md new file mode 100644 index 000000000..416310eba --- /dev/null +++ b/tests/testthat/_snaps/ab1.md @@ -0,0 +1,4 @@ +# results are consistent + + c(63.0932857200897, 157.745612219962) + diff --git a/tests/testthat/_snaps/analyze_sims/sim_results.csv b/tests/testthat/_snaps/analyze_sims/sim_results.csv new file mode 100644 index 000000000..26d29d0d8 --- /dev/null +++ b/tests/testthat/_snaps/analyze_sims/sim_results.csv @@ -0,0 +1,25 @@ +lambda.sim,sample_size,Bias,Mean_Est_SE,Empirical_SE,RMSE,Mean_CI_Width,CI_Coverage +0.05,50,0.00320384,0.0129978,0.0134506,0.0134958,0.0529853,0.95 +0.05,100,0.00340194,0.00929504,0.0117095,0.0119092,0.037163,0.8 +0.05,150,0.00156837,0.00740015,0.0106099,0.0104595,0.0293993,0.9 +0.05,200,-0.00217932,0.00612167,0.00978295,0.00978112,0.0242544,0.85 +0.1,50,0.010051,0.0207562,0.0274411,0.0285725,0.0832707,0.85 +0.1,100,0.0042780200000000004,0.0142954,0.0157375,0.0159244,0.0567184,0.9 +0.1,150,0.00746141,0.0118862,0.0166083,0.0178246,0.0469621,0.8 +0.1,200,0.00500536,0.0101522,0.0123592,0.0130448,0.0400357,0.85 +0.15,50,0.00840848,0.0269729,0.0307942,0.03117,0.107739,0.95 +0.15,100,0.0292375,0.020418,0.0221396,0.0363385,0.0807066,0.6 +0.15,150,0.018591,0.0163714,0.0255764,0.0310978,0.0645648,0.8 +0.15,200,0.00982785,0.0134798,0.0200743,0.0218955,0.053082,0.75 +0.2,50,0.0454037,0.037084,0.0551894,0.0703923,0.147523,0.7 +0.2,100,0.0226022,0.02423,0.0365067,0.0421541,0.0957066,0.75 +0.2,150,0.0275992,0.0199784,0.0244225,0.0364465,0.0787022,0.65 +0.2,200,0.0218642,0.0170185,0.0279525,0.034933,0.0669634,0.7 +0.5,50,0.117874,0.0851499,0.153676,0.190604,0.337864,0.5 +0.5,100,0.0653509,0.0543313,0.0806827,0.102249,0.214238,0.7 +0.5,150,0.060862,0.0440988,0.0470198,0.0761873,0.173549,0.7 +0.5,200,0.0831555,0.0398219,0.0585539,0.100856,0.156565,0.5 +0.8,50,0.251405,0.152342,0.339305,0.415423,0.605277,0.6 +0.8,100,0.23398,0.105511,0.179434,0.292119,0.416368,0.5 +0.8,150,0.190503,0.0813865,0.110503,0.218842,0.320412,0.3 +0.8,200,0.155172,0.0677062,0.0872712,0.176957,0.266259,0.35 diff --git a/tests/testthat/_snaps/as_curve_params.md b/tests/testthat/_snaps/as_sr_params.md similarity index 99% rename from tests/testthat/_snaps/as_curve_params.md rename to tests/testthat/_snaps/as_sr_params.md index 53ae9923f..159955e98 100644 --- a/tests/testthat/_snaps/as_curve_params.md +++ b/tests/testthat/_snaps/as_sr_params.md @@ -1,4 +1,4 @@ -# `as_curve_params()` produces expected results +# `as_sr_params()` produces expected results Code test_data diff --git a/tests/testthat/_snaps/as_curve_params/curve-data.csv b/tests/testthat/_snaps/as_sr_params/curve-data.csv similarity index 100% rename from tests/testthat/_snaps/as_curve_params/curve-data.csv rename to tests/testthat/_snaps/as_sr_params/curve-data.csv diff --git a/tests/testthat/_snaps/autoplot.curve_params/autoplot-curve-params.svg b/tests/testthat/_snaps/autoplot.curve_params/autoplot-curve-params.svg new file mode 100644 index 000000000..34e5a857c --- /dev/null +++ b/tests/testthat/_snaps/autoplot.curve_params/autoplot-curve-params.svg @@ -0,0 +1,311 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +HlyE_IgA + + + + + + + + + +HlyE_IgG + + + +0 +400 +800 +1200 + +0 +400 +800 +1200 + +0.1 +10.0 +1,000.0 +Days since fever onset +ELISA units +MCMC chain + + + +10% quantile +50% quantile +90% quantile +autoplot.curve_params + + diff --git a/tests/testthat/_snaps/autoplot.pop_data/age-scatter-no-strat.svg b/tests/testthat/_snaps/autoplot.pop_data/age-scatter-no-strat.svg index 5806eb8f0..fe6676773 100644 --- a/tests/testthat/_snaps/autoplot.pop_data/age-scatter-no-strat.svg +++ b/tests/testthat/_snaps/autoplot.pop_data/age-scatter-no-strat.svg @@ -21,250 +21,308 @@ - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -0.0 -0.1 -1.0 -10.0 - - - - - - - - - -5 -10 -15 -20 -25 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +HlyE_IgA + + + + + + + + + + +HlyE_IgG + + + + + + + +5 +10 +15 +20 +25 + + + + + +5 +10 +15 +20 +25 +0.0 +0.1 +1.0 +10.0 + + + + Age -Antibody Response Value +Antibody Response Value Quantitative Antibody Responses by Age diff --git a/tests/testthat/_snaps/autoplot.pop_data/age-scatter-strat-country.svg b/tests/testthat/_snaps/autoplot.pop_data/age-scatter-strat-country.svg index dfaaa6bcf..e96679c0d 100644 --- a/tests/testthat/_snaps/autoplot.pop_data/age-scatter-strat-country.svg +++ b/tests/testthat/_snaps/autoplot.pop_data/age-scatter-strat-country.svg @@ -21,261 +21,320 @@ - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + -0.0 -0.1 -1.0 -10.0 - - - - - - - - - -5 -10 -15 -20 -25 -Age -Antibody Response Value - -catchment - - - - - - -aku -kgh + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +HlyE_IgA + + + + + + + + + + +HlyE_IgG + + + + + + + +5 +10 +15 +20 +25 + + + + + +5 +10 +15 +20 +25 +0.0 +0.1 +1.0 +10.0 + + + + +Age +Antibody Response Value + +catchment + + + + + + +aku +kgh Quantitative Antibody Responses by Age diff --git a/tests/testthat/_snaps/autoplot.pop_data/density-log.svg b/tests/testthat/_snaps/autoplot.pop_data/density-log.svg new file mode 100644 index 000000000..cf2508be7 --- /dev/null +++ b/tests/testthat/_snaps/autoplot.pop_data/density-log.svg @@ -0,0 +1,144 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +HlyE_IgG + + + + + + + + + + +HlyE_IgA + + + + + + + + +0.00 +0.01 +0.10 +1.00 +10.00 +100.00 +0.0 +0.5 +1.0 + + + +0.0 +0.5 +1.0 + + + +Quantitative antibody response +Frequency + +catchment + + + + +aku +kgh +Distribution of Cross-sectional Antibody Responses + + diff --git a/tests/testthat/_snaps/autoplot.pop_data/density.svg b/tests/testthat/_snaps/autoplot.pop_data/density.svg index 36c0a94b3..df9ac9e7e 100644 --- a/tests/testthat/_snaps/autoplot.pop_data/density.svg +++ b/tests/testthat/_snaps/autoplot.pop_data/density.svg @@ -138,9 +138,9 @@ catchment - + - + aku kgh Distribution of Cross-sectional Antibody Responses diff --git a/tests/testthat/_snaps/autoplot.seroincidence.by/seroinc-plot.svg b/tests/testthat/_snaps/autoplot.seroincidence.by/seroinc-plot.svg new file mode 100644 index 000000000..f41658d6e --- /dev/null +++ b/tests/testthat/_snaps/autoplot.seroincidence.by/seroinc-plot.svg @@ -0,0 +1,166 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +-400 +-350 +-300 + + + + + + + + +0.00 +0.25 +0.50 +0.75 +1.00 +incidence rate (events per person:year) +log(likelihood) + + + + + + + +HlyE_IgG + HlyE_IgA +est.incidence +lambda_start +Stratum 1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +-450 +-400 +-350 +-300 +-250 + + + + + + + + + + +0.00 +0.25 +0.50 +0.75 +1.00 +incidence rate (events per person:year) +log(likelihood) + + + + + + + +HlyE_IgG + HlyE_IgA +est.incidence +lambda_start +Stratum 2 + + diff --git a/tests/testthat/_snaps/autoplot.sim_results/autoplot-sim-results.svg b/tests/testthat/_snaps/autoplot.sim_results/autoplot-sim-results.svg new file mode 100644 index 000000000..48f31853c --- /dev/null +++ b/tests/testthat/_snaps/autoplot.sim_results/autoplot-sim-results.svg @@ -0,0 +1,123 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.1 +0.2 +0.3 + + + + + + + + +50 +100 +150 +200 +sample_size +Empirical_SE + +lambda.sim + + + + + + + + + + + + + + + + + + +0.05 +0.1 +0.15 +0.2 +0.5 +0.8 +autoplot-sim-results + + diff --git a/tests/testthat/_snaps/autoplot.summary.seroincidence.by.md b/tests/testthat/_snaps/autoplot.summary.seroincidence.by.md new file mode 100644 index 000000000..d34432df6 --- /dev/null +++ b/tests/testthat/_snaps/autoplot.summary.seroincidence.by.md @@ -0,0 +1,20 @@ +# error on plot type + + Code + plot1 <- autoplot(est2sum, xvar = "ageCat", type = "whisker", dodge_width = 0.1, + color_var = "catchment", CI = TRUE) + Condition + Error in `autoplot()`: + ! Invalid plot `type` specified: "whisker". + i Please choose either 'scatter' or 'bar'. + +# error on incorrect yvar + + Code + plot1 <- autoplot(est2sum, yvar = "fake", type = "bar", dodge_width = 0.1, + color_var = "catchment", CI = TRUE) + Condition + Error in `strat_ests_barplot()`: + ! The variable `fake` specified by argument `yvar` does not exist in `object`. + Please choose a column that exists in `object`. + diff --git a/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-barplot-palette.svg b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-barplot-palette.svg new file mode 100644 index 000000000..8509e1e0f --- /dev/null +++ b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-barplot-palette.svg @@ -0,0 +1,88 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +16+ +5-15 +<5 + + + + + + +0 +200 +400 +Seroconversion rate per 1000 person-years +ageCat + + + + + +aku +kgh + + diff --git a/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-barplot.svg b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-barplot.svg new file mode 100644 index 000000000..04d6788a5 --- /dev/null +++ b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-barplot.svg @@ -0,0 +1,88 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +16+ +5-15 +<5 + + + + + + +0 +200 +400 +Seroconversion rate per 1000 person-years +ageCat + + + + + +aku +kgh + + diff --git a/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-ci-lines.svg b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-ci-lines.svg new file mode 100644 index 000000000..61806abd0 --- /dev/null +++ b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-ci-lines.svg @@ -0,0 +1,84 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.2 +0.4 +0.6 + + + + + + + +<5 +5-15 +16+ +ageCat +Estimated incidence rate + +Catchment Area + + + + + + + + +aku +kgh +strat-est-plot-CI-lines + + diff --git a/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-ci.svg b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-ci.svg index a5d7a6099..1c399f4f6 100644 --- a/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-ci.svg +++ b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-ci.svg @@ -28,35 +28,53 @@ - - - - - - - - + + + + + + + + + + + + + + + + + + 0.0 -0.1 -0.2 +0.2 +0.4 +0.6 - - - - -aku -kgh -catchment + + + + + + +<5 +5-15 +16+ +ageCat Estimated incidence rate - -`nlm()` convergence code - - - -1 + +Catchment Area + + + + + + +aku +kgh strat-est-plot-CI diff --git a/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-no-ci.svg b/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-no-ci.svg deleted file mode 100644 index d0d04703e..000000000 --- a/tests/testthat/_snaps/autoplot.summary.seroincidence.by/strat-est-plot-no-ci.svg +++ /dev/null @@ -1,65 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0.00 -0.05 -0.10 -0.15 -0.20 - - - - - - - -aku -kgh -catchment -Estimated incidence rate - -`nlm()` convergence code - - -1 -strat-est-plot-no-CI - - diff --git a/tests/testthat/_snaps/compare_seroincidence.md b/tests/testthat/_snaps/compare_seroincidence.md new file mode 100644 index 000000000..e3f43f143 --- /dev/null +++ b/tests/testthat/_snaps/compare_seroincidence.md @@ -0,0 +1,74 @@ +# compare_seroincidence works with seroincidence.by object + + WAoAAAACAAQFAgACAwAAAAMTAAAADAAAABAAAAABAAQACQAAAAlTdHJhdHVtIDEAAAAQAAAA + AQAEAAkAAAAJU3RyYXR1bSAyAAAAEAAAAAEABAAJAAAAA2FrdQAAABAAAAABAAQACQAAAANr + Z2gAAAAOAAAAAT/B7HG3u31GAAAADgAAAAE/yY2Xe8TPHwAAAA4AAAABv66ElxAlR2QAAAAO + AAAAAT+i8aCT13qAAAAADgAAAAG/+caFd3xITAAAAA4AAAABP7twmZAu1MYAAAAOAAAAAb/A + 6WvUlXr8AAAADgAAAAE/inIEyC10qAAABAIAAAABAAQACQAAAAVuYW1lcwAAABAAAAAMAAQA + CQAAAAlTdHJhdHVtXzEABAAJAAAACVN0cmF0dW1fMgAEAAkAAAALY2F0Y2htZW50LjEABAAJ + AAAAC2NhdGNobWVudC4yAAQACQAAABBpbmNpZGVuY2UucmF0ZS4xAAQACQAAABBpbmNpZGVu + Y2UucmF0ZS4yAAQACQAAAApkaWZmZXJlbmNlAAQACQAAAAJTRQAEAAkAAAALei5zdGF0aXN0 + aWMABAAJAAAAB3AudmFsdWUABAAJAAAABkNJLmx3cgAEAAkAAAAGQ0kudXByAAAEAgAAAAEA + BAAJAAAACXJvdy5uYW1lcwAAAA0AAAABAAAAAQAABAIAAAABAAQACQAAAAVjbGFzcwAAABAA + AAAEAAQACQAAABtjb21wYXJpc29uLnNlcm9pbmNpZGVuY2UuYnkABAAJAAAABnRibF9kZgAE + AAkAAAADdGJsAAQACQAAAApkYXRhLmZyYW1lAAAEAgAAAAEABAAJAAAACGNvdmVyYWdlAAAA + DgAAAAE/7mZmZmZmZgAABAIAAAABAAQACQAAAAtzdHJhdGFfdmFycwAAABAAAAABAAQACQAA + AAljYXRjaG1lbnQAAAQCAAAAAQAEAAkAAAAMYW50aWdlbl9pc29zAAAAEAAAAAIABAAJAAAA + CEhseUVfSWdHAAQACQAAAAhIbHlFX0lnQQAAAP4= + +# compare_seroincidence works with multiple strata variables + + WAoAAAACAAQFAgACAwAAAAMTAAAADgAAABAAAAAPAAQACQAAAAlTdHJhdHVtIDEABAAJAAAA + CVN0cmF0dW0gMQAEAAkAAAAJU3RyYXR1bSAxAAQACQAAAAlTdHJhdHVtIDEABAAJAAAACVN0 + cmF0dW0gMQAEAAkAAAAJU3RyYXR1bSAyAAQACQAAAAlTdHJhdHVtIDIABAAJAAAACVN0cmF0 + dW0gMgAEAAkAAAAJU3RyYXR1bSAyAAQACQAAAAlTdHJhdHVtIDMABAAJAAAACVN0cmF0dW0g + MwAEAAkAAAAJU3RyYXR1bSAzAAQACQAAAAlTdHJhdHVtIDQABAAJAAAACVN0cmF0dW0gNAAE + AAkAAAAJU3RyYXR1bSA1AAAAEAAAAA8ABAAJAAAACVN0cmF0dW0gMgAEAAkAAAAJU3RyYXR1 + bSAzAAQACQAAAAlTdHJhdHVtIDQABAAJAAAACVN0cmF0dW0gNQAEAAkAAAAJU3RyYXR1bSA2 + AAQACQAAAAlTdHJhdHVtIDMABAAJAAAACVN0cmF0dW0gNAAEAAkAAAAJU3RyYXR1bSA1AAQA + CQAAAAlTdHJhdHVtIDYABAAJAAAACVN0cmF0dW0gNAAEAAkAAAAJU3RyYXR1bSA1AAQACQAA + AAlTdHJhdHVtIDYABAAJAAAACVN0cmF0dW0gNQAEAAkAAAAJU3RyYXR1bSA2AAQACQAAAAlT + dHJhdHVtIDYAAAAQAAAADwAEAAkAAAADYWt1AAQACQAAAANha3UABAAJAAAAA2FrdQAEAAkA + AAADYWt1AAQACQAAAANha3UABAAJAAAAA2FrdQAEAAkAAAADYWt1AAQACQAAAANha3UABAAJ + AAAAA2FrdQAEAAkAAAADYWt1AAQACQAAAANha3UABAAJAAAAA2FrdQAEAAkAAAADa2doAAQA + CQAAAANrZ2gABAAJAAAAA2tnaAAAAw0AAAAPAAAAAQAAAAEAAAABAAAAAQAAAAEAAAACAAAA + AgAAAAIAAAACAAAAAwAAAAMAAAADAAAAAQAAAAEAAAACAAAEAgAAAAEABAAJAAAABmxldmVs + cwAAABAAAAADAAQACQAAAAI8NQAEAAkAAAAENS0xNQAEAAkAAAADMTYrAAAEAgAAAAEABAAJ + AAAABWNsYXNzAAAAEAAAAAEABAAJAAAABmZhY3RvcgAAAP4AAAAQAAAADwAEAAkAAAADYWt1 + AAQACQAAAANha3UABAAJAAAAA2tnaAAEAAkAAAADa2doAAQACQAAAANrZ2gABAAJAAAAA2Fr + dQAEAAkAAAADa2doAAQACQAAAANrZ2gABAAJAAAAA2tnaAAEAAkAAAADa2doAAQACQAAAANr + Z2gABAAJAAAAA2tnaAAEAAkAAAADa2doAAQACQAAAANrZ2gABAAJAAAAA2tnaAAAAw0AAAAP + AAAAAgAAAAMAAAABAAAAAgAAAAMAAAADAAAAAQAAAAIAAAADAAAAAQAAAAIAAAADAAAAAgAA + AAMAAAADAAAEAgAAAf8AAAAQAAAAAwAEAAkAAAACPDUABAAJAAAABDUtMTUABAAJAAAAAzE2 + KwAABAIAAAL/AAAAEAAAAAEABAAJAAAABmZhY3RvcgAAAP4AAAAOAAAADz+dcYqyz3TbP51x + irLPdNs/nXGKss902z+dcYqyz3TbP51xirLPdNs/wu3QCbzYrD/C7dAJvNisP8Lt0Am82Kw/ + wu3QCbzYrD/N1VjCWzOmP83VWMJbM6Y/zdVYwlszpj/Cw/+whtrQP8LD/7CG2tA/xYBBe3iW + XQAAAA4AAAAPP8Lt0Am82Kw/zdVYwlszpj/Cw/+whtrQP8WAQXt4ll0/1QiF6BDzgj/N1VjC + WzOmP8LD/7CG2tA/xYBBe3iWXT/VCIXoEPOCP8LD/7CG2tA/xYBBe3iWXT/VCIXoEPOCP8WA + QXt4ll0/1QiF6BDzgj/VCIXoEPOCAAAADgAAAA+/vn89ZsXUIb/KJydsAUULv74rnLRZ2Gm/ + wdIQJR6nwr/TMW084/w0v7XPEXE8tfQ/VOgsmv7uAL+Uk4uN3e2Iv8cjO8ZlDlg/tiKyI6ix + rD+wqi6NxTqSv7h3ZhuNZry/leIOV43caL/HTQwfmww0v8SQylSpUKcAAAAOAAAADz+i/yhl + JDNjP7FVBma5Mn0/s/qFmbh9tj+kQPVPtjRhP7h63t1xxIU/slS4ExLrrj+02dxMRopkP6eC + bZYNCmY/uTJu4s6ZsD+5ZVYFqk/WP7KqEsElUs8/vRH0oH5kID+1JQ0zJORZP764uLr/3iU/ + uXDKwbrP9AAAAA4AAAAPwAmvqAaJdDXACCSSVJ5eMr/4KYIDJWsYwAwn2o392pbACRbKKJWX + fr/zCSI3AP1CP5AK+80l8u2/3AHvAWEGMb/9YnXlsu2FP+vkUA/qdZs/7JJc5id4zL/q7qlO + Dq1+v9CPBIkXsga/+EVGEahtQr/53jklc3VfAAAADgAAAA8/VbBrNUA72z9k2rHgZ0vNP8DE + 6aT+C9s/PFbpvtm+uj9cDGwECBPgP834tiHvCkQ/75mZ4cc/Bz/lLFhRhVdMP7D3mevhhA4/ + 2InhmK/DQz/Xza3C8+YbP9mZfN/JuAM/6XeOYOwZNz/AjIjQyqneP7seUuAXaGcAAAAOAAAA + D7/IjoYBOgdfv9WRreZ4IEy/0VT5Au15Fb/LvqVbNl0Gv98wIwlhWpq/zN5QcNtpzr/ERTF+ + /55Rv7wu1mDNFpi/1+pFWnqKRr+7o7BIM628v7Pqq25s1ga/1FxXR80NhL/HdPM26Gg9v9q0 + KpyPFUy/1r+ayMR+7QAAAA4AAAAPv6fC3ZYvMwi/slXmFiSS/j+h+VVGBGcGv6+V67wbyfi/ + vMrdwZp3OD+sPPv+es9oP8SY0jFrmgk/seUQmd4f1D+I4TKCr33AP9H6RSPhREU/yp+ERPul + lT/AQUh0DLRLP8H8b6EE8SM/qzjz56BIwD+hdoOg2XIwAAAEAgAAAAEABAAJAAAABW5hbWVz + AAAAEAAAAA4ABAAJAAAACVN0cmF0dW1fMQAEAAkAAAAJU3RyYXR1bV8yAAQACQAAAAtjYXRj + aG1lbnQuMQAEAAkAAAAIYWdlQ2F0LjEABAAJAAAAC2NhdGNobWVudC4yAAQACQAAAAhhZ2VD + YXQuMgAEAAkAAAAQaW5jaWRlbmNlLnJhdGUuMQAEAAkAAAAQaW5jaWRlbmNlLnJhdGUuMgAE + AAkAAAAKZGlmZmVyZW5jZQAEAAkAAAACU0UABAAJAAAAC3ouc3RhdGlzdGljAAQACQAAAAdw + LnZhbHVlAAQACQAAAAZDSS5sd3IABAAJAAAABkNJLnVwcgAABAIAAAABAAQACQAAAAlyb3cu + bmFtZXMAAAANAAAAAoAAAAAAAAAPAAAEAgAAAv8AAAAQAAAABAAEAAkAAAAbY29tcGFyaXNv + bi5zZXJvaW5jaWRlbmNlLmJ5AAQACQAAAAZ0YmxfZGYABAAJAAAAA3RibAAEAAkAAAAKZGF0 + YS5mcmFtZQAABAIAAAABAAQACQAAAAhjb3ZlcmFnZQAAAA4AAAABP+5mZmZmZmYAAAQCAAAA + AQAEAAkAAAALc3RyYXRhX3ZhcnMAAAAQAAAAAgAEAAkAAAAJY2F0Y2htZW50AAQACQAAAAZh + Z2VDYXQAAAQCAAAAAQAEAAkAAAAMYW50aWdlbl9pc29zAAAAEAAAAAIABAAJAAAACEhseUVf + SWdHAAQACQAAAAhIbHlFX0lnQQAAAP4= + diff --git a/tests/testthat/_snaps/darwin/compare_seroincidence.md b/tests/testthat/_snaps/darwin/compare_seroincidence.md new file mode 100644 index 000000000..aab70ba28 --- /dev/null +++ b/tests/testthat/_snaps/darwin/compare_seroincidence.md @@ -0,0 +1,18 @@ +# compare_seroincidence works with two seroincidence objects + + Code + result + Output + + Two-sample z-test for difference in seroincidence rates + + data: seroincidence estimates + z = 1.611, p-value = 0.1072 + alternative hypothesis: true difference in incidence rates is not equal to 0 + 95 percent confidence interval: + -0.0129128 0.1321235 + sample estimates: + incidence rate 1 incidence rate 2 difference + 0.19963354 0.14002820 0.05960533 + + diff --git a/tests/testthat/_snaps/est.incidence.md b/tests/testthat/_snaps/est.incidence.md deleted file mode 100644 index fbf3bcab1..000000000 --- a/tests/testthat/_snaps/est.incidence.md +++ /dev/null @@ -1,19 +0,0 @@ -# est.incidence() produces expected results for typhoid data - - Code - summary(typhoid_results, coverage = 0.95) - Output - # A tibble: 1 x 10 - est.start incidence.rate SE CI.lwr CI.upr coverage log.lik iterations - - 1 0.1 0.166 0.0178 0.135 0.205 0.95 -524. 5 - # i 2 more variables: antigen.isos , nlm.convergence.code - ---- - - structure(list(minimum = 523.575044823023, estimate = -1.7955958453869, - gradient = 3.60891331241403e-06, hessian = structure(86.991906300701, dim = c(1L, - 1L)), code = 1L, iterations = 5L), class = c("seroincidence", - "list"), lambda_start = 0.1, antigen_isos = c("HlyE_IgG", "HlyE_IgA" - )) - diff --git a/tests/testthat/_snaps/est_seroincidence.md b/tests/testthat/_snaps/est_seroincidence.md new file mode 100644 index 000000000..7417bc4fa --- /dev/null +++ b/tests/testthat/_snaps/est_seroincidence.md @@ -0,0 +1,97 @@ +# results are as expected for typhoid data + + Code + summary(typhoid_results, coverage = 0.95) + Output + # A tibble: 1 x 10 + est.start incidence.rate SE CI.lwr CI.upr coverage log.lik iterations + + 1 0.1 0.166 0.0178 0.135 0.205 0.95 -524. 5 + # i 2 more variables: antigen.isos , nlm.convergence.code + +--- + + structure(list(minimum = 523.575044823023, estimate = -1.7955958453869, + gradient = 3.60891331241403e-06, hessian = structure(86.991906300701, dim = c(1L, + 1L)), code = 1L, iterations = 5L), class = c("seroincidence", + "list"), lambda_start = 0.1, antigen_isos = c("HlyE_IgG", "HlyE_IgA" + )) + +# verbose output is consistent + + Code + est_seroincidence(pop_data = sees_pop_data_pk_100, sr_param = typhoid_curves_nostrat_100, + noise_param = example_noise_params_pk, antigen_isos = c("HlyE_IgG", + "HlyE_IgA"), verbose = TRUE) + Message + i nrow(sr_params) = 200 + Initial negative log-likelihood: 533.379886031329 + about to call `nlm()` + Output + iteration = 0 + Step: + [1] 0 + Parameter: + [1] -2.3025851 + Function Value + [1] 533.37989 + Gradient: + [1] -35.939944 + + iteration = 1 + Step: + [1] 0.33515619 + Parameter: + [1] -1.9674289 + Function Value + [1] 524.8067 + Gradient: + [1] -14.024947 + + iteration = 2 + Step: + [1] 0.21449 + Parameter: + [1] -1.7529389 + Function Value + [1] 523.65497 + Gradient: + [1] 3.7657315 + + iteration = 3 + Step: + [1] -0.04540084 + Parameter: + [1] -1.7983397 + Function Value + [1] 523.57537 + Gradient: + [1] -0.23844672 + + iteration = 4 + Step: + [1] 0.0027035963 + Parameter: + [1] -1.7956361 + Function Value + [1] 523.57504 + Gradient: + [1] -0.0035022135 + + iteration = 5 + Parameter: + [1] -1.7955958 + Function Value + [1] 523.57504 + Gradient: + [1] 3.6089133e-06 + + Relative gradient close to zero. + Current iterate is probably solution. + + `seroincidence` object estimated given the following setup: + a) `antigen_isos`: HlyE_IgG, HlyE_IgA + b) `lambda_start`: 0.1 + Call the `summary()` function to obtain output results. + Call the `autoplot()` function to graph the log-likelihood curve. + diff --git a/tests/testthat/_snaps/est.incidence.by.md b/tests/testthat/_snaps/est_seroincidence_by.md similarity index 86% rename from tests/testthat/_snaps/est.incidence.by.md rename to tests/testthat/_snaps/est_seroincidence_by.md index 0679befa4..24777df09 100644 --- a/tests/testthat/_snaps/est.incidence.by.md +++ b/tests/testthat/_snaps/est_seroincidence_by.md @@ -1,4 +1,4 @@ -# `est.incidence.by()` produces consistent results for typhoid data +# `est_seroincidence_by()` produces consistent results for sample data structure(list("Stratum 1" = structure(list(minimum = 269.456336163178, estimate = -1.9659114149187, gradient = -9.97551535545146e-06, @@ -17,13 +17,13 @@ # a warning is produced when `strata = NULL Code - est.incidence.by(strata = NULL, pop_data = sees_pop_data_pk_100, curve_param = typhoid_curves_nostrat_100, + est_seroincidence_by(strata = NULL, pop_data = sees_pop_data_pk_100, sr_param = typhoid_curves_nostrat_100, noise_param = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA")) Condition Warning: The `strata` argument to `est.incidence.by()` is missing. - i If you do not want to stratify your data, consider using the `est.incidence()` function to simplify your code and avoid this warning. + i If you do not want to stratify your data, consider using the `est_seroincidence()` function to simplify your code and avoid this warning. i Since the `strata` argument is empty, `est.incidence.by()` will return a object, instead of a object. Output `seroincidence` object estimated given the following setup: diff --git a/tests/testthat/_snaps/f_dev.md b/tests/testthat/_snaps/f_dev.md index 2a1411332..b1c8c4a28 100644 --- a/tests/testthat/_snaps/f_dev.md +++ b/tests/testthat/_snaps/f_dev.md @@ -8,3 +8,17 @@ 283.883900391513, 280.994834667768, 278.438022140511, 276.171332889367, 274.159545559505, 272.372931568773, 270.786191204759) +# `f_dev()` handles censored data + + 0.548585701501278 + +--- + + c(0.063482102497246, 0.146664773685652, 0.548585701501278, 0.632155591621672, + 0.821834759847586, 0.821834759847586, 0.821834759847586) + +--- + + c(0.389528012302306, 0.245924981049843, 0.668575822086022, 0.632155591621672, + 0.632155591621672, 0.632155591621672, 0.632155591621672) + diff --git a/tests/testthat/_snaps/graph.curve.params/curve-black-chains.svg b/tests/testthat/_snaps/graph.curve.params/curve-black-chains.svg new file mode 100644 index 000000000..0e064bf93 --- /dev/null +++ b/tests/testthat/_snaps/graph.curve.params/curve-black-chains.svg @@ -0,0 +1,313 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +HlyE_IgA + + + + + + + + + +HlyE_IgG + + + +0 +400 +800 +1200 + +0 +400 +800 +1200 + +0.1 +10.0 +1,000.0 +Days since fever onset +ELISA units + + + + + + +5% quantile +55% quantile +95% quantile +curve-black-chains + + diff --git a/tests/testthat/_snaps/graph.curve.params/curve-custom-quantiles.svg b/tests/testthat/_snaps/graph.curve.params/curve-custom-quantiles.svg new file mode 100644 index 000000000..b31090a5a --- /dev/null +++ b/tests/testthat/_snaps/graph.curve.params/curve-custom-quantiles.svg @@ -0,0 +1,110 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +HlyE_IgA + + + + + + + + + +HlyE_IgG + + + +0 +400 +800 +1200 + +0 +400 +800 +1200 + +0.1 +10.0 +1,000.0 +Days since fever onset +ELISA units + + + +5% quantile +55% quantile +95% quantile +curve-custom-quantiles + + diff --git a/tests/testthat/_snaps/graph.curve.params/curve-quantiles-and-samples.svg b/tests/testthat/_snaps/graph.curve.params/curve-quantiles-and-samples.svg index a004e6b62..0edb09f1b 100644 --- a/tests/testthat/_snaps/graph.curve.params/curve-quantiles-and-samples.svg +++ b/tests/testthat/_snaps/graph.curve.params/curve-quantiles-and-samples.svg @@ -18,6 +18,7 @@ + @@ -28,116 +29,116 @@ - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -150,116 +151,116 @@ - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -293,22 +294,22 @@ 800 1200 -0.1 -10.0 -1,000.0 +0.1 +10.0 +1,000.0 Days since fever onset ELISA units -MCMC chain - - - - - -1 -2 -median -10% quantile -90% quantile +MCMC chain + + + + + +1 +2 +10% quantile +50% quantile +90% quantile curve-quantiles-and-samples diff --git a/tests/testthat/_snaps/graph.curve.params/curve-quantiles.svg b/tests/testthat/_snaps/graph.curve.params/curve-quantiles.svg index cbc4e2137..982c81465 100644 --- a/tests/testthat/_snaps/graph.curve.params/curve-quantiles.svg +++ b/tests/testthat/_snaps/graph.curve.params/curve-quantiles.svg @@ -18,95 +18,93 @@ + - - + + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + - - + + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + - - + + - -HlyE_IgA + +HlyE_IgA - - + + - -HlyE_IgG + +HlyE_IgG - -0 -400 -800 -1200 - -0 -400 -800 -1200 - -1 -10 -100 -1,000 -Days since fever onset + +0 +400 +800 +1200 + +0 +400 +800 +1200 + +0.1 +10.0 +1,000.0 +Days since fever onset ELISA units - - - -10% quantile -90% quantile -median -curve-quantiles + + + +10% quantile +50% quantile +90% quantile +curve-quantiles diff --git a/tests/testthat/_snaps/graph.curve.params/curve-samples-log-x.svg b/tests/testthat/_snaps/graph.curve.params/curve-samples-log-x.svg new file mode 100644 index 000000000..8986a6132 --- /dev/null +++ b/tests/testthat/_snaps/graph.curve.params/curve-samples-log-x.svg @@ -0,0 +1,321 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +HlyE_IgA + + + + + + + + + +HlyE_IgG + + + +0.1 +1.0 +10.0 +100.0 +1,000.0 + +0.1 +1.0 +10.0 +100.0 +1,000.0 + +0.1 +10.0 +1,000.0 +Days since fever onset +ELISA units +MCMC chain + + + + + +1 +2 +10% quantile +50% quantile +90% quantile +curve-samples-log_x + + diff --git a/tests/testthat/_snaps/graph.curve.params/curve-samples.svg b/tests/testthat/_snaps/graph.curve.params/curve-samples.svg index 0ff02d8c2..05b9b5ddc 100644 --- a/tests/testthat/_snaps/graph.curve.params/curve-samples.svg +++ b/tests/testthat/_snaps/graph.curve.params/curve-samples.svg @@ -18,6 +18,7 @@ + @@ -28,113 +29,113 @@ - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -147,113 +148,113 @@ - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -287,9 +288,9 @@ 800 1200 -0.1 -10.0 -1,000.0 +0.1 +10.0 +1,000.0 Days since fever onset ELISA units MCMC chain diff --git a/tests/testthat/_snaps/linux/compare_seroincidence.md b/tests/testthat/_snaps/linux/compare_seroincidence.md new file mode 100644 index 000000000..08e842f30 --- /dev/null +++ b/tests/testthat/_snaps/linux/compare_seroincidence.md @@ -0,0 +1,18 @@ +# compare_seroincidence works with two seroincidence objects + + Code + result + Output + + Two-sample z-test for difference in seroincidence rates + + data: seroincidence estimates + z = 1.611, p-value = 0.1072 + alternative hypothesis: true difference in incidence rates is not equal to 0 + 95 percent confidence interval: + -0.01291279 0.13212345 + sample estimates: + incidence rate 1 incidence rate 2 difference + 0.19963354 0.14002820 0.05960533 + + diff --git a/tests/testthat/_snaps/print.seroincidence.by.md b/tests/testthat/_snaps/print.seroincidence.by.md index 83648c02f..6bc9b6083 100644 --- a/tests/testthat/_snaps/print.seroincidence.by.md +++ b/tests/testthat/_snaps/print.seroincidence.by.md @@ -9,7 +9,7 @@ This object is a list of `seroincidence` objects, with added meta-data attributes: `antigen_isos` - Character vector of antigen isotypes used in analysis. - `Strata` - Input parameter strata of function `est.incidence.by()` + `Strata` - Input parameter strata of function `est_seroincidence_by()` Call the `summary()` function to obtain output results. diff --git a/tests/testthat/_snaps/sim_pop_data_multi/pop_data_multi.csv b/tests/testthat/_snaps/sim_pop_data_multi/pop_data_multi.csv index 93304d65e..af947ecdc 100644 --- a/tests/testthat/_snaps/sim_pop_data_multi/pop_data_multi.csv +++ b/tests/testthat/_snaps/sim_pop_data_multi/pop_data_multi.csv @@ -1,10001 +1,15001 @@ -age,id,antigen_iso,value,lambda.sim,cluster -3.53,1,HlyE_IgA,0.757356,0.05,1 -3.53,1,HlyE_IgG,0.520421,0.05,1 -2.27,2,HlyE_IgA,0.818508,0.05,1 -2.27,2,HlyE_IgG,0.707485,0.05,1 -9.05,3,HlyE_IgA,0.150392,0.05,1 -9.05,3,HlyE_IgG,0.506036,0.05,1 -5.94,4,HlyE_IgA,0.837328,0.05,1 -5.94,4,HlyE_IgG,0.870043,0.05,1 -9.88,5,HlyE_IgA,0.296693,0.05,1 -9.88,5,HlyE_IgG,0.272395,0.05,1 -0.46,6,HlyE_IgA,0.366182,0.05,1 -0.46,6,HlyE_IgG,0.32505,0.05,1 -2.67,7,HlyE_IgA,0.685483,0.05,1 -2.67,7,HlyE_IgG,0.429879,0.05,1 -5.65,8,HlyE_IgA,0.423016,0.05,1 -5.65,8,HlyE_IgG,0.392611,0.05,1 -5.53,9,HlyE_IgA,0.448711,0.05,1 -5.53,9,HlyE_IgG,0.464039,0.05,1 -6.21,10,HlyE_IgA,0.721066,0.05,1 -6.21,10,HlyE_IgG,2.00096,0.05,1 -1.98,11,HlyE_IgA,0.20662,0.05,1 -1.98,11,HlyE_IgG,0.636274,0.05,1 -1.56,12,HlyE_IgA,0.473856,0.05,1 -1.56,12,HlyE_IgG,0.509714,0.05,1 -5.35,13,HlyE_IgA,0.347482,0.05,1 -5.35,13,HlyE_IgG,0.576382,0.05,1 -0.53,14,HlyE_IgA,0.871964,0.05,1 -0.53,14,HlyE_IgG,0.701732,0.05,1 -8.67,15,HlyE_IgA,0.496802,0.05,1 -8.67,15,HlyE_IgG,0.667926,0.05,1 -8.96,16,HlyE_IgA,1.59436,0.05,1 -8.96,16,HlyE_IgG,2.38523,0.05,1 -8.62,17,HlyE_IgA,0.640458,0.05,1 -8.62,17,HlyE_IgG,30.5785,0.05,1 -5.7,18,HlyE_IgA,0.988588,0.05,1 -5.7,18,HlyE_IgG,0.623984,0.05,1 -6.74,19,HlyE_IgA,0.362629,0.05,1 -6.74,19,HlyE_IgG,0.470004,0.05,1 -2.53,20,HlyE_IgA,2.02732,0.05,1 -2.53,20,HlyE_IgG,1496.22,0.05,1 -9.15,21,HlyE_IgA,0.116825,0.05,1 -9.15,21,HlyE_IgG,0.420959,0.05,1 -8.8,22,HlyE_IgA,0.315195,0.05,1 -8.8,22,HlyE_IgG,0.439954,0.05,1 -2.69,23,HlyE_IgA,0.456437,0.05,1 -2.69,23,HlyE_IgG,0.501798,0.05,1 -8.66,24,HlyE_IgA,0.63082,0.05,1 -8.66,24,HlyE_IgG,0.182757,0.05,1 -3,25,HlyE_IgA,0.625864,0.05,1 -3,25,HlyE_IgG,0.817629,0.05,1 -0.63,26,HlyE_IgA,0.112744,0.05,1 -0.63,26,HlyE_IgG,0.338796,0.05,1 -7.54,27,HlyE_IgA,0.39961,0.05,1 -7.54,27,HlyE_IgG,0.320337,0.05,1 -2.36,28,HlyE_IgA,0.617571,0.05,1 -2.36,28,HlyE_IgG,0.513845,0.05,1 -8.89,29,HlyE_IgA,0.553631,0.05,1 -8.89,29,HlyE_IgG,0.954865,0.05,1 -6.48,30,HlyE_IgA,809.283,0.05,1 -6.48,30,HlyE_IgG,30.0221,0.05,1 -8.2,31,HlyE_IgA,0.717314,0.05,1 -8.2,31,HlyE_IgG,0.256721,0.05,1 -5.49,32,HlyE_IgA,0.652137,0.05,1 -5.49,32,HlyE_IgG,0.783192,0.05,1 -3.91,33,HlyE_IgA,0.442891,0.05,1 -3.91,33,HlyE_IgG,0.398846,0.05,1 -9.98,34,HlyE_IgA,0.585922,0.05,1 -9.98,34,HlyE_IgG,0.382025,0.05,1 -6.52,35,HlyE_IgA,0.434013,0.05,1 -6.52,35,HlyE_IgG,0.514917,0.05,1 -2.29,36,HlyE_IgA,0.679751,0.05,1 -2.29,36,HlyE_IgG,0.402258,0.05,1 -3.8,37,HlyE_IgA,0.338475,0.05,1 -3.8,37,HlyE_IgG,0.433155,0.05,1 -2.01,38,HlyE_IgA,0.95388,0.05,1 -2.01,38,HlyE_IgG,0.811151,0.05,1 -1.52,39,HlyE_IgA,0.80270300000000006,0.05,1 -1.52,39,HlyE_IgG,0.514549,0.05,1 -7.08,40,HlyE_IgA,0.501302,0.05,1 -7.08,40,HlyE_IgG,0.258173,0.05,1 -7.33,41,HlyE_IgA,0.126101,0.05,1 -7.33,41,HlyE_IgG,0.540212,0.05,1 -3.74,42,HlyE_IgA,0.54187,0.05,1 -3.74,42,HlyE_IgG,0.505896,0.05,1 -7.62,43,HlyE_IgA,0.45461,0.05,1 -7.62,43,HlyE_IgG,0.559519,0.05,1 -6.27,44,HlyE_IgA,0.641723,0.05,1 -6.27,44,HlyE_IgG,0.675166,0.05,1 -9.05,45,HlyE_IgA,26.9616,0.05,1 -9.05,45,HlyE_IgG,52.7251,0.05,1 -2.58,46,HlyE_IgA,0.610145,0.05,1 -2.58,46,HlyE_IgG,0.908863,0.05,1 -2.56,47,HlyE_IgA,0.627547,0.05,1 -2.56,47,HlyE_IgG,0.354255,0.05,1 -7.94,48,HlyE_IgA,0.565011,0.05,1 -7.94,48,HlyE_IgG,0.584268,0.05,1 -9.03,49,HlyE_IgA,0.494711,0.05,1 -9.03,49,HlyE_IgG,0.472405,0.05,1 -5.7,50,HlyE_IgA,2.99669,0.05,1 -5.7,50,HlyE_IgG,134.091,0.05,1 -9.54,51,HlyE_IgA,0.718728,0.05,1 -9.54,51,HlyE_IgG,0.584489,0.05,1 -0.48,52,HlyE_IgA,0.195877,0.05,1 -0.48,52,HlyE_IgG,0.184285,0.05,1 -1.02,53,HlyE_IgA,98.4574,0.05,1 -1.02,53,HlyE_IgG,5065.37,0.05,1 -9.56,54,HlyE_IgA,0.631364,0.05,1 -9.56,54,HlyE_IgG,0.41432,0.05,1 -8.25,55,HlyE_IgA,0.522691,0.05,1 -8.25,55,HlyE_IgG,0.542056,0.05,1 -8.63,56,HlyE_IgA,0.563916,0.05,1 -8.63,56,HlyE_IgG,0.541728,0.05,1 -8.11,57,HlyE_IgA,0.458996,0.05,1 -8.11,57,HlyE_IgG,0.535566,0.05,1 -1.67,58,HlyE_IgA,0.736355,0.05,1 -1.67,58,HlyE_IgG,0.391294,0.05,1 -3.07,59,HlyE_IgA,0.42064,0.05,1 -3.07,59,HlyE_IgG,0.311784,0.05,1 -7.15,60,HlyE_IgA,106.366,0.05,1 -7.15,60,HlyE_IgG,168.18,0.05,1 -6.68,61,HlyE_IgA,15.1038,0.05,1 -6.68,61,HlyE_IgG,25.7071,0.05,1 -8.45,62,HlyE_IgA,0.581226,0.05,1 -8.45,62,HlyE_IgG,0.248931,0.05,1 -9.36,63,HlyE_IgA,0.519586,0.05,1 -9.36,63,HlyE_IgG,0.451352,0.05,1 -6.91,64,HlyE_IgA,0.71877,0.05,1 -6.91,64,HlyE_IgG,0.043813,0.05,1 -5.57,65,HlyE_IgA,0.539674,0.05,1 -5.57,65,HlyE_IgG,0.296516,0.05,1 -4.27,66,HlyE_IgA,0.456468,0.05,1 -4.27,66,HlyE_IgG,0.793374,0.05,1 -3.81,67,HlyE_IgA,0.326477,0.05,1 -3.81,67,HlyE_IgG,0.324429,0.05,1 -0.3,68,HlyE_IgA,0.199652,0.05,1 -0.3,68,HlyE_IgG,0.444905,0.05,1 -3.5,69,HlyE_IgA,0.328381,0.05,1 -3.5,69,HlyE_IgG,0.892983,0.05,1 -5.86,70,HlyE_IgA,0.171078,0.05,1 -5.86,70,HlyE_IgG,0.318071,0.05,1 -4.79,71,HlyE_IgA,0.204388,0.05,1 -4.79,71,HlyE_IgG,0.77087,0.05,1 -0.82,72,HlyE_IgA,0.807896,0.05,1 -0.82,72,HlyE_IgG,0.346036,0.05,1 -7.26,73,HlyE_IgA,0.69084,0.05,1 -7.26,73,HlyE_IgG,0.858963,0.05,1 -9.85,74,HlyE_IgA,0.397659,0.05,1 -9.85,74,HlyE_IgG,0.467506,0.05,1 -7.15,75,HlyE_IgA,0.382159,0.05,1 -7.15,75,HlyE_IgG,0.257855,0.05,1 -9.07,76,HlyE_IgA,0.356264,0.05,1 -9.07,76,HlyE_IgG,0.230123,0.05,1 -5.41,77,HlyE_IgA,0.462769,0.05,1 -5.41,77,HlyE_IgG,0.638912,0.05,1 -5.6,78,HlyE_IgA,0.518003,0.05,1 -5.6,78,HlyE_IgG,0.237031,0.05,1 -4.86,79,HlyE_IgA,0.750516,0.05,1 -4.86,79,HlyE_IgG,0.54093,0.05,1 -5.43,80,HlyE_IgA,0.331273,0.05,1 -5.43,80,HlyE_IgG,0.661672,0.05,1 -5.77,81,HlyE_IgA,1.28145,0.05,1 -5.77,81,HlyE_IgG,1.47943,0.05,1 -7.91,82,HlyE_IgA,4.09252,0.05,1 -7.91,82,HlyE_IgG,7.97472,0.05,1 -2.62,83,HlyE_IgA,1.37724,0.05,1 -2.62,83,HlyE_IgG,100.919,0.05,1 -6.35,84,HlyE_IgA,0.287204,0.05,1 -6.35,84,HlyE_IgG,0.490109,0.05,1 -5.88,85,HlyE_IgA,0.149619,0.05,1 -5.88,85,HlyE_IgG,0.746815,0.05,1 -7.47,86,HlyE_IgA,0.634066,0.05,1 -7.47,86,HlyE_IgG,0.100768,0.05,1 -4.38,87,HlyE_IgA,0.713388,0.05,1 -4.38,87,HlyE_IgG,0.733765,0.05,1 -1.85,88,HlyE_IgA,0.500935,0.05,1 -1.85,88,HlyE_IgG,0.229304,0.05,1 -3.33,89,HlyE_IgA,0.212624,0.05,1 -3.33,89,HlyE_IgG,0.35957,0.05,1 -5.92,90,HlyE_IgA,1.44665,0.05,1 -5.92,90,HlyE_IgG,38.7811,0.05,1 -1.07,91,HlyE_IgA,0.205253,0.05,1 -1.07,91,HlyE_IgG,0.518253,0.05,1 -7.85,92,HlyE_IgA,1.18125,0.05,1 -7.85,92,HlyE_IgG,3.21254,0.05,1 -1.36,93,HlyE_IgA,0.276043,0.05,1 -1.36,93,HlyE_IgG,0.217807,0.05,1 -9.86,94,HlyE_IgA,0.758874,0.05,1 -9.86,94,HlyE_IgG,1.21994,0.05,1 -1.93,95,HlyE_IgA,0.77072,0.05,1 -1.93,95,HlyE_IgG,0.518651,0.05,1 -6.27,96,HlyE_IgA,0.409223,0.05,1 -6.27,96,HlyE_IgG,0.664473,0.05,1 -3.73,97,HlyE_IgA,0.575763,0.05,1 -3.73,97,HlyE_IgG,0.34842,0.05,1 -4.55,98,HlyE_IgA,0.3207,0.05,1 -4.55,98,HlyE_IgG,0.578259,0.05,1 -4.97,99,HlyE_IgA,0.474694,0.05,1 -4.97,99,HlyE_IgG,0.652978,0.05,1 -2.05,100,HlyE_IgA,0.829847,0.05,1 -2.05,100,HlyE_IgG,0.96676,0.05,1 -0.55,1,HlyE_IgA,0.359606,0.05,2 -0.55,1,HlyE_IgG,0.726537,0.05,2 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+1.66,48,HlyE_IgG,0.232478,0.3,50,10 +1.59,49,HlyE_IgA,0.717771,0.3,50,10 +1.59,49,HlyE_IgG,0.502472,0.3,50,10 +2.42,50,HlyE_IgA,0.409948,0.3,50,10 +2.42,50,HlyE_IgG,0.551265,0.3,50,10 diff --git a/tests/testthat/_snaps/strata/strata-ests.csv b/tests/testthat/_snaps/strata/strata-ests.csv new file mode 100644 index 000000000..2d1954389 --- /dev/null +++ b/tests/testthat/_snaps/strata/strata-ests.csv @@ -0,0 +1,10 @@ +Stratum,ageCat,Country,n +Stratum 1,<5,Bangladesh,101 +Stratum 2,<5,Nepal,171 +Stratum 3,<5,Pakistan,126 +Stratum 4,5-15,Bangladesh,256 +Stratum 5,5-15,Nepal,378 +Stratum 6,5-15,Pakistan,261 +Stratum 7,16+,Bangladesh,44 +Stratum 8,16+,Nepal,211 +Stratum 9,16+,Pakistan,107 diff --git a/tests/testthat/_snaps/stratify_data.md b/tests/testthat/_snaps/stratify_data.md index 19748bf92..72127b491 100644 --- a/tests/testthat/_snaps/stratify_data.md +++ b/tests/testthat/_snaps/stratify_data.md @@ -23,7 +23,7 @@ $antigen_isos [1] "HlyE_IgA" "HlyE_IgG" - $curve_params + $sr_params # 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AP4AAAMTAAAABQAAAA4AAAACQATDzgANHOhAAuFiospgWAAAAA4AAAACP9HgZLISU5g/wqRX + 3cpmjwAAAA4AAAACP+BFPcbXYck/+YNhwTKHMwAAAA4AAAACQVMS0AAAAABBUxLQAAAAAAAA + ABAAAAACAAQACQAAAAhIbHlFX0lnQQAEAAkAAAAISGx5RV9JZ0cAAAQCAAAD/wAAABAAAAAF + AAQACQAAAAJudQAEAAkAAAADZXBzAAQACQAAAAV5LmxvdwAEAAkAAAAGeS5oaWdoAAQACQAA + AAthbnRpZ2VuX2lzbwAABAIAAAT/AAAADQAAAAKAAAAA/////gAABAIAAAL/AAAAEAAAAAQA + BAAJAAAADG5vaXNlX3BhcmFtcwAEAAkAAAAGdGJsX2RmAAQACQAAAAN0YmwABAAJAAAACmRh + dGEuZnJhbWUAAAQCAAAF/wAAABAAAAAEAAQACQAAAAhIbHlFX0lnQQAEAAkAAAAISGx5RV9J + Z0cABAAJAAAAB0xQU19JZ0EABAAJAAAAB0xQU19JZ0cAAAD+AAAEAgAAA/8AAAAQAAAABAAE + AAkAAAAIcG9wX2RhdGEABAAJAAAADGFudGlnZW5faXNvcwAEAAkAAAAJc3JfcGFyYW1zAAQA + CQAAAAxub2lzZV9wYXJhbXMAAAQCAAAC/wAAABAAAAACAAQACQAAABliaW9tYXJrZXJfZGF0 + YV9hbmRfcGFyYW1zAAQACQAAAARsaXN0AAAA/gAABAIAAAP/AAAAEAAAAAIABAAJAAAACVN0 + cmF0dW0gMQAEAAkAAAAJU3RyYXR1bSAyAAAEAgAABf8AAAAQAAAAAgAEAAkAAAAISGx5RV9J + Z0EABAAJAAAACEhseUVfSWdHAAAEAgAAAAEABAAJAAAABnN0cmF0YQAAAxMAAAADAAAAEAAA + AAIABAAJAAAACVN0cmF0dW0gMQAEAAkAAAAJU3RyYXR1bSAyAAAAEAAAAAIABAAJAAAAA2Fr + dQAEAAkAAAADa2doAAAADQAAAAIAAAA1AAAALwAABAIAAAP/AAAAEAAAAAMABAAJAAAAB1N0 + cmF0dW0ABAAJAAAACWNhdGNobWVudAAEAAkAAAABbgAABAIAAAT/AAAADQAAAAKAAAAA//// + /gAABAIAAAL/AAAAEAAAAAMABAAJAAAABnRibF9kZgAEAAkAAAADdGJsAAQACQAAAApkYXRh + LmZyYW1lAAAEAgAAAAEABAAJAAAAC3N0cmF0YV92YXJzAAAAEAAAAAEABAAJAAAACWNhdGNo + bWVudAAAAP4AAAQCAAAC/wAAABAAAAACAAQACQAAAB5iaW9tYXJrZXJfZGF0YV9hbmRfcGFy + YW1zLmxpc3QABAAJAAAABGxpc3QAAAD+ # stratify_data() produces consistent results with no strata - WAoAAAACAAQEAgACAwAAAAITAAAAAQAAAxMAAAAEAAADEwAAAAMAAAAOAAAAyD/iLYdMuu6p + WAoAAAACAAQEAQACAwAAAAITAAAAAQAAAxMAAAAEAAADEwAAAAMAAAAOAAAAyD/iLYdMuu6p QBa+jE14JU0/86fO30MTcj/xW0jz5L6xP/beNg0cDBRACH7SvbAs4j/o76/BtVLEP/5ay8ck fXQ/9oq+T+T4RUAI08kFQgk6P5+FlLKHr8BAHHYNr0i2BQAAAAAAAAAAQAc5mhtjd41AGY/x P8jC0j/3HOaE/CAZQA4fFqI2fstABl4YcdWfHUARlGcAjI88QDolsdnXTFg/zsXBZnhrMkAL @@ -672,11 +672,11 @@ AApkYXRhLmZyYW1lAAAEAgAABf8AAAAQAAAABAAEAAkAAAAISGx5RV9JZ0EABAAJAAAACEhs eUVfSWdHAAQACQAAAAdMUFNfSWdBAAQACQAAAAdMUFNfSWdHAAAA/gAAABAAAAACAAQACQAA AAhIbHlFX0lnQQAEAAkAAAAISGx5RV9JZ0cAAAQCAAAD/wAAABAAAAAEAAQACQAAAAhwb3Bf - ZGF0YQAEAAkAAAAMY3VydmVfcGFyYW1zAAQACQAAAAxub2lzZV9wYXJhbXMABAAJAAAADGFu - dGlnZW5faXNvcwAABAIAAAL/AAAAEAAAAAIABAAJAAAAGWJpb21hcmtlcl9kYXRhX2FuZF9w - YXJhbXMABAAJAAAABGxpc3QAAAD+AAAEAgAAA/8AAAAQAAAAAQAEAAkAAAAIYWxsIGRhdGEA - AAQCAAAF/wAAABAAAAACAAQACQAAAAhIbHlFX0lnQQAEAAkAAAAISGx5RV9JZ0cAAAQCAAAA - AQAEAAkAAAAGc3RyYXRhAAADEwAAAAEAAAAKAAAAAYAAAAAAAAQCAAAC/wAAABAAAAADAAQA - CQAAAAZ0YmxfZGYABAAJAAAAA3RibAAEAAkAAAAKZGF0YS5mcmFtZQAABAIAAAT/AAAADQAA - AAKAAAAA/////wAABAIAAAP/AAAAEAAAAAEABAAJAAAAB1N0cmF0dW0AAAD+AAAA/g== + ZGF0YQAEAAkAAAAJc3JfcGFyYW1zAAQACQAAAAxub2lzZV9wYXJhbXMABAAJAAAADGFudGln + ZW5faXNvcwAABAIAAAL/AAAAEAAAAAIABAAJAAAAGWJpb21hcmtlcl9kYXRhX2FuZF9wYXJh + bXMABAAJAAAABGxpc3QAAAD+AAAEAgAAA/8AAAAQAAAAAQAEAAkAAAAIYWxsIGRhdGEAAAQC + AAAF/wAAABAAAAACAAQACQAAAAhIbHlFX0lnQQAEAAkAAAAISGx5RV9JZ0cAAAQCAAAAAQAE + AAkAAAAGc3RyYXRhAAADEwAAAAEAAAAKAAAAAYAAAAAAAAQCAAAC/wAAABAAAAADAAQACQAA + AAZ0YmxfZGYABAAJAAAAA3RibAAEAAkAAAAKZGF0YS5mcmFtZQAABAIAAAT/AAAADQAAAAKA + AAAA/////wAABAIAAAP/AAAAEAAAAAEABAAJAAAAB1N0cmF0dW0AAAD+AAAA/g== diff --git a/tests/testthat/_snaps/warn_missing_strata.md b/tests/testthat/_snaps/warn_missing_strata.md index 0f7410e43..43d789cff 100644 --- a/tests/testthat/_snaps/warn_missing_strata.md +++ b/tests/testthat/_snaps/warn_missing_strata.md @@ -16,7 +16,7 @@ Condition Warning: `sees_pop_data_pk_100` is missing `place` and will only be stratified by `Country` - i To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. + i To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est_seroincidence_by()`. Output [1] "Country" diff --git a/tests/testthat/_snaps/windows/compare_seroincidence.md b/tests/testthat/_snaps/windows/compare_seroincidence.md new file mode 100644 index 000000000..08e842f30 --- /dev/null +++ b/tests/testthat/_snaps/windows/compare_seroincidence.md @@ -0,0 +1,18 @@ +# compare_seroincidence works with two seroincidence objects + + Code + result + Output + + Two-sample z-test for difference in seroincidence rates + + data: seroincidence estimates + z = 1.611, p-value = 0.1072 + alternative hypothesis: true difference in incidence rates is not equal to 0 + 95 percent confidence interval: + -0.01291279 0.13212345 + sample estimates: + incidence rate 1 incidence rate 2 difference + 0.19963354 0.14002820 0.05960533 + + diff --git a/tests/testthat/fixtures/make-test_sim_results.R b/tests/testthat/fixtures/make-test_sim_results.R new file mode 100644 index 000000000..1b04551d8 --- /dev/null +++ b/tests/testthat/fixtures/make-test_sim_results.R @@ -0,0 +1,53 @@ +dmcmc <- typhoid_curves_nostrat_100 + +n_cores <- 2 + +nclus <- 20 +# cross-sectional sample size +nrep <- c(50, 100, 150, 200) + +# incidence rate in e +lambdas <- c(.05, .1, .15, .2, .5, .8) +antibodies <- c("HlyE_IgA", "HlyE_IgG") +dlims <- rbind( + "HlyE_IgA" = c(min = 0, max = 0.5), + "HlyE_IgG" = c(min = 0, max = 0.5) +) +sim_df <- + sim_pop_data_multi( + n_cores = n_cores, + lambdas = lambdas, + nclus = nclus, + sample_sizes = nrep, + age_range = lifespan, + antigen_isos = antibodies, + renew_params = FALSE, + add_noise = TRUE, + curve_params = dmcmc, + noise_limits = dlims, + format = "long" + ) +cond <- tibble::tibble( + antigen_iso = c("HlyE_IgG", "HlyE_IgA"), + nu = c(0.5, 0.5), # Biologic noise (nu) + eps = c(0, 0), # M noise (eps) + y.low = c(1, 1), # low cutoff (llod) + y.high = c(5e6, 5e6) +) +ests <- + est_seroincidence_by( + pop_data = sim_df, + curve_params = dmcmc, + noise_params = cond, + num_cores = n_cores, + strata = c("lambda.sim", "sample_size", "cluster"), + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + verbose = FALSE, + build_graph = FALSE, # slows down the function substantially + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + +ests |> + summary() |> + readr::write_rds(file = "tests/testthat/fixtures/test_sim_results.rds") diff --git a/tests/testthat/fixtures/test_sim_results.rds b/tests/testthat/fixtures/test_sim_results.rds new file mode 100644 index 000000000..97d6689af Binary files /dev/null and b/tests/testthat/fixtures/test_sim_results.rds differ diff --git a/tests/testthat/helper-noise_ymax.R b/tests/testthat/helper-noise_ymax.R new file mode 100644 index 000000000..f3b59309b --- /dev/null +++ b/tests/testthat/helper-noise_ymax.R @@ -0,0 +1,46 @@ +noise_params_test0 <- function(ymax = 0.5, yval = 0.6) { + + cur_antibody <- "HlyE_IgA" + + # load in longitudinal parameters + curve_params <- + serocalculator::typhoid_curves_nostrat_100 |> + dplyr::filter(.data$antigen_iso == cur_antibody) |> + dplyr::slice_head(n = 100) + + if (!is.element("d", names(curve_params))) { + curve_params <- + curve_params |> + dplyr::mutate( + alpha = .data$alpha * 365.25, + d = .data$r - 1 + ) + } + + xs_data <- serocalculator::sees_pop_data_pk_100 |> + dplyr::filter( + .data$antigen_iso == cur_antibody + ) |> + dplyr::slice_head(n = 1) |> + dplyr::mutate(value = yval) + + # Load noise params + noise_params <- tibble( + antigen_iso = cur_antibody, + nu = 0.5, # Biologic noise (nu) + eps = 0, # M noise (eps) + y.low = 0, # low cutoff (llod) + y.high = ymax # high cutoff (y.high) + ) + + lambda <- 0.1 + f_dev0( + lambda = lambda, + csdata = xs_data, + lnpars = curve_params, + cond = noise_params + ) + +} + +noise_params_test <- Vectorize(noise_params_test0, c("ymax", "yval")) diff --git a/tests/testthat/test-ab.R b/tests/testthat/test-ab.R index d859b01bd..ea2027855 100644 --- a/tests/testthat/test-ab.R +++ b/tests/testthat/test-ab.R @@ -1,32 +1,32 @@ test_that("`ab()` works consistently", { - par1 <- matrix( - c( - 1.11418923843475, 1, 0.12415057798022207, 0.24829344792968783, - 0.01998946878312856, 0.0012360802436587237, 1.297194045996013, - 1.3976510415108334, 1, 0.2159993563893431, 0.4318070551383313, - 0.0015146395107173347, 0.0003580062906750277, 1.5695811573082081 - ), - nrow = 7L, - ncol = 2L, - dimnames = list( - params = c("y0", "b0", "mu0", "mu1", "c1", "alpha", "shape_r"), - antigen_iso = c("HlyE_IgA", "HlyE_IgG") - ) - ) - t <- 0:1444 - blims <- matrix( - rep(c(0, 0.5), each = 2L), - nrow = 2L, - ncol = 2L, - dimnames = list(c("HlyE_IgA", "HlyE_IgG"), c("min", "max")) - ) - preds <- ab(t = t, par = par1, blims = blims) + par1 <- matrix( + c( + 1.11418923843475, 1, 0.12415057798022207, 0.24829344792968783, + 0.01998946878312856, 0.0012360802436587237, 1.297194045996013, + 1.3976510415108334, 1, 0.2159993563893431, 0.4318070551383313, + 0.0015146395107173347, 0.0003580062906750277, 1.5695811573082081 + ), + nrow = 7L, + ncol = 2L, + dimnames = list( + params = c("y0", "b0", "mu0", "mu1", "c1", "alpha", "shape_r"), + antigen_iso = c("HlyE_IgA", "HlyE_IgG") + ) + ) + t <- 0:1444 + blims <- matrix( + rep(c(0, 0.5), each = 2L), + nrow = 2L, + ncol = 2L, + dimnames = list(c("HlyE_IgA", "HlyE_IgG"), c("min", "max")) + ) + preds <- ab(t = t, par = par1, blims = blims) - colnames(preds) <- colnames(par1) + colnames(preds) <- colnames(par1) - preds2 <- preds |> as_tibble() + preds2 <- preds |> as_tibble() - ssdtools:::expect_snapshot_data(preds2, name = "ab-preds") + expect_snapshot_data(preds2, name = "ab-preds") }) diff --git a/tests/testthat/test-ab0.R b/tests/testthat/test-ab0.R index ddd8cab1b..75fe928dc 100644 --- a/tests/testthat/test-ab0.R +++ b/tests/testthat/test-ab0.R @@ -6,9 +6,10 @@ test_that("`ab0()` produces consistent results", { t1 = 9.5, alpha = 0.01, r = 1, + iter = 1, antigen_iso = "test" ) |> - as_curve_params() + as_sr_params() calc1 <- ab0(curve_params = params1, t = 9.4) @@ -27,9 +28,10 @@ test_that("`ab0()` produces consistent results", { t1 = 9.5, alpha = 0.01, r = 2, + iter = 1, antigen_iso = "test" ) |> - as_curve_params() + as_sr_params() calc3 <- ab0(curve_params = params2, t = 9.4) diff --git a/tests/testthat/test-ab1.R b/tests/testthat/test-ab1.R new file mode 100644 index 000000000..a6567bf0c --- /dev/null +++ b/tests/testthat/test-ab1.R @@ -0,0 +1,12 @@ +test_that("results are consistent", { + params <- + serocalculator::typhoid_curves_nostrat_100 |> + head(2) + + params |> + dplyr::select(-c(antigen_iso, iter)) |> + dplyr::rename(shape = r) |> + dplyr::mutate(t = 10) |> + do.call(what = ab1) |> + expect_snapshot_value(style = "deparse") +}) diff --git a/tests/testthat/test-analyze_sims.R b/tests/testthat/test-analyze_sims.R new file mode 100644 index 000000000..d668c5af3 --- /dev/null +++ b/tests/testthat/test-analyze_sims.R @@ -0,0 +1,13 @@ +test_that( + desc = "results are consistent", + code = { + test_sim_results <- + test_path("fixtures", "test_sim_results.rds") |> + readr::read_rds() + + test_sim_results |> + analyze_sims() |> + expect_snapshot_data(name = "sim_results") + + } +) diff --git a/tests/testthat/test-as_curve_params.R b/tests/testthat/test-as_curve_params.R deleted file mode 100644 index 8b463c7c1..000000000 --- a/tests/testthat/test-as_curve_params.R +++ /dev/null @@ -1,36 +0,0 @@ -test_that("`as_curve_params()` produces an error - when non-curve data is provided", { - library(magrittr) - expect_error( - object = curve_data <- - serocalculator_example("example_pop_data.csv") %>% # pop data - read.csv() %>% - as_curve_params(), - class = "not curve_params" - ) - }) - -test_that("`as_curve_params()` produces an error - when `data` is not a data.frame", - { - library(magrittr) - expect_error(object = - "example_curve_params.csv" %>% # string (not data frame) - as_curve_params(), class = "not data.frame") - }) - -test_that("`as_curve_params()` produces expected results", { - library(dplyr) - test_data <- serocalculator_example("example_curve_params.csv") %>% - read.csv(row.names = 1) %>% - slice_head(n = 100) %>% - as_curve_params() - - expect_snapshot(test_data) - - expect_snapshot_value(x = test_data, style = "serialize") - - test_data %>% ssdtools:::expect_snapshot_data(name = "curve-data") - - -}) diff --git a/tests/testthat/test-as_sr_params.R b/tests/testthat/test-as_sr_params.R new file mode 100644 index 000000000..2ee73ed56 --- /dev/null +++ b/tests/testthat/test-as_sr_params.R @@ -0,0 +1,36 @@ +test_that("`as_sr_params()` produces an error + when non-curve data is provided", { + library(magrittr) + expect_error( + object = curve_data <- + serocalculator_example("example_pop_data.csv") |> # pop data + read.csv() |> + as_sr_params(), + class = "not curve_params" + ) + }) + +test_that("`as_sr_params()` produces an error when `data` is not a data.frame", + { + library(magrittr) + expect_error(object = + "example_curve_params.csv" |> # string (not data frame) + as_sr_params(), class = "not data.frame") + } +) + +test_that("`as_sr_params()` produces expected results", { + library(dplyr) + test_data <- serocalculator_example("example_curve_params.csv") |> + read.csv(row.names = 1) |> + slice_head(n = 100) |> + as_sr_params() + + expect_snapshot(test_data) + + expect_snapshot_value(x = test_data, style = "serialize") + + test_data |> expect_snapshot_data(name = "curve-data") + + +}) diff --git a/tests/testthat/test-autoplot.curve_params.R b/tests/testthat/test-autoplot.curve_params.R new file mode 100644 index 000000000..6d3959eda --- /dev/null +++ b/tests/testthat/test-autoplot.curve_params.R @@ -0,0 +1,18 @@ +test_that( + desc = "results are consistent", + code = { + + curve <- + serocalculator_example("example_curve_params.csv") |> + read.csv() |> + as_sr_params() |> + filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) |> + autoplot() + + curve |> + vdiffr::expect_doppelganger( + title = "autoplot.curve_params" + ) + } + +) diff --git a/tests/testthat/test-autoplot.pop_data.R b/tests/testthat/test-autoplot.pop_data.R index 2e87563b0..62227d1aa 100644 --- a/tests/testthat/test-autoplot.pop_data.R +++ b/tests/testthat/test-autoplot.pop_data.R @@ -3,7 +3,7 @@ test_that("`autoplot.pop_data()` raise { xs_data <- sees_pop_data_pk_100 - expect_error(object = xs_data %>% + expect_error(object = xs_data |> autoplot(strata = "catchment", type = "den")) }) @@ -12,7 +12,7 @@ test_that("`autoplot.pop_data()` raise { xs_data <- sees_pop_data_pk_100 - expect_error(object = xs_data %>% + expect_error(object = xs_data |> autoplot(strata = "strat1", type = "density")) }) @@ -20,24 +20,30 @@ test_that("`autoplot.pop_data()` produces stable results for `type = 'density'`", { skip_if(getRversion() < "4.4.1") # 4.3.3 had issues - xs_data <- sees_pop_data_pk_100 %>% - autoplot(strata = "catchment", type = "density") %>% + xs_data <- sees_pop_data_pk_100 |> + autoplot(strata = "catchment", type = "density") |> vdiffr::expect_doppelganger(title = "density") + + xs_data <- sees_pop_data_pk_100 |> + autoplot(strata = "catchment", + type = "density", + log = TRUE) |> + vdiffr::expect_doppelganger(title = "density-log") }) test_that("`autoplot.pop_data()` produces stable results for `type = 'age-scatter'`", { - xs_data <- sees_pop_data_pk_100 %>% - autoplot(strata = "catchment", type = "age-scatter") %>% + xs_data <- sees_pop_data_pk_100 |> + autoplot(strata = "catchment", type = "age-scatter") |> vdiffr::expect_doppelganger(title = "age_scatter_strat_country") }) test_that("`autoplot.pop_data()` produces stable results for `type = 'age-scatter', strata = NULL`", { - xs_data <- sees_pop_data_pk_100 %>% - autoplot(strata = NULL, type = "age-scatter") %>% + xs_data <- sees_pop_data_pk_100 |> + autoplot(strata = NULL, type = "age-scatter") |> vdiffr::expect_doppelganger(title = "age_scatter_no_strat") }) diff --git a/tests/testthat/test-autoplot.seroincidence.by.R b/tests/testthat/test-autoplot.seroincidence.by.R new file mode 100644 index 000000000..ef4142536 --- /dev/null +++ b/tests/testthat/test-autoplot.seroincidence.by.R @@ -0,0 +1,32 @@ +test_that("Build graphs not TRUE", { + + est2 <- est_seroincidence_by( + strata = c("catchment"), + pop_data = sees_pop_data_pk_100, + sr_params = typhoid_curves_nostrat_100, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + build_graph = FALSE + ) |> + autoplot(est2) |> + + expect_error() +}) + +test_that("Build graphs works as expected", { + + est2 <- est_seroincidence_by( + strata = c("catchment"), + pop_data = sees_pop_data_pk_100, + sr_params = typhoid_curves_nostrat_100, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + build_graph = TRUE + ) |> + autoplot() |> + vdiffr::expect_doppelganger(title = "seroinc-plot") +}) diff --git a/tests/testthat/test-autoplot.sim_results.R b/tests/testthat/test-autoplot.sim_results.R new file mode 100644 index 000000000..b077c9b37 --- /dev/null +++ b/tests/testthat/test-autoplot.sim_results.R @@ -0,0 +1,13 @@ +test_that("results are consistent", { + + test_sim_results <- + test_path("fixtures", "test_sim_results.rds") |> + readr::read_rds() + test_sim_results |> + analyze_sims() |> + autoplot() |> + vdiffr::expect_doppelganger(title = "autoplot-sim-results") + + + +}) diff --git a/tests/testthat/test-autoplot.summary.seroincidence.by.R b/tests/testthat/test-autoplot.summary.seroincidence.by.R index 578efe985..61c3a3bac 100644 --- a/tests/testthat/test-autoplot.summary.seroincidence.by.R +++ b/tests/testthat/test-autoplot.summary.seroincidence.by.R @@ -1,37 +1,226 @@ -test_that("results are consistent", { +test_that( + desc = "scatterplot results are consistent", + code = { - library(dplyr) - library(ggplot2) + withr::local_package("dplyr") + withr::local_package("ggplot2") - xs_data <- - sees_pop_data_pk_100 + xs_data <- + sees_pop_data_pk_100 - curve <- - typhoid_curves_nostrat_100 |> - filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) + curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) - noise <- - example_noise_params_pk + noise <- + example_noise_params_pk - est2 <- est.incidence.by( - strata = c("catchment"), - pop_data = xs_data, - curve_params = curve, - noise_params = noise, - curve_strata_varnames = NULL, - noise_strata_varnames = NULL, - antigen_isos = c("HlyE_IgG", "HlyE_IgA"), - num_cores = 2 # Allow for parallel processing to decrease run time - ) + est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + num_cores = 2 # Allow for parallel processing to decrease run time + ) - est2sum <- summary(est2) + est2sum <- summary(est2) |> + mutate(catchment = catchment |> + labelled::set_label_attribute("Catchment Area")) - plot1 <- autoplot(est2sum, "catchment", CI = TRUE) + plot1 <- autoplot(est2sum, + xvar = "ageCat", + type = "scatter", + dodge_width = 0.1, + color_var = "catchment", + CI = TRUE) - plot1 |> vdiffr::expect_doppelganger(title = "strat-est-plot-CI") + plot1 |> vdiffr::expect_doppelganger(title = "strat-est-plot-CI") - plot2 <- autoplot(est2sum, "catchment", CI = FALSE) + plot2 <- autoplot(est2sum, + xvar = "ageCat", + type = "scatter", + CI = TRUE, + dodge_width = 0.1, + group_var = "catchment", + color_var = "catchment") - plot2 |> vdiffr::expect_doppelganger(title = "strat-est-plot-no-CI") + plot2 |> vdiffr::expect_doppelganger(title = "strat-est-plot-CI-lines") -}) + + } +) + +test_that( + desc = "barplot results are consistent", + code = { + + withr::local_package("dplyr") + withr::local_package("ggplot2") + + xs_data <- + sees_pop_data_pk_100 + + curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) + + noise <- + example_noise_params_pk + + est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + num_cores = 2 # Allow for parallel processing to decrease run time + ) + + est2sum <- summary(est2) + + plot1 <- autoplot(est2sum, + yvar = "ageCat", + type = "bar", + dodge_width = 0.1, + color_var = "catchment", + CI = TRUE) + + plot1 |> vdiffr::expect_doppelganger(title = "strat-est-barplot") + } +) + +test_that( + desc = "error on plot type", + code = { + + withr::local_package("dplyr") + withr::local_package("ggplot2") + + xs_data <- + sees_pop_data_pk_100 + + curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) + + noise <- + example_noise_params_pk + + est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + num_cores = 2 # Allow for parallel processing to decrease run time + ) + + est2sum <- summary(est2) + + expect_snapshot( + error = TRUE, + x = { + plot1 <- autoplot(est2sum, + xvar = "ageCat", + type = "whisker", #invalid plot type + dodge_width = 0.1, + color_var = "catchment", + CI = TRUE) + } + ) + } +) + +test_that( + desc = "error on incorrect yvar", + code = { + withr::local_package("dplyr") + withr::local_package("ggplot2") + + xs_data <- + sees_pop_data_pk_100 + + curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) + + noise <- + example_noise_params_pk + + est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + num_cores = 2 # Allow for parallel processing to decrease run time + ) + + est2sum <- summary(est2) + + expect_snapshot( + error = TRUE, + x = { + plot1 <- autoplot(est2sum, + yvar = "fake", #invalid variable name + type = "bar", + dodge_width = 0.1, + color_var = "catchment", + CI = TRUE) + + } + ) + } +) + +test_that( + desc = "color palette works as expected", + code = { + withr::local_package("dplyr") + withr::local_package("ggplot2") + + xs_data <- + sees_pop_data_pk_100 + + curve <- + typhoid_curves_nostrat_100 |> + filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) + + noise <- + example_noise_params_pk + + est2 <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = xs_data, + sr_params = curve, + noise_params = noise, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + num_cores = 2 # Allow for parallel processing to decrease run time + ) + + est2sum <- summary(est2) + + new_color_palette <- c("#EA6552", "#8F4B86", "#0099B4FF") + + plot1 <- autoplot(est2sum, + yvar = "ageCat", + type = "bar", + dodge_width = 0.1, + color_var = "catchment", + color_palette = new_color_palette, #manual color palette + CI = TRUE) + + plot1 |> vdiffr::expect_doppelganger(title = "strat-est-barplot-palette") + } +) diff --git a/tests/testthat/test-check_strata.R b/tests/testthat/test-check_strata.R index 69c0bbc04..281a4fa3f 100644 --- a/tests/testthat/test-check_strata.R +++ b/tests/testthat/test-check_strata.R @@ -17,15 +17,27 @@ test_that("`check_strata()` throws an error when `strata` is not a `character`", test_that( desc = - "`check_strata()` warns when some strata of `pop_data` + "`check_strata()` warns when some strata of `pop_data` entirely lack some biomarkers", code = { sees_pop_data_100 |> dplyr::filter(Country == "Nepal", catchment == "kavre" | antigen_iso == "HlyE_IgA") |> - check_strata( - strata = "catchment" - ) |> + check_strata( + strata = "catchment" + ) |> expect_warning(class = "strata missing some biomarkers") } ) + +test_that( + desc = + "`check_strata()` can handle when there are no partial matches", + code = { + sees_pop_data_100 |> + check_strata( + strata = "Country2" + ) |> + expect_error(class = "missing_var") + } +) diff --git a/tests/testthat/test-compare_seroincidence.R b/tests/testthat/test-compare_seroincidence.R new file mode 100644 index 000000000..ccafdb7c4 --- /dev/null +++ b/tests/testthat/test-compare_seroincidence.R @@ -0,0 +1,172 @@ +test_that("compare_seroincidence works with two seroincidence objects", { + withr::local_package("dplyr") + + # Create two separate estimates + est1 <- est_seroincidence( + pop_data = sees_pop_data_pk_100 |> filter(catchment == "kgh"), + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + est2 <- est_seroincidence( + pop_data = sees_pop_data_pk_100 |> filter(catchment == "aku"), + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + result <- compare_seroincidence(est1, est2) + + # Check that result is an htest object with correct structure + expect_s3_class(result, "htest") + expect_snapshot(result, variant = tolower(Sys.info()[["sysname"]])) +}) + +test_that("compare_seroincidence works with seroincidence.by object", { + withr::local_package("dplyr") + + # Create stratified estimates + est_by <- est_seroincidence_by( + strata = "catchment", + pop_data = sees_pop_data_pk_100, + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + curve_strata_varnames = NULL, + noise_strata_varnames = NULL + ) + + result <- compare_seroincidence(est_by) + + # Check that result is a tibble with correct class and structure + expect_s3_class(result, "tbl_df") + expect_s3_class(result, "comparison.seroincidence.by") + + # Use snapshot to validate structure and values + expect_snapshot_value(result, style = "serialize", tolerance = 1e-4) +}) + +test_that("compare_seroincidence works with multiple strata variables", { + withr::local_package("dplyr") + + # Create stratified estimates with multiple variables + est_by <- est_seroincidence_by( + strata = c("catchment", "ageCat"), + pop_data = sees_pop_data_pk_100, + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + curve_strata_varnames = NULL, + noise_strata_varnames = NULL + ) + + result <- compare_seroincidence(est_by) + + # Validate structure via snapshot + expect_snapshot_value(result, style = "serialize", tolerance = 1e-4) +}) + +test_that("compare_seroincidence errors appropriately", { + withr::local_package("dplyr") + + est1 <- est_seroincidence( + pop_data = sees_pop_data_pk_100 |> filter(catchment == "kgh"), + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + # Missing y parameter + expect_error( + compare_seroincidence(est1), + class = "rlang_error" + ) + + # Wrong class for y + expect_error( + compare_seroincidence(est1, y = "not a seroincidence object"), + class = "rlang_error" + ) + + # Wrong class for x + expect_error( + compare_seroincidence("not a seroincidence object"), + class = "rlang_error" + ) + + # Fewer than 2 strata + est_by_single <- est_seroincidence_by( + strata = "catchment", + pop_data = sees_pop_data_pk_100 |> filter(catchment == "kgh"), + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + curve_strata_varnames = NULL, + noise_strata_varnames = NULL + ) + + expect_error( + compare_seroincidence(est_by_single), + class = "rlang_error" + ) +}) + +test_that("compare_seroincidence respects coverage parameter", { + withr::local_package("dplyr") + + est1 <- est_seroincidence( + pop_data = sees_pop_data_pk_100 |> filter(catchment == "kgh"), + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + est2 <- est_seroincidence( + pop_data = sees_pop_data_pk_100 |> filter(catchment == "aku"), + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + result_95 <- compare_seroincidence(est1, est2, coverage = 0.95) + result_90 <- compare_seroincidence(est1, est2, coverage = 0.90) + + # Check that confidence levels are set correctly + expect_equal(attr(result_95$conf.int, "conf.level"), 0.95) + expect_equal(attr(result_90$conf.int, "conf.level"), 0.90) + + # Check that 90% CI is narrower than 95% CI + ci_width_95 <- result_95$conf.int[2] - result_95$conf.int[1] + ci_width_90 <- result_90$conf.int[2] - result_90$conf.int[1] + expect_true(ci_width_90 < ci_width_95) +}) + +test_that( + "compare_seroincidence warns when y is provided for seroincidence.by", + { + withr::local_package("dplyr") + + est_by <- est_seroincidence_by( + strata = "catchment", + pop_data = sees_pop_data_pk_100, + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + curve_strata_varnames = NULL, + noise_strata_varnames = NULL + ) + + est1 <- est_seroincidence( + pop_data = sees_pop_data_pk_100 |> filter(catchment == "kgh"), + sr_params = typhoid_curves_nostrat_100, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + expect_warning( + compare_seroincidence(est_by, y = est1), + class = "rlang_warning" + ) + } +) diff --git a/tests/testthat/test-df_to_array.R b/tests/testthat/test-df_to_array.R index 39b760aa7..4e94e8a2b 100644 --- a/tests/testthat/test-df_to_array.R +++ b/tests/testthat/test-df_to_array.R @@ -1,7 +1,7 @@ test_that("df_to_array() produces consistent results", { - library(dplyr) - library(tidyr) - df <- iris %>% + withr::local_package("dplyr") + withr::local_package("tidyr") + df <- iris |> tidyr::pivot_longer( names_to = "parameter", cols = c( @@ -10,9 +10,9 @@ test_that("df_to_array() produces consistent results", { "Petal.Width", "Petal.Length" ) - ) %>% + ) |> mutate(parameter = factor(parameter, levels = unique(parameter))) - arr <- df %>% + arr <- df |> serocalculator:::df_to_array(dim_var_names = c("parameter", "Species")) - arr %>% expect_snapshot_value(style = "serialize") + arr |> expect_snapshot_value(style = "serialize") }) diff --git a/tests/testthat/test-est.incidence.R b/tests/testthat/test-est.incidence.R deleted file mode 100644 index 730c5ff17..000000000 --- a/tests/testthat/test-est.incidence.R +++ /dev/null @@ -1,35 +0,0 @@ -test_that("est.incidence() produces expected results for typhoid data", { - typhoid_results <- est.incidence( - pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, - noise_param = example_noise_params_pk, - antigen_isos = c("HlyE_IgG", "HlyE_IgA") - ) - - expect_snapshot(x = summary(typhoid_results, coverage = .95)) - - expect_snapshot_value(typhoid_results, style = "deparse", tolerance = 1e-4) - -}) - -test_that( - "`est.incidence()` produces consistent results - regardless of whether data colnames are standardized.", - { - est_true <- est.incidence( - pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, - noise_param = example_noise_params_pk, - antigen_isos = c("HlyE_IgG", "HlyE_IgA") - ) - - est_false <- est.incidence( - pop_data = sees_pop_data_pk_100_old_names, - curve_param = typhoid_curves_nostrat_100, - noise_param = example_noise_params_pk, - antigen_isos = c("HlyE_IgG", "HlyE_IgA") - ) - - expect_equal(est_true, est_false) - } -) diff --git a/tests/testthat/test-est_seroincidence.R b/tests/testthat/test-est_seroincidence.R new file mode 100644 index 000000000..3b8432e9e --- /dev/null +++ b/tests/testthat/test-est_seroincidence.R @@ -0,0 +1,68 @@ +test_that("results are as expected for typhoid data", { + typhoid_results <- est_seroincidence( + pop_data = sees_pop_data_pk_100, + sr_param = typhoid_curves_nostrat_100, + noise_param = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + expect_snapshot(x = summary(typhoid_results, coverage = .95)) + + expect_snapshot_value(typhoid_results, style = "deparse", tolerance = 1e-4) + +}) + +test_that( + "results are consistent + regardless of whether data colnames are standardized.", + { + est_true <- est_seroincidence( + pop_data = sees_pop_data_pk_100, + sr_param = typhoid_curves_nostrat_100, + noise_param = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + est_false <- est_seroincidence( + pop_data = sees_pop_data_pk_100_old_names, + sr_param = typhoid_curves_nostrat_100, + noise_param = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) + + expect_equal(est_true, est_false) + } +) + +test_that( + "verbose output is consistent", + code = { + skip_on_os("mac") + withr::local_options( + list( + width = 80, + digits = 8)) + + est_seroincidence( + pop_data = sees_pop_data_pk_100, + sr_param = typhoid_curves_nostrat_100, + noise_param = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + verbose = TRUE + ) |> + expect_snapshot() + } +) + +test_that( + "lifecycle warning works as expected", + code = { + lifecycle_test <- est.incidence( + pop_data = sees_pop_data_pk_100, + sr_param = typhoid_curves_nostrat_100, + noise_param = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA") + ) |> + expect_warning() + } +) diff --git a/tests/testthat/test-est.incidence.by.R b/tests/testthat/test-est_seroincidence_by.R similarity index 70% rename from tests/testthat/test-est.incidence.by.R rename to tests/testthat/test-est_seroincidence_by.R index 5fb392411..d2aa00b1a 100644 --- a/tests/testthat/test-est.incidence.by.R +++ b/tests/testthat/test-est_seroincidence_by.R @@ -1,15 +1,15 @@ test_that( - desc = "est.incidence.by() warns about missing data", + desc = "est_seroincidence_by() warns about missing data", code = { - library(dplyr) - library(readr) + withr::local_package("dplyr") + withr::local_package("readr") - est.incidence.by( + est_seroincidence_by( pop_data = sees_pop_data_pk_100 |> dplyr::filter(catchment == "kgh" | antigen_iso == "HlyE_IgA"), - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_sees |> dplyr::filter(Country == "Nepal"), @@ -22,15 +22,16 @@ test_that( ) -test_that("est.incidence.by() warns about missing data", { +test_that("est_seroincidence_by() warns about missing data", { - library(dplyr) - library(readr) + withr::local_package("dplyr") + withr::local_package("readr") - est.incidence.by( + + est_seroincidence_by( pop_data = sees_pop_data_pk_100 |> tail(-1), - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_sees |> dplyr::filter(Country == "Nepal"), strata = "catchment", @@ -40,11 +41,11 @@ test_that("est.incidence.by() warns about missing data", { expect_warning(class = "incomplete-obs") }) -test_that("`est.incidence.by()` warns user when strata is missing", { +test_that("`est_seroincidence_by()` warns user when strata is missing", { expect_warning( - est.incidence.by( + est_seroincidence_by( pop_data = sees_pop_data_pk_100, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA") ), @@ -53,14 +54,14 @@ test_that("`est.incidence.by()` warns user when strata is missing", { }) test_that( - "`est.incidence.by()` aborts when elements that don't exactly + "`est_seroincidence_by()` aborts when elements that don't exactly match the columns of `pop_data` are provided", { expect_error( - object = est.incidence.by( + object = est_seroincidence_by( strata = c("ag", "catch", "Count"), pop_data = sees_pop_data_pk_100, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), num_cores = 8, @@ -75,13 +76,13 @@ test_that( test_that( - desc = "`est.incidence.by()` produces consistent results for typhoid data", + desc = "`est_seroincidence_by()` produces consistent results for sample data", code = { withr::local_options(width = 80) - typhoid_results <- est.incidence.by( + typhoid_results <- est_seroincidence_by( strata = "catchment", pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, + sr_param = typhoid_curves_nostrat_100, curve_strata_varnames = NULL, noise_strata_varnames = NULL, noise_param = example_noise_params_pk, @@ -98,13 +99,13 @@ test_that( ) test_that( - "`est.incidence.by()` produces expected results + "`est_seroincidence_by()` produces expected results regardless of whether varnames have been standardized.", { - est_true <- est.incidence.by( + est_true <- est_seroincidence_by( strata = c("catchment"), pop_data = sees_pop_data_pk_100, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), curve_strata_varnames = NULL, @@ -112,10 +113,10 @@ test_that( num_cores = 1 # Allow for parallel processing to decrease run time ) - est_false <- est.incidence.by( + est_false <- est_seroincidence_by( strata = c("catchment"), pop_data = sees_pop_data_pk_100_old_names, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, curve_strata_varnames = NULL, noise_strata_varnames = NULL, @@ -129,14 +130,14 @@ test_that( test_that( - "`est.incidence.by()` produces expected results + "`est_seroincidence_by()` produces expected results regardless of whether using parallel processing or not.", { - ests_1_core <- est.incidence.by( + ests_1_core <- est_seroincidence_by( strata = c("catchment"), pop_data = sees_pop_data_pk_100, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), curve_strata_varnames = NULL, @@ -144,10 +145,10 @@ test_that( num_cores = 1 ) - ests_2_cores <- est.incidence.by( + ests_2_cores <- est_seroincidence_by( strata = c("catchment"), pop_data = sees_pop_data_pk_100_old_names, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, curve_strata_varnames = NULL, noise_strata_varnames = NULL, @@ -160,7 +161,7 @@ test_that( ) test_that( - "`est.incidence.by()` produces expected results + "`est_seroincidence_by()` produces expected results regardless of whether using verbose messaging or not. with single core.", { @@ -168,10 +169,10 @@ test_that( capture.output( file = nullfile(), { - ests_verbose_sc <- est.incidence.by( + ests_verbose_sc <- est_seroincidence_by( strata = c("catchment"), pop_data = sees_pop_data_pk_100, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), curve_strata_varnames = NULL, @@ -182,11 +183,11 @@ test_that( } ) - ests_non_verbose_sc <- est.incidence.by( + ests_non_verbose_sc <- est_seroincidence_by( verbose = FALSE, strata = c("catchment"), pop_data = sees_pop_data_pk_100_old_names, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, curve_strata_varnames = NULL, noise_strata_varnames = NULL, @@ -199,15 +200,15 @@ test_that( ) test_that( - "`est.incidence.by()` produces expected results + "`est_seroincidence_by()` produces expected results regardless of whether using verbose messaging or not with multi-core processing.", { - ests_verbose_mc <- est.incidence.by( + ests_verbose_mc <- est_seroincidence_by( strata = c("catchment"), pop_data = sees_pop_data_pk_100, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), curve_strata_varnames = NULL, @@ -217,11 +218,11 @@ test_that( ) |> suppressMessages() - ests_non_verbose_mc <- est.incidence.by( + ests_non_verbose_mc <- est_seroincidence_by( verbose = FALSE, strata = c("catchment"), pop_data = sees_pop_data_pk_100_old_names, - curve_params = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_params = example_noise_params_pk, curve_strata_varnames = NULL, noise_strata_varnames = NULL, @@ -240,10 +241,10 @@ test_that( test_that( "a warning is produced when `strata = NULL", code = { - est.incidence.by( + est_seroincidence_by( strata = NULL, pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, + sr_param = typhoid_curves_nostrat_100, noise_param = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA") ) |> @@ -252,17 +253,17 @@ test_that( ) test_that("results are consistent with `strata = NULL`", { - typhoid_results_simple <- est.incidence( + typhoid_results_simple <- est_seroincidence( pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, + sr_params = typhoid_curves_nostrat_100, noise_param = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA") ) - typhoid_results_nullstrata <- est.incidence.by( + typhoid_results_nullstrata <- est_seroincidence_by( strata = NULL, pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, + sr_param = typhoid_curves_nostrat_100, noise_param = example_noise_params_pk, antigen_isos = c("HlyE_IgG", "HlyE_IgA") ) |> suppressWarnings() @@ -273,3 +274,17 @@ test_that("results are consistent with `strata = NULL`", { ) }) + +test_that("deprecate warning is as expected", { + est2 <- est.incidence.by( + strata = c("catchment"), + pop_data = sees_pop_data_pk_100, + sr_params = typhoid_curves_nostrat_100, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + noise_params = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + build_graph = TRUE + ) |> + expect_warning() +}) diff --git a/tests/testthat/test-f_dev.R b/tests/testthat/test-f_dev.R index 4ed754372..f913d196c 100644 --- a/tests/testthat/test-f_dev.R +++ b/tests/testthat/test-f_dev.R @@ -1,47 +1,48 @@ test_that("`f_dev0()` and `f_dev()` produce stable results", { - - library(dplyr) - library(tibble) + withr::local_package("dplyr") + withr::local_package("tibble") # load in longitudinal parameters - curve_params = typhoid_curves_nostrat_100 + curve_params <- typhoid_curves_nostrat_100 xs_data <- sees_pop_data_pk_100 - #Load noise params + # Load noise params noise_params <- tibble( antigen_iso = c("HlyE_IgG", "HlyE_IgA"), - nu = c(0.5, 0.5), # Biologic noise (nu) - eps = c(0, 0), # M noise (eps) - y.low = c(1, 1), # low cutoff (llod) - y.high = c(5e6, 5e6)) # high cutoff (y.high) + nu = c(0.5, 0.5), # Biologic noise (nu) + eps = c(0, 0), # M noise (eps) + y.low = c(1, 1), # low cutoff (llod) + y.high = c(5e6, 5e6) # high cutoff (y.high) + ) - cur_antibody = "HlyE_IgA" + cur_antibody <- "HlyE_IgA" - cur_data = - xs_data %>% + cur_data <- + xs_data |> dplyr::filter( - .data$antigen_iso == cur_antibody) %>% - slice_head(n = 100) + .data$antigen_iso == cur_antibody + ) |> + dplyr::slice_head(n = 100) - cur_curve_params = - curve_params %>% - dplyr::filter(.data$antigen_iso == cur_antibody) %>% - slice_head(n = 100) + cur_curve_params <- + curve_params |> + dplyr::filter(.data$antigen_iso == cur_antibody) |> + dplyr::slice_head(n = 100) - cur_noise_params = - noise_params %>% + cur_noise_params <- + noise_params |> dplyr::filter(.data$antigen_iso == cur_antibody) - if(!is.element('d', names(cur_curve_params))) - { - cur_curve_params = - cur_curve_params %>% + if (!is.element("d", names(cur_curve_params))) { + cur_curve_params <- + cur_curve_params |> dplyr::mutate( alpha = .data$alpha * 365.25, - d = .data$r - 1) + d = .data$r - 1 + ) } - lambda = 0.1 + lambda <- 0.1 expect_snapshot_value( x = f_dev0( lambda = lambda, @@ -49,9 +50,10 @@ test_that("`f_dev0()` and `f_dev()` produce stable results", { lnpars = cur_curve_params, cond = cur_noise_params ), - style = "deparse") + style = "deparse" + ) - lambdas = seq(0.1, 0.2, by = .01) + lambdas <- seq(0.1, 0.2, by = .01) expect_snapshot_value( x = f_dev( lambda = lambdas, @@ -59,6 +61,68 @@ test_that("`f_dev0()` and `f_dev()` produce stable results", { lnpars = cur_curve_params, cond = cur_noise_params ), - style = "deparse") - + style = "deparse" + ) }) + +test_that( + desc = "`f_dev()` handles censored data", + code = { + withr::local_package("dplyr") + withr::local_package("tibble") + + cur_antibody <- "HlyE_IgA" + + # load in longitudinal parameters + curve_params <- + typhoid_curves_nostrat_100 |> + dplyr::filter(.data$antigen_iso == cur_antibody) |> + dplyr::slice_head(n = 100) + + if (!is.element("d", names(curve_params))) { + curve_params <- + curve_params |> + dplyr::mutate( + alpha = .data$alpha * 365.25, + d = .data$r - 1 + ) + } + + xs_data <- sees_pop_data_pk_100 |> + dplyr::filter( + .data$antigen_iso == cur_antibody + ) |> + dplyr::slice_head(n = 1) |> + dplyr::mutate(value = .6) + + # Load noise params + noise_params <- tibble( + antigen_iso = "HlyE_IgA", + nu = 0.5, # Biologic noise (nu) + eps = 0, # M noise (eps) + y.low = 0, # low cutoff (llod) + y.high = .5 # high cutoff (y.high) + ) + + + lambda <- 0.1 + expect_snapshot_value( + x = f_dev0( + lambda = lambda, + csdata = xs_data, + lnpars = curve_params, + cond = noise_params + ), + style = "deparse" + ) + + c(.1, .2, .5, .6, .61, 10, 20) |> + noise_params_test(ymax = _, yval = 0.6) |> + expect_snapshot_value(style = "deparse") + + c(.1, .2, .5, .6, .61, 10, 20) |> + noise_params_test(ymax = 0.6, yval = _) |> + expect_snapshot_value(style = "deparse") + + } +) diff --git a/tests/testthat/test-graph.curve.params.R b/tests/testthat/test-graph.curve.params.R index 75d048277..1bed1c55d 100644 --- a/tests/testthat/test-graph.curve.params.R +++ b/tests/testthat/test-graph.curve.params.R @@ -1,21 +1,67 @@ test_that( desc = "results are consistent", + code = { + curve <- typhoid_curves_nostrat_100 |> + dplyr::filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) |> + dplyr::mutate(.by = antigen_iso, chain = rep(1:2, times = n() / 2)) + + # 1. Default quantiles: c(0.1, 0.5, 0.9) + plot1 <- graph.curve.params( + curve, + n_curves = 0 + ) + plot1 |> vdiffr::expect_doppelganger(title = "curve-quantiles") + + # 2. Default quantiles + all MCMC samples + plot2 <- graph.curve.params( + curve, + n_curves = Inf + ) + plot2 |> vdiffr::expect_doppelganger(title = "curve-quantiles-and-samples") + + # 3. Test that disabling quantiles works correctly (curves only) + plot3 <- graph.curve.params( + curve, + n_curves = Inf, + quantiles = NULL + ) + + plot3 |> vdiffr::expect_doppelganger(title = "curve-samples") + + # 4. Test that custom numeric quantiles are drawn correctly + plot4 <- graph.curve.params( + curve, + n_curves = 0, + quantiles = c(0.05, 0.55, 0.95) + ) + plot4 |> vdiffr::expect_doppelganger(title = "curve-custom-quantiles") + + # 5. Test that chain_color = FALSE works correctly + plot5 <- graph.curve.params( + curve, + n_curves = Inf, + quantiles = c(0.05, 0.55, 0.95), + chain_color = FALSE + ) + plot5 |> vdiffr::expect_doppelganger(title = "curve-black-chains") + } +) + +test_that( + desc = "results are consistent with log_x", code = { curve <- typhoid_curves_nostrat_100 |> dplyr::filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) |> dplyr::mutate(.by = antigen_iso, chain = rep(1:2, times = n() / 2)) - plot1 <- graph.curve.params(curve) - plot1 |> vdiffr::expect_doppelganger(title = "curve-quantiles") - plot2 <- graph.curve.params(curve, show_all_curves = TRUE) - plot2 |> - vdiffr::expect_doppelganger(title = "curve-quantiles-and-samples") + + # Test that log_x argument works with samples only plot3 <- graph.curve.params( curve, - show_all_curves = TRUE, - show_quantiles = FALSE + n_curves = Inf, + log_x = TRUE ) plot3 |> - vdiffr::expect_doppelganger(title = "curve-samples") + vdiffr::expect_doppelganger(title = "curve-samples-log_x") } ) diff --git a/tests/testthat/test-load_sr_params.R b/tests/testthat/test-load_sr_params.R new file mode 100644 index 000000000..323d05931 --- /dev/null +++ b/tests/testthat/test-load_sr_params.R @@ -0,0 +1,34 @@ +test_that( + desc = "`load_sr_params()` produces expected results", + code = { + expect_no_error( + sr_params_true <- + load_sr_params( + serocalculator_example("example_curve_params.rds") + ) + ) + } +) + +test_that( + desc = "`load_sr_params()` produces error with non-curve data", + code = { + expect_error( + sr_params_true <- + load_sr_params( + serocalculator_example("example_pop_data.rds") + ) + ) + } +) + +test_that( + desc = "non filepath produces error", + code = { + expect_error( + expect_warning( + load_sr_params("non file path") + ) + ) + } +) diff --git a/tests/testthat/test-plot_curve_params_one_ab.R b/tests/testthat/test-plot_curve_params_one_ab.R index f631191d6..9fc625b21 100644 --- a/tests/testthat/test-plot_curve_params_one_ab.R +++ b/tests/testthat/test-plot_curve_params_one_ab.R @@ -6,9 +6,10 @@ test_that("`plot_curve_params_one_ab()` produces consistent results", { t1 = 9.5, alpha = 0.01, r = 1, + iter = 1, antigen_iso = "test" ) |> - as_curve_params() + as_sr_params() fig1 <- diff --git a/tests/testthat/test-print.seroincidence.R b/tests/testthat/test-print.seroincidence.R index 137f10170..0fc7929d3 100644 --- a/tests/testthat/test-print.seroincidence.R +++ b/tests/testthat/test-print.seroincidence.R @@ -4,13 +4,13 @@ test_that("results are consistent", { xs_data <- sees_pop_data_pk_100 curve <- - typhoid_curves_nostrat_100 %>% + typhoid_curves_nostrat_100 |> filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) noise <- example_noise_params_pk - est1 <- est.incidence( + est1 <- est_seroincidence( pop_data = xs_data, - curve_params = curve, + sr_params = curve, noise_params = noise, antigen_isos = c("HlyE_IgG", "HlyE_IgA"), ) diff --git a/tests/testthat/test-print.seroincidence.by.R b/tests/testthat/test-print.seroincidence.by.R index 40a3884d5..c9b21162e 100644 --- a/tests/testthat/test-print.seroincidence.by.R +++ b/tests/testthat/test-print.seroincidence.by.R @@ -1,20 +1,19 @@ test_that( - desc = "print() method works consistently", - code = { + desc = "print() method works consistently", + code = { - withr::local_options(width = 80) - typhoid_results <- est.incidence.by( - strata = "catchment", - pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, - curve_strata_varnames = NULL, - noise_strata_varnames = NULL, - noise_param = example_noise_params_pk, - antigen_isos = c("HlyE_IgG", "HlyE_IgA"), - # Allow for parallel processing to decrease run time - num_cores = 1 - ) + withr::local_options(width = 80) + typhoid_results <- est_seroincidence_by( + strata = "catchment", + pop_data = sees_pop_data_pk_100, + sr_param = typhoid_curves_nostrat_100, + curve_strata_varnames = NULL, + noise_strata_varnames = NULL, + noise_param = example_noise_params_pk, + antigen_isos = c("HlyE_IgG", "HlyE_IgA"), + # Allow for parallel processing to decrease run time + num_cores = 1 + ) - expect_snapshot(x = print(typhoid_results)) - -}) + expect_snapshot(x = print(typhoid_results)) + }) diff --git a/tests/testthat/test-print.summary.seroincidence.by.R b/tests/testthat/test-print.summary.seroincidence.by.R index 85878c0d9..5f072d199 100644 --- a/tests/testthat/test-print.summary.seroincidence.by.R +++ b/tests/testthat/test-print.summary.seroincidence.by.R @@ -1,9 +1,9 @@ test_that("print method works consistently", { withr::local_options(width = 80) - typhoid_results <- est.incidence.by( + typhoid_results <- est_seroincidence_by( strata = "catchment", pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, + sr_param = typhoid_curves_nostrat_100, curve_strata_varnames = NULL, noise_strata_varnames = NULL, noise_param = example_noise_params_pk, diff --git a/tests/testthat/test-sim_pop_data.R b/tests/testthat/test-sim_pop_data.R index 432d882d0..898e975de 100644 --- a/tests/testthat/test-sim_pop_data.R +++ b/tests/testthat/test-sim_pop_data.R @@ -38,5 +38,5 @@ test_that("`sim_pop_data()` produces consistent results", { format = "long" ) - ssdtools:::expect_snapshot_data(csdata, "sim_pop_data") + expect_snapshot_data(csdata, name = "sim_pop_data") }) diff --git a/tests/testthat/test-sim_pop_data_multi.R b/tests/testthat/test-sim_pop_data_multi.R index 35e8e1e14..a953d60dc 100644 --- a/tests/testthat/test-sim_pop_data_multi.R +++ b/tests/testthat/test-sim_pop_data_multi.R @@ -1,4 +1,6 @@ test_that("`sim_pop_data_multi()` works consistently", { + skip_on_cran() + skip_on_os("linux") # Load curve parameters dmcmc <- typhoid_curves_nostrat_100 @@ -10,12 +12,11 @@ test_that("`sim_pop_data_multi()` works consistently", { # Simulated incidence rate per person-year lambdas <- c(.05, .1, .15, .2, .3) - # Range covered in simulations lifespan <- c(0, 10) # Cross-sectional sample size - nrep <- 100 + sample_sizes <- c(100, 50) # Biologic noise distribution dlims <- rbind( @@ -26,7 +27,7 @@ test_that("`sim_pop_data_multi()` works consistently", { pop_data_multi <- sim_pop_data_multi( curve_params = dmcmc, lambdas = lambdas, - n_samples = nrep, + sample_sizes = sample_sizes, age_range = lifespan, antigen_isos = antibodies, n_mcmc_samples = 0, @@ -38,5 +39,5 @@ test_that("`sim_pop_data_multi()` works consistently", { ) pop_data_multi |> - ssdtools:::expect_snapshot_data(name = "pop_data_multi") + expect_snapshot_data(name = "pop_data_multi") }) diff --git a/tests/testthat/test-strata.R b/tests/testthat/test-strata.R new file mode 100644 index 000000000..9f6b3bfed --- /dev/null +++ b/tests/testthat/test-strata.R @@ -0,0 +1,8 @@ +test_that("results are consistent", { + + sees_typhoid_ests_strat |> + strata() |> + expect_snapshot_data(name = "strata-ests") + + +}) diff --git a/tests/testthat/test-summary.seroincidence.by.R b/tests/testthat/test-summary.seroincidence.by.R index cfea7f704..1e05fcc27 100644 --- a/tests/testthat/test-summary.seroincidence.by.R +++ b/tests/testthat/test-summary.seroincidence.by.R @@ -1,10 +1,10 @@ test_that("`summary.seroincidence.by()` produces consistent results", { typhoid_results <- - est.incidence.by( + est_seroincidence_by( strata = "catchment", pop_data = sees_pop_data_pk_100, - curve_param = typhoid_curves_nostrat_100, + sr_param = typhoid_curves_nostrat_100, curve_strata_varnames = NULL, noise_strata_varnames = NULL, noise_param = example_noise_params_pk, diff --git a/vignettes/.gitignore b/vignettes/.gitignore index 732bb38c3..ffd4306b2 100644 --- a/vignettes/.gitignore +++ b/vignettes/.gitignore @@ -10,3 +10,5 @@ rsconnect *.docx /.quarto/ *.rmarkdown + +**/*.quarto_ipynb diff --git a/vignettes/articles/_antibody-response-model.qmd b/vignettes/articles/_antibody-response-model.qmd index 0e0cf9ee4..d71a375f3 100644 --- a/vignettes/articles/_antibody-response-model.qmd +++ b/vignettes/articles/_antibody-response-model.qmd @@ -110,7 +110,7 @@ library(dplyr) # Import longitudinal antibody parameters from OSF curves <- "https://osf.io/download/rtw5k/" %>% - load_curve_params() %>% + load_sr_params() %>% filter(iter < 50) curve1 <- diff --git a/vignettes/articles/enteric_fever_example.Rmd b/vignettes/articles/enteric_fever_example.Rmd index 057735bd5..16746e4b3 100644 --- a/vignettes/articles/enteric_fever_example.Rmd +++ b/vignettes/articles/enteric_fever_example.Rmd @@ -72,19 +72,19 @@ We will first load the longitudinal curve parameters to set the antibody decay p Column Name | Description -------------- | ----------- - y0 | Baseline antibody concentration - y1 | Peak antibody concentration - t1 | Time to peak antibody concentration - alpha | Antibody decay rate - r | Antibody decay shape +y0 | Baseline antibody concentration +y1 | Peak antibody concentration +t1 | Time to peak antibody concentration +alpha | Antibody decay rate +r | Antibody decay shape *Note that variable names are case-sensitive* ```{r curve, message=FALSE} # Import longitudinal antibody parameters from OSF curves <- - "https://osf.io/download/rtw5k/" %>% - load_curve_params() + "https://osf.io/download/rtw5k/" |> + load_sr_params() ``` ### Visualize curve parameters @@ -93,8 +93,10 @@ We can graph the decay curves with an `autoplot()` method: ```{r, fig.width=7, fig.height=4} # Visualize curve parameters -curves %>% filter(antigen_iso == "HlyE_IgA" | - antigen_iso == "HlyE_IgG") %>% +curves |> + filter( + antigen_iso %in% c("HlyE_IgA", "HlyE_IgG") + ) |> autoplot() ``` @@ -110,14 +112,14 @@ We have selected hemolysin E (*HlyE*) as our target antigen and *IgG* and *IgA* Column Name | Description ----------- | ----------- - value | Quantitative antibody response - age | Numeric age +value | Quantitative antibody response +age | Numeric age *Note that variable names are case sensitive* ```{r data, message = FALSE} #Import cross-sectional data from OSF and rename required variables -xs_data <- readr::read_rds("https://osf.io/download//n6cp3/") %>% +xs_data <- readr::read_rds("https://osf.io/download//n6cp3/") |> as_pop_data() ``` @@ -127,7 +129,7 @@ xs_data <- readr::read_rds("https://osf.io/download//n6cp3/") %>% We can compute numerical summaries of our cross-sectional antibody data with a `summary()` method for `pop_data` objects: ```{r} -xs_data %>% summary() +xs_data |> summary() ``` @@ -142,7 +144,7 @@ We examine our cross-sectional antibody data by visualizing the distribution of #color palette country_pal <- c("#EA6552", "#8F4B86", "#0099B4FF") -xs_data %>% autoplot(strata = "Country", type = "density") + +xs_data |> autoplot(strata = "Country", type = "density") + scale_fill_manual(values = country_pal) ``` @@ -152,8 +154,8 @@ We see that across countries, our data is highly skewed with the majority of res ```{r logplot, message = FALSE} # Create log transformed plots -xs_data %>% - mutate(Country = fct_relevel(Country, "Bangladesh", "Pakistan", "Nepal")) %>% +xs_data |> + mutate(Country = fct_relevel(Country, "Bangladesh", "Pakistan", "Nepal")) |> autoplot(strata = "Country", type = "density") + scale_fill_manual(values = country_pal) + scale_x_log10(labels = scales::label_comma()) @@ -167,22 +169,16 @@ Let's also take a look at how antibody responses change by age. ```{r plot-age, message=FALSE, warning = FALSE} #Plot antibody responses by age -ggplot(data = xs_data %>% - mutate(Country = fct_relevel( - Country, "Bangladesh", "Pakistan", "Nepal" - )), -aes(x = age, y = value, color = Country)) + - geom_point(size = 0.6, alpha = 0.7) + - geom_smooth(method = "lm", se = FALSE) + - scale_y_log10() + - theme_linedraw() + - labs(title = "Quantitative Antibody Results by Age", x = "Age", y = "Value") + +xs_data |> + autoplot( + strata = "Country", + type = "age-scatter" + ) + scale_color_manual(values = country_pal) ``` -In this plot, a steeper slope indicates a higher incidence. We can see that the highest burden is in Bangladesh. Nepal has a slightly higher incidence in the older group (higher slope). - +In this plot, we can see that the highest burden is in Bangladesh, but Nepal has the steepest slope and experiences the greatest change in seroconversion rates by age, with lower exposures at younger ages and higher exposures at older ages. ### Load noise parameters @@ -198,16 +194,16 @@ Measurement noise, $\varepsilon$ ("epsilon"), represents measurement error from Column Name | Description ----------- | ----------- - y.low | Lower limit of detection of the assay - nu | Biologic noise - y.high | Upper limit of detection of the assay - eps | Measurement noise +y.low | Lower limit of detection of the assay +nu | Biologic noise +y.high | Upper limit of detection of the assay +eps | Measurement noise *Note that variable names are case-sensitive.* ``` {r noise, message=FALSE} # Import noise parameters from OSF -noise <- url("https://osf.io/download//hqy4v/") %>% readRDS() +noise <- url("https://osf.io/download//hqy4v/") |> readRDS() ``` ## Estimate Seroincidence @@ -219,10 +215,10 @@ Using the function `est.incidence`, we filter to sites in Pakistan and define th ```{r est} # Using est.incidence (no strata) -est1 <- est.incidence( - pop_data = xs_data %>% filter(Country == "Pakistan"), - curve_param = curves, - noise_param = noise %>% filter(Country == "Pakistan"), +est1 <- est_seroincidence( + pop_data = xs_data |> filter(Country == "Pakistan"), + sr_params = curves, + noise_params = noise |> filter(Country == "Pakistan"), antigen_isos = c("HlyE_IgG", "HlyE_IgA") ) @@ -238,10 +234,10 @@ Let's compare estimates across all countries and by age group. ```{r estbycountry} #Using est.incidence.by (strata) -est_country_age <- est.incidence.by( +est_country_age <- est_seroincidence_by( strata = c("Country", "ageCat"), pop_data = xs_data, - curve_params = curves, + sr_params = curves, curve_strata_varnames = NULL, noise_params = noise, noise_strata_varnames = "Country", @@ -261,59 +257,99 @@ Without this, a warning appears: "*curve_params is missing all strata variables, To stratify based on variables that exist in a longitudinal curve parameters dataset, specify variables using `curve_strata_varnames`, similar to how `noise_strata_varnames` is used for "noise" above. -Finally, let's visualize our seroincidence estimates by country and age category. +Finally, let's visualize our seroincidence estimates by country and age category using `autoplot()`: ```{r} +# Save summary(est_country_age) +est_country_agedf <- summary(est_country_age) + # Plot seroincidence estimates +autoplot( + est_country_agedf, + type = "bar", + yvar = "ageCat", + color_var = "Country", + color_palette = country_pal, + CIs = TRUE +) +``` -## Save summary(est_country_age) as a dataframe and sort by incidence rate -est_country_agedf <- summary(est_country_age) %>% - mutate( - Country = fct_relevel(Country, "Bangladesh", "Pakistan", "Nepal"), - ageCat = factor(ageCat) - ) - -## Create plot by country and age category -ggplot(est_country_agedf, - aes( - y = fct_rev(ageCat), - x = incidence.rate * 1000, #rescale incidence - fill = Country - )) + - geom_bar(stat = "identity", - position = position_dodge(), - show.legend = TRUE) + - geom_errorbar( - aes(xmin = CI.lwr * 1000, xmax = CI.upr * 1000), #rescale CIs - position = position_dodge(width = 0.9), - width = .2 - ) + - labs(title = "Enteric Fever Seroincidence by Country and Age", - x = "Seroincidence rate per 1000 person-years", - y = "Age Category", - fill = "Country") + - theme_linedraw() + - theme(axis.text.y = element_text(size = 11), - axis.text.x = element_text(size = 11)) + - scale_x_continuous(expand = c(0, 10)) + - scale_fill_manual(values = country_pal) +## Comparing Seroincidence Rates + +After estimating seroincidence rates across different groups, we may want to statistically compare these rates to determine if observed differences are significant. The `compare_seroincidence()` function performs two-sample z-tests to compare seroincidence rates. + +### Comparing Two Individual Estimates + +First, let's compare seroincidence rates between two countries directly. We'll estimate seroincidence for Bangladesh and Nepal separately, then compare them: + +```{r compare_two} +# Estimate seroincidence for Bangladesh +est_bangladesh <- est_seroincidence( + pop_data = xs_data |> filter(Country == "Bangladesh"), + sr_params = curves, + noise_params = noise |> filter(Country == "Bangladesh"), + antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +# Estimate seroincidence for Nepal +est_nepal <- est_seroincidence( + pop_data = xs_data |> filter(Country == "Nepal"), + sr_params = curves, + noise_params = noise |> filter(Country == "Nepal"), + antigen_isos = c("HlyE_IgG", "HlyE_IgA") +) + +# Compare the two estimates +comparison <- compare_seroincidence(est_bangladesh, est_nepal) +print(comparison) ``` +The output follows the standard `htest` format in R, providing the z-statistic, p-value, estimates for both groups, and a confidence interval for the difference in incidence rates. + +### Comparing All Pairs of Stratified Estimates + +For stratified analyses, we can compare all pairs of strata at once. This is particularly useful when we have multiple groups and want a comprehensive view of all pairwise differences: + +```{r compare_strata} +# Compare all pairs of country-age combinations +comparisons_table <- compare_seroincidence(est_country_age) + +# Display the results +print(comparisons_table) +``` + +This produces a table with one row for each pairwise comparison, showing: +- The two strata being compared +- Incidence rates for each group +- The difference in rates +- Standard error of the difference +- Z-statistic +- P-value +- 95% confidence interval for the difference + +We can use this to identify which differences are statistically significant (typically using p < 0.05 as a threshold). + ```{r include=FALSE} #Calculate output values -rate_bangla <- round(est_country_agedf$incidence.rate[3] * 1000) +rate_bangla_5_15 <- est_country_agedf |> + dplyr::filter(Country == "Bangladesh", ageCat == "5-15") |> + pull("incidence.rate") + +rate_bangla_5_15 <- round(rate_bangla_5_15 * 1000) + +rate_nepal_5_15 <- est_country_agedf |> + dplyr::filter(Country == "Nepal", ageCat == "5-15") |> + pull("incidence.rate") -rate_nepal <- round(est_country_agedf$incidence.rate[1] * 1000) +rate_nepal_5_15 <- round(rate_nepal_5_15 * 1000) -rate_ratio_bangla_nepal <- round(rate_bangla / rate_nepal) +rate_ratio_bangla_nepal <- round(rate_bangla_5_15 / rate_nepal_5_15) ``` ## Conclusions -We find that Bangladesh has the highest overall seroincidence of enteric fever with a rate of `r rate_bangla` per 1000 person-years, as well as the highest seroincidence by age category. -In comparison, Nepal has a seroincidence rate over `r rate_ratio_bangla_nepal` times lower than that of Bangladesh (`r rate_nepal` per 1000 person-years) and the lowest age-specific seroincidence rates of the three countries in the study. -**serocalculator** provides an efficient tool to conduct this analysis and produce actionable results. +We estimate that Bangladesh has the highest enteric fever seroconversion rates across all age groups, with the highest rates observed among 5- to 15-year-olds (`r rate_bangla_5_15` per 1000 person-years). In this age group, the seroconversion rate in Bangladesh is `r rate_ratio_bangla_nepal` times higher than in Nepal, where the rate is `r rate_nepal_5_15` per 1000 person-years. These findings highlight substantial geographic variation in enteric fever transmission, emphasizing the need for targeted prevention strategies. **serocalculator** offers an efficient and reproducible approach to estimating seroconversion rates, enabling data-driven insights for disease surveillance and public health decision-making. ## Acknowledgments diff --git a/vignettes/articles/scrubTyphus_example.Rmd b/vignettes/articles/scrubTyphus_example.Rmd index 9199e4a34..091a09115 100644 --- a/vignettes/articles/scrubTyphus_example.Rmd +++ b/vignettes/articles/scrubTyphus_example.Rmd @@ -18,11 +18,21 @@ This vignette reproduces the analysis for: [**Estimating the seroincidence of sc ## Methods -The **serocalculator** R package provides a rapid and computationally simple method for calculating seroconversion rates, as originally published in @Simonsen_2009 and @Teunis_2012, and further developed in subsequent publications by @de_Graaf_2014, @Teunis_2016, and @Teunis_2020. In short, longitudinal seroresponses from confirmed cases with a known symptom onset date are assumed to represent the time course of human serum antibodies against a specific pathogen. Therefore, by using these longitudinal antibody dynamics with any cross–sectional sample of the same antibodies in a human population, an incidence estimate can be calculated. +The **serocalculator** R package provides a rapid and computationally simple method for calculating seroconversion rates, +as originally published in @Simonsen_2009 and @Teunis_2012, +and further developed in subsequent publications by @de_Graaf_2014, @Teunis_2016, and @Teunis_2020. +In short, +longitudinal seroresponses from confirmed cases with a known symptom onset date +are assumed to represent the time course of human serum antibodies against a specific pathogen. +Therefore, +by using these longitudinal antibody dynamics with any cross–sectional sample of the same antibodies in a human population, +an incidence estimate can be calculated. ### The Seroincidence Estimator -The **serocalculator** package was designed to calculate the incidence of seroconversion by using the longitudinal seroresponse characteristics. The distribution of serum antibody concentrations in a cross–sectional population sample is calculated as a function of the longitudinal seroresponse and the frequency of seroconversion (or seroincidence). Given the seroresponse, this marginal distribution of antibody concentrations can be fitted to the cross-sectional data and thereby providing a means to estimate the seroincidence. +The **serocalculator** package was designed to calculate the incidence of seroconversion by using the longitudinal seroresponse characteristics. +The distribution of serum antibody concentrations in a cross–sectional population sample is calculated as a function of the longitudinal seroresponse and the frequency of seroconversion (or seroincidence). +Given the seroresponse, this marginal distribution of antibody concentrations can be fitted to the cross-sectional data and thereby providing a means to estimate the seroincidence. @@ -75,8 +85,8 @@ We will first load the longitudinal curve parameters to set the antibody decay p # Import longitudinal antibody parameters from OSF curves <- - "https://osf.io/download/u5gxh/" %>% - load_curve_params() + "https://osf.io/download/u5gxh/" |> + load_sr_params() ``` #### Visualize curve parameters @@ -84,7 +94,7 @@ curves <- We can graph the decay curves with an `autoplot()` method: ```{r} -curves %>% autoplot() +curves |> autoplot() ``` @@ -108,7 +118,7 @@ xs_data <- load_pop_data( We can check that `xs_data` has the correct formatting using the `check_pop_data()` function: ```{r, message = TRUE} -xs_data %>% check_pop_data(verbose = TRUE) +xs_data |> check_pop_data(verbose = TRUE) ``` #### Summarize antibody data @@ -116,7 +126,7 @@ xs_data %>% check_pop_data(verbose = TRUE) We can compute numerical summaries of our cross-sectional antibody data with a `summary()` method for `pop_data` objects: ```{r} -xs_data %>% summary(strata = "country") +xs_data |> summary(strata = "country") ``` @@ -151,10 +161,10 @@ Column Name | Description ```{r message=FALSE, warning=FALSE} # biologic noise -b_noise <- xs_data %>% - group_by(antigen_iso) %>% - filter(!is.na(value)) %>% - filter(age < 40) %>% # restrict to young ages to capture recent exposures +b_noise <- xs_data |> + group_by(antigen_iso) |> + filter(!is.na(value)) |> + filter(age < 40) |> # restrict to young ages to capture recent exposures do({ set.seed(54321) # Fit the mixture model @@ -183,7 +193,7 @@ noise <- data.frame( eps = c(0.2, 0.2), # M noise (eps) y.low = c(0.2, 0.2), # low cutoff (llod) y.high = c(200, 200) -) %>% # high cutoff (y.high) +) |> # high cutoff (y.high) mutate(across(where(is.numeric), round, digits = 2)) ``` @@ -194,10 +204,10 @@ Now we are ready to begin estimating seroincidence. We will use `est.incidence.b ```{r estby} # Using est.incidence.by (strata) -est <- est.incidence.by( +est <- est_seroincidence_by( strata = c("country"), pop_data = xs_data, - curve_params = curves, + sr_params = curves, noise_params = noise, antigen_isos = c("OT56kda_IgG"), num_cores = 8 # Allow for parallel processing to decrease run time @@ -214,10 +224,10 @@ Now we are ready to begin estimating seroincidence. We will use `est.incidence.b ```{r estby2} # Using est.incidence.by (strata) -est2 <- est.incidence.by( +est2 <- est_seroincidence_by( strata = c("country", "ageQ"), pop_data = xs_data, - curve_params = curves, + sr_params = curves, noise_params = noise, antigen_isos = c("OT56kda_IgG"), num_cores = 8 # Allow for parallel processing to decrease run time @@ -239,7 +249,7 @@ Let's visualize our seroincidence estimates by strata. # Plot seroincidence estimates # Save summary(est) as a dataframe -estdf <- summary(est) %>% +estdf <- summary(est) |> mutate(ageQ = "Overall") # Save summary(est2) as a dataframe diff --git a/vignettes/articles/simulate_xsectionalData.qmd b/vignettes/articles/simulate_xsectionalData.qmd index 3772bf4c7..c7b608ae8 100644 --- a/vignettes/articles/simulate_xsectionalData.qmd +++ b/vignettes/articles/simulate_xsectionalData.qmd @@ -1,10 +1,17 @@ --- -title: "Generate a simulated cross-sectional sample and estimate seroincidence" -subtitle: "Simulation of Enteric Fever using HlyE IgG and/or HlyE IgA" +title: "Simulation studies" description: "A demonstration of the accuracy of the estimation approach" bibliography: ../references.bib --- +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.width = 7, + fig.height = 5 +) +``` This vignette shows how to simulate a cross-sectional sample of seroresponses for incident infections as a Poisson process with @@ -51,16 +58,6 @@ the chosen number is fixed and reused in any subsequent infection. This is for diagnostic purposes. - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>", - fig.width = 7, - fig.height = 5 -) -``` - # Simulate a single dataset ## load model parameters @@ -75,8 +72,8 @@ library(ggbeeswarm) # for plotting library(dplyr) dmcmc <- "https://osf.io/download/rtw5k" |> - load_curve_params() |> - dplyr::filter(iter < 500) # reduce number of mcmc samples for speed + load_sr_params() |> + dplyr::filter(iter < 50) # reduce number of mcmc samples for speed ``` ## visualize antibody decay model @@ -86,24 +83,23 @@ of model parameters using a `autoplot.curve_params()` method for the `autoplot()` function: ```{r} -dmcmc |> autoplot(n_curves = 50) +dmcmc |> autoplot(show_quantiles = FALSE, n_curves = 100) ``` We can use a logarithmic scale for the x-axis if desired: ```{r} -dmcmc |> autoplot(log_x = TRUE, n_curves = 50) +dmcmc |> autoplot(show_quantiles = FALSE, log_x = TRUE, n_curves = 100) ``` -We can graph the median, 10%, and 90% quantiles of the model using the `graph.curve.params()` function: +We can add the median, 10%, and 90% quantiles of the model: ```{r, "graph-curve-params"} # Specify the antibody-isotype responses to include in analyses antibodies <- c("HlyE_IgA", "HlyE_IgG") dmcmc |> - graph.curve.params(antigen_isos = antibodies) |> - print() + autoplot(show_quantiles = TRUE, n_curves = 100) ``` @@ -173,8 +169,8 @@ csdata |> aes(x = as.factor(antigen_iso), y = value) + geom_beeswarm( - size = .2, - alpha = .3, + size = .5, + alpha = .5, aes(color = antigen_iso), show.legend = FALSE ) + @@ -259,18 +255,18 @@ print(lik_both) ## estimate incidence -We can estimate incidence with `est.incidence()`: +We can estimate incidence with `est_seroincidence()`: ```{r "est.incidence"} -est1 <- est.incidence( +est1 <- est_seroincidence( pop_data = csdata, - curve_params = dmcmc, + sr_params = dmcmc, noise_params = cond, lambda_start = .1, build_graph = TRUE, verbose = verbose, print_graph = FALSE, # display the log-likelihood curve while - #`est.incidence()` is running + #`est_seroincidence()` is running antigen_isos = antibodies ) ``` @@ -317,7 +313,7 @@ sim_df <- n_cores = n_cores, lambdas = lambdas, nclus = nclus, - n_samples = nrep, + sample_sizes = nrep, age_range = lifespan, antigen_isos = antibodies, renew_params = renew_params, @@ -343,19 +339,20 @@ sim_df |> geom_boxplot(outlier.colour = NA, fill = NA) + scale_y_log10() + facet_wrap(~ antigen_iso + lambda.sim, nrow = 2) + - theme_linedraw() + theme_linedraw() + + theme(legend.position = "bottom") ``` ## Estimate incidence in each cluster ```{r, "est-by-stratum"} ests <- - est.incidence.by( + est_seroincidence_by( pop_data = sim_df, - curve_params = dmcmc, + sr_params = dmcmc, noise_params = cond, num_cores = n_cores, - strata = c("lambda.sim", "cluster"), + strata = c("sample_size", "lambda.sim", "cluster"), curve_strata_varnames = NULL, noise_strata_varnames = NULL, verbose = verbose, @@ -420,9 +417,9 @@ ests_summary |> Solutions to `nlm()` exit codes 3-5: -* 3: decrease the `stepmin` argument to `est.incidence()`/`est.incidence.by()` -* 4: increase the `iterlim` argument to `est.incidence()`/`est.incidence.by()` -* 5: increase the `stepmax` argument to `est.incidence()`/`est.incidence.by()` +* 3: decrease the `stepmin` argument to `est_seroincidence()`/`est_seroincidence_by()` +* 4: increase the `iterlim` argument to `est_seroincidence()`/`est_seroincidence_by()` +* 5: increase the `stepmax` argument to `est_seroincidence()`/`est_seroincidence_by()` We can extract the indices of problematic strata, if there are any: @@ -452,8 +449,9 @@ Finally, we can look at our simulation results: library(ggplot2) ests_summary |> - autoplot(xvar = "lambda.sim", - CI = TRUE, + autoplot(type = "scatter", + xvar = "lambda.sim", + CI = TRUE, dodge_width = .05) + ggplot2::geom_function( fun = function(x) x, @@ -465,6 +463,25 @@ ests_summary |> ``` +--- + +We can analyze the simulation results with `analyze_sims()`: + +```{r} +ests_summary |> analyze_sims() +``` + +--- + +We can graph the analysis results with an `autoplot()` method: + +```{r} + +ests_summary |> analyze_sims() |> autoplot(statistic = "Empirical_SE") + +``` + + ## Effect of `renew_params` Setting `renew_params = TRUE` is more realistic, @@ -478,7 +495,7 @@ sim_df_renew <- n_cores = n_cores, lambdas = lambdas, nclus = nclus, - n_samples = nrep, + sample_sizes = nrep, age_range = lifespan, antigen_isos = antibodies, renew_params = TRUE, @@ -489,12 +506,12 @@ sim_df_renew <- ) ests_renew <- - est.incidence.by( + est_seroincidence_by( pop_data = sim_df_renew, - curve_params = dmcmc, + sr_params = dmcmc, noise_params = cond, num_cores = n_cores, - strata = c("lambda.sim", "cluster"), + strata = c("sample_size", "lambda.sim", "cluster"), curve_strata_varnames = NULL, noise_strata_varnames = NULL, verbose = verbose, @@ -511,8 +528,9 @@ ests_renew_summary <- ```{r, "graph_renew"} ests_renew_summary |> - autoplot(xvar = "lambda.sim", - CI = TRUE, + autoplot(type = "scatter", + xvar = "lambda.sim", + CI = TRUE, dodge_width = .05) + ggplot2::geom_function( fun = function(x) x, diff --git a/vignettes/methodology.qmd b/vignettes/methodology.qmd index 6358f7d1a..5ebe616a3 100644 --- a/vignettes/methodology.qmd +++ b/vignettes/methodology.qmd @@ -195,7 +195,7 @@ library(dplyr) # Import longitudinal antibody parameters from OSF curves <- "https://osf.io/download/rtw5k/" %>% - load_curve_params() %>% + load_sr_params() %>% filter(iter < 50) # Import cross-sectional data from OSF and rename required variables: