From 21be691a909fd5924f974de518746c2f8ca7ddb2 Mon Sep 17 00:00:00 2001 From: Ernest Guevarra Date: Thu, 5 Jan 2023 00:33:54 +0000 Subject: [PATCH] update repo; fix #46; fix #47; fix #48 --- .Rbuildignore | 2 + .github/CODE_OF_CONDUCT.md | 148 +- .github/CONTRIBUTING.md | 12 +- .github/workflows/R-CMD-check.yaml | 4 +- .github/workflows/test-coverage.yaml | 60 +- DESCRIPTION | 5 +- NEWS.md | 16 +- README.Rmd | 17 +- README.md | 43 +- codecov.yml | 14 + docs/404.html | 251 +- docs/CODE_OF_CONDUCT.html | 312 +- docs/CONTRIBUTING.html | 270 +- docs/LICENSE.html | 397 +- docs/articles/ad.html | 1718 ++- .../figure-html/unnamed-chunk-102-1.png | Bin 20402 -> 20699 bytes .../figure-html/unnamed-chunk-11-1.png | Bin 24028 -> 24253 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 23402 -> 23708 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 26016 -> 26388 bytes .../figure-html/unnamed-chunk-15-2.png | Bin 28723 -> 26437 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 21926 -> 21632 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a/.Rbuildignore b/.Rbuildignore index 5bf89a7..ecb88ea 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -23,3 +23,5 @@ ^pkgdown$ ^cran-comments\.md$ ^CRAN-RELEASE$ +^CODE_OF_CONDUCT\.md$ +^codecov\.yml$ diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md index b52c263..361d8bd 100644 --- a/.github/CODE_OF_CONDUCT.md +++ b/.github/CODE_OF_CONDUCT.md @@ -1,48 +1,126 @@ -# Contributor Code of Conduct +# Contributor Covenant Code of Conduct -As contributors and maintainers of this project, and in the interest of -fostering an open and welcoming community, we pledge to respect all people who -contribute through reporting issues, posting feature requests, updating -documentation, submitting pull requests or patches, and other activities. +## Our Pledge -We are committed to making participation in this project a harassment-free -experience for everyone, regardless of level of experience, gender, gender -identity and expression, sexual orientation, disability, personal appearance, -body size, race, ethnicity, age, religion, or nationality. +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. -Examples of unacceptable behavior by participants include: +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. -* The use of sexualized language or imagery -* Personal attacks -* Trolling or insulting/derogatory comments +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks * Public or private harassment -* Publishing other's private information, such as physical or electronic - addresses, without explicit permission -* Other unethical or unprofessional conduct +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. +## Scope -By adopting this Code of Conduct, project maintainers commit themselves to -fairly and consistently applying these principles to every aspect of managing -this project. Project maintainers who do not follow or enforce the Code of -Conduct may be permanently removed from the project team. +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. -This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. +## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting a project maintainer at contact@guevarra.io. All -complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. Maintainers are -obligated to maintain confidentiality with regard to the reporter of an incident. +reported to the community leaders responsible for enforcement at ernest@guevarra.io. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], -version 1.3.0, available at -[http://contributor-covenant.org/version/1/3/0/][version] +version 2.1, available at +. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][https://github.com/mozilla/inclusion]. + +For answers to common questions about this code of conduct, see the FAQ at +. Translations are available at . -[homepage]: http://contributor-covenant.org -[version]: http://contributor-covenant.org/version/1/3/0/ +[homepage]: https://www.contributor-covenant.org diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index a60010e..c524b87 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -2,7 +2,7 @@ ## Bugs -* Submit an issue on the [issues page](https://github.com/como-ph/oxcgrt/issues) +* Submit an issue on the [issues page](https://github.com/nutriverse/nipnTK/issues) ## Code contributions @@ -11,14 +11,14 @@ * Clone your version on your account down to your machine from your account ``` -git clone https://github.com//oxcgrt.git +git clone https://github.com//nipnTK.git ``` -* Make sure to track progress upstream i.e., on our version of `oxcgrt` -at `oxcgrt/covidphdata`, by doing +* Make sure to track progress upstream i.e., on our version of `nipnTK` +at `nutriverse/nipnTK`, by doing ``` -git remote add upstream https://github.com/como-ph/oxcgrt.git +git remote add upstream https://github.com/nutriverse/nipnTK.git ``` * Before making changes make sure to pull changes in from `upstream` by doing @@ -32,4 +32,4 @@ documentation * Push up changes to your account -* Submit a pull request at `como-ph/oxcgrt` +* Submit a pull request at `nutriverse/nipnTK` diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 52c2b27..a3ac618 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -18,7 +18,7 @@ jobs: fail-fast: false matrix: config: - - {os: macOS-latest, r: 'release'} + - {os: macos-latest, r: 'release'} - {os: windows-latest, r: 'release'} - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'release'} @@ -29,7 +29,7 @@ jobs: R_KEEP_PKG_SOURCE: yes steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: r-lib/actions/setup-pandoc@v2 diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml index 5910c1a..2c5bb50 100644 --- a/.github/workflows/test-coverage.yaml +++ b/.github/workflows/test-coverage.yaml @@ -1,48 +1,50 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: - branches: - - main - - master + branches: [main, master] pull_request: - branches: - - main - - master + branches: [main, master] name: test-coverage jobs: test-coverage: - runs-on: macOS-latest + runs-on: ubuntu-latest env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - - uses: r-lib/actions/setup-r@v1 + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true - - uses: r-lib/actions/setup-pandoc@v1 + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::covr + needs: coverage - - name: Query dependencies + - name: Test coverage run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") + covr::codecov( + quiet = FALSE, + clean = FALSE, + install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package") + ) shell: Rscript {0} - - name: Cache R packages - uses: actions/cache@v2 - with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - - - name: Install dependencies + - name: Show testthat output + if: always() run: | - install.packages(c("remotes")) - remotes::install_deps(dependencies = TRUE) - remotes::install_cran("covr") - shell: Rscript {0} + ## -------------------------------------------------------------------- + find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true + shell: bash - - name: Test coverage - run: covr::codecov() - shell: Rscript {0} + - name: Upload test results + if: failure() + uses: actions/upload-artifact@v3 + with: + name: coverage-test-failures + path: ${{ runner.temp }}/package diff --git a/DESCRIPTION b/DESCRIPTION index 430d1b9..348e909 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -30,11 +30,12 @@ Suggests: knitr, rmarkdown, tufte, - spelling + spelling, + covr Encoding: UTF-8 Language: en-GB LazyData: true -RoxygenNote: 7.2.1 +RoxygenNote: 7.2.3 Roxygen: list(markdown = TRUE) URL: https://nutriverse.io/nipnTK/, https://github.com/nutriverse/nipnTK BugReports: https://github.com/nutriverse/nipnTK/issues diff --git a/NEWS.md b/NEWS.md index 6005d31..b8b2cc2 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,18 +1,26 @@ -## nipnTK 0.1.1.9000 +# nipnTK 0.1.1.9000 Second release of `nipnTK`. This is a GitHub-only development release. In this release: -### General updates +## General updates * remove `appveyor.yml` and the Appveyor ci/cd workflow * update GitHub Actions workflow to latest 5 system standard check +* update GitHub Actions workflow for coverage testing -## nipnTK 0.1.0 +* change default git branch name from master to main -This is the first [CRAN](https://cran.r-project.org) release of `nipnTK`. +* add CITATION + +* update CONTRIBUTOR guidelines + +* upgrade website to bootstrap 5 +# nipnTK 0.1.0 + +This is the first [CRAN](https://cran.r-project.org) release of `nipnTK`. diff --git a/README.Rmd b/README.Rmd index 21afc98..e9c1cd0 100644 --- a/README.Rmd +++ b/README.Rmd @@ -21,16 +21,15 @@ library(nipnTK) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) +[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) [![CRAN](https://img.shields.io/cran/v/nipnTK.svg)](https://cran.r-project.org/package=nipnTK) [![cran checks](https://cranchecks.info/badges/summary/nipnTK)](https://cran.r-project.org/web/checks/check_results_nipnTK.html) [![CRAN](https://img.shields.io/cran/l/nipnTK.svg)](https://CRAN.R-project.org/package=nipnTK) [![CRAN](http://cranlogs.r-pkg.org/badges/nipnTK)](https://cran.r-project.org/package=nipnTK) [![CRAN](http://cranlogs.r-pkg.org/badges/grand-total/nipnTK)](https://cran.r-project.org/package=nipnTK) -[![R build status](https://github.com/nutriverse/nipnTK/workflows/R-CMD-check/badge.svg)](https://github.com/nutriverse/nipnTK/actions) -[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/nutriverse/nipnTK?branch=master&svg=true)](https://ci.appveyor.com/project/nutriverse/nipnTK) -[![R build status](https://github.com/nutriverse/nipnTK/workflows/test-coverage/badge.svg)](https://github.com/nutriverse/nipnTK/actions) -[![codecov](https://codecov.io/gh/nutriverse/nipnTK/branch/master/graph/badge.svg)](https://codecov.io/gh/nutriverse/nipnTK) +[![R-CMD-check](https://github.com/nutriverse/nipnTK/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/nutriverse/nipnTK/actions/workflows/R-CMD-check.yaml) +[![test-coverage](https://github.com/nutriverse/nipnTK/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/nutriverse/nipnTK/actions/workflows/test-coverage.yaml) +[![Codecov test coverage](https://codecov.io/gh/nutriverse/nipnTK/branch/main/graph/badge.svg)](https://app.codecov.io/gh/nutriverse/nipnTK?branch=main) [![CodeFactor](https://www.codefactor.io/repository/github/nutriverse/nipntk/badge)](https://www.codefactor.io/repository/github/nutriverse/nipntk) [![DOI](https://zenodo.org/badge/118171028.svg)](https://zenodo.org/badge/latestdoi/118171028) @@ -88,6 +87,14 @@ These activities and a proposed order in which they should be performed are show knitr::include_graphics("man/figures/nipnWorkflow.png") ``` +## Citation + +If you find the `nipnTK` package useful, please cite using the suggested citation provided by a call to the `citation` function as follows: + +```{r cite} +citation("nipnTK") +``` + ## Community guidelines Feedback, bug reports and feature requests are welcome; file issues or seek support [here](https://github.com/nutriverse/nipnTK/issues). If you would like to contribute to the package, please see our [contributing guidelines](https://nutriverse.io/nipnTK/CONTRIBUTING.html). diff --git a/README.md b/README.md index d9ef620..06000d4 100644 --- a/README.md +++ b/README.md @@ -8,20 +8,18 @@ [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) +[![Lifecycle: +stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) [![CRAN](https://img.shields.io/cran/v/nipnTK.svg)](https://cran.r-project.org/package=nipnTK) [![cran checks](https://cranchecks.info/badges/summary/nipnTK)](https://cran.r-project.org/web/checks/check_results_nipnTK.html) [![CRAN](https://img.shields.io/cran/l/nipnTK.svg)](https://CRAN.R-project.org/package=nipnTK) [![CRAN](http://cranlogs.r-pkg.org/badges/nipnTK)](https://cran.r-project.org/package=nipnTK) [![CRAN](http://cranlogs.r-pkg.org/badges/grand-total/nipnTK)](https://cran.r-project.org/package=nipnTK) -[![R build -status](https://github.com/nutriverse/nipnTK/workflows/R-CMD-check/badge.svg)](https://github.com/nutriverse/nipnTK/actions) -[![AppVeyor Build -Status](https://ci.appveyor.com/api/projects/status/github/nutriverse/nipnTK?branch=master&svg=true)](https://ci.appveyor.com/project/nutriverse/nipnTK) -[![R build -status](https://github.com/nutriverse/nipnTK/workflows/test-coverage/badge.svg)](https://github.com/nutriverse/nipnTK/actions) -[![codecov](https://codecov.io/gh/nutriverse/nipnTK/branch/master/graph/badge.svg)](https://codecov.io/gh/nutriverse/nipnTK) +[![R-CMD-check](https://github.com/nutriverse/nipnTK/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/nutriverse/nipnTK/actions/workflows/R-CMD-check.yaml) +[![test-coverage](https://github.com/nutriverse/nipnTK/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/nutriverse/nipnTK/actions/workflows/test-coverage.yaml) +[![Codecov test +coverage](https://codecov.io/gh/nutriverse/nipnTK/branch/main/graph/badge.svg)](https://app.codecov.io/gh/nutriverse/nipnTK?branch=main) [![CodeFactor](https://www.codefactor.io/repository/github/nutriverse/nipntk/badge)](https://www.codefactor.io/repository/github/nutriverse/nipntk) [![DOI](https://zenodo.org/badge/118171028.svg)](https://zenodo.org/badge/latestdoi/118171028) @@ -49,7 +47,7 @@ variables. ## Requirements - - [R](https://cran.r-project.org) version 3.4 or higher +- [R](https://cran.r-project.org) version 3.4 or higher Extensive use is made of the [R](https://cran.r-project.org) language and environment for statistical computing. This is a free and powerful @@ -113,6 +111,33 @@ are shown below: +## Citation + +If you find the `nipnTK` package useful, please cite using the suggested +citation provided by a call to the `citation` function as follows: + +``` r +citation("nipnTK") +#> +#> To cite nipnTK in publications use: +#> +#> Mark Myatt and Ernest Guevarra (2023). nipnTK: National Information +#> Platforms for Nutrition (NiPN) Data Quality Toolkit R package version +#> 0.1.1.9000 URL https://nutriverse.io/nipnTK/ DOI +#> 10.5281/zenodo.4297897 +#> +#> A BibTeX entry for LaTeX users is +#> +#> @Manual{, +#> title = {nipnTK: National Information Platforms for Nutrition (NiPN) Data Quality Toolkit}, +#> author = {{Mark Myatt} and {Ernest Guevarra}}, +#> year = {2023}, +#> note = {R package version 0.1.1.9000}, +#> url = {https://nutriverse.io/nipnTK/}, +#> doi = {10.5281/zenodo.4297897}, +#> } +``` + ## Community guidelines Feedback, bug reports and feature requests are welcome; file issues or diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 0000000..04c5585 --- /dev/null +++ b/codecov.yml @@ -0,0 +1,14 @@ +comment: false + +coverage: + status: + project: + default: + target: auto + threshold: 1% + informational: true + patch: + default: + target: auto + threshold: 1% + informational: true diff --git a/docs/404.html b/docs/404.html index 1435e81..b1aa8e0 100644 --- a/docs/404.html +++ b/docs/404.html @@ -1,214 +1,125 @@ - - - - + + + + - - + Page not found (404) • nipnTK - - - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + - - - - - - - - - - -
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Developed by Mark Myatt, Ernest Guevarra.

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+ - - diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html index 77cb722..21eadbc 100644 --- a/docs/CODE_OF_CONDUCT.html +++ b/docs/CODE_OF_CONDUCT.html @@ -1,232 +1,162 @@ - - - - - - - -Contributor Code of Conduct • nipnTK - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Contributor Covenant Code of Conduct • nipnTK + Skip to contents + +
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Contributor Code of Conduct

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Contributor Covenant Code of Conduct

+ Source: .github/CODE_OF_CONDUCT.md
-
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As contributors and maintainers of this project, and in the interest of fostering an open and welcoming community, we pledge to respect all people who contribute through reporting issues, posting feature requests, updating documentation, submitting pull requests or patches, and other activities.

-

We are committed to making participation in this project a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion, or nationality.

-

Examples of unacceptable behavior by participants include:

-
    -
  • The use of sexualized language or imagery
    • -
  • Personal attacks
    • -
  • Trolling or insulting/derogatory comments
    • +
    +

    Our Pledge

    +

    We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation.

    +

    We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.

    +
    +
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    Our Standards

    +

    Examples of behavior that contributes to a positive environment for our community include:

    +
    • Demonstrating empathy and kindness toward other people
      • +
    • Being respectful of differing opinions, viewpoints, and experiences
      • +
    • Giving and gracefully accepting constructive feedback
      • +
    • Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
      • +
    • Focusing on what is best not just for us as individuals, but for the overall community
      • +

    Examples of unacceptable behavior include:

    +
    • The use of sexualized language or imagery, and sexual attention or advances of any kind
      • +
    • Trolling, insulting or derogatory comments, and personal or political attacks
    • Public or private harassment
      • -
    • Publishing other’s private information, such as physical or electronic addresses, without explicit permission
      • -
    • Other unethical or unprofessional conduct
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    Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.

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    By adopting this Code of Conduct, project maintainers commit themselves to fairly and consistently applying these principles to every aspect of managing this project. Project maintainers who do not follow or enforce the Code of Conduct may be permanently removed from the project team.

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    This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community.

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    Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting a project maintainer at . All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. Maintainers are obligated to maintain confidentiality with regard to the reporter of an incident.

    -

    This Code of Conduct is adapted from the Contributor Covenant, version 1.3.0, available at http://contributor-covenant.org/version/1/3/0/

    +
  • Publishing others’ private information, such as a physical or email address, without their explicit permission
    • +
  • Other conduct which could reasonably be considered inappropriate in a professional setting
    • +
+
+

Enforcement Responsibilities

+

Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.

+

Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.

+
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Scope

+

This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.

+
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+

Enforcement

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Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at . All complaints will be reviewed and investigated promptly and fairly.

+

All community leaders are obligated to respect the privacy and security of the reporter of any incident.

+
+
+

Enforcement Guidelines

+

Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:

+
+

1. Correction

+

Community Impact: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.

+

Consequence: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.

+
+
+

2. Warning

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Community Impact: A violation through a single incident or series of actions.

+

Consequence: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.

+
+
+

3. Temporary Ban

+

Community Impact: A serious violation of community standards, including sustained inappropriate behavior.

+

Consequence: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.

+
+
+

4. Permanent Ban

+

Community Impact: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.

+

Consequence: A permanent ban from any sort of public interaction within the community.

+
+
+
+

Attribution

+

This Code of Conduct is adapted from the Contributor Covenant, version 2.1, available at https://www.contributor-covenant.org/version/2/1/code_of_conduct.html.

+

Community Impact Guidelines were inspired by [Mozilla’s code of conduct enforcement ladder][https://github.com/mozilla/inclusion].

+

For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.

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Developed by Mark Myatt, Ernest Guevarra.

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Developed by Mark Myatt, Ernest Guevarra.

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Site built with pkgdown 1.6.1.

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Site built with pkgdown 2.0.7.

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+ - - - + diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html index 3c37636..3206e5b 100644 --- a/docs/CONTRIBUTING.html +++ b/docs/CONTRIBUTING.html @@ -1,244 +1,116 @@ - - - - - - - -Contributing • nipnTK - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Contributing • nipnTK + Skip to contents + +
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Contributing

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+

Contributing

+ Source: .github/CONTRIBUTING.md
-
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-Bugs

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  • Submit an issue on the issues page +
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    Bugs

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    -Code contributions

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    • Fork this repository to your Github account

      • +
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    Code contributions

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    • Fork this repository to your Github account

    • Clone your version on your account down to your machine from your account

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    git clone https://github.com/<yourgithubusername>/oxcgrt.git
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    • Before making changes make sure to pull changes in from upstream by doing either git fetch upstream then merge later or git pull upstream to fetch and merge in one step

      • +
    git clone https://github.com/<yourgithubusername>/nipnTK.git
    +
    • Make sure to track progress upstream i.e., on our version of nipnTK at nutriverse/nipnTK, by doing
      • +
    git remote add upstream https://github.com/nutriverse/nipnTK.git
    +

    • Before making changes make sure to pull changes in from upstream by doing either git fetch upstream then merge later or git pull upstream to fetch and merge in one step

    • Make your changes on a new feature branch

    • Please write a test or tests for your changes if they affect code and not just documentation

    • Push up changes to your account

      • -

    • Submit a pull request at como-ph/oxcgrt

      • -
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  • Submit a pull request at nutriverse/nipnTK

    • +
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    -
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    Developed by Mark Myatt, Ernest Guevarra.

    +
    +

    Developed by Mark Myatt, Ernest Guevarra.

    -
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    Site built with pkgdown 1.6.1.

    +
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    Site built with pkgdown 2.0.7.

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    + - - - + diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 724ed5b..cc2db45 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -1,191 +1,83 @@ - - - - - - - -GNU General Public License • nipnTK - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -GNU General Public License • nipnTK + + Skip to contents + +
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    GNU General Public License

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    GNU General Public License

    + Source: LICENSE.md
    -

    Version 3, 29 June 2007
    Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/>

    +

    Version 3, 29 June 2007
    Copyright © 2007 Free Software Foundation, Inc. <http://fsf.org/>

    Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

    -
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    -Preamble

    +
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    Preamble

    The GNU General Public License is a free, copyleft license for software and other kinds of works.

    The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.

    When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.

    @@ -197,12 +89,10 @@

    Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.

    The precise terms and conditions for copying, distribution and modification follow.

    -
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    -TERMS AND CONDITIONS

    -
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    -0. Definitions

    +
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    TERMS AND CONDITIONS

    +
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    0. Definitions

    “This License” refers to version 3 of the GNU General Public License.

    “Copyright” also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.

    “The Program” refers to any copyrightable work licensed under this License. Each licensee is addressed as “you”. “Licensees” and “recipients” may be individuals or organizations.

    @@ -212,9 +102,8 @@

    To “convey” a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying.

    An interactive user interface displays “Appropriate Legal Notices” to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion.

    -
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    -1. Source Code

    +
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    1. Source Code

    The “source code” for a work means the preferred form of the work for making modifications to it. “Object code” means any non-source form of a work.

    A “Standard Interface” means an interface that either is an official standard defined by a recognized standards body, or, in the case of interfaces specified for a particular programming language, one that is widely used among developers working in that language.

    The “System Libraries” of an executable work include anything, other than the work as a whole, that (a) is included in the normal form of packaging a Major Component, but which is not part of that Major Component, and (b) serves only to enable use of the work with that Major Component, or to implement a Standard Interface for which an implementation is available to the public in source code form. A “Major Component”, in this context, means a major essential component (kernel, window system, and so on) of the specific operating system (if any) on which the executable work runs, or a compiler used to produce the work, or an object code interpreter used to run it.

    @@ -222,31 +111,26 @@

    The Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source.

    The Corresponding Source for a work in source code form is that same work.

    -
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    -2. Basic Permissions

    +
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    2. Basic Permissions

    All rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law.

    You may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. You may convey covered works to others for the sole purpose of having them make modifications exclusively for you, or provide you with facilities for running those works, provided that you comply with the terms of this License in conveying all material for which you do not control copyright. Those thus making or running the covered works for you must do so exclusively on your behalf, under your direction and control, on terms that prohibit them from making any copies of your copyrighted material outside their relationship with you.

    Conveying under any other circumstances is permitted solely under the conditions stated below. Sublicensing is not allowed; section 10 makes it unnecessary.

    -
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    Developed by Mark Myatt, Ernest Guevarra.

    +
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    Developed by Mark Myatt, Ernest Guevarra.

    -
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    Site built with pkgdown 1.6.1.

    +
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    Site built with pkgdown 2.0.7.

    -
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    +
    + - - - + diff --git a/docs/articles/ad.html b/docs/articles/ad.html index 30fc455..a2acae0 100644 --- a/docs/articles/ad.html +++ b/docs/articles/ad.html @@ -4,7 +4,8 @@ - + + Distributions of variables and indices • nipnTK @@ -12,170 +13,149 @@ - - - + + + - - + + - -
    -
    - +
    -
    -
    -

    Distributions of variables and indices

    -

    Mark Myatt

    +
    +

    Distributions of variables and indices

    +

    Mark Myatt

    - Source: vignettes/ad.Rmd - - + Source: vignettes/ad.Rmd +
    ad.Rmd
    -
    -

    -Assessing the distribution of anthropometric variables, indices, and indicators

    -

    In this section we will examine the distribution of anthropometric variables (e.g. weight, height, and MUAC), anthropometric indices (e.g. WHZ, HAZ, and WHZ), and anthropometric indicators (e.g. wasted, stunted, and underweight).

    -

    Topics such as the distribution of age, age by sex, age-heaping, and digit preference are covered in other sections of this toolkit.

    +
    +

    Assessing the distribution of anthropometric variables, indices, and +indicators +

    +

    In this section we will examine the distribution of anthropometric +variables (e.g. weight, height, and MUAC), anthropometric indices +(e.g. WHZ, HAZ, and WHZ), and anthropometric indicators (e.g. wasted, +stunted, and underweight).

    +

    Topics such as the distribution of age, age by sex, age-heaping, and +digit preference are covered in other sections of this toolkit.

    We will retrieve a survey dataset:

    -
    svy <- read.table("dist.ex01.csv", header = TRUE, sep = ",") head(svy)
    -

    The file dist.ex01.csv is a comma-separated-value (CSV) file containing anthropometric data from a SMART survey in Kabul, Afghanistan.

    +
    svy <- read.table("dist.ex01.csv", header = TRUE, sep = ",") head(svy)
    +

    The file dist.ex01.csv is a comma-separated-value +(CSV) file containing anthropometric data from a SMART survey in Kabul, +Afghanistan.

    -
    -

    -Numerical summaries

    -

    The summary() function in R provides a six-figure summary (i.e. minimum, first quartile, median, means, third quartile, and maximum) of a numeric variable. For example:

    +
    +

    Numerical summaries +

    +

    The summary() function in R provides a six-figure +summary (i.e. minimum, first quartile, median, means, third quartile, +and maximum) of a numeric variable. For example:

    -summary(svy$weight)
    +summary(svy$weight)

    returns:

    -
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>    4.90    9.00   11.00   11.13   13.10   20.70
    +
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#>    4.90    9.00   11.00   11.13   13.10   20.70

    The six-figure summary does not report the standard deviation.

    -

    The sd() function in R calculates the standard deviation. For example:

    +

    The sd() function in R calculates the standard +deviation. For example:

    -sd(svy$weight)
    +sd(svy$weight)

    returns:

    -
    #> [1] 2.802065
    -

    The sd() function may return NA. This will happen if there are missing values in the specified variable.

    -

    If this happens you can instruct the function to ignore missing values:

    +
    #> [1] 2.802065
    +

    The sd() function may return NA. This +will happen if there are missing values in the specified variable.

    +

    If this happens you can instruct the function to ignore missing +values:

    -sd(svy$weight, na.rm = TRUE)
    +sd(svy$weight, na.rm = TRUE)

    returns the same value:

    -
    #> [1] 2.802065
    -

    Using the na.rm parameter in this way (i.e. specifying na.rm = TRUE) works with many descriptive functions in R (see table below).

    - +
    #> [1] 2.802065
    +

    Using the na.rm parameter in this way (i.e. specifying +na.rm = TRUE) works with many descriptive functions in R +(see table below).

    +
    @@ -295,237 +275,331 @@

    Some descriptive functions in R
    -* If a function returns NA this may be because the variable contains missing values.
    Use na.rm = TRUE to instruct the function to ignore missing values. +* If a function returns NA this may be because the variable +contains missing values.
    Use na.rm = TRUE to instruct the function +to ignore missing values.
    -
    -

    -Graphical and numerical summaries

    -

    Numerical summaries are useful for checking that data are within an expected range.

    -

    Graphical methods are often more informative than numerical summaries.

    -

    A key graphical method for examining the distribution of a variable is the histogram. For example:

    +
    +

    Graphical and numerical summaries +

    +

    Numerical summaries are useful for checking that data are within an +expected range.

    +

    Graphical methods are often more informative than numerical +summaries.

    +

    A key graphical method for examining the distribution of a variable +is the histogram. For example:

    -hist(svy$weight)
    -

    displays a histogram of the weight variable in the example dataset (see figure below).

    +hist(svy$weight)
    +

    displays a histogram of the weight variable in the example dataset +(see figure below).

    -

    We need to be careful when examining the distribution of measurements, as they may vary by sex. For example:

    +

    We need to be careful when examining the distribution of +measurements, as they may vary by sex. For example:

    -hist(svy$height)
    -

    will display the heights of both males and females. That is, it will display two separate distributions as if they were a single distribution.

    +hist(svy$height)
    +

    will display the heights of both males and females. That is, it will +display two separate distributions as if they were a single +distribution.

    -

    In this case it is sensible to look at the data for males and the data for females using separate histograms:

    +

    In this case it is sensible to look at the data for males and the +data for females using separate histograms:

    -hist(svy$height[svy$sex == 1]) 
-hist(svy$height[svy$sex == 2])
    +hist(svy$height[svy$sex == 1]) +hist(svy$height[svy$sex == 2])

    or using a box-plot:

    -boxplot(svy$height ~ svy$sex, names = c("M", "F"), 
-        xlab = "Sex", ylab = "Height (cm)", main = "Height by sex")
    +boxplot(svy$height ~ svy$sex, names = c("M", "F"), + xlab = "Sex", ylab = "Height (cm)", main = "Height by sex")

    Numerical summaries can also be used:

    -by(svy$height, svy$sex, summary)
    +by(svy$height, svy$sex, summary)

    This returns:

    -
    #> svy$sex: 1
-#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>   56.20   75.00   81.95   82.49   90.00  110.50 
-#> ------------------------------------------------------------ 
-#> svy$sex: 2
-#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>   58.00   73.25   80.30   81.30   88.95  109.50
    +
    #> svy$sex: 1
+#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#>   56.20   75.00   81.95   82.49   90.00  110.50 
+#> ------------------------------------------------------------ 
+#> svy$sex: 2
+#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#>   58.00   73.25   80.30   81.30   88.95  109.50
    -
    -

    -Normal distributions

    -

    With anthropometric variables and indices we usually expect a symmetrical (or nearly symmetrical) “bell-shaped” distribution. The variables and indices of interest are usually:

    +
    +

    Normal distributions +

    +

    With anthropometric variables and indices we usually expect a +symmetrical (or nearly symmetrical) “bell-shaped” distribution. The +variables and indices of interest are usually:

    -hist(svy$muac) 
-hist(svy$haz) 
-hist(svy$waz) 
-hist(svy$whz)
    +hist(svy$muac) +hist(svy$haz) +hist(svy$waz) +hist(svy$whz)

    These plots are shown below.

    Histograms showing the distribution of anthropometric indices in the example datasetHistograms showing the distribution of anthropometric indices in the example dataset

    -Histograms showing the distribution of anthropometric indices in the example dataset +Histograms showing the distribution of anthropometric indices in the +example dataset

    -

    The number and size of the “intervals” (breaks) used when plotting a histogram is calculated to produce a useful plot. The intervals used are based on the range of the data.

    -

    You can specify a different set of breaks for the hist() function to use. For example:

    +

    The number and size of the “intervals” (breaks) used +when plotting a histogram is calculated to produce a useful plot. The +intervals used are based on the range of the data.

    +

    You can specify a different set of breaks for the +hist() function to use. For example:

    -hist(svy$haz, breaks = "scott")
    +hist(svy$haz, breaks = "scott")

    -

    calculates intervals using the standard deviation and the sample size. This:

    +

    calculates intervals using the standard deviation and the sample +size. This:

    -hist(svy$haz, breaks = "FD")
    +hist(svy$haz, breaks = "FD")

    calculates intervals using the inter-quartile range. This:

    -hist(svy$haz, breaks = 40)
    +hist(svy$haz, breaks = 40)

    will use about 40 intervals. This:

    -hist(svy$haz, 
-     breaks = seq(from = floor(min(svy$haz)), to = ceiling(max(svy$haz)), by = 0.5))
    +hist(svy$haz, + breaks = seq(from = floor(min(svy$haz)), to = ceiling(max(svy$haz)), by = 0.5))

    -

    uses intervals that are 0.5 z-scores wide over the full range of haz.

    -

    All of these plots show nearly symmetrical “bell-shaped” distributions.

    -

    The ideal symmetrical “bell-shaped” distribution is the normal distribution.

    -

    There are a number of ways of assessing whether a variable is normally distributed.

    -

    The first way of assessing whether a variable is normally distributed is a simple “by-eye” assessment as we have already done using histograms.

    -

    The NiPN data quality toolkit provides an R language function called histNormal() that can help with “by-eye” assessments by superimposing a normal curve on a histogram of the variable of interest:

    +

    uses intervals that are 0.5 z-scores wide over the +full range of haz.

    +

    All of these plots show nearly symmetrical “bell-shaped” +distributions.

    +

    The ideal symmetrical “bell-shaped” distribution is the normal +distribution.

    +

    There are a number of ways of assessing whether a variable is +normally distributed.

    +

    The first way of assessing whether a variable is normally distributed +is a simple “by-eye” assessment as we have already done using +histograms.

    +

    The NiPN data quality toolkit provides an R language function called +histNormal() that can help with “by-eye” assessments by +superimposing a normal curve on a histogram of the variable of +interest:

    -histNormal(svy$muac)
-histNormal(svy$haz)
-histNormal(svy$waz)
-histNormal(svy$whz)
    -

    These plots are shown below. All variables appear to be approximately normally distributed.

    +histNormal(svy$muac) +histNormal(svy$haz) +histNormal(svy$waz) +histNormal(svy$whz) +

    These plots are shown below. All variables appear to be approximately +normally distributed.

    Histograms of anthropometric indices with normal curves superimposedHistograms of anthropometric indices with normal curves superimposed

    Histograms of anthropometric indices with normal curves superimposed

    -

    Changing the breaks parameter may make a histogram easier to “read”. For example:

    +

    Changing the breaks parameter may make a histogram +easier to “read”. For example:

    -histNormal(svy$haz, breaks = 15)
    +histNormal(svy$haz, breaks = 15)

    -

    Another graphical method for assessing whether a variable is normally distributed is the normal quantile-quantile plot. These are easy to produce using R.

    -

    The NiPN data quality toolkit provided a helper function called qqNormalPlot() that produces a slightly enhanced normal quantile-quantile plot:

    +

    Another graphical method for assessing whether a variable is normally +distributed is the normal quantile-quantile plot. These are +easy to produce using R.

    +

    The NiPN data quality toolkit provided a helper function called +qqNormalPlot() that produces a slightly enhanced normal +quantile-quantile plot:

    -qqNormalPlot(svy$whz)
    +qqNormalPlot(svy$whz)

    -

    This plot is shown below (with annotations). In this example the tails of the distribution contain more cases than would be expected in a perfectly normally distributed variable.

    +

    This plot is shown below (with annotations). In this example the +tails of the distribution contain more cases than would be expected in a +perfectly normally distributed variable.

    -Annotated normal quantile-quantile plot of the whz variable in the example dataset

    -Annotated normal quantile-quantile plot of the whz variable in the example dataset +Annotated normal quantile-quantile plot of the whz variable in the example dataset

    +Annotated normal quantile-quantile plot of the whz variable in the +example dataset

    We should examine all of the relevant variables:

    -qqNormalPlot(svy$muac) 
-qqNormalPlot(svy$haz) 
-qqNormalPlot(svy$waz) 
-qqNormalPlot(svy$whz)
    -

    These plots are shown below. There is evidence of small deviations from normality in muac, haz, and whz.

    +qqNormalPlot(svy$muac) +qqNormalPlot(svy$haz) +qqNormalPlot(svy$waz) +qqNormalPlot(svy$whz) +

    These plots are shown below. There is evidence of small deviations +from normality in muac, haz, and +whz.

    Normal quantile-quantile plots of anthropometric indices in the example datasetNormal quantile-quantile plots of anthropometric indices in the example dataset

    -Normal quantile-quantile plots of anthropometric indices in the example dataset +Normal quantile-quantile plots of anthropometric indices in the example +dataset

    -

    A final way of assessing normality is to use a formal statistical significance test. The preferred test is the Shapiro-Wilk test of normality:

    +

    A final way of assessing normality is to use a formal statistical +significance test. The preferred test is the Shapiro-Wilk test of +normality:

    -shapiro.test(svy$muac) 
-#> 
-#>  Shapiro-Wilk normality test
-#> 
-#> data:  svy$muac
-#> W = 0.99496, p-value = 0.005495
-shapiro.test(svy$haz) 
-#> 
-#>  Shapiro-Wilk normality test
-#> 
-#> data:  svy$haz
-#> W = 0.99348, p-value = 0.0007455
-shapiro.test(svy$waz) 
-#> 
-#>  Shapiro-Wilk normality test
-#> 
-#> data:  svy$waz
-#> W = 0.99827, p-value = 0.5358
-shapiro.test(svy$whz)
-#> 
-#>  Shapiro-Wilk normality test
-#> 
-#> data:  svy$whz
-#> W = 0.99078, p-value = 2.777e-05
    -

    These tests indicate that muac, haz, and whz are significantly non-normal. Examination of the histograms and the normal quantile-quantile plots show that the deviation from normality in these indices are not particular marked. All indices have symmetrical, or nearly symmetrical, “bell-shaped” distributions.

    -

    We need to be careful when using significance tests such as the Shapiro-Wilk test of normality because the results can be strongly influenced by the sample size.

    -

    Small sample sizes can lead to tests missing large effects and large sample sizes can lead to tests identifying small effects as highly significant.

    -

    The analysis above found some highly significant but small deviations from normality that would probably not have been detected by a significance test if a smaller sample size had been used.

    -

    We can simulate a considerably smaller sample size by taking, for example, every fourth muac value:

    +shapiro.test(svy$muac) +#> +#> Shapiro-Wilk normality test +#> +#> data: svy$muac +#> W = 0.99496, p-value = 0.005495 +shapiro.test(svy$haz) +#> +#> Shapiro-Wilk normality test +#> +#> data: svy$haz +#> W = 0.99348, p-value = 0.0007455 +shapiro.test(svy$waz) +#> +#> Shapiro-Wilk normality test +#> +#> data: svy$waz +#> W = 0.99827, p-value = 0.5358 +shapiro.test(svy$whz) +#> +#> Shapiro-Wilk normality test +#> +#> data: svy$whz +#> W = 0.99078, p-value = 2.777e-05 +

    These tests indicate that muac, +haz, and whz are significantly +non-normal. Examination of the histograms and the normal +quantile-quantile plots show that the deviation from normality in these +indices are not particular marked. All indices have symmetrical, or +nearly symmetrical, “bell-shaped” distributions.

    +

    We need to be careful when using significance tests such as the +Shapiro-Wilk test of normality because the results can be strongly +influenced by the sample size.

    +

    Small sample sizes can lead to tests missing large effects and large +sample sizes can lead to tests identifying small effects as highly +significant.

    +

    The analysis above found some highly significant but small deviations +from normality that would probably not have been detected by a +significance test if a smaller sample size had been used.

    +

    We can simulate a considerably smaller sample size by taking, for +example, every fourth muac value:

    -length(svy$muac)
-#> [1] 873
-oneQuarter <- svy$muac[seq(from = 1, to = length(svy$muac), by = 4)] 
-length(oneQuarter)
-#> [1] 219
    +length(svy$muac) +#> [1] 873 +oneQuarter <- svy$muac[seq(from = 1, to = length(svy$muac), by = 4)] +length(oneQuarter) +#> [1] 219

    Inspecting this smaller sample graphically:

    -histNormal(oneQuarter) 
-qqNormalPlot(oneQuarter)
    +histNormal(oneQuarter) +qqNormalPlot(oneQuarter)

    -

    yields results similar to those found when the complete sample was used, but the formal test:

    +

    yields results similar to those found when the complete sample was +used, but the formal test:

    -shapiro.test(oneQuarter)
-#> 
-#>  Shapiro-Wilk normality test
-#> 
-#> data:  oneQuarter
-#> W = 0.98836, p-value = 0.0724
    +shapiro.test(oneQuarter) +#> +#> Shapiro-Wilk normality test +#> +#> data: oneQuarter +#> W = 0.98836, p-value = 0.0724

    is no longer significant at p < 0.05.

    -

    If a distribution appears to be normal (i.e. has a symmetrical, or nearly symmetrical, “bell-shaped” distribution) then it is usually safe to assume normality and to use statistical procedures that assume normality. Formal tests for normality can be misleading when sample sizes of more than a few hundred cases are used. Graphical methods are not very useful when sample sizes are small. Formal test are not very useful when sample sizes are large. The sample sizes of most anthropometry surveys will be large enough to cause formal tests for normality to identify small deviations from normality as highly significant.

    +

    If a distribution appears to be normal (i.e. has a symmetrical, or +nearly symmetrical, “bell-shaped” distribution) then it is usually safe +to assume normality and to use statistical procedures that assume +normality. Formal tests for normality can be misleading when sample +sizes of more than a few hundred cases are used. Graphical methods are +not very useful when sample sizes are small. Formal test are not very +useful when sample sizes are large. The sample sizes of most +anthropometry surveys will be large enough to cause formal tests for +normality to identify small deviations from normality as highly +significant.

    -
    -

    -Skew and kurtosis

    -

    Skew is a measure of the asymmetry of a distribution about its mean. Skew can be zero, positive, or negative. Zero skew is found when the distribution is perfectly symmetrical. Positive skew is found when there is a long right tail to the distribution and the mass of the distribution is concentrated to the left. Negative skew is found when there is a long left tail to the distribution and the mass of the distribution is concentrated to the right. We can usually see skew in histograms. We can also calculate a skewness statistic and test if this is significantly different from zero.

    -

    Kurtosis is a measure of how much a distribution is concentrated about the mean. Kurtosis can be zero, positive, or negative. Zero kurtosis is found when a variable is normally distributed. Positive kurtosis is found when the mass of the distribution is concentrated about the mean and there are very few values far from the mean. Negative kurtosis is found when the mass of the distribution is concentrated in the tails of the distribution. We can usually see kurtosis in histograms. We can also calculate a kurtosis statistic and test if this is significantly different from zero.

    -

    The NiPN data quality toolkit provides an R language function called skewKurt() that calculates skewness and kurtosis statistics and tests whether they differ significantly from zero. Here we apply the skewKurt() function to the muac variable in the example dataset:

    +
    +

    Skew and kurtosis +

    +

    Skew is a measure of the asymmetry of a distribution about its mean. +Skew can be zero, positive, or negative. Zero skew is found when the +distribution is perfectly symmetrical. Positive skew is found when there +is a long right tail to the distribution and the mass of the +distribution is concentrated to the left. Negative skew is found when +there is a long left tail to the distribution and the mass of the +distribution is concentrated to the right. We can usually see skew in +histograms. We can also calculate a skewness statistic and test if this +is significantly different from zero.

    +

    Kurtosis is a measure of how much a distribution is concentrated +about the mean. Kurtosis can be zero, positive, or negative. Zero +kurtosis is found when a variable is normally distributed. Positive +kurtosis is found when the mass of the distribution is concentrated +about the mean and there are very few values far from the mean. Negative +kurtosis is found when the mass of the distribution is concentrated in +the tails of the distribution. We can usually see kurtosis in +histograms. We can also calculate a kurtosis statistic and test if this +is significantly different from zero.

    +

    The NiPN data quality toolkit provides an R language function called +skewKurt() that calculates skewness and kurtosis statistics +and tests whether they differ significantly from zero. Here we apply the +skewKurt() function to the muac variable in the example +dataset:

    -skewKurt(svy$muac)
    +skewKurt(svy$muac)

    This returns:

    -
    #> 
-#>  Skewness and kurtosis
-#> 
-#> Skewness = +0.0525   SE = 0.0828 z = 0.6348  p = 0.5256
-#> Kurtosis = -0.2412   SE = 0.1653 z = 1.4586  p = 0.1447
    -

    There is positive skew and negative kurtosis. Neither is significantly different from zero.

    -

    Applying the skewKurt() function to the haz variable in the example dataset:

    +
    #> 
+#>  Skewness and kurtosis
+#> 
+#> Skewness = +0.0525   SE = 0.0828 z = 0.6348  p = 0.5256
+#> Kurtosis = -0.2412   SE = 0.1653 z = 1.4586  p = 0.1447
    +

    There is positive skew and negative kurtosis. Neither is +significantly different from zero.

    +

    Applying the skewKurt() function to the +haz variable in the example dataset:

    -skewKurt(svy$haz)
    +skewKurt(svy$haz)

    returns:

    -
    #> 
-#>  Skewness and kurtosis
-#> 
-#> Skewness = +0.3074   SE = 0.0828 z = 3.7149  p = 0.0002
-#> Kurtosis = +0.2074   SE = 0.1653 z = 1.2545  p = 0.2097
    -

    There is a positive skew and a positive kurtosis. The skew is significantly different from zero. The skew can be seen in the histogram:

    +
    #> 
+#>  Skewness and kurtosis
+#> 
+#> Skewness = +0.3074   SE = 0.0828 z = 3.7149  p = 0.0002
+#> Kurtosis = +0.2074   SE = 0.1653 z = 1.2545  p = 0.2097
    +

    There is a positive skew and a positive kurtosis. The skew is +significantly different from zero. The skew can be seen in the +histogram:

    -histNormal(svy$haz, breaks = "scott")
    +histNormal(svy$haz, breaks = "scott")

    -

    Applying the skewKurt() function to the waz variable in the example dataset:

    +

    Applying the skewKurt() function to the +waz variable in the example dataset:

    -skewKurt(svy$waz)
    +skewKurt(svy$waz)

    returns:

    -
    #> 
-#>  Skewness and kurtosis
-#> 
-#> Skewness = -0.0128   SE = 0.0828 z = 0.1541  p = 0.8775
-#> Kurtosis = +0.1805   SE = 0.1653 z = 1.0919  p = 0.2749
    -

    There is negative skew and positive kurtosis. Neither is significantly different from zero.

    -

    Applying the skewKurt() function to the whz variable in the example dataset:

    +
    #> 
+#>  Skewness and kurtosis
+#> 
+#> Skewness = -0.0128   SE = 0.0828 z = 0.1541  p = 0.8775
+#> Kurtosis = +0.1805   SE = 0.1653 z = 1.0919  p = 0.2749
    +

    There is negative skew and positive kurtosis. Neither is +significantly different from zero.

    +

    Applying the skewKurt() function to the +whz variable in the example dataset:

    -skewKurt(svy$whz)
    +skewKurt(svy$whz)

    returns:

    -
    #> 
-#>  Skewness and kurtosis
-#> 
-#> Skewness = +0.0823   SE = 0.0828 z = 0.9946  p = 0.3199
-#> Kurtosis = +0.7528   SE = 0.1653 z = 4.5530  p = 0.0000
    -

    There is a positive skew and a positive kurtosis. The kurtosis is significantly different from zero. The kurtosis can be seen on the histogram:

    +
    #> 
+#>  Skewness and kurtosis
+#> 
+#> Skewness = +0.0823   SE = 0.0828 z = 0.9946  p = 0.3199
+#> Kurtosis = +0.7528   SE = 0.1653 z = 4.5530  p = 0.0000
    +

    There is a positive skew and a positive kurtosis. The kurtosis is +significantly different from zero. The kurtosis can be seen on the +histogram:

    -histNormal(svy$whz, breaks = "scott")
    +histNormal(svy$whz, breaks = "scott")

    -

    as the tall central columns that exceed the expected values shown by the overlaid normal distribution.

    -

    Skew and kurtosis are both used in SMART plausibility checks. Table below shows how skew and kurtosis statistics are applied by SMART.

    - +

    as the tall central columns that exceed the expected values shown by +the overlaid normal distribution.

    +

    Skew and kurtosis are both used in SMART plausibility checks. Table +below shows how skew and kurtosis statistics are applied by SMART.

    +
    -The range of absolute values of skewness and kurtosis statistics that are applied by SMART (2015) +The range of absolute values of skewness and kurtosis statistics that +are applied by SMART (2015)
    @@ -571,560 +645,758 @@

    -* This is the value of a number ignoring its negative or positive sign.
    The absolute value of -0.2412 is 0.2412. The absolute value of +0.2412 is also 0.2412. +* This is the value of a number ignoring its negative or +positive sign.
    The absolute value of -0.2412 is 0.2412. The absolute +value of +0.2412 is also 0.2412.
    -

    The whz variable in the example dataset is considered “problematic” according to this scheme because kurtosis is above 0.6.

    -

    Care should be exercised when using statistical significance tests to classify data as “problematic”. The use of thresholds and ranges for skew and kurtosis statistics is usually a better approach than relying on tests of statistical significance. Significance tests can be strongly affected by sample size. Small sample sizes can lead to tests missing large effects and large sample sizes can lead to tests identifying small effects as highly significant. If a distribution appears to be normal (i.e. has a symmetrical, or nearly symmetrical, “bell-shaped” distribution) then it is usually safe to assume normality and to use statistical procedures that assume normality.

    -

    It is important to remember that the normal distribution is a mathematical abstraction. There is nothing compelling the real world to conform to the normal distribution. The normal distribution has become reified:

    +

    The whz variable in the example dataset is +considered “problematic” according to this scheme because kurtosis is +above 0.6.

    +

    Care should be exercised when using statistical significance tests to +classify data as “problematic”. The use of thresholds and ranges for +skew and kurtosis statistics is usually a better approach than relying +on tests of statistical significance. Significance tests can be strongly +affected by sample size. Small sample sizes can lead to tests missing +large effects and large sample sizes can lead to tests identifying small +effects as highly significant. If a distribution appears to be normal +(i.e. has a symmetrical, or nearly symmetrical, “bell-shaped” +distribution) then it is usually safe to assume normality and to use +statistical procedures that assume normality.

    +

    It is important to remember that the normal distribution is a +mathematical abstraction. There is nothing compelling the real world to +conform to the normal distribution. The normal distribution has become +reified:

    -Everyone is sure of this [the normal distribution] … experimentalists believe that it is a mathematical theorem, and the mathematicians that it is an experimentally determined fact. +Everyone is sure of this [the normal distribution] … experimentalists +believe that it is a mathematical theorem, and the mathematicians that +it is an experimentally determined fact.
    — Henri Poincaré (1912), Calcul des Probabilités
    -

    The data we see may be representative of reality even when it fails tests for normality.

    -

    Tests for normality are useful when selecting statistical methods that rely on normality. They are less useful for determining data quality. If data follows a symmetrical, or nearly symmetrical, “bell-shaped” distribution then it will usually safe to use.

    +

    The data we see may be representative of reality even when it fails +tests for normality.

    +

    Tests for normality are useful when selecting statistical methods +that rely on normality. They are less useful for determining data +quality. If data follows a symmetrical, or nearly symmetrical, +“bell-shaped” distribution then it will usually safe to use.

    -
    -

    -Deviation from normality

    -

    Some anthropometric survey methods (e.g. SMART) use deviations from perfect normality as an indicator of poor data quality. This is not a sensible approach because deviations from normality are not necessarily due to poor quality data; they can be due to sampling a mixed population. This is easy to demonstrate with some simulated data.

    -

    We will assume that we have a population consisting of two groups:

    -

    Group 1 : 75% of the population, mean = -0.48, sd = 0.87

    -

    Group 2 : 25% of the population, mean = -1.04, sd = 1.10

    -

    and that we take a sample size = 1000 from the whole population. We can simulate this:

    +
    +

    Deviation from normality +

    +

    Some anthropometric survey methods (e.g. SMART) use deviations from +perfect normality as an indicator of poor data quality. This is not a +sensible approach because deviations from normality are not necessarily +due to poor quality data; they can be due to sampling a mixed +population. This is easy to demonstrate with some simulated data.

    +

    We will assume that we have a population consisting of two +groups:

    +

    Group 1 : 75% of the population, mean = -0.48, sd = +0.87

    +

    Group 2 : 25% of the population, mean = -1.04, sd = +1.10

    +

    and that we take a sample size = 1000 from the whole population. We +can simulate this:

    -set.seed(0)
-g1 <- rnorm(n = 750, mean = -0.48, sd = 0.87) 
-g2 <- rnorm(n = 250, mean = -1.04, sd = 1.11) 
-g1g2 <- c(g1, g2)
    -

    The distributions in the two subgroups (g1 and g2) are both normally distributed:

    +set.seed(0) +g1 <- rnorm(n = 750, mean = -0.48, sd = 0.87) +g2 <- rnorm(n = 250, mean = -1.04, sd = 1.11) +g1g2 <- c(g1, g2)
    +

    The distributions in the two subgroups (g1 and +g2) are both normally distributed:

    +histNormal(g1) +qqNormalPlot(g1) +shapiro.test(g1) +skewKurt(g1)

    -
    #> 
-#>  Shapiro-Wilk normality test
-#> 
-#> data:  g1
-#> W = 0.99725, p-value = 0.2411
-#> 
-#>  Skewness and kurtosis
-#> 
-#> Skewness = +0.1149   SE = 0.0893 z = 1.2867  p = 0.1982
-#> Kurtosis = -0.1869   SE = 0.1783 z = 1.0483  p = 0.2945
    +
    #> 
+#>  Shapiro-Wilk normality test
+#> 
+#> data:  g1
+#> W = 0.99725, p-value = 0.2411
+#> 
+#>  Skewness and kurtosis
+#> 
+#> Skewness = +0.1149   SE = 0.0893 z = 1.2867  p = 0.1982
+#> Kurtosis = -0.1869   SE = 0.1783 z = 1.0483  p = 0.2945
    +histNormal(g2) +qqNormalPlot(g2) +shapiro.test(g2) +skewKurt(g2)

    -
    #> 
-#>  Shapiro-Wilk normality test
-#> 
-#> data:  g2
-#> W = 0.9947, p-value = 0.5363
-#> 
-#>  Skewness and kurtosis
-#> 
-#> Skewness = +0.0317   SE = 0.1540 z = 0.2058  p = 0.8369
-#> Kurtosis = -0.1282   SE = 0.3068 z = 0.4178  p = 0.6761
    -

    but the distribution in the entire sample (g1g2) is not normal:

    +
    #> 
+#>  Shapiro-Wilk normality test
+#> 
+#> data:  g2
+#> W = 0.9947, p-value = 0.5363
+#> 
+#>  Skewness and kurtosis
+#> 
+#> Skewness = +0.0317   SE = 0.1540 z = 0.2058  p = 0.8369
+#> Kurtosis = -0.1282   SE = 0.3068 z = 0.4178  p = 0.6761
    +

    but the distribution in the entire sample (g1g2) is +not normal:

    -histNormal(g1g2) 
-qqNormalPlot(g1g2) 
-shapiro.test(g1g2) 
-skewKurt(g1g2)
    +histNormal(g1g2) +qqNormalPlot(g1g2) +shapiro.test(g1g2) +skewKurt(g1g2)

    The Shapiro-Wilk test of normality returns:

    -
    #> 
-#>  Shapiro-Wilk normality test
-#> 
-#> data:  g1g2
-#> W = 0.99671, p-value = 0.03514
    +
    #> 
+#>  Shapiro-Wilk normality test
+#> 
+#> data:  g1g2
+#> W = 0.99671, p-value = 0.03514

    There is statistically significant negative skew:

    -
    #> 
-#>  Skewness and kurtosis
-#> 
-#> Skewness = -0.1767   SE = 0.0773 z = 2.2851  p = 0.0223
-#> Kurtosis = +0.2894   SE = 0.1545 z = 1.8728  p = 0.0611
    +
    #> 
+#>  Skewness and kurtosis
+#> 
+#> Skewness = -0.1767   SE = 0.0773 z = 2.2851  p = 0.0223
+#> Kurtosis = +0.2894   SE = 0.1545 z = 1.8728  p = 0.0611

    There is, however, nothing wrong with the sample or with the data

    -

    The distribution in the entire sample (g1g2) is called a “mixture of Gaussians” (the term “Gaussian” refers to the normal distribution in this context).

    +

    The distribution in the entire sample (g1g2) is +called a “mixture of Gaussians” (the term “Gaussian” refers to the +normal distribution in this context).

    We can see this mixture of Gaussians with:

    -hist(g1, col=rgb(0.2, 0.2, 0.2, 0.5),
-     breaks = seq(-5, 3, 0.5), xlab = "", main = "")
-hist(g2, col=rgb(0.8, 0.8, 0.8, 0.5), breaks = seq(-5, 3, 0.5), add = TRUE) 
-title(main = "Histogram of g1 and g2", xlab = "z-score")
    +hist(g1, col=rgb(0.2, 0.2, 0.2, 0.5), + breaks = seq(-5, 3, 0.5), xlab = "", main = "") +hist(g2, col=rgb(0.8, 0.8, 0.8, 0.5), breaks = seq(-5, 3, 0.5), add = TRUE) +title(main = "Histogram of g1 and g2", xlab = "z-score")

    -

    In this case the mixture was already known. There are a number of methods for revealing the underlying mixture when the components of the mixture are unknown. These techniques are not covered in this toolkit. We will, however, continue with an example in which the components of the mixture are suspected.

    -

    We expect to see small deviations from normality in most survey datasets. This will often be the case when a survey samples subjects over a wide area covering, for example, several agro-ecological zones, socio-economic groups, or ethnic groups. This will almost always be the case, particularly with large surveys such as DHS, MICS, and national SMART surveys.

    -

    Another reason for non-normality is that one (or more) of the survey teams has a systematic bias in making a measurement. Identifying the “offending” survey team by examining and testing for normality separately in all combinations of data from \(n ~ – ~ 1\) survey teams can be attempted. If (e.g.) there were three teams then we would need to separately test data from:

    +

    In this case the mixture was already known. There are a number of +methods for revealing the underlying mixture when the components of the +mixture are unknown. These techniques are not covered in this toolkit. +We will, however, continue with an example in which the components of +the mixture are suspected.

    +

    We expect to see small deviations from normality in most survey +datasets. This will often be the case when a survey samples subjects +over a wide area covering, for example, several agro-ecological zones, +socio-economic groups, or ethnic groups. This will almost always be the +case, particularly with large surveys such as DHS, MICS, and national +SMART surveys.

    +

    Another reason for non-normality is that one (or more) of the survey +teams has a systematic bias in making a measurement. Identifying the +“offending” survey team by examining and testing for normality +separately in all combinations of data from \(n ~ – ~ 1\) survey teams can be attempted. +If (e.g.) there were three teams then we would need to separately test +data from:

    Team 1 and Team 2 (Team 3 excluded)

    Team 1 and Team 3 (Team 2 excluded)

    Team 2 and Team 3 (Team 1 excluded)

    -

    to see if the deviation from normality disappears when a particular team’s data are excluded. There is, however, a problem with this type of analysis. In cluster-sampled surveys, teams often sample adjacent primary sampling units (clusters). When this occurs the “exclude one team” analysis cannot distinguish between differences due to spatial heterogeneity (i.e. patchiness) and differences due to a team having a systematic measurement bias.

    +

    to see if the deviation from normality disappears when a particular +team’s data are excluded. There is, however, a problem with this type of +analysis. In cluster-sampled surveys, teams often sample adjacent +primary sampling units (clusters). When this occurs the “exclude one +team” analysis cannot distinguish between differences due to spatial +heterogeneity (i.e. patchiness) and differences due to a team having a +systematic measurement bias.

    -
    -

    -The standard deviation and alternatives

    -

    The standard deviation is sometimes considered to be useful measure of data quality when applied to z-scores.

    -

    We can use the sd() function to find the standard deviation. For example:

    +
    +

    The standard deviation and alternatives +

    +

    The standard deviation is sometimes considered to be useful measure +of data quality when applied to z-scores.

    +

    We can use the sd() function to find the standard +deviation. For example:

    -sd(svy$whz)
    +sd(svy$whz)

    returns:

    -
    #> [1] 1.323469
    +
    #> [1] 1.323469

    1.323469

    -

    This may produce misleading values if applied to raw data. This procedure should only be applied to cleaned data from which erroneous data and flagged records have been censored.

    -

    SMART guidelines state that the acceptable range for the standard deviation of the weight-for-height z-scores (whz) is 0.8 to 1.2 when flagging criteria have been applied and flagged records have been censored. Standard deviations outside this range are considered to indicate poor survey quality. Note that SMART does not define threshold for anthropometric indices other than weight-for-height z-scores. It is important to note that a standard deviation above 1.2 may be due to sampling from a mixed population rather than due to poor data quality.

    -

    The flag column in the example dataset contains a flagging code in which the codes 2, 3, 6, or 7 indicate potential problems with weight and / or height. We should calculate the standard deviation of the whz variable using only the data in which records with these flagging codes are censored and there is no oedema recorded:

    +

    This may produce misleading values if applied to raw data. This +procedure should only be applied to cleaned data from which erroneous +data and flagged records have been censored.

    +

    SMART guidelines state that the acceptable range for the standard +deviation of the weight-for-height z-scores (whz) is +0.8 to 1.2 when flagging criteria have been applied and flagged records +have been censored. Standard deviations outside this range are +considered to indicate poor survey quality. Note that SMART does not +define threshold for anthropometric indices other than weight-for-height +z-scores. It is important to note that a standard deviation above 1.2 +may be due to sampling from a mixed population rather than due to poor +data quality.

    +

    The flag column in the example dataset contains a +flagging code in which the codes 2, 3, +6, or 7 indicate potential problems +with weight and / or height. We should calculate the standard deviation +of the whz variable using only the data in which +records with these flagging codes are censored and there is no oedema +recorded:

    -sd(svy$whz[!(svy$flag %in% c(2, 3, 6, 7) | svy$oedema == 1)])
    -

    The ! character specifies a logical “not”. The standard deviation is, therefore, calculated using records in which the flag variable does not contain 2, 3, 6, or 7 or oedema is not recorded as being present.

    -

    The standard deviation for whz when flagged records and oedema cases are censored is:

    -
    #> [1] 1.141944
    +sd(svy$whz[!(svy$flag %in% c(2, 3, 6, 7) | svy$oedema == 1)])
    +

    The ! character specifies a logical “not”. The standard +deviation is, therefore, calculated using records in which the +flag variable does not contain +2, 3, 6, or +7 or oedema is not recorded as being +present.

    +

    The standard deviation for whz when flagged records +and oedema cases are censored is:

    +
    #> [1] 1.141944

    This is within the SMART acceptable range of 0.8 to 1.2.

    -

    The problem with using the standard deviation with raw data is that it is a non-robust statistic. This means that it can be strongly influenced by outliers. For example:

    +

    The problem with using the standard deviation with raw data is that +it is a non-robust statistic. This means that it can be strongly +influenced by outliers. For example:

    -sd(c(4.55, 5.93, 2.68, 5.61, 3.53, 4.78, 3.60, 5.82, 4.41, 5.42))
    +sd(c(4.55, 5.93, 2.68, 5.61, 3.53, 4.78, 3.60, 5.82, 4.41, 5.42))

    returns:

    -
    #> [1] 1.097533
    -

    Adding a single outlier (e.g. data entered as 7.84 rather than as 4.78):

    +
    #> [1] 1.097533
    +

    Adding a single outlier (e.g. data entered as 7.84 +rather than as 4.78):

    -sd(c(4.55, 5.93, 2.68, 5.61, 3.53, 7.84, 3.60, 5.82, 4.41, 5.42))
    +sd(c(4.55, 5.93, 2.68, 5.61, 3.53, 7.84, 3.60, 5.82, 4.41, 5.42))

    returns:

    -
    #> [1] 1.496963
    -

    In this example a single outlier has strongly influenced the standard deviation.

    -

    There are a number of robust estimators for the standard deviation. R provides the mad() function to calculate an adjusted median absolute deviation (MAD).

    -

    The median absolute deviation (MAD) is defined as the median of the absolute deviations from the median. It is the median of the absolute values of the differences between the individual data points and the median of the data:

    -

    \[ MAD ~ = ~ ( | x_i ~ - ~ median(x) | ) \]

    -

    The calculated MAD is adjusted to make it consistent with the standard deviation:

    -

    \[ \hat{\sigma} ~ = ~ k ~ \times ~ MAD \]

    -

    where k is a constant scaling factor, which depends upon the distribution. For the normal distribution:

    +
    #> [1] 1.496963
    +

    In this example a single outlier has strongly influenced the standard +deviation.

    +

    There are a number of robust estimators for the standard deviation. R +provides the mad() function to calculate an adjusted +median absolute deviation (MAD).

    +

    The median absolute deviation (MAD) is defined as the median of the +absolute deviations from the median. It is the median of the absolute +values of the differences between the individual data points and the +median of the data:

    +

    \[ MAD ~ = ~ ( | x_i ~ - ~ median(x) | ) +\]

    +

    The calculated MAD is adjusted to make it consistent with the +standard deviation:

    +

    \[ \hat{\sigma} ~ = ~ k ~ \times ~ +MAD \]

    +

    where k is a constant scaling factor, which depends upon the +distribution. For the normal distribution:

    \[ k ~ = ~ 1.4826 \]

    -

    The mad() function in R function returns the adjusted MAD:

    -

    \[ \hat{\sigma} ~ = ~ 1.4826 ~ \times ~ MAD \]

    +

    The mad() function in R function returns the adjusted +MAD:

    +

    \[ \hat{\sigma} ~ = ~ 1.4826 ~ \times ~ +MAD \]

    This is a robust estimate of the standard deviation.

    -

    This estimator is preferred when a sample is taken from a mixed population (this is almost always the case) and when the distribution has “fat” or “heavy” tails, as is the case with the whz variable in the example dataset.

    -

    Using the mad() function with the raw WHZ data:

    +

    This estimator is preferred when a sample is taken from a mixed +population (this is almost always the case) and when the distribution +has “fat” or “heavy” tails, as is the case with the whz +variable in the example dataset.

    +

    Using the mad() function with the raw WHZ data:

    -mad(svy$whz)
    +mad(svy$whz)

    This returns:

    -
    #> [1] 1.156428
    -

    We would usually want to calculate the adjusted MAD of the whz variable using only the data in which records with flagging codes relevant to whz and cases of oedema are censored:

    +
    #> [1] 1.156428
    +

    We would usually want to calculate the adjusted MAD of the whz +variable using only the data in which records with flagging codes +relevant to whz and cases of oedema are censored:

    -mad(svy$whz[!(svy$flag %in% c(2, 3, 6, 7) | svy$oedema == 1)])
    +mad(svy$whz[!(svy$flag %in% c(2, 3, 6, 7) | svy$oedema == 1)])

    This returns:

    -
    #> [1] 1.097124
    -

    The use of the standard deviation and robust equivalents such as the adjusted MAD with simple thresholds is problematic. Data that is a mixture of Gaussians distributions will tend to have large standard deviations even when there is no systematic error and nothing is wrong with the sample. Checks on the standard deviation in large surveys should, therefore, be performed on the smallest spatial strata above the PSU or cluster level. This reduces but does not eliminate the problem of sampling from mixed populations.

    +
    #> [1] 1.097124
    +

    The use of the standard deviation and robust equivalents such as the +adjusted MAD with simple thresholds is problematic. Data that is a +mixture of Gaussians distributions will tend to have large standard +deviations even when there is no systematic error and nothing is wrong +with the sample. Checks on the standard deviation in large surveys +should, therefore, be performed on the smallest spatial strata above the +PSU or cluster level. This reduces but does not eliminate the problem of +sampling from mixed populations.

    We will retrieve a dataset and examine within-strata MADs:

    -svy <- read.table("flag.ex03.csv", header = TRUE, sep = ",")
-head(svy)
    -
    #>   psu region state age sex weight height   haz   waz   whz
-#> 1   1     SE  Abia  12   2    7.4   72.1 -0.74 -1.58 -1.69
-#> 2   1     SE  Abia  33   1   13.3   94.2  0.04 -0.33 -0.52
-#> 3   1     SE  Abia  44   2   14.1   98.6 -0.41 -0.63 -0.57
-#> 4   1     SE  Abia  40   2   15.8   99.3  0.39  0.59  0.55
-#> 5   1     SE  Abia  23   2   10.1   83.9 -0.51 -0.90 -0.92
-#> 6   1     SE  Abia  24   1   13.9   88.7  0.52  1.18  1.22
    -

    The file flag.ex03.csv is a comma-separated-value (CSV) file containing anthropometric data from a national SMART survey in Nigeria.

    -

    The data stored in the file flag.ex03.csv were collected using methods similar to MICS and DHS surveys. The only difference is that the survey concentrated on anthropometric data in children aged between 6 and 59 months. In this exercise we will concentrate on WHZ.

    -

    Data are stratified by region and by state within region. We will create a new variable that combines region and state:

    +svy <- read.table("flag.ex03.csv", header = TRUE, sep = ",") +head(svy) +
    #>   psu region state age sex weight height   haz   waz   whz
+#> 1   1     SE  Abia  12   2    7.4   72.1 -0.74 -1.58 -1.69
+#> 2   1     SE  Abia  33   1   13.3   94.2  0.04 -0.33 -0.52
+#> 3   1     SE  Abia  44   2   14.1   98.6 -0.41 -0.63 -0.57
+#> 4   1     SE  Abia  40   2   15.8   99.3  0.39  0.59  0.55
+#> 5   1     SE  Abia  23   2   10.1   83.9 -0.51 -0.90 -0.92
+#> 6   1     SE  Abia  24   1   13.9   88.7  0.52  1.18  1.22
    +

    The file flag.ex03.csv is a comma-separated-value +(CSV) file containing anthropometric data from a national SMART survey +in Nigeria.

    +

    The data stored in the file flag.ex03.csv were collected using +methods similar to MICS and DHS surveys. The only difference is that the +survey concentrated on anthropometric data in children aged between 6 +and 59 months. In this exercise we will concentrate on WHZ.

    +

    Data are stratified by region and by +state within region. We will create a +new variable that combines region and +state:

    -svy$regionState <- paste(svy$region, svy$state, sep = ":")
-head(svy)
-#>   psu region state age sex weight height   haz   waz   whz regionState
-#> 1   1     SE  Abia  12   2    7.4   72.1 -0.74 -1.58 -1.69     SE:Abia
-#> 2   1     SE  Abia  33   1   13.3   94.2  0.04 -0.33 -0.52     SE:Abia
-#> 3   1     SE  Abia  44   2   14.1   98.6 -0.41 -0.63 -0.57     SE:Abia
-#> 4   1     SE  Abia  40   2   15.8   99.3  0.39  0.59  0.55     SE:Abia
-#> 5   1     SE  Abia  23   2   10.1   83.9 -0.51 -0.90 -0.92     SE:Abia
-#> 6   1     SE  Abia  24   1   13.9   88.7  0.52  1.18  1.22     SE:Abia
-table(svy$regionState)
-#> 
-#>       NC:Benue NC:FCT (Abuja)        NC:Kogi       NC:Kwara    NC:Nasarawa 
-#>            386            363            326            392            430 
-#>       NC:Niger     NC:Plateau     NE:Adamawa      NE:Bauchi       NE:Borno 
-#>            589            503            410            804            558 
-#>       NE:Gombe      NE:Taraba        NE:Yobe      NW:Jigawa      NW:Kaduna 
-#>            643            421            689            711            536 
-#>        NW:Kano     NW:Katsina       NW:Kebbi      NW:Sokoto     NW:Zamfara 
-#>            671            657            728            646            668 
-#>        SE:Abia     SE:Anambra      SE:Ebonyi       SE:Enugu         SE:Imo 
-#>            334            390            455            418            371 
-#>   SS:Akwa-Ibom     SS:Bayelsa SS:Cross River       SS:Delta         SS:Edo 
-#>            331            330            376            346            480 
-#>      SS:Rivers       SW:Ekiti       SW:Lagos        SW:Ogun        SW:Ondo 
-#>            315            376            640            566            426 
-#>        SW:Osun         SW:Oyo 
-#>            435            610
    -

    We can examine the adjusted MAD for whz for each combination of region and state in the survey dataset using:

    +svy$regionState <- paste(svy$region, svy$state, sep = ":") +head(svy) +#> psu region state age sex weight height haz waz whz regionState +#> 1 1 SE Abia 12 2 7.4 72.1 -0.74 -1.58 -1.69 SE:Abia +#> 2 1 SE Abia 33 1 13.3 94.2 0.04 -0.33 -0.52 SE:Abia +#> 3 1 SE Abia 44 2 14.1 98.6 -0.41 -0.63 -0.57 SE:Abia +#> 4 1 SE Abia 40 2 15.8 99.3 0.39 0.59 0.55 SE:Abia +#> 5 1 SE Abia 23 2 10.1 83.9 -0.51 -0.90 -0.92 SE:Abia +#> 6 1 SE Abia 24 1 13.9 88.7 0.52 1.18 1.22 SE:Abia +table(svy$regionState) +#> +#> NC:Benue NC:FCT (Abuja) NC:Kogi NC:Kwara NC:Nasarawa +#> 386 363 326 392 430 +#> NC:Niger NC:Plateau NE:Adamawa NE:Bauchi NE:Borno +#> 589 503 410 804 558 +#> NE:Gombe NE:Taraba NE:Yobe NW:Jigawa NW:Kaduna +#> 643 421 689 711 536 +#> NW:Kano NW:Katsina NW:Kebbi NW:Sokoto NW:Zamfara +#> 671 657 728 646 668 +#> SE:Abia SE:Anambra SE:Ebonyi SE:Enugu SE:Imo +#> 334 390 455 418 371 +#> SS:Akwa-Ibom SS:Bayelsa SS:Cross River SS:Delta SS:Edo +#> 331 330 376 346 480 +#> SS:Rivers SW:Ekiti SW:Lagos SW:Ogun SW:Ondo +#> 315 376 640 566 426 +#> SW:Osun SW:Oyo +#> 435 610 +

    We can examine the adjusted MAD for whz for each +combination of region and state in the +survey dataset using:

    -by(svy$whz, svy$regionState, mad, na.rm = TRUE)
-#> svy$regionState: NC:Benue
-#> [1] 0.941451
-#> ------------------------------------------------------------ 
-#> svy$regionState: NC:FCT (Abuja)
-#> [1] 0.96369
-#> ------------------------------------------------------------ 
-#> svy$regionState: NC:Kogi
-#> [1] 0.993342
-#> ------------------------------------------------------------ 
-#> svy$regionState: NC:Kwara
-#> [1] 0.993342
-#> ------------------------------------------------------------ 
-#> svy$regionState: NC:Nasarawa
-#> [1] 0.926625
-#> ------------------------------------------------------------ 
-#> svy$regionState: NC:Niger
-#> [1] 0.978516
-#> ------------------------------------------------------------ 
-#> svy$regionState: NC:Plateau
-#> [1] 1.022994
-#> ------------------------------------------------------------ 
-#> svy$regionState: NE:Adamawa
-#> [1] 1.045233
-#> ------------------------------------------------------------ 
-#> svy$regionState: NE:Bauchi
-#> [1] 1.18608
-#> ------------------------------------------------------------ 
-#> svy$regionState: NE:Borno
-#> [1] 1.030407
-#> ------------------------------------------------------------ 
-#> svy$regionState: NE:Gombe
-#> [1] 1.082298
-#> ------------------------------------------------------------ 
-#> svy$regionState: NE:Taraba
-#> [1] 1.008168
-#> ------------------------------------------------------------ 
-#> svy$regionState: NE:Yobe
-#> [1] 1.022994
-#> ------------------------------------------------------------ 
-#> svy$regionState: NW:Jigawa
-#> [1] 1.200906
-#> ------------------------------------------------------------ 
-#> svy$regionState: NW:Kaduna
-#> [1] 0.985929
-#> ------------------------------------------------------------ 
-#> svy$regionState: NW:Kano
-#> [1] 1.156428
-#> ------------------------------------------------------------ 
-#> svy$regionState: NW:Katsina
-#> [1] 1.022994
-#> ------------------------------------------------------------ 
-#> svy$regionState: NW:Kebbi
-#> [1] 0.926625
-#> ------------------------------------------------------------ 
-#> svy$regionState: NW:Sokoto
-#> [1] 0.926625
-#> ------------------------------------------------------------ 
-#> svy$regionState: NW:Zamfara
-#> [1] 1.052646
-#> ------------------------------------------------------------ 
-#> svy$regionState: SE:Abia
-#> [1] 0.904386
-#> ------------------------------------------------------------ 
-#> svy$regionState: SE:Anambra
-#> [1] 0.926625
-#> ------------------------------------------------------------ 
-#> svy$regionState: SE:Ebonyi
-#> [1] 0.904386
-#> ------------------------------------------------------------ 
-#> svy$regionState: SE:Enugu
-#> [1] 0.919212
-#> ------------------------------------------------------------ 
-#> svy$regionState: SE:Imo
-#> [1] 0.88956
-#> ------------------------------------------------------------ 
-#> svy$regionState: SS:Akwa-Ibom
-#> [1] 0.904386
-#> ------------------------------------------------------------ 
-#> svy$regionState: SS:Bayelsa
-#> [1] 1.11195
-#> ------------------------------------------------------------ 
-#> svy$regionState: SS:Cross River
-#> [1] 0.971103
-#> ------------------------------------------------------------ 
-#> svy$regionState: SS:Delta
-#> [1] 0.971103
-#> ------------------------------------------------------------ 
-#> svy$regionState: SS:Edo
-#> [1] 0.971103
-#> ------------------------------------------------------------ 
-#> svy$regionState: SS:Rivers
-#> [1] 1.052646
-#> ------------------------------------------------------------ 
-#> svy$regionState: SW:Ekiti
-#> [1] 1.030407
-#> ------------------------------------------------------------ 
-#> svy$regionState: SW:Lagos
-#> [1] 0.837669
-#> ------------------------------------------------------------ 
-#> svy$regionState: SW:Ogun
-#> [1] 0.911799
-#> ------------------------------------------------------------ 
-#> svy$regionState: SW:Ondo
-#> [1] 0.978516
-#> ------------------------------------------------------------ 
-#> svy$regionState: SW:Osun
-#> [1] 0.904386
-#> ------------------------------------------------------------ 
-#> svy$regionState: SW:Oyo
-#> [1] 0.956277
    -

    The long output can be made more compact, easier to read, and easier to work with:

    +by(svy$whz, svy$regionState, mad, na.rm = TRUE) +#> svy$regionState: NC:Benue +#> [1] 0.941451 +#> ------------------------------------------------------------ +#> svy$regionState: NC:FCT (Abuja) +#> [1] 0.96369 +#> ------------------------------------------------------------ +#> svy$regionState: NC:Kogi +#> [1] 0.993342 +#> ------------------------------------------------------------ +#> svy$regionState: NC:Kwara +#> [1] 0.993342 +#> ------------------------------------------------------------ +#> svy$regionState: NC:Nasarawa +#> [1] 0.926625 +#> ------------------------------------------------------------ +#> svy$regionState: NC:Niger +#> [1] 0.978516 +#> ------------------------------------------------------------ +#> svy$regionState: NC:Plateau +#> [1] 1.022994 +#> ------------------------------------------------------------ +#> svy$regionState: NE:Adamawa +#> [1] 1.045233 +#> ------------------------------------------------------------ +#> svy$regionState: NE:Bauchi +#> [1] 1.18608 +#> ------------------------------------------------------------ +#> svy$regionState: NE:Borno +#> [1] 1.030407 +#> ------------------------------------------------------------ +#> svy$regionState: NE:Gombe +#> [1] 1.082298 +#> ------------------------------------------------------------ +#> svy$regionState: NE:Taraba +#> [1] 1.008168 +#> ------------------------------------------------------------ +#> svy$regionState: NE:Yobe +#> [1] 1.022994 +#> ------------------------------------------------------------ +#> svy$regionState: NW:Jigawa +#> [1] 1.200906 +#> ------------------------------------------------------------ +#> svy$regionState: NW:Kaduna +#> [1] 0.985929 +#> ------------------------------------------------------------ +#> svy$regionState: NW:Kano +#> [1] 1.156428 +#> ------------------------------------------------------------ +#> svy$regionState: NW:Katsina +#> [1] 1.022994 +#> ------------------------------------------------------------ +#> svy$regionState: NW:Kebbi +#> [1] 0.926625 +#> ------------------------------------------------------------ +#> svy$regionState: NW:Sokoto +#> [1] 0.926625 +#> ------------------------------------------------------------ +#> svy$regionState: NW:Zamfara +#> [1] 1.052646 +#> ------------------------------------------------------------ +#> svy$regionState: SE:Abia +#> [1] 0.904386 +#> ------------------------------------------------------------ +#> svy$regionState: SE:Anambra +#> [1] 0.926625 +#> ------------------------------------------------------------ +#> svy$regionState: SE:Ebonyi +#> [1] 0.904386 +#> ------------------------------------------------------------ +#> svy$regionState: SE:Enugu +#> [1] 0.919212 +#> ------------------------------------------------------------ +#> svy$regionState: SE:Imo +#> [1] 0.88956 +#> ------------------------------------------------------------ +#> svy$regionState: SS:Akwa-Ibom +#> [1] 0.904386 +#> ------------------------------------------------------------ +#> svy$regionState: SS:Bayelsa +#> [1] 1.11195 +#> ------------------------------------------------------------ +#> svy$regionState: SS:Cross River +#> [1] 0.971103 +#> ------------------------------------------------------------ +#> svy$regionState: SS:Delta +#> [1] 0.971103 +#> ------------------------------------------------------------ +#> svy$regionState: SS:Edo +#> [1] 0.971103 +#> ------------------------------------------------------------ +#> svy$regionState: SS:Rivers +#> [1] 1.052646 +#> ------------------------------------------------------------ +#> svy$regionState: SW:Ekiti +#> [1] 1.030407 +#> ------------------------------------------------------------ +#> svy$regionState: SW:Lagos +#> [1] 0.837669 +#> ------------------------------------------------------------ +#> svy$regionState: SW:Ogun +#> [1] 0.911799 +#> ------------------------------------------------------------ +#> svy$regionState: SW:Ondo +#> [1] 0.978516 +#> ------------------------------------------------------------ +#> svy$regionState: SW:Osun +#> [1] 0.904386 +#> ------------------------------------------------------------ +#> svy$regionState: SW:Oyo +#> [1] 0.956277 +

    The long output can be made more compact, easier to read, and easier +to work with:

    -mads <- by(svy$whz, svy$regionState, mad, na.rm = TRUE)
-mads <- round(mads[1:length(mads)], 2)
-mads
-#> svy$regionState
-#>       NC:Benue NC:FCT (Abuja)        NC:Kogi       NC:Kwara    NC:Nasarawa 
-#>           0.94           0.96           0.99           0.99           0.93 
-#>       NC:Niger     NC:Plateau     NE:Adamawa      NE:Bauchi       NE:Borno 
-#>           0.98           1.02           1.05           1.19           1.03 
-#>       NE:Gombe      NE:Taraba        NE:Yobe      NW:Jigawa      NW:Kaduna 
-#>           1.08           1.01           1.02           1.20           0.99 
-#>        NW:Kano     NW:Katsina       NW:Kebbi      NW:Sokoto     NW:Zamfara 
-#>           1.16           1.02           0.93           0.93           1.05 
-#>        SE:Abia     SE:Anambra      SE:Ebonyi       SE:Enugu         SE:Imo 
-#>           0.90           0.93           0.90           0.92           0.89 
-#>   SS:Akwa-Ibom     SS:Bayelsa SS:Cross River       SS:Delta         SS:Edo 
-#>           0.90           1.11           0.97           0.97           0.97 
-#>      SS:Rivers       SW:Ekiti       SW:Lagos        SW:Ogun        SW:Ondo 
-#>           1.05           1.03           0.84           0.91           0.98 
-#>        SW:Osun         SW:Oyo 
-#>           0.90           0.96
    +mads <- by(svy$whz, svy$regionState, mad, na.rm = TRUE) +mads <- round(mads[1:length(mads)], 2) +mads +#> svy$regionState +#> NC:Benue NC:FCT (Abuja) NC:Kogi NC:Kwara NC:Nasarawa +#> 0.94 0.96 0.99 0.99 0.93 +#> NC:Niger NC:Plateau NE:Adamawa NE:Bauchi NE:Borno +#> 0.98 1.02 1.05 1.19 1.03 +#> NE:Gombe NE:Taraba NE:Yobe NW:Jigawa NW:Kaduna +#> 1.08 1.01 1.02 1.20 0.99 +#> NW:Kano NW:Katsina NW:Kebbi NW:Sokoto NW:Zamfara +#> 1.16 1.02 0.93 0.93 1.05 +#> SE:Abia SE:Anambra SE:Ebonyi SE:Enugu SE:Imo +#> 0.90 0.93 0.90 0.92 0.89 +#> SS:Akwa-Ibom SS:Bayelsa SS:Cross River SS:Delta SS:Edo +#> 0.90 1.11 0.97 0.97 0.97 +#> SS:Rivers SW:Ekiti SW:Lagos SW:Ogun SW:Ondo +#> 1.05 1.03 0.84 0.91 0.98 +#> SW:Osun SW:Oyo +#> 0.90 0.96

    The saved mads object can be summarised:

    -summary(mads)
    +summary(mads)

    This returns:

    -
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>  0.8400  0.9300  0.9800  0.9892  1.0300  1.2000
    +
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#>  0.8400  0.9300  0.9800  0.9892  1.0300  1.2000

    A table can also be useful:

    -table(mads)
-#> mads
-#> 0.84 0.89  0.9 0.91 0.92 0.93 0.94 0.96 0.97 0.98 0.99 1.01 1.02 1.03 1.05 1.08 
-#>    1    1    4    1    1    4    1    2    3    2    3    1    3    2    3    1 
-#> 1.11 1.16 1.19  1.2 
-#>    1    1    1    1
    -

    In this example the adjusted MAD of the whz variable is within the limits 0.8 to 1.2 for all combinations of region and state.

    -

    Note that we combined region and state. We did this to avoid potential problems with duplicate state names (i.e. the same state name used in more than one region).

    -

    In the previous exercise we used the raw (i.e. without flagging) data. It is better to use only the data in which records with flagging codes relevant to whz and cases of oedema are censored.

    -

    This is a national SMART survey so we will use SMART flagging criteria. We will use the national.SMART() function to add SMART flags to the survey dataset:

    +table(mads) +#> mads +#> 0.84 0.89 0.9 0.91 0.92 0.93 0.94 0.96 0.97 0.98 0.99 1.01 1.02 1.03 1.05 1.08 +#> 1 1 4 1 1 4 1 2 3 2 3 1 3 2 3 1 +#> 1.11 1.16 1.19 1.2 +#> 1 1 1 1 +

    In this example the adjusted MAD of the whz variable +is within the limits 0.8 to 1.2 for all combinations of +region and state.

    +

    Note that we combined region and +state. We did this to avoid potential problems with +duplicate state names (i.e. the same +state name used in more than one +region).

    +

    In the previous exercise we used the raw (i.e. without flagging) +data. It is better to use only the data in which records with flagging +codes relevant to whz and cases of oedema are +censored.

    +

    This is a national SMART survey so we will use SMART flagging +criteria. We will use the national.SMART() function to add +SMART flags to the survey dataset:

    -svyFlagged <- national.SMART(x = svy, strata = "regionState")
    +svyFlagged <- national.SMART(x = svy, strata = "regionState")

    We need to exclude records with flagging codes relevant to whz:

    -svyFlagged <- svyFlagged[!(svyFlagged$flagSMART %in% c(2, 3, 6, 7)), ]
    -

    Note that oedema is not recorded in the dataset so we cannot exclude oedema cases.

    -

    We can now calculate the MAD for whz in each stratum:

    +svyFlagged <- svyFlagged[!(svyFlagged$flagSMART %in% c(2, 3, 6, 7)), ] +

    Note that oedema is not recorded in the dataset so we cannot exclude +oedema cases.

    +

    We can now calculate the MAD for whz in each +stratum:

    -mads <- by(svyFlagged$whz, svyFlagged$regionState, mad, na.rm = TRUE)
-mads <- round(mads[1:length(mads)], 2)
-mads
-#> svyFlagged$regionState
-#>       NC:Benue NC:FCT (Abuja)        NC:Kogi       NC:Kwara    NC:Nasarawa 
-#>           0.92           0.95           0.99           0.96           0.92 
-#>       NC:Niger     NC:Plateau     NE:Adamawa      NE:Bauchi       NE:Borno 
-#>           0.93           1.02           1.02           1.17           1.02 
-#>       NE:Gombe      NE:Taraba        NE:Yobe      NW:Jigawa      NW:Kaduna 
-#>           1.06           0.98           0.99           1.17           0.96 
-#>        NW:Kano     NW:Katsina       NW:Kebbi      NW:Sokoto     NW:Zamfara 
-#>           1.10           1.01           0.90           0.90           1.02 
-#>        SE:Abia     SE:Anambra      SE:Ebonyi       SE:Enugu         SE:Imo 
-#>           0.87           0.91           0.90           0.90           0.87 
-#>   SS:Akwa-Ibom     SS:Bayelsa SS:Cross River       SS:Delta         SS:Edo 
-#>           0.87           1.05           0.92           0.95           0.96 
-#>      SS:Rivers       SW:Ekiti       SW:Lagos        SW:Ogun        SW:Ondo 
-#>           1.01           1.01           0.85           0.90           0.96 
-#>        SW:Osun         SW:Oyo 
-#>           0.89           0.95
    +mads <- by(svyFlagged$whz, svyFlagged$regionState, mad, na.rm = TRUE) +mads <- round(mads[1:length(mads)], 2) +mads +#> svyFlagged$regionState +#> NC:Benue NC:FCT (Abuja) NC:Kogi NC:Kwara NC:Nasarawa +#> 0.92 0.95 0.99 0.96 0.92 +#> NC:Niger NC:Plateau NE:Adamawa NE:Bauchi NE:Borno +#> 0.93 1.02 1.02 1.17 1.02 +#> NE:Gombe NE:Taraba NE:Yobe NW:Jigawa NW:Kaduna +#> 1.06 0.98 0.99 1.17 0.96 +#> NW:Kano NW:Katsina NW:Kebbi NW:Sokoto NW:Zamfara +#> 1.10 1.01 0.90 0.90 1.02 +#> SE:Abia SE:Anambra SE:Ebonyi SE:Enugu SE:Imo +#> 0.87 0.91 0.90 0.90 0.87 +#> SS:Akwa-Ibom SS:Bayelsa SS:Cross River SS:Delta SS:Edo +#> 0.87 1.05 0.92 0.95 0.96 +#> SS:Rivers SW:Ekiti SW:Lagos SW:Ogun SW:Ondo +#> 1.01 1.01 0.85 0.90 0.96 +#> SW:Osun SW:Oyo +#> 0.89 0.95

    The saved mads object can be summarised:

    -summary(mads)
    +summary(mads)

    This returns:

    -
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>  0.8500  0.9000  0.9600  0.9665  1.0100  1.1700
    -

    In this analysis the adjusted MAD of the whz variable is within the limits 0.8 to 1.2 for all combinations of region and state.

    +
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#>  0.8500  0.9000  0.9600  0.9665  1.0100  1.1700
    +

    In this analysis the adjusted MAD of the whz +variable is within the limits 0.8 to 1.2 for all combinations of +region and state.

    -
    -

    -Measures of dispersion

    -

    Measures of dispersion summarise how cases (e.g. children classified as wasted, stunted, or underweight) are distributed across a survey’s primary sampling units (e.g. clusters).

    +
    +

    Measures of dispersion +

    +

    Measures of dispersion summarise how cases (e.g. children classified +as wasted, stunted, or underweight) are distributed across a survey’s +primary sampling units (e.g. clusters).

    We will retrieve a survey dataset:

    -svy <- read.table("flag.ex01.csv", header = TRUE, sep = ",") 
-head(svy)
    -
    #>   psu child age sex weight height muac oedema   haz   waz   whz
-#> 1   1     1  20   2    6.1   82.5  127      2 -0.07 -4.54 -6.03
-#> 2   1     2  13   2    6.4   70.4  116      2 -1.83 -3.04 -2.93
-#> 3   1     3  15   1    7.1   67.5  124      2 -4.60 -3.34 -1.25
-#> 4   1     4  15   1    7.2   75.4  130      2 -1.48 -3.22 -3.57
-#> 5   1     5  15   1    7.4   70.0  124      2 -3.61 -2.99 -1.61
-#> 6   1     6  18   2    7.7   70.6  130      2 -3.48 -2.40 -0.82
    -

    The file flag.ex01.csv is a comma-separated-value (CSV) file containing anthropometric data from a recent SMART survey in Sudan.

    +svy <- read.table("flag.ex01.csv", header = TRUE, sep = ",") +head(svy)
    +
    #>   psu child age sex weight height muac oedema   haz   waz   whz
+#> 1   1     1  20   2    6.1   82.5  127      2 -0.07 -4.54 -6.03
+#> 2   1     2  13   2    6.4   70.4  116      2 -1.83 -3.04 -2.93
+#> 3   1     3  15   1    7.1   67.5  124      2 -4.60 -3.34 -1.25
+#> 4   1     4  15   1    7.2   75.4  130      2 -1.48 -3.22 -3.57
+#> 5   1     5  15   1    7.4   70.0  124      2 -3.61 -2.99 -1.61
+#> 6   1     6  18   2    7.7   70.6  130      2 -3.48 -2.40 -0.82
    +

    The file flag.ex01.csv is a comma-separated-value +(CSV) file containing anthropometric data from a recent SMART survey in +Sudan.

    We will apply WHO flagging criteria to the data:

    -svy$flag <- 0
-svy$flag <- ifelse(!is.na(svy$haz) & (svy$haz < -6 | svy$haz > 6), svy$flag + 1, svy$flag)
-svy$flag <- ifelse(!is.na(svy$whz) & (svy$whz < -5 | svy$whz > 5), svy$flag + 2, svy$flag)
-svy$flag <- ifelse(!is.na(svy$waz) & (svy$waz < -6 | svy$waz > 5), svy$flag + 4, svy$flag)
    +svy$flag <- 0 +svy$flag <- ifelse(!is.na(svy$haz) & (svy$haz < -6 | svy$haz > 6), svy$flag + 1, svy$flag) +svy$flag <- ifelse(!is.na(svy$whz) & (svy$whz < -5 | svy$whz > 5), svy$flag + 2, svy$flag) +svy$flag <- ifelse(!is.na(svy$waz) & (svy$waz < -6 | svy$waz > 5), svy$flag + 4, svy$flag)

    We should exclude flagged records:

    -svy <- svy[svy$flag == 0, ]
    +svy <- svy[svy$flag == 0, ]

    We will apply a case-definition for being stunted:

    -svy$stunted <- ifelse(svy$haz < -2, 1, 2)
    -

    We can examine the distribution of stunted cases across the primary sampling units in this survey:

    +svy$stunted <- ifelse(svy$haz < -2, 1, 2) +

    We can examine the distribution of stunted cases across the primary +sampling units in this survey:

    -table(svy$psu, svy$stunted)
-#>     
-#>       1  2
-#>   1   8 20
-#>   2  11 14
-#>   3   7 22
-#>   4   6 23
-#>   5   7 15
-#>   6  11 20
-#>   7  11 14
-#>   8  14 12
-#>   9  12 18
-#>   10 10  9
-#>   11 12 16
-#>   12  9 13
-#>   13  9 13
-#>   14  5 21
-#>   15 12  9
-#>   16  8 17
-#>   17  6 23
-#>   18  8 21
-#>   19 10 12
-#>   20  6 20
-#>   21 11 18
-#>   22 11 14
-#>   23 12  6
-#>   24  8 15
-#>   25 10 19
-#>   26 10  8
-#>   27 12  9
-#>   28  6 14
-#>   29 14 10
-#>   30 11 18
    +table(svy$psu, svy$stunted) +#> +#> 1 2 +#> 1 8 20 +#> 2 11 14 +#> 3 7 22 +#> 4 6 23 +#> 5 7 15 +#> 6 11 20 +#> 7 11 14 +#> 8 14 12 +#> 9 12 18 +#> 10 10 9 +#> 11 12 16 +#> 12 9 13 +#> 13 9 13 +#> 14 5 21 +#> 15 12 9 +#> 16 8 17 +#> 17 6 23 +#> 18 8 21 +#> 19 10 12 +#> 20 6 20 +#> 21 11 18 +#> 22 11 14 +#> 23 12 6 +#> 24 8 15 +#> 25 10 19 +#> 26 10 8 +#> 27 12 9 +#> 28 6 14 +#> 29 14 10 +#> 30 11 18

    We only need the counts of cases in each primary sampling unit:

    -table(svy$psu, svy$stunted)[,1]
-#>  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 
-#>  8 11  7  6  7 11 11 14 12 10 12  9  9  5 12  8  6  8 10  6 11 11 12  8 10 10 
-#> 27 28 29 30 
-#> 12  6 14 11
-barplot(table(svy$psu, svy$stunted)[,1], xlab = "PSU", ylab = "Cases", cex.names = 0.5)
    +table(svy$psu, svy$stunted)[,1] +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 +#> 8 11 7 6 7 11 11 14 12 10 12 9 9 5 12 8 6 8 10 6 11 11 12 8 10 10 +#> 27 28 29 30 +#> 12 6 14 11 +barplot(table(svy$psu, svy$stunted)[,1], xlab = "PSU", ylab = "Cases", cex.names = 0.5)

    It will be useful to keep this for later use:

    -casesPerPSU <- table(svy$psu, svy$stunted)[,1] 
-casesPerPSU
-#>  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 
-#>  8 11  7  6  7 11 11 14 12 10 12  9  9  5 12  8  6  8 10  6 11 11 12  8 10 10 
-#> 27 28 29 30 
-#> 12  6 14 11
    -

    We are interested in how cases are distributed across the primary sampling units.

    -

    There are three general patterns. These are random, clumped, and uniform.

    -

    We can identify the pattern to which the example data most likely belongs using an index of dispersion.

    -

    The simplest index of dispersion, and the one used by SMART, is the variance to mean ratio:

    -

    \[ \text{Variance to mean ratio} ~ = ~ \frac{s ^ 2}{\overline{\chi}} \]

    -

    The interpretation of the variance to mean ratio is straightforward:

    +casesPerPSU <- table(svy$psu, svy$stunted)[,1] +casesPerPSU +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 +#> 8 11 7 6 7 11 11 14 12 10 12 9 9 5 12 8 6 8 10 6 11 11 12 8 10 10 +#> 27 28 29 30 +#> 12 6 14 11 +

    We are interested in how cases are distributed across the primary +sampling units.

    +

    There are three general patterns. These are random, +clumped, and uniform.

    +

    We can identify the pattern to which the example data most likely +belongs using an index of dispersion.

    +

    The simplest index of dispersion, and the one used by SMART, is the +variance to mean ratio:

    +

    \[ \text{Variance to mean ratio} ~ = ~ +\frac{s ^ 2}{\overline{\chi}} \]

    +

    The interpretation of the variance to mean ratio is +straightforward:

    Variance to mean ratio ≈ 1 Random

    -

    Variance to mean ratio > 1 Clumped (i.e. more clumped than random)

    -

    Variance to mean ratio < 1 Uniform (i.e. more uniform than random)

    -

    The value of the variance to mean ratio can range between zero (maximum uniformity) and the total number of cases in the data (maximum clumping). Maximum uniformity is found when the same number of cases are found in every primary sampling unit. Maximum clumping is found when all cases are found in one primary sampling unit.

    +

    Variance to mean ratio > 1 Clumped (i.e. more clumped than +random)

    +

    Variance to mean ratio < 1 Uniform (i.e. more uniform than +random)

    +

    The value of the variance to mean ratio can range between zero +(maximum uniformity) and the total number of cases in the data (maximum +clumping). Maximum uniformity is found when the same number of cases are +found in every primary sampling unit. Maximum clumping is found when all +cases are found in one primary sampling unit.

    With the example data:

    -varianceCasesPerPSU <- var(casesPerPSU)
-meanCasesPerPSU <- sum(casesPerPSU) / length(casesPerPSU) 
-V2M <- varianceCasesPerPSU / meanCasesPerPSU
-V2M
-#> [1] 0.6393127
    -

    The observed variance to mean ratio (0.6393127) suggests that the distribution of cases across primary sampling units is not completely uniform, but neither is it random.

    -

    A formal (Chi-squared) test can be performed. The Chi-squared test statistic can be calculated using:

    +varianceCasesPerPSU <- var(casesPerPSU) +meanCasesPerPSU <- sum(casesPerPSU) / length(casesPerPSU) +V2M <- varianceCasesPerPSU / meanCasesPerPSU +V2M +#> [1] 0.6393127 +

    The observed variance to mean ratio (0.6393127) +suggests that the distribution of cases across primary sampling units is +not completely uniform, but neither is it random.

    +

    A formal (Chi-squared) test can be performed. The +Chi-squared test statistic can be calculated using:

    -sum((casesPerPSU - meanCasesPerPSU)^2) / meanCasesPerPSU
    +sum((casesPerPSU - meanCasesPerPSU)^2) / meanCasesPerPSU

    This returns:

    -
    #> [1] 18.54007
    +
    #> [1] 18.54007

    18.54007

    The critical values for this test statistic can be found using:

    -qchisq(p = c(0.025, 0.975), df = length(casesPerPSU) - 1)
    +qchisq(p = c(0.025, 0.975), df = length(casesPerPSU) - 1)

    This returns:

    -qchisq(p = c(0.025, 0.975), df = length(casesPerPSU) - 1)
    +qchisq(p = c(0.025, 0.975), df = length(casesPerPSU) - 1)

    16.04707 45.72229

    -

    If the Chi-squared test statistic was below 16.04707 then we would conclude that the pattern of cases across primary sampling units in the example data is uniform. This is not the case in the example data.

    -

    If the Chi-squared test statistic was above 45.72229 then we would conclude that the pattern of cases across primary sampling units in the example data is clumped. This is not the case in the example data.

    -

    Since the Chi-squared test statistic falls between 16.04707 and 45.72229 we conclude that the pattern of cases across primary sampling units in the example data is random.

    -

    There are problems with the variance to mean ratio. Some clearly non-random patterns can produce variance to mean ratios of one. The variance to mean ratio is also strongly influenced by the total number of cases present in the data when clumping is present.

    +

    If the Chi-squared test statistic was below 16.04707 +then we would conclude that the pattern of cases across primary sampling +units in the example data is uniform. This is not the case in the +example data.

    +

    If the Chi-squared test statistic was above 45.72229 +then we would conclude that the pattern of cases across primary sampling +units in the example data is clumped. This is not the case in the +example data.

    +

    Since the Chi-squared test statistic falls between +16.04707 and 45.72229 we conclude that +the pattern of cases across primary sampling units in the example data +is random.

    +

    There are problems with the variance to mean ratio. Some clearly +non-random patterns can produce variance to mean ratios of one. The +variance to mean ratio is also strongly influenced by the total number +of cases present in the data when clumping is present.

    A better measure is Green’s Index of Dispersion:

    -

    \[ \text{Green's Index} ~ = ~ \frac{ \left ( \frac{s ^ 2}{\overline{\chi}} \right ) ~ - ~ 1}{n ~ - ~ 1} \]

    -

    Green’s Index corrects the variance to mean ratio for the total number of cases present in the data.

    -

    The value of Green’s Index can range between $ -1 / (n - 1) $ for maximum uniformity (specific to the dataset) and one for maximum clumping. The interpretation of Green’s Index is straightforward:

    +

    \[ \text{Green's Index} ~ = ~ \frac{ +\left ( \frac{s ^ 2}{\overline{\chi}} \right ) ~ - ~ 1}{n ~ - ~ 1} +\]

    +

    Green’s Index corrects the variance to mean ratio for the total +number of cases present in the data.

    +

    The value of Green’s Index can range between $ -1 / (n - 1) $ for +maximum uniformity (specific to the dataset) and one for maximum +clumping. The interpretation of Green’s Index is straightforward:

    Green’s Index ≈ 0 Random

    Green’s Index > 0 Clumped (i.e. more clumped than random)

    Green’s Index < 0 Uniform (i.e. more uniform than random)

    -

    The sampling distribution of Green’s Index is not well described. The NiPN data quality toolkit provides the greenIndex() function that overcomes this problem. This R language function uses the bootstrap technique to estimate Green’s Index and test whether the distribution of cases across primary sampling units is random.

    -

    The greenIndex() function requires you to specify the name of the survey dataset, the name of the variable specifying the primary sampling unit, and the name of the variable specifying case status. With the example data:

    +

    The sampling distribution of Green’s Index is not well described. The +NiPN data quality toolkit provides the greenIndex() +function that overcomes this problem. This R language function uses the +bootstrap technique to estimate Green’s Index and test whether the +distribution of cases across primary sampling units is random.

    +

    The greenIndex() function requires you to specify the +name of the survey dataset, the name of the variable specifying the +primary sampling unit, and the name of the variable specifying case +status. With the example data:

    -greensIndex(data = svy, psu = "psu", case = "stunted")
    +greensIndex(data = svy, psu = "psu", case = "stunted")

    this returns:

    -
    #> 
-#>  Green's Index of Dispersion
-#> 
-#> Green's Index (GI) of Dispersion  = -0.0013, 95% CI = (-0.0022, -0.0004)
-#> Maximum uniformity for this data  = -0.0035
-#>                          p-value  =  0.0000
    -

    The point estimate of Green’s Index (-0.0013) is below zero and the p-value of the test for a random distribution of cases across primary sampling units (0.0040) is below 0.05. The distribution of cases across primary sampling units in the example data is significantly more uniform than it is random. We can see this graphically using:

    +
    #> 
+#>  Green's Index of Dispersion
+#> 
+#> Green's Index (GI) of Dispersion  = -0.0013, 95% CI = (-0.0022, -0.0004)
+#> Maximum uniformity for this data  = -0.0035
+#>                          p-value  =  0.0000
    +

    The point estimate of Green’s Index (-0.0013) is +below zero and the p-value of the test for a random distribution of +cases across primary sampling units (0.0040) is below +0.05. The distribution of cases across primary sampling units in the +example data is significantly more uniform than it is random. We can see +this graphically using:

    -table(svy$psu, svy$stunted)[,1]
-#>  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 
-#>  8 11  7  6  7 11 11 14 12 10 12  9  9  5 12  8  6  8 10  6 11 11 12  8 10 10 
-#> 27 28 29 30 
-#> 12  6 14 11
-barplot(table(svy$psu, svy$stunted)[,1], xlab = "PSU", ylab = "Cases", cex.names = 0.5)
-abline(h = sum(casesPerPSU) / length(casesPerPSU), lty = 2)
    +table(svy$psu, svy$stunted)[,1] +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 +#> 8 11 7 6 7 11 11 14 12 10 12 9 9 5 12 8 6 8 10 6 11 11 12 8 10 10 +#> 27 28 29 30 +#> 12 6 14 11 +barplot(table(svy$psu, svy$stunted)[,1], xlab = "PSU", ylab = "Cases", cex.names = 0.5) +abline(h = sum(casesPerPSU) / length(casesPerPSU), lty = 2)

    -

    The dashed line on the plot marks the mean number of cases found in each primary sampling unit. A uniform distribution would show all bars ending close to this line (see figure above).

    -

    SMART uses the variance to mean ratio as a test of data quality. Green’s Index is a more robust choice because it can be used to compare samples that vary in overall sample size and the number of sampling units used.

    -

    The idea behind using a measure of dispersion to judge data quality is a belief that the distribution of cases of malnutrition across primary sampling units should always be random. If this is not the case then the data are considered to be suspect. The problem with this approach is that deviations from random can reflect the true distribution of cases in the survey area. This may occur when the survey area comprises, for example, more than one livelihood zone. It is also less likely to be the case for conditions, such as wasting and oedema, that are associated with infectious disease and so may be more clumped than randomly distributed across primary sampling units. This may become a particular problem when proximity sampling is used to collect the within-cluster samples.

    -

    Measures of dispersion are problematic when used as measures of data quality and should be interpreted with caution. The exception to this rule is finding maximum, or almost maximum, uniformity or maximum, or almost maximum, clumping. A finding of maximum uniformity is likely only when data have been fabricated. A finding of maximum clumping may indicate poor data collection and / or poor data management.

    +

    The dashed line on the plot marks the mean number of cases found in +each primary sampling unit. A uniform distribution would show all bars +ending close to this line (see figure above).

    +

    SMART uses the variance to mean ratio as a test of data quality. +Green’s Index is a more robust choice because it can be used to compare +samples that vary in overall sample size and the number of sampling +units used.

    +

    The idea behind using a measure of dispersion to judge data quality +is a belief that the distribution of cases of malnutrition across +primary sampling units should always be random. If this is not the case +then the data are considered to be suspect. The problem with this +approach is that deviations from random can reflect the true +distribution of cases in the survey area. This may occur when the survey +area comprises, for example, more than one livelihood zone. It is also +less likely to be the case for conditions, such as wasting and oedema, +that are associated with infectious disease and so may be more clumped +than randomly distributed across primary sampling units. This may become +a particular problem when proximity sampling is used to collect the +within-cluster samples.

    +

    Measures of dispersion are problematic when used as measures of data +quality and should be interpreted with caution. The exception to this +rule is finding maximum, or almost maximum, uniformity or maximum, or +almost maximum, clumping. A finding of maximum uniformity is likely only +when data have been fabricated. A finding of maximum clumping may +indicate poor data collection and / or poor data management.

    - - - - + -
    -

    Developed by Mark Myatt, Ernest Guevarra.

    +
    +

    +

    Developed by Mark Myatt, Ernest Guevarra.

    -
    -

    Site built with pkgdown 1.6.1.

    +
    +

    +

    Site built with pkgdown 2.0.7.

    -
    +
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    -
    -
    -
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    -

    Age heaping

    -

    Mark Myatt

    +
    +
    +

    Age heaping

    +

    Mark Myatt

    - Source: vignettes/ah.Rmd - - + Source: vignettes/ah.Rmd +
    ah.Rmd
    -

    This section depends on some of the material presented in the section relating to digit preference.

    -

    Age heaping is the tendency to report children’s ages to the nearest year or adults’ ages to the nearest multiple of five or ten years. Age heaping is very common. This is a major reason why data from nutritional anthropometry surveys is often analysed and reported using broad age groups.

    +

    This section depends on some of the material presented in the section +relating to digit preference.

    +

    Age heaping is the tendency to report children’s ages to the nearest +year or adults’ ages to the nearest multiple of five or ten years. Age +heaping is very common. This is a major reason why data from nutritional +anthropometry surveys is often analysed and reported using broad age +groups.

    We will retrieve a survey dataset:

    -svy <- read.table("dp.ex02.csv", header = TRUE, sep = ",")
    -

    The file dp.ex02.csv is a comma-separated-value (CSV) file containing anthropometric data from a SMART survey in Kabul, Afghanistan.

    +svy <- read.table("dp.ex02.csv", header = TRUE, sep = ",")
    +

    The file dp.ex02.csv is a comma-separated-value +(CSV) file containing anthropometric data from a SMART survey in Kabul, +Afghanistan.

    The first few records in this dataset can be seen using:

    -head(svy)
    +head(svy)

    This returns:

    -
    #>   psu age sex weight height muac oedema
-#> 1   1   6   1    7.3   65.0  146      2
-#> 2   1  42   2   12.5   89.5  156      2
-#> 3   1  23   1   10.6   78.1  149      2
-#> 4   1  18   1   12.8   81.5  160      2
-#> 5   1  52   1   12.1   87.3  152      2
-#> 6   1  36   2   16.9   93.0  190      2
    -
    -

    -Summarising, tabulating, and visualising age data

    +
    #>   psu age sex weight height muac oedema
+#> 1   1   6   1    7.3   65.0  146      2
+#> 2   1  42   2   12.5   89.5  156      2
+#> 3   1  23   1   10.6   78.1  149      2
+#> 4   1  18   1   12.8   81.5  160      2
+#> 5   1  52   1   12.1   87.3  152      2
+#> 6   1  36   2   16.9   93.0  190      2
    +
    +

    Summarising, tabulating, and visualising age data +

    The variable of interest is age (age in months):

    -summary(svy$age)
-#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>     6.0    18.0    30.0    30.4    42.0    59.0
    -

    Tables can be difficult to use with ungrouped age data because there are usually many different values:

    +summary(svy$age) +#> Min. 1st Qu. Median Mean 3rd Qu. Max. +#> 6.0 18.0 30.0 30.4 42.0 59.0
    +

    Tables can be difficult to use with ungrouped age data because there +are usually many different values:

    -table(svy$age)
-#> 
-#>  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 
-#> 10 17 25 13 19 23 38 11 11 17  9 14 26  9 17 14 24 12 31  8 13  9 21 14 38 14 
-#> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 
-#> 16 23 22 18 57  8 13  9 11 12 19 10 13 14 12 14 44  6  9  6  5  8 12 13 12  8 
-#> 58 59 
-#> 13  9
    -

    The fullTable() function from the NiPN data-quality toolkit is preferred as this will include values with zero counts:

    +table(svy$age) +#> +#> 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 +#> 10 17 25 13 19 23 38 11 11 17 9 14 26 9 17 14 24 12 31 8 13 9 21 14 38 14 +#> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 +#> 16 23 22 18 57 8 13 9 11 12 19 10 13 14 12 14 44 6 9 6 5 8 12 13 12 8 +#> 58 59 +#> 13 9
    +

    The fullTable() function from the NiPN data-quality +toolkit is preferred as this will include values with zero counts:

    -fullTable(svy$age, values = 6:59)
-#>  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 
-#> 10 17 25 13 19 23 38 11 11 17  9 14 26  9 17 14 24 12 31  8 13  9 21 14 38 14 
-#> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 
-#> 16 23 22 18 57  8 13  9 11 12 19 10 13 14 12 14 44  6  9  6  5  8 12 13 12  8 
-#> 58 59 
-#> 13  9
    -

    We used the fullTable() function here because it returns a table containing cells for every value specified by the values parameter. The returned table will also only contain cells for the values specified by the values parameter. The default for the values parameter is the range of the variable being tabulated. This means that the values parameter can be sometimes be omitted:

    +fullTable(svy$age, values = 6:59) +#> 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 +#> 10 17 25 13 19 23 38 11 11 17 9 14 26 9 17 14 24 12 31 8 13 9 21 14 38 14 +#> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 +#> 16 23 22 18 57 8 13 9 11 12 19 10 13 14 12 14 44 6 9 6 5 8 12 13 12 8 +#> 58 59 +#> 13 9
    +

    We used the fullTable() function here because it returns +a table containing cells for every value specified by the +values parameter. The returned table will also only +contain cells for the values specified by the values +parameter. The default for the values parameter is the +range of the variable being tabulated. This means that the values +parameter can be sometimes be omitted:

    -fullTable(svy$age)
-#>  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 
-#> 10 17 25 13 19 23 38 11 11 17  9 14 26  9 17 14 24 12 31  8 13  9 21 14 38 14 
-#> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 
-#> 16 23 22 18 57  8 13  9 11 12 19 10 13 14 12 14 44  6  9  6  5  8 12 13 12  8 
-#> 58 59 
-#> 13  9
    -

    Omitting the values parameter only works reliably for numeric variables containing whole numbers. If the variable being tabulated is a character variable or is a numeric variable containing one or more numbers with decimal places then you should specify the values parameter.

    -

    A graphical analysis is usually more informative than a tabular analysis:

    +fullTable(svy$age) +#> 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 +#> 10 17 25 13 19 23 38 11 11 17 9 14 26 9 17 14 24 12 31 8 13 9 21 14 38 14 +#> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 +#> 16 23 22 18 57 8 13 9 11 12 19 10 13 14 12 14 44 6 9 6 5 8 12 13 12 8 +#> 58 59 +#> 13 9
    +

    Omitting the values parameter only works reliably +for numeric variables containing whole numbers. If the variable being +tabulated is a character variable or is a numeric variable containing +one or more numbers with decimal places then you should specify the +values parameter.

    +

    A graphical analysis is usually more informative than a tabular +analysis:

    -barplot(fullTable(svy$age, values = 6:59), 
-        xlab = "Age (months)", ylab = "Frequency", las = 3, cex.names = 0.6)
    +barplot(fullTable(svy$age, values = 6:59), + xlab = "Age (months)", ylab = "Frequency", las = 3, cex.names = 0.6)

    -

    We expect all ages to be present in roughly equal frequency or with frequency reducing slowly with age due to mortality. We can see that there is marked age-heaping at 12, 18, 24, 30, 36, and 48 months (see figure above). This is very common when age is reported by mothers. This is because of a tendency for mothers and other carers to round ages to whole years or half years.

    -

    Note that we used values = 6:59 with the fullTable() function from the NiPN data quality toolkit. We did this because it is the range of values that should be present in the age variable.

    +

    We expect all ages to be present in roughly equal frequency or with +frequency reducing slowly with age due to mortality. We can see that +there is marked age-heaping at 12, 18, 24, 30, 36, and 48 months (see +figure above). This is very common when age is reported by mothers. This +is because of a tendency for mothers and other carers to round ages to +whole years or half years.

    +

    Note that we used values = 6:59 with the +fullTable() function from the NiPN data quality toolkit. We +did this because it is the range of values that should be present in the +age variable.

    -
    -

    -Age heaping in children

    -

    Age heaping can seriously affect survey results for indices that include an age component (e.g. height- for-age and weight-for age). The effect is important when there is systematic rounding up or systematic rounding down. Systematic rounding can lead to bias. If rounding is systematically down then indices will be biased upwards and prevalence biased downwards. If rounding is systematically up then indices will be biased downwards and prevalence biased upwards.

    -

    A useful way of looking at age heaping when age is recorded in months is to examine the remainders when the ages are divided by 12.

    -

    The R language provides a special operator (%%) to help with this:

    +
    +

    Age heaping in children +

    +

    Age heaping can seriously affect survey results for indices that +include an age component (e.g. height- for-age and weight-for age). The +effect is important when there is systematic rounding up or systematic +rounding down. Systematic rounding can lead to bias. If rounding is +systematically down then indices will be biased upwards and prevalence +biased downwards. If rounding is systematically up then indices will be +biased downwards and prevalence biased upwards.

    +

    A useful way of looking at age heaping when age is recorded in months +is to examine the remainders when the ages are divided by 12.

    +

    The R language provides a special operator (%%) to help with +this:

    -rem <- svy$age %% 12
-remTable <- fullTable(rem, values = 0:11)
-remTable
-#>   0   1   2   3   4   5   6   7   8   9  10  11 
-#> 170  33  46  41  46  48 105  63  83  72  90  76
-prop.table(remTable) * 100
-#>         0         1         2         3         4         5         6         7 
-#> 19.473081  3.780069  5.269187  4.696449  5.269187  5.498282 12.027491  7.216495 
-#>         8         9        10        11 
-#>  9.507446  8.247423 10.309278  8.705613
-barplot(remTable, xlab = "Age (months) %% 12", ylab = "Frequency") 
-abline(h = sum(remTable / 12), lty = 3)
    +rem <- svy$age %% 12 +remTable <- fullTable(rem, values = 0:11) +remTable +#> 0 1 2 3 4 5 6 7 8 9 10 11 +#> 170 33 46 41 46 48 105 63 83 72 90 76 +prop.table(remTable) * 100 +#> 0 1 2 3 4 5 6 7 +#> 19.473081 3.780069 5.269187 4.696449 5.269187 5.498282 12.027491 7.216495 +#> 8 9 10 11 +#> 9.507446 8.247423 10.309278 8.705613 +barplot(remTable, xlab = "Age (months) %% 12", ylab = "Frequency") +abline(h = sum(remTable / 12), lty = 3)

    -chisq.test(remTable)
-#> 
-#>  Chi-squared test for given probabilities
-#> 
-#> data:  remTable
-#> X-squared = 214.96, df = 11, p-value < 2.2e-16
    -

    The NiPN data quality toolkit provides an R language function called ageHeaping() that performs this age-heaping analysis. Applying this function to the example data:

    +chisq.test(remTable) +#> +#> Chi-squared test for given probabilities +#> +#> data: remTable +#> X-squared = 214.96, df = 11, p-value < 2.2e-16
    +

    The NiPN data quality toolkit provides an R language function called +ageHeaping() that performs this age-heaping analysis. +Applying this function to the example data:

    -ageHeaping(svy$age)
    +ageHeaping(svy$age)

    This returns:

    -
    #> 
-#>  Age-heaping Analysis
-#> 
-#> data:    Remainder of svy$age / 12
-#> X-squared = 214.9588, df = 11, p-value = 0.0000
    -

    The output of the ageHeaping() function can be saved for later use:

    +
    #> 
+#>  Age-heaping Analysis
+#> 
+#> data:    Remainder of svy$age / 12
+#> X-squared = 214.9588, df = 11, p-value = 0.0000
    +

    The output of the ageHeaping() function can be saved for +later use:

    -ah12 <- ageHeaping(svy$age)
    -

    The saved output contains the Chi-squared test and frequency tables of the final digits (counts and percentages). These can be accessed using:

    +ah12 <- ageHeaping(svy$age) +

    The saved output contains the Chi-squared test and frequency tables +of the final digits (counts and percentages). These can be accessed +using:

    -ah12
-#> 
-#>  Age-heaping Analysis
-#> 
-#> data:    Remainder of svy$age / 12
-#> X-squared = 214.9588, df = 11, p-value = 0.0000
-ah12$X2
-#> X-squared 
-#>  214.9588
-ah12$df
-#> df 
-#> 11
-ah12$p
-#> [1] 5.791598e-40
-ah12$tab
-#> Remainder of svy$age / 12
-#>   0   1   2   3   4   5   6   7   8   9  10  11 
-#> 170  33  46  41  46  48 105  63  83  72  90  76
-ah12$pct
-#> Remainder of svy$age / 12
-#>    0    1    2    3    4    5    6    7    8    9   10   11 
-#> 19.5  3.8  5.3  4.7  5.3  5.5 12.0  7.2  9.5  8.2 10.3  8.7
    +ah12 +#> +#> Age-heaping Analysis +#> +#> data: Remainder of svy$age / 12 +#> X-squared = 214.9588, df = 11, p-value = 0.0000 +ah12$X2 +#> X-squared +#> 214.9588 +ah12$df +#> df +#> 11 +ah12$p +#> [1] 5.791598e-40 +ah12$tab +#> Remainder of svy$age / 12 +#> 0 1 2 3 4 5 6 7 8 9 10 11 +#> 170 33 46 41 46 48 105 63 83 72 90 76 +ah12$pct +#> Remainder of svy$age / 12 +#> 0 1 2 3 4 5 6 7 8 9 10 11 +#> 19.5 3.8 5.3 4.7 5.3 5.5 12.0 7.2 9.5 8.2 10.3 8.7

    The saved results may also be plotted:

    -plot(ah12, main = "Age-heaping (remainder of age / 12)")
    +plot(ah12, main = "Age-heaping (remainder of age / 12)")

    The resulting plot is shown below.

    -

    The ageHeaping() function assumes that you want to examine the remainder after dividing by twelve. This is useful when working with ages that are recorded in months. It may also be useful to use other divisors, such as examining the remainder after dividing by six:

    +

    The ageHeaping() function assumes that you want to +examine the remainder after dividing by twelve. This is useful when +working with ages that are recorded in months. It may also be useful to +use other divisors, such as examining the remainder after dividing by +six:

    -ah6 <- ageHeaping(svy$age, divisor = 6) 
-print(ah6)
-#> 
-#>  Age-heaping Analysis
-#> 
-#> data:    Remainder of svy$age / 6
-#> X-squared = 145.0275, df = 5, p-value = 0.0000
-plot(ah6)
    +ah6 <- ageHeaping(svy$age, divisor = 6) +print(ah6) +#> +#> Age-heaping Analysis +#> +#> data: Remainder of svy$age / 6 +#> X-squared = 145.0275, df = 5, p-value = 0.0000 +plot(ah6)

    -

    This shows the extent of age heaping at whole and half-years (see figure above).

    +

    This shows the extent of age heaping at whole and half-years (see +figure above).

    -
    -

    -Age heaping in adults

    -

    Using ten and five as divisors can be useful when dealing with data for adults in which ages are recorded in whole years. For example:

    +
    +

    Age heaping in adults +

    +

    Using ten and five as divisors can be useful when dealing with data +for adults in which ages are recorded in whole years. For example:

    -svy <- read.table("ah.ex01.csv", header = TRUE, sep = ",") 
-head(svy)
    +svy <- read.table("ah.ex01.csv", header = TRUE, sep = ",") +head(svy)
    -svy <- ah.ex01 
-head(svy)
    -

    The file ah.ex01.csv is a comma-separated-value (CSV) file containing anthropometric data from a Rapid Assessment Method for Older People (RAM-OP) survey in the Dadaab refugee camp in Garissa, Kenya. This is a survey of people aged sixty years and older.

    +svy <- ah.ex01 +head(svy)
    +

    The file ah.ex01.csv is a comma-separated-value +(CSV) file containing anthropometric data from a Rapid Assessment Method +for Older People (RAM-OP) survey in the Dadaab refugee camp in Garissa, +Kenya. This is a survey of people aged sixty years and older.

    The variable of interest is age (age in years):

    -summary(svy$age)
-#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>     6.0    18.0    30.0    30.4    42.0    59.0
    -

    Care should be exercised when specifying the divisor to use in the analysis of age heaping. Not all calendars use base ten. Amongst Han Chinese, for example, age heaping may occur with a twelve-year cycle corresponding to preferred animal years in the Chinese calendar. An analysis of age heaping that concentrates on specific digits (e.g. zero and five) or on decimal intervals will not be appropriate in all populations. It is advisable, therefore to use simple tabulation and visualisation techniques to heap decide an appropriate divisor.

    +summary(svy$age) +#> Min. 1st Qu. Median Mean 3rd Qu. Max. +#> 6.0 18.0 30.0 30.4 42.0 59.0 +

    Care should be exercised when specifying the divisor +to use in the analysis of age heaping. Not all calendars use base ten. +Amongst Han Chinese, for example, age heaping may occur with a +twelve-year cycle corresponding to preferred animal years in the Chinese +calendar. An analysis of age heaping that concentrates on specific +digits (e.g. zero and five) or on decimal intervals will not be +appropriate in all populations. It is advisable, therefore to use simple +tabulation and visualisation techniques to heap decide an appropriate +divisor.

    With the example data:

    -summary(svy$age)
-#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>     6.0    18.0    30.0    30.4    42.0    59.0
-fullTable(svy$age)
-#>  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 
-#> 10 17 25 13 19 23 38 11 11 17  9 14 26  9 17 14 24 12 31  8 13  9 21 14 38 14 
-#> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 
-#> 16 23 22 18 57  8 13  9 11 12 19 10 13 14 12 14 44  6  9  6  5  8 12 13 12  8 
-#> 58 59 
-#> 13  9
-barplot(fullTable(svy$age), 
-        xlab = "Age (years)", ylab = "Frequency", las = 3, cex.names = 0.6)
    +summary(svy$age) +#> Min. 1st Qu. Median Mean 3rd Qu. Max. +#> 6.0 18.0 30.0 30.4 42.0 59.0 +fullTable(svy$age) +#> 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 +#> 10 17 25 13 19 23 38 11 11 17 9 14 26 9 17 14 24 12 31 8 13 9 21 14 38 14 +#> 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 +#> 16 23 22 18 57 8 13 9 11 12 19 10 13 14 12 14 44 6 9 6 5 8 12 13 12 8 +#> 58 59 +#> 13 9 +barplot(fullTable(svy$age), + xlab = "Age (years)", ylab = "Frequency", las = 3, cex.names = 0.6)

    -

    shows age-heaping at decades and half-decades (see figure above). In this survey using a divisor of 10 would be appropriate:

    +

    shows age-heaping at decades and half-decades (see figure above). In +this survey using a divisor of 10 would be +appropriate:

    -ah10 <- ageHeaping(svy$age, divisor = 10) 
-print(ah10)
-#> 
-#>  Age-heaping Analysis
-#> 
-#> data:    Remainder of svy$age / 10
-#> X-squared = 70.31042, df = 9, p-value = 0.0000
-plot(ah10)
    +ah10 <- ageHeaping(svy$age, divisor = 10) +print(ah10) +#> +#> Age-heaping Analysis +#> +#> data: Remainder of svy$age / 10 +#> X-squared = 70.31042, df = 9, p-value = 0.0000 +plot(ah10)

    -

    There is pronounced age heaping at decades and, to a lesser extent, half-decades in these data (see figure above).

    -

    It may also be useful to use other divisors, such as examining the remainder after dividing by five:

    +

    There is pronounced age heaping at decades and, to a lesser extent, +half-decades in these data (see figure above).

    +

    It may also be useful to use other divisors, such as examining the +remainder after dividing by five:

    -ah5 <- ageHeaping(svy$age, divisor = 5) 
-print(ah5)
-#> 
-#>  Age-heaping Analysis
-#> 
-#> data:    Remainder of svy$age / 5
-#> X-squared = 10.39633, df = 4, p-value = 0.0343
-plot(ah5)
    +ah5 <- ageHeaping(svy$age, divisor = 5) +print(ah5) +#> +#> Age-heaping Analysis +#> +#> data: Remainder of svy$age / 5 +#> X-squared = 10.39633, df = 4, p-value = 0.0343 +plot(ah5)

    -

    This shows the extent of age heaping at whole and half decades (see figure above).

    +

    This shows the extent of age heaping at whole and half decades (see +figure above).

    - - - - + -
    -

    Developed by Mark Myatt, Ernest Guevarra.

    +
    +

    +

    Developed by Mark Myatt, Ernest Guevarra.

    -
    -

    Site built with pkgdown 1.6.1.

    +
    +

    +

    Site built with pkgdown 2.0.7.

    -
    +
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    -
    - +
    -
    -
    -

    Age and sex distributions

    -

    Mark Myatt

    +
    +

    Age and sex distributions

    +

    Mark Myatt

    - Source: vignettes/as.Rmd - - + Source: vignettes/as.Rmd +
    as.Rmd
    -
    -

    -Age and sex distributions (children’s data)

    -

    Age heaping is the tendency to report children’s ages to the nearest year or adult ages to the nearest multiple of 5 or 10 years. Age heaping is very common. It is a major reason why data from nutritional anthropometry surveys are often analysed and reported using broad age-groups. The commonest age-groups used with children’s data are 6 to 17 months, 18 to 29 months, 30 to 41 months, 42 to 53 months, and 54 to 59 months (see figure below). These are known as year-centred age-groups. Note that the last age-group covers only six months but is nominally centred at five years. Other age-groups may be used for specific analyses. The techniques presented here can be adapted to work with other age- groups.

    -

    +
    +

    Age and sex distributions (children’s data) +

    +

    Age heaping is the tendency to report children’s ages to the nearest +year or adult ages to the nearest multiple of 5 or 10 years. Age heaping +is very common. It is a major reason why data from nutritional +anthropometry surveys are often analysed and reported using broad +age-groups. The commonest age-groups used with children’s data are 6 to +17 months, 18 to 29 months, 30 to 41 months, 42 to 53 months, and 54 to +59 months (see figure below). These are known as year-centred +age-groups. Note that the last age-group covers only six months but is +nominally centred at five years. Other age-groups may be used for +specific analyses. The techniques presented here can be adapted to work +with other age- groups.

    +

    We will retrieve a survey dataset:

    -svy <- read.table("dp.ex02.csv", header = TRUE, sep = ",") 
-head(svy)
    -
    #>   psu age sex weight height muac oedema
-#> 1   1   6   1    7.3   65.0  146      2
-#> 2   1  42   2   12.5   89.5  156      2
-#> 3   1  23   1   10.6   78.1  149      2
-#> 4   1  18   1   12.8   81.5  160      2
-#> 5   1  52   1   12.1   87.3  152      2
-#> 6   1  36   2   16.9   93.0  190      2
    -

    The dataset dp.ex02 is a comma-separated-value (CSV) file containing anthropometric data from a SMART survey in Kabul, Afghanistan.

    -
    -

    -Tabulation and visualisation

    -

    The NiPN data quality toolkit provides an R language function called recode() that makes it easy to recode and group data. We will use the recode() function to group the data in the age variable (age in months) into year-centred age-groups.

    +svy <- read.table("dp.ex02.csv", header = TRUE, sep = ",") +head(svy)
    +
    #>   psu age sex weight height muac oedema
+#> 1   1   6   1    7.3   65.0  146      2
+#> 2   1  42   2   12.5   89.5  156      2
+#> 3   1  23   1   10.6   78.1  149      2
+#> 4   1  18   1   12.8   81.5  160      2
+#> 5   1  52   1   12.1   87.3  152      2
+#> 6   1  36   2   16.9   93.0  190      2
    +

    The dataset dp.ex02 is a comma-separated-value (CSV) +file containing anthropometric data from a SMART survey in Kabul, +Afghanistan.

    +
    +

    Tabulation and visualisation +

    +

    The NiPN data quality toolkit provides an R language function called +recode() that makes it easy to recode and group data. We +will use the recode() function to group the data in the age +variable (age in months) into year-centred age-groups.

    -svy$ycag <- recode(svy$age, "6:17=1; 18:29=2; 30:41=3; 42:53=4; 54:59=5")
-head(svy)
-#>   psu age sex weight height muac oedema ycag
-#> 1   1   6   1    7.3   65.0  146      2    1
-#> 2   1  42   2   12.5   89.5  156      2    4
-#> 3   1  23   1   10.6   78.1  149      2    2
-#> 4   1  18   1   12.8   81.5  160      2    2
-#> 5   1  52   1   12.1   87.3  152      2    4
-#> 6   1  36   2   16.9   93.0  190      2    3
    +svy$ycag <- recode(svy$age, "6:17=1; 18:29=2; 30:41=3; 42:53=4; 54:59=5") +head(svy) +#> psu age sex weight height muac oedema ycag +#> 1 1 6 1 7.3 65.0 146 2 1 +#> 2 1 42 2 12.5 89.5 156 2 4 +#> 3 1 23 1 10.6 78.1 149 2 2 +#> 4 1 18 1 12.8 81.5 160 2 2 +#> 5 1 52 1 12.1 87.3 152 2 4 +#> 6 1 36 2 16.9 93.0 190 2 3

    A tabular analysis can be performed:

    -table(svy$ycag, svy$sex) 
-#>    
-#>       1   2
-#>   1 101 106
-#>   2 102  96
-#>   3 126 115
-#>   4  78  82
-#>   5  31  36
-prop.table(table(svy$ycag, svy$sex)) * 100
-#>    
-#>             1         2
-#>   1 11.569301 12.142039
-#>   2 11.683849 10.996564
-#>   3 14.432990 13.172967
-#>   4  8.934708  9.392898
-#>   5  3.550974  4.123711
    -

    The table() function performs a cross-tabulation. The first variable specified (svy$ycag in this example) is the row variable. The second variable specified (svy$sex in this example) is the column variable.

    -

    -

    It is useful to examine row percentages and column percentages in tables of age-group by sex. We should look at row percentages:

    +table(svy$ycag, svy$sex) +#> +#> 1 2 +#> 1 101 106 +#> 2 102 96 +#> 3 126 115 +#> 4 78 82 +#> 5 31 36 +prop.table(table(svy$ycag, svy$sex)) * 100 +#> +#> 1 2 +#> 1 11.569301 12.142039 +#> 2 11.683849 10.996564 +#> 3 14.432990 13.172967 +#> 4 8.934708 9.392898 +#> 5 3.550974 4.123711
    +

    The table() function performs a cross-tabulation. The +first variable specified (svy$ycag in this example) is the +row variable. The second variable specified (svy$sex in +this example) is the column variable.

    +

    +

    It is useful to examine row percentages and column percentages in +tables of age-group by sex. We should look at row percentages:

    -prop.table(table(svy$ycag, svy$sex), margin = 1) * 100
    +prop.table(table(svy$ycag, svy$sex), margin = 1) * 100

    This returns:

    -
    #>    
-#>            1        2
-#>   1 48.79227 51.20773
-#>   2 51.51515 48.48485
-#>   3 52.28216 47.71784
-#>   4 48.75000 51.25000
-#>   5 46.26866 53.73134
    -

    Which shows approximately equal proportions of males and females in each year-centred age-group. We specified margin = 1 with the prop.table() function because we wanted row percentages.

    +
    #>    
+#>            1        2
+#>   1 48.79227 51.20773
+#>   2 51.51515 48.48485
+#>   3 52.28216 47.71784
+#>   4 48.75000 51.25000
+#>   5 46.26866 53.73134
    +

    Which shows approximately equal proportions of males and females in +each year-centred age-group. We specified margin = 1 with +the prop.table() function because we wanted row +percentages.

    We should also look at column percentages:

    -prop.table(table(svy$ycag, svy$sex), margin = 2) * 100
    +prop.table(table(svy$ycag, svy$sex), margin = 2) * 100

    This returns:

    -
    #>    
-#>             1         2
-#>   1 23.059361 24.367816
-#>   2 23.287671 22.068966
-#>   3 28.767123 26.436782
-#>   4 17.808219 18.850575
-#>   5  7.077626  8.275862
    -

    We expect there to be approximately equal proportions of children in the age-groups centred at 1, 2, 3, and 4 years and a smaller proportion (i.e. about half that in the other age-groups) in the age-group centred at 5 years. We specified margin = 2 with the prop.table() function because we wanted column percentages.

    -

    A graphical analysis using a population pyramid can be useful. The NiPN data quality toolkit provides an R language function called pyramid.plot() for plotting population pyramids:

    +
    #>    
+#>             1         2
+#>   1 23.059361 24.367816
+#>   2 23.287671 22.068966
+#>   3 28.767123 26.436782
+#>   4 17.808219 18.850575
+#>   5  7.077626  8.275862
    +

    We expect there to be approximately equal proportions of children in +the age-groups centred at 1, 2, 3, and 4 years and a smaller proportion +(i.e. about half that in the other age-groups) in the age-group centred +at 5 years. We specified margin = 2 with the +prop.table() function because we wanted column +percentages.

    +

    A graphical analysis using a population pyramid can be useful. The +NiPN data quality toolkit provides an R language function +called pyramid.plot() for plotting population pyramids:

    -pyramid.plot(svy$ycag, svy$sex)
    +pyramid.plot(svy$ycag, svy$sex)

    -

    We can make a more informative plot by specifying a title and axis labels:

    +

    We can make a more informative plot by specifying a title and axis +labels:

    -pyramid.plot(svy$ycag, svy$sex, 
-             main = "Distribution of age by sex",
-             xlab = "Frequency (Males | Females)", 
-             ylab = "Year-centred age-group")
    +pyramid.plot(svy$ycag, svy$sex, + main = "Distribution of age by sex", + xlab = "Frequency (Males | Females)", + ylab = "Year-centred age-group")

    and applying shading:

    -pyramid.plot(svy$ycag, svy$sex, 
-             main = "Distribution of age by sex",
-             xlab = "Frequency (Males | Females)", 
-             ylab = "Year-centred age-group",
-             col = c("grey80", "white"))
    +pyramid.plot(svy$ycag, svy$sex, + main = "Distribution of age by sex", + xlab = "Frequency (Males | Females)", + ylab = "Year-centred age-group", + col = c("grey80", "white"))

    or colours:

    -pyramid.plot(svy$ycag, svy$sex, 
-             main = "Distribution of age by sex",
-             xlab = "Frequency (Males | Females)", 
-             ylab = "Year-centred age-group",
-             col = c("lightblue", "pink"))
    +pyramid.plot(svy$ycag, svy$sex, + main = "Distribution of age by sex", + xlab = "Frequency (Males | Females)", + ylab = "Year-centred age-group", + col = c("lightblue", "pink"))

    -

    We expect there to be approximately equal numbers of children in the age-groups centred at 1, 2, 3, and 4 years and a smaller number (i.e. about half the number in the other age-groups) in the age-group centred at 5 years. There should also be approximately equal numbers of males and females. This is what we see in the population pyramid below.

    +

    We expect there to be approximately equal numbers of children in the +age-groups centred at 1, 2, 3, and 4 years and a smaller number +(i.e. about half the number in the other age-groups) in the age-group +centred at 5 years. There should also be approximately equal numbers of +males and females. This is what we see in the population pyramid +below.

    -pyramid.plot(svy$ycag, svy$sex, 
-             main = "Distribution of age by sex",
-             xlab = "Frequency (Males | Females)", 
-             ylab = "Year-centred age-group")
    +pyramid.plot(svy$ycag, svy$sex, + main = "Distribution of age by sex", + xlab = "Frequency (Males | Females)", + ylab = "Year-centred age-group")

    -

    The pyramid.plot() function uses the values of the grouped age variable as y-axis value labels.

    -

    We can assign descriptive text values using the recode() function. For example:

    +

    The pyramid.plot() function uses the values of the +grouped age variable as y-axis value labels.

    +

    We can assign descriptive text values using the recode() +function. For example:

    -svy$ageLabel <- recode(svy$age, "6:29='< 30 months'; 30:hi='30 month or older'")
-
-pyramid.plot(svy$ageLabel, 
-             svy$sex, 
-             main = "Distribution of age by sex", 
-             xlab = "Frequency (Males | Females)", 
-             ylab = "Age-group")
    +svy$ageLabel <- recode(svy$age, "6:29='< 30 months'; 30:hi='30 month or older'") +#> Warning in recode(svy$age, "6:29='< 30 months'; 30:hi='30 month or older'"): NAs +#> introduced by coercion + +pyramid.plot(svy$ageLabel, + svy$sex, + main = "Distribution of age by sex", + xlab = "Frequency (Males | Females)", + ylab = "Age-group")

    -

    We can also use a factor type variable. This type of variable allows labels to be specified:

    +

    We can also use a factor type variable. This type of variable allows +labels to be specified:

    -svy$ageLabel <- factor(svy$ycag,
-                       labels = c("6:17", "18:29", "30:41", "42:53", "54:59"))
-
-pyramid.plot(svy$ageLabel, 
-             svy$sex, 
-             main = "Distribution of age by sex", 
-             xlab = "Frequency (Males | Females)", 
-             ylab = "Year-centred age-group")
    +svy$ageLabel <- factor(svy$ycag, + labels = c("6:17", "18:29", "30:41", "42:53", "54:59")) + +pyramid.plot(svy$ageLabel, + svy$sex, + main = "Distribution of age by sex", + xlab = "Frequency (Males | Females)", + ylab = "Year-centred age-group")

    -

    The cut() function may also be used:

    +

    The cut() function may also be used:

    -svy$ageCuts <- cut(svy$age, breaks = c(0, 17, 29, 41, 53, 59))
-
-pyramid.plot(svy$ageCuts, 
-             svy$sex, 
-             main = "Age-group (months) ",
-             xlab = "Frequency (Males | Females)", 
-             ylab = "Year-centred age-group",
-             cex.names = 0.9)
    +svy$ageCuts <- cut(svy$age, breaks = c(0, 17, 29, 41, 53, 59)) + +pyramid.plot(svy$ageCuts, + svy$sex, + main = "Age-group (months) ", + xlab = "Frequency (Males | Females)", + ylab = "Year-centred age-group", + cex.names = 0.9)

    -

    The cut() function is a versatile grouping function. It is explained in more detail later in this section.

    -

    The cex.names parameter of the pyramid.plot() function allows us to change the size of the value labels on the y-axis. The value for cex.names is a magnification factor. Values above one make the labels larger than the default. Values below one make the labels smaller than the default.

    +

    The cut() function is a versatile grouping function. It +is explained in more detail later in this section.

    +

    The cex.names parameter of the +pyramid.plot() function allows us to change the size of the +value labels on the y-axis. The value for cex.names is a +magnification factor. Values above one make the labels larger than the +default. Values below one make the labels smaller than the default.

    -
    -

    -Simple testing

    -

    It is possible to perform a formal test on the distribution of age-groups by sex. A simple test is:

    +
    +

    Simple testing +

    +

    It is possible to perform a formal test on the distribution of +age-groups by sex. A simple test is:

    -chisq.test(table(svy$ycag, svy$sex))
    +chisq.test(table(svy$ycag, svy$sex))

    This yields:

    -
    #> 
-#>  Pearson's Chi-squared test
-#> 
-#> data:  table(svy$ycag, svy$sex)
-#> X-squared = 1.2675, df = 4, p-value = 0.8669
    -

    In this example the p-value is not below 0.05 so we accept the null hypothesis that there is no significant association between age and sex. This is an important test as it tests whether the distribution of ages is similar for males and females. It does not, however, test whether the age structure in the sample meets expectations. This requires a test that compares observed numbers with expected numbers derived from an external source (e.g. census data) or from a demographic model.

    -
    -

    -A model of the expected age structure

    -

    A simple model-based method for calculating expected numbers is exponential decay in a population in which births and deaths balance each other and with a 1:1 male to female sex ratio. Under this model the proportion surviving in each group at each year can be calculated as:

    +
    #> 
+#>  Pearson's Chi-squared test
+#> 
+#> data:  table(svy$ycag, svy$sex)
+#> X-squared = 1.2675, df = 4, p-value = 0.8669
    +

    In this example the p-value is not below 0.05 so we accept the null +hypothesis that there is no significant association between age and sex. +This is an important test as it tests whether the distribution of ages +is similar for males and females. It does not, however, test whether the +age structure in the sample meets expectations. This requires a test +that compares observed numbers with expected numbers derived from an +external source (e.g. census data) or from a demographic model.

    +
    +

    A model of the expected age structure +

    +

    A simple model-based method for calculating expected numbers is +exponential decay in a population in which births and deaths balance +each other and with a 1:1 male to female sex ratio. Under this model the +proportion surviving in each group at each year can be calculated +as:

    \[ p ~ = ~ e ^ {-zt} \]

    -

    in which e is the base of the natural logarithm (approximately 2.7183), z is the mortality rate associated with each time period, and t is time. Time (t) starts at zero for the purposes of computation. Age can be used as a measure of time since birth. We should use 0 for the first year-centred age-group, 1 for the second year-centred age-group, and so-on. This is the rationale for us using t <- 0:4 below.

    -

    With five year-centred age-groups and a mortality rate of 1 / 10,000 / day, the expected proportions surviving at each year can be calculated as:

    +

    in which e is the base of the natural logarithm (approximately +2.7183), z is the mortality rate associated with each time period, and t +is time. Time (t) starts at zero for the purposes of computation. Age +can be used as a measure of time since birth. We should use 0 for the +first year-centred age-group, 1 for the second year-centred age-group, +and so-on. This is the rationale for us using t <- 0:4 +below.

    +

    With five year-centred age-groups and a mortality rate of 1 / 10,000 +/ day, the expected proportions surviving at each year can be calculated +as:

    -z <- (1 / 10000) * 365.25 
-
-t <- 0:4
-
-p <- exp(-z * t)
-
-p
    +z <- (1 / 10000) * 365.25 + +t <- 0:4 + +p <- exp(-z * t) + +p

    This yields the following survival probabilities:

    -z <- (1 / 10000) * 365.25 
-
-t <- 0:4
-
-p <- exp(-z * t)
-
-p
-#> [1] 1.0000000 0.9641340 0.9295544 0.8962149 0.8640713
    -

    We need to specify the duration (i.e. the number of years) represented by each age-group:

    +z <- (1 / 10000) * 365.25 + +t <- 0:4 + +p <- exp(-z * t) + +p +#> [1] 1.0000000 0.9641340 0.9295544 0.8962149 0.8640713
    +

    We need to specify the duration (i.e. the number of years) +represented by each age-group:

    -d <- c(1, 1, 1, 1, 0.5)
    -

    We can then calculate expected proportions of children in each age-group:

    +d <- c(1, 1, 1, 1, 0.5)
    +

    We can then calculate expected proportions of children in each +age-group:

    -ep <- d * p / sum(d * p) 
-
-ep
    +ep <- d * p / sum(d * p) + +ep

    This gives:

    -
    #> [1] 0.2368580 0.2283628 0.2201724 0.2122757 0.1023311
    +
    #> [1] 0.2368580 0.2283628 0.2201724 0.2122757 0.1023311

    We can now calculate expected numbers:

    -
    expected <- ep * sum(table(svy$ycag)) names(expected) <- 1:5
-
-expected
    +
    expected <- ep * sum(table(svy$ycag)) names(expected) <- 1:5
+
+expected

    giving:

    -
    #>         1         2         3         4         5 
-#> 206.77703 199.36076 192.21049 185.31667  89.33505
    -

    A formal test would compare the observed numbers with the expected numbers. The observed numbers can be found using:

    +
    #>         1         2         3         4         5 
+#> 206.77703 199.36076 192.21049 185.31667  89.33505
    +

    A formal test would compare the observed numbers with the expected +numbers. The observed numbers can be found using:

    -observed <- table(svy$ycag) 
-
-observed
    +observed <- table(svy$ycag) + +observed

    This gives:

    -
    #> 
-#>   1   2   3   4   5 
-#> 207 198 241 160  67
    -

    It can be useful to examine observed and expected numbers graphically:

    +
    #> 
+#>   1   2   3   4   5 
+#> 207 198 241 160  67
    +

    It can be useful to examine observed and expected numbers +graphically:

    -par(mfcol = c(1, 2))
-barplot(observed, main = "Observed", xlab = "Age group", ylab = "Frequency", ylim = c(0, 250))
-barplot(expected, main = "Expected", xlab = "Age group", ylab = "Frequency", ylim = c(0, 250))
    +par(mfcol = c(1, 2)) +barplot(observed, main = "Observed", xlab = "Age group", ylab = "Frequency", ylim = c(0, 250)) +barplot(expected, main = "Expected", xlab = "Age group", ylab = "Frequency", ylim = c(0, 250))

    We will calculate a Chi-squared test statistic:

    -

    \[ \chi ^ 2 ~ = ~ \sum \frac{(\text{observed} - \text{expected}) ^ 2}{\text{expected}} \]

    +

    \[ \chi ^ 2 ~ = ~ \sum +\frac{(\text{observed} - \text{expected}) ^ 2}{\text{expected}} +\]

    using:

    -X2 <- sum((observed - expected) ^ 2 / expected)
    +X2 <- sum((observed - expected) ^ 2 / expected)

    which yields a Chi-Squared test statistic of:

    We can find the p-value using:

    -pchisq(X2, df = 4, lower.tail = FALSE)
    +pchisq(X2, df = 4, lower.tail = FALSE)

    This gives:

    -
    #> [1] 0.000259395
    -

    In this example the age distribution is significantly different from expected numbers calculated using a simple demographic model.

    -

    Note that we specify the degrees of freedom (df) for the Chi-Squared test as the number of age-groups minus one. As we have five age-groups we specify df = 4. The degrees of freedom (df) that we need to specify will depend on the number of age-groups that we use. It is always the number of age-groups minus one. If, for example, there are ten age-groups we would need to specify df = 9.

    -

    The NiPN data quality toolkit provides an R function called ageChildren() that performs the model- based Chi-Squared test:

    +
    #> [1] 0.000259395
    +

    In this example the age distribution is significantly different from +expected numbers calculated using a simple demographic model.

    +

    Note that we specify the degrees of freedom (df) for the +Chi-Squared test as the number of age-groups minus one. As we have five +age-groups we specify df = 4. The degrees of freedom +(df) that we need to specify will depend on the number of +age-groups that we use. It is always the number of age-groups minus one. +If, for example, there are ten age-groups we would need to specify +df = 9.

    +

    The NiPN data quality toolkit provides an R function called +ageChildren() that performs the model- based Chi-Squared +test:

    -ageChildren(svy$age, u5mr = 1)
    +ageChildren(svy$age, u5mr = 1)

    which returns:

    -
    #> 
-#>  Age Test (Children)
-#> 
-#> X-squared = 21.4366, df = 4, p = 0.0003
    -

    Note that we specified the under five years mortality rate as 1 / 10,000 / day using u5mr = 1. Another, more appropriate, rate may be specified.

    -

    The ageChildren() function calculates year-centred age-groups for children aged between six and fifty-nine months by default. This is a standard survey population and is used in SMART and many other surveys. The use of year-centred age-groups is also standard practice. The commands that are given above can, however, be adapted for use with different age-groups.

    -

    The output of the ageChildren() function can be saved for later use:

    +
    #> 
+#>  Age Test (Children)
+#> 
+#> X-squared = 21.4366, df = 4, p = 0.0003
    +

    Note that we specified the under five years mortality rate as 1 / +10,000 / day using u5mr = 1. Another, more appropriate, +rate may be specified.

    +

    The ageChildren() function calculates year-centred +age-groups for children aged between six and fifty-nine months by +default. This is a standard survey population and is used in SMART and +many other surveys. The use of year-centred age-groups is also standard +practice. The commands that are given above can, however, be adapted for +use with different age-groups.

    +

    The output of the ageChildren() function can be saved +for later use:

    -ac <- ageChildren(svy$age, u5mr = 1)
    -

    The saved output contains the Chi-squared test results and tables of observed and expected values. These can be accessed using:

    +ac <- ageChildren(svy$age, u5mr = 1) +

    The saved output contains the Chi-squared test results and tables of +observed and expected values. These can be accessed using:

    -ac
-#> 
-#>  Age Test (Children)
-#> 
-#> X-squared = 21.4366, df = 4, p = 0.0003
-
-ac$X2
-#> [1] 21.43662
-
-ac$df
-#> [1] 4
-
-ac$p 
-#> [1] 0.000259395
-
-ac$observed 
-#>   1   2   3   4   5 
-#> 207 198 241 160  67
-
-ac$expected
-#>         1         2         3         4         5 
-#> 206.77703 199.36076 192.21049 185.31667  89.33505
    +ac +#> +#> Age Test (Children) +#> +#> X-squared = 21.4366, df = 4, p = 0.0003 + +ac$X2 +#> [1] 21.43662 + +ac$df +#> [1] 4 + +ac$p +#> [1] 0.000259395 + +ac$observed +#> 1 2 3 4 5 +#> 207 198 241 160 67 + +ac$expected +#> 1 2 3 4 5 +#> 206.77703 199.36076 192.21049 185.31667 89.33505

    The saved results may also be plotted:

    -plot(ac)
    +plot(ac)

    -

    The ageChildren() function can be applied to each sex separately. To males:

    +

    The ageChildren() function can be applied to each sex +separately. To males:

    -acM <- ageChildren(svy$age[svy$sex == 1], u5mr = 1) 
-
-acM
-#> 
-#>  Age Test (Children)
-#> 
-#> X-squared = 15.8496, df = 4, p = 0.0032
-
-plot(acM)
    +acM <- ageChildren(svy$age[svy$sex == 1], u5mr = 1) + +acM +#> +#> Age Test (Children) +#> +#> X-squared = 15.8496, df = 4, p = 0.0032 + +plot(acM)

    and to females:

    -acF <- ageChildren(svy$age[svy$sex == 2], u5mr = 1) 
-
-acF
-#> 
-#>  Age Test (Children)
-#> 
-#> X-squared = 6.8429, df = 4, p = 0.1444
-
-plot(acF)
    +acF <- ageChildren(svy$age[svy$sex == 2], u5mr = 1) + +acF +#> +#> Age Test (Children) +#> +#> X-squared = 6.8429, df = 4, p = 0.1444 + +plot(acF)

    An easier way of doing this is:

    -by(svy$age, svy$sex, ageChildren, u5mr = 1)
-#> svy$sex: 1
-#> 
-#>  Age Test (Children)
-#> 
-#> X-squared = 15.8496, df = 4, p = 0.0032
-#> 
-#> ------------------------------------------------------------ 
-#> svy$sex: 2
-#> 
-#>  Age Test (Children)
-#> 
-#> X-squared = 6.8429, df = 4, p = 0.1444
    -

    The test statistics should be interpreted with caution. A significant test result may, for example, be due to the use of an inappropriate model to generate expected numbers.

    +by(svy$age, svy$sex, ageChildren, u5mr = 1) +#> svy$sex: 1 +#> +#> Age Test (Children) +#> +#> X-squared = 15.8496, df = 4, p = 0.0032 +#> +#> ------------------------------------------------------------ +#> svy$sex: 2 +#> +#> Age Test (Children) +#> +#> X-squared = 6.8429, df = 4, p = 0.1444 +

    The test statistics should be interpreted with caution. A significant +test result may, for example, be due to the use of an inappropriate +model to generate expected numbers.

    A significant result in this particular test may be due to:

      -

    • Specifying an inappropriate under five years mortality rate: This is a particular problem because the specified under five years mortality rate is assumed to have applied for five years prior to data being collected.

      • -

    • The assumption of a 1:1 male to female sex ratio: This is a particular problem in setting in which there is sex-selective abortion and sex-selective infanticide.

      • +

    • Specifying an inappropriate under five years mortality +rate: This is a particular problem because the specified under +five years mortality rate is assumed to have applied for five years +prior to data being collected.

      • +

    • The assumption of a 1:1 male to female sex +ratio: This is a particular problem in setting in which there +is sex-selective abortion and sex-selective infanticide.

    -

    The model is crude. Mortality is related to age. Younger children have a greater mortality risk than older children and only an average under five years mortality rate is used. A more sophisticated model could be used but, in many settings, we will not have the data required to use such a model.

    -

    It should also be noted that the sample sizes used in most survey can cause tests to yield statistically significant results for small differences between observed and expected numbers.

    +

    The model is crude. Mortality is related to age. Younger children +have a greater mortality risk than older children and only an average +under five years mortality rate is used. A more sophisticated model +could be used but, in many settings, we will not have the data required +to use such a model.

    +

    It should also be noted that the sample sizes used in most survey can +cause tests to yield statistically significant results for small +differences between observed and expected numbers.

    -
    -

    -Use of census data

    -

    The use of simple demographic models is far from ideal. It is usually better to calculate the expected proportions from census data. A useful source of census data is the United States Census Bureau’s International Data Base:

    -

    https://www.census.gov/data-tools/demo/idb/informationGateway.php

    -

    The population in single year age-groups for 0, 1, 2, 3, and 4 years for Afghanistan in 2015 was:

    - +
    +

    Use of census data +

    +

    The use of simple demographic models is far from ideal. It is usually +better to calculate the expected proportions from census data. A useful +source of census data is the United +States Census Bureau’s International Data Base:

    +

    https://www.census.gov/data-tools/demo/idb/informationGateway.php

    +

    The population in single year age-groups for 0, 1, 2, 3, and 4 years +for Afghanistan in 2015 was:

    +
    Age @@ -559,358 +631,438 @@

    We can calculate expected values from these data:

    -pop <- c(1148379, 1062635, 1015688, 981288, 950875) 
-ep <- pop / sum(pop)
    -

    With a sample size of \(n = 900\) the expected number in each age-group would be:

    +pop <- c(1148379, 1062635, 1015688, 981288, 950875) +ep <- pop / sum(pop)
    +

    With a sample size of \(n = 900\) +the expected number in each age-group would be:

    -expected <- ep * 900
-expected
-#> [1] 200.3427 185.3841 177.1939 171.1925 165.8868
    -

    These expected values can be used in a Chi-squared test as is illustrated above.

    -

    Census data may also be used to estimate the under five years’ mortality rate (U5MR) which can then be used with the ageChildren() function.

    -

    The model of exponential decay in a population in which births and deaths balance each other with a 1:1 male to female sex ratio:

    +expected <- ep * 900 +expected +#> [1] 200.3427 185.3841 177.1939 171.1925 165.8868 +

    These expected values can be used in a Chi-squared test as is +illustrated above.

    +

    Census data may also be used to estimate the under five years’ +mortality rate (U5MR) which can then be used with the +ageChildren() function.

    +

    The model of exponential decay in a population in which births and +deaths balance each other with a 1:1 male to female sex ratio:

    \[ p ~ = ~ e ^ {-zt} \]

    -

    means that we can, given an age-distribution, estimate mortality by fitting the model:

    -

    \[ \log_e(n) ~ = ~ \alpha ~ + ~ \beta t \]

    -

    where \(n\) is the count of children in each age-group.

    -

    The absolute value of the β coefficient is the point estimate of the mortality rate (z). Using the 2015 population data for Afghanistan:

    +

    means that we can, given an age-distribution, estimate mortality by +fitting the model:

    +

    \[ \log_e(n) ~ = ~ \alpha ~ + ~ \beta t +\]

    +

    where \(n\) is the count of children +in each age-group.

    +

    The absolute value of the β coefficient is the point estimate of the +mortality rate (z). Using the 2015 population data for Afghanistan:

    -t <- 0:4 
-lm(log(pop) ~ t)
    +t <- 0:4 +lm(log(pop) ~ t)

    This gives:

    -
    #> 
-#> Call:
-#> lm(formula = log(pop) ~ t)
-#> 
-#> Coefficients:
-#> (Intercept)            t  
-#>    13.93601     -0.04571
    -

    The value reported under t is the \(\beta\) coefficient (-0.04571). The absolute value of the \(\beta\) coefficient (i.e. the value without the sign) is 0.04571. This is the point estimate of the mortality rate. Expressed as the number of deaths / 10,000 persons / day:

    +
    #> 
+#> Call:
+#> lm(formula = log(pop) ~ t)
+#> 
+#> Coefficients:
+#> (Intercept)            t  
+#>    13.93601     -0.04571
    +

    The value reported under t is the \(\beta\) coefficient +(-0.04571). The absolute value of the \(\beta\) coefficient (i.e. the value without +the sign) is 0.04571. This is the point estimate of the +mortality rate. Expressed as the number of deaths / 10,000 persons / +day:

    -(0.04571 / 365.25) * 10000
    +(0.04571 / 365.25) * 10000

    this is:

    -
    #> [1] 1.251472
    -

    We can use this estimate with the ageChildren() function:

    +
    #> [1] 1.251472
    +

    We can use this estimate with the ageChildren() +function:

    -ageChildren(svy$age, u5mr = 1.251472)
-#> 
-#>  Age Test (Children)
-#> 
-#> X-squared = 20.4744, df = 4, p = 0.0004
    -
    -

    -The age ratio

    -

    A much simpler and less problematic age-related test of survey and data quality is the age ratio test.

    +ageChildren(svy$age, u5mr = 1.251472) +#> +#> Age Test (Children) +#> +#> X-squared = 20.4744, df = 4, p = 0.0004
    +
    +

    The age ratio +

    +

    A much simpler and less problematic age-related test of survey and +data quality is the age ratio test.

    The age ratio is defined as:

    -

    \[ \text{Age ratio} ~ = ~ \frac{\text{number of children aged between 6 and 29 months}}{\text{number of children aged between 30 and 59 months}} \]

    -

    We will use the recode() function from NiPN data quality toolkit to create the relevant age-groups:

    +

    \[ \text{Age ratio} ~ = ~ +\frac{\text{number of children aged between 6 and 29 +months}}{\text{number of children aged between 30 and 59 months}} +\]

    +

    We will use the recode() function from NiPN data quality +toolkit to create the relevant age-groups:

    -svy$ageGroup <- recode(svy$age, "6:29=1; 30:59=2")
-head(svy)
-#>   psu age sex weight height muac oedema ycag ageLabel ageCuts ageGroup
-#> 1   1   6   1    7.3   65.0  146      2    1     6:17  (0,17]        1
-#> 2   1  42   2   12.5   89.5  156      2    4    42:53 (41,53]        2
-#> 3   1  23   1   10.6   78.1  149      2    2    18:29 (17,29]        1
-#> 4   1  18   1   12.8   81.5  160      2    2    18:29 (17,29]        1
-#> 5   1  52   1   12.1   87.3  152      2    4    42:53 (41,53]        2
-#> 6   1  36   2   16.9   93.0  190      2    3    30:41 (29,41]        2
    +svy$ageGroup <- recode(svy$age, "6:29=1; 30:59=2") +head(svy) +#> psu age sex weight height muac oedema ycag ageLabel ageCuts ageGroup +#> 1 1 6 1 7.3 65.0 146 2 1 6:17 (0,17] 1 +#> 2 1 42 2 12.5 89.5 156 2 4 42:53 (41,53] 2 +#> 3 1 23 1 10.6 78.1 149 2 2 18:29 (17,29] 1 +#> 4 1 18 1 12.8 81.5 160 2 2 18:29 (17,29] 1 +#> 5 1 52 1 12.1 87.3 152 2 4 42:53 (41,53] 2 +#> 6 1 36 2 16.9 93.0 190 2 3 30:41 (29,41] 2

    The observed age ratio is:

    -sum(svy$ageGroup == 1) / sum(svy$ageGroup == 2)
    +sum(svy$ageGroup == 1) / sum(svy$ageGroup == 2)

    which gives:

    -
    #> [1] 0.8653846
    -

    It is often easier to work with proportions than with ratios so we only need to calculate the proportion in the younger age-group:

    +
    #> [1] 0.8653846
    +

    It is often easier to work with proportions than with ratios so we +only need to calculate the proportion in the younger age-group:

    -sum(svy$ageGroup == 1) / sum(table(svy$ageGroup))
    +sum(svy$ageGroup == 1) / sum(table(svy$ageGroup))

    which gives:

    -
    #> [1] 0.4639175
    -

    We can calculate an expected value using census data or a simple demographic model. The simplest approach is to use a standard value. SMART surveys often use the ratio 0.85:1.

    -

    We only need to calculate the expected proportion in the younger group. For the ratio 0.85:1 this is:

    +
    #> [1] 0.4639175
    +

    We can calculate an expected value using census data or a simple +demographic model. The simplest approach is to use a standard value. +SMART surveys often use the ratio 0.85:1.

    +

    We only need to calculate the expected proportion in the younger +group. For the ratio 0.85:1 this is:

    -p <- 0.85 / (0.85 + 1)
    +p <- 0.85 / (0.85 + 1)

    This gives:

    -
    #> [1] 0.4594595
    -

    The observed proportion (0.4639175) and expected proportion (0.4594595) are so similar that a formal test of statistical significance is not required in this case.

    +
    #> [1] 0.4594595
    +

    The observed proportion (0.4639175) and expected proportion +(0.4594595) are so similar that a formal test of statistical +significance is not required in this case.

    Formal testing can be done using a Chi-squared test:

    -prop.test(sum(svy$ageGroup == 1), sum(table(svy$ageGroup)), p = 0.4594595)
    +prop.test(sum(svy$ageGroup == 1), sum(table(svy$ageGroup)), p = 0.4594595)

    This returns:

    -
    #> 
-#>  1-sample proportions test with continuity correction
-#> 
-#> data:  sum(svy$ageGroup == 1) out of sum(table(svy$ageGroup)), null probability 0.4594595
-#> X-squared = 0.053062, df = 1, p-value = 0.8178
-#> alternative hypothesis: true p is not equal to 0.4594595
-#> 95 percent confidence interval:
-#>  0.4304994 0.4976573
-#> sample estimates:
-#>         p 
-#> 0.4639175
    -

    The age ratio in the example data is not significantly different from the expected age ratio.

    -

    The NiPN data quality toolkit provide an R function called ageRatioTest() that performs the age ratio test:

    +
    #> 
+#>  1-sample proportions test with continuity correction
+#> 
+#> data:  sum(svy$ageGroup == 1) out of sum(table(svy$ageGroup)), null probability 0.4594595
+#> X-squared = 0.053062, df = 1, p-value = 0.8178
+#> alternative hypothesis: true p is not equal to 0.4594595
+#> 95 percent confidence interval:
+#>  0.4304994 0.4976573
+#> sample estimates:
+#>         p 
+#> 0.4639175
    +

    The age ratio in the example data is not significantly different from +the expected age ratio.

    +

    The NiPN data quality toolkit provide an R function called +ageRatioTest() that performs the age ratio test:

    -ageRatioTest(svy$age, ratio = 0.85)
    +ageRatioTest(svy$age, ratio = 0.85)

    This returns:

    -
    #> 
-#>      Age Ratio Test (children's data)
-#> 
-#>                     Expected age ratio = 0.8500
-#> Expected proportion aged 6 - 29 months = 0.4595
-#> 
-#>                     Observed age ratio = 0.8654
-#> Observed proportion aged 6 - 29 months = 0.4639
-#> 
-#> X-squared = 0.0531, p = 0.8178
    -

    The ratio parameter of the ageRatioTest() function allows you to specify an expected age ratio other than 0.85:1.

    -

    Note that the ageRatioTest() function applies the test to data from children aged between 6 and 59 months only (all other ages are ignored).

    -

    The age ratio test might be applied to data from both sexes (as above) and to each sex separately:

    +
    #> 
+#>      Age Ratio Test (children's data)
+#> 
+#>                     Expected age ratio = 0.8500
+#> Expected proportion aged 6 - 29 months = 0.4595
+#> 
+#>                     Observed age ratio = 0.8654
+#> Observed proportion aged 6 - 29 months = 0.4639
+#> 
+#> X-squared = 0.0531, p = 0.8178
    +

    The ratio parameter of the ageRatioTest() +function allows you to specify an expected age ratio other than +0.85:1.

    +

    Note that the ageRatioTest() function applies the test +to data from children aged between 6 and 59 months only (all other ages +are ignored).

    +

    The age ratio test might be applied to data from both sexes (as +above) and to each sex separately:

    -by(svy$age, svy$sex, ageRatioTest, ratio = 0.85)
-#> svy$sex: 1
-#> 
-#>      Age Ratio Test (children's data)
-#> 
-#>                     Expected age ratio = 0.8500
-#> Expected proportion aged 6 - 29 months = 0.4595
-#> 
-#>                     Observed age ratio = 0.8638
-#> Observed proportion aged 6 - 29 months = 0.4635
-#> 
-#> X-squared = 0.0145, p = 0.9041
-#> 
-#> ------------------------------------------------------------ 
-#> svy$sex: 2
-#> 
-#>      Age Ratio Test (children's data)
-#> 
-#>                     Expected age ratio = 0.8500
-#> Expected proportion aged 6 - 29 months = 0.4595
-#> 
-#>                     Observed age ratio = 0.8670
-#> Observed proportion aged 6 - 29 months = 0.4644
-#> 
-#> X-squared = 0.0247, p = 0.8750
    -

    The example data meets expectations regarding the age ratio for all children and for male and female children separately.

    +by(svy$age, svy$sex, ageRatioTest, ratio = 0.85) +#> svy$sex: 1 +#> +#> Age Ratio Test (children's data) +#> +#> Expected age ratio = 0.8500 +#> Expected proportion aged 6 - 29 months = 0.4595 +#> +#> Observed age ratio = 0.8638 +#> Observed proportion aged 6 - 29 months = 0.4635 +#> +#> X-squared = 0.0145, p = 0.9041 +#> +#> ------------------------------------------------------------ +#> svy$sex: 2 +#> +#> Age Ratio Test (children's data) +#> +#> Expected age ratio = 0.8500 +#> Expected proportion aged 6 - 29 months = 0.4595 +#> +#> Observed age ratio = 0.8670 +#> Observed proportion aged 6 - 29 months = 0.4644 +#> +#> X-squared = 0.0247, p = 0.8750 +

    The example data meets expectations regarding the age ratio for all +children and for male and female children separately.

    -
    -

    -Age and sex distributions : Adults and general population surveys

    -

    A key test of survey quality is whether the survey data represents the population in terms of the age and sex distribution. We can test this by comparison with census data.

    +
    +

    Age and sex distributions : Adults and general population +surveys +

    +

    A key test of survey quality is whether the survey data represents +the population in terms of the age and sex distribution. We can test +this by comparison with census data.

    We will retrieve some example data:

    -svy <- read.table("as.ex01.csv", header = TRUE, sep = ",") 
-head(svy)
    -
    #>   age sex
-#> 1  44   2
-#> 2   1   2
-#> 3  15   2
-#> 4   7   1
-#> 5  14   1
-#> 6  14   1
    -

    These data are taken from household rosters collected as part of a household survey in Tanzania. We will use census data taken from the Wolfram|Alpha knowledge engine:

    -

    http://www.wolframalpha.com/input/?i=Tanzania+age+distribution

    -

    Another useful source of census data is the United States Census Bureau’s International Data Base:

    -

    https://www.census.gov/data-tools/demo/idb/informationGateway.php

    -

    The pyramid plot produced by Wolfram|Alpha is shown in the figure below.

    -

    -

    The table produced by Wolfram|Alpha was downloaded and stored in a CSV file:

    +svy <- read.table("as.ex01.csv", header = TRUE, sep = ",") +head(svy)
    +
    #>   age sex
+#> 1  44   2
+#> 2   1   2
+#> 3  15   2
+#> 4   7   1
+#> 5  14   1
+#> 6  14   1
    +

    These data are taken from household rosters collected as part of a +household survey in Tanzania. We will use census data taken from the +Wolfram|Alpha knowledge engine:

    +

    http://www.wolframalpha.com/input/?i=Tanzania+age+distribution

    +

    Another useful source of census data is the United States Census +Bureau’s International Data Base:

    +

    https://www.census.gov/data-tools/demo/idb/informationGateway.php

    +

    The pyramid plot produced by Wolfram|Alpha is shown in the figure +below.

    +

    +

    The table produced by Wolfram|Alpha was downloaded and stored in a +CSV file:

    -ref <- read.table("as.ex02.csv", header = TRUE, sep = ",")
-ref
    -
    #>         age   Males Females     All
-#> 1     [0,5) 4043000 3969000 8012000
-#> 2    [5,10) 3336000 3284000 6620000
-#> 3   [10,15) 2775000 2742000 5517000
-#> 4   [15,20) 2386000 2372000 4758000
-#> 5   [20,25) 2076000 2073000 4149000
-#> 6   [25,30) 1753000 1750000 3503000
-#> 7   [30,35) 1453000 1432000 2885000
-#> 8   [35,40) 1142000 1099000 2241000
-#> 9   [40,45)  873000  846000 1719000
-#> 10  [45,50)  673000  699000 1372000
-#> 11  [50,55)  538000  601000 1139000
-#> 12  [55,60)  433000  503000  936000
-#> 13  [60,65)  357000  426000  783000
-#> 14  [65,70)  266000  319000  585000
-#> 15  [70,75)  182000  222000  404000
-#> 16  [75,80)  108000  137000  245000
-#> 17  [80,85)   51000   68000  119000
-#> 18  [85,90)   17000   25000   42000
-#> 19  [90,95)    3000    6000    9000
-#> 20 [95,100)       0    1000    1000
    -

    The age-groups are expressed using the form specified in ISO 31-11, an international standard that applies to mathematical symbols. The form [a,b) expresses the interval \(a ≤ x < b\). For example, [30,35) is used to indicate the set {30, 31, 32, 33, 34} of ages in years. The form [a,b) is said to be closed on the left and open on the right.

    -

    The reference data (ref) uses five-year age-groups. We will create the same age-groups in the example dataset.

    +ref <- read.table("as.ex02.csv", header = TRUE, sep = ",") +ref
    +
    #>         age   Males Females     All
+#> 1     [0,5) 4043000 3969000 8012000
+#> 2    [5,10) 3336000 3284000 6620000
+#> 3   [10,15) 2775000 2742000 5517000
+#> 4   [15,20) 2386000 2372000 4758000
+#> 5   [20,25) 2076000 2073000 4149000
+#> 6   [25,30) 1753000 1750000 3503000
+#> 7   [30,35) 1453000 1432000 2885000
+#> 8   [35,40) 1142000 1099000 2241000
+#> 9   [40,45)  873000  846000 1719000
+#> 10  [45,50)  673000  699000 1372000
+#> 11  [50,55)  538000  601000 1139000
+#> 12  [55,60)  433000  503000  936000
+#> 13  [60,65)  357000  426000  783000
+#> 14  [65,70)  266000  319000  585000
+#> 15  [70,75)  182000  222000  404000
+#> 16  [75,80)  108000  137000  245000
+#> 17  [80,85)   51000   68000  119000
+#> 18  [85,90)   17000   25000   42000
+#> 19  [90,95)    3000    6000    9000
+#> 20 [95,100)       0    1000    1000
    +

    The age-groups are expressed using the form specified in ISO 31-11, +an international standard that applies to mathematical symbols. The form +[a,b) expresses the interval \(a ≤ x < +b\). For example, [30,35) is used to indicate the set {30, 31, +32, 33, 34} of ages in years. The form [a,b) is said to be +closed on the left and open on the right.

    +

    The reference data (ref) uses five-year age-groups. We +will create the same age-groups in the example dataset.

    We should first check the range of ages in the example data:

    -range(svy$age)
    +range(svy$age)

    which returns:

    -
    #> [1]  0 93
    -

    The R language provides a function that makes it easy to create ISO 31-11 groupings from raw data:

    +
    #> [1]  0 93
    +

    The R language provides a function that makes it easy to create ISO +31-11 groupings from raw data:

    -svy$ageGroup <-cut(svy$age, 
-                   breaks = seq(from = 0, to = 95, by = 5),
-                   include.lowest = TRUE, right = FALSE)
    -

    Using include.lowest = TRUE tells the cut() function to include the lowest breaks value (zero in this case). Using right = FALSE tells the cut() function to use groupings that are closed on the left. This combination of parameters creates the same “closed on the left” and “open on the right” age-groups as are used in the reference (ref) data:

    +svy$ageGroup <-cut(svy$age, + breaks = seq(from = 0, to = 95, by = 5), + include.lowest = TRUE, right = FALSE) +

    Using include.lowest = TRUE tells the cut() +function to include the lowest breaks value (zero in this case). Using +right = FALSE tells the cut() function to use +groupings that are closed on the left. This combination of parameters +creates the same “closed on the left” and “open on the right” age-groups +as are used in the reference (ref) data:

    -table(svy$ageGroup)
-#> 
-#>   [0,5)  [5,10) [10,15) [15,20) [20,25) [25,30) [30,35) [35,40) [40,45) [45,50) 
-#>    1598    1268    1072     808     870     575     580     385     424     258 
-#> [50,55) [55,60) [60,65) [65,70) [70,75) [75,80) [80,85) [85,90) [90,95] 
-#>     284     128     165      82      98      51      60      18      12
    +table(svy$ageGroup) +#> +#> [0,5) [5,10) [10,15) [15,20) [20,25) [25,30) [30,35) [35,40) [40,45) [45,50) +#> 1598 1268 1072 808 870 575 580 385 424 258 +#> [50,55) [55,60) [60,65) [65,70) [70,75) [75,80) [80,85) [85,90) [90,95] +#> 284 128 165 82 98 51 60 18 12

    A tabular analysis of age-group by sex can be produced using:

    -table(svy$ageGroup, svy$sex)
-#>          
-#>             1   2
-#>   [0,5)   821 777
-#>   [5,10)  637 631
-#>   [10,15) 547 525
-#>   [15,20) 389 419
-#>   [20,25) 342 528
-#>   [25,30) 343 232
-#>   [30,35) 250 330
-#>   [35,40) 177 208
-#>   [40,45) 206 218
-#>   [45,50) 125 133
-#>   [50,55) 162 122
-#>   [55,60)  70  58
-#>   [60,65)  87  78
-#>   [65,70)  33  49
-#>   [70,75)  47  51
-#>   [75,80)  22  29
-#>   [80,85)  24  36
-#>   [85,90)  10   8
-#>   [90,95]   1  11
    +table(svy$ageGroup, svy$sex) +#> +#> 1 2 +#> [0,5) 821 777 +#> [5,10) 637 631 +#> [10,15) 547 525 +#> [15,20) 389 419 +#> [20,25) 342 528 +#> [25,30) 343 232 +#> [30,35) 250 330 +#> [35,40) 177 208 +#> [40,45) 206 218 +#> [45,50) 125 133 +#> [50,55) 162 122 +#> [55,60) 70 58 +#> [60,65) 87 78 +#> [65,70) 33 49 +#> [70,75) 47 51 +#> [75,80) 22 29 +#> [80,85) 24 36 +#> [85,90) 10 8 +#> [90,95] 1 11

    A visual inspection is useful:

    -pyramid.plot(svy$ageGroup, svy$sex)
    +pyramid.plot(svy$ageGroup, svy$sex)

    We can make this easier to read:

    -pyramid.plot(svy$ageGroup, 
-             svy$sex, 
-             main = "Age-group by sex",
-             xlab = "Number (Males | Females)", 
-             ylab = "", 
-             las = 1, 
-             cex.names = 0.9)
    +pyramid.plot(svy$ageGroup, + svy$sex, + main = "Age-group by sex", + xlab = "Number (Males | Females)", + ylab = "", + las = 1, + cex.names = 0.9)

    -

    Note that we specified ylab = "" because it is clear that the category labels represent age-groups and to prevent the y-axis label from obscuring the category labels, which happens with:

    +

    Note that we specified ylab = "" because it is clear +that the category labels represent age-groups and to prevent the y-axis +label from obscuring the category labels, which happens with:

    -pyramid.plot(svy$ageGroup, 
-             svy$sex, 
-             main = "Age-group by sex",
-             xlab = "Number (Males | Females)", 
-             ylab = "Age-group", 
-             las = 1,
-             cex.names = 0.9)
    +pyramid.plot(svy$ageGroup, + svy$sex, + main = "Age-group by sex", + xlab = "Number (Males | Females)", + ylab = "Age-group", + las = 1, + cex.names = 0.9)

    -

    It is possible to alter the number of lines of text in margins of the plot, reduce the size of the age-group labels, and place the y-axis label on a specific line in the left margin of the plot in order to make a clearer plot:

    +

    It is possible to alter the number of lines of text in margins of the +plot, reduce the size of the age-group labels, and place the y-axis +label on a specific line in the left margin of the plot in order to make +a clearer plot:

    -par(mar = c(5, 5, 4, 2))
-
-pyramid.plot(svy$ageGroup, 
-             svy$sex, 
-             main = "Age-group by sex",
-             xlab = "Number (Males | Females)", 
-             ylab = "", 
-             las = 1, 
-             cex.names = 0.8)
-
-title(ylab = "Age-group", line = 4)
    +par(mar = c(5, 5, 4, 2)) + +pyramid.plot(svy$ageGroup, + svy$sex, + main = "Age-group by sex", + xlab = "Number (Males | Females)", + ylab = "", + las = 1, + cex.names = 0.8) + +title(ylab = "Age-group", line = 4)

    -

    The easiest way of checking whether the survey data represents the general population in terms of the age and sex distribution is to compare the observed (figure on right) and expected (figure on left) distributions.

    -

    -

    The general shapes of the two distributions are similar. Some of the lumpiness in figure on the right is due to age heaping in the adult ages at decades and half-decades:

    +

    The easiest way of checking whether the survey data represents the +general population in terms of the age and sex distribution is to +compare the observed (figure on right) and expected (figure on left) +distributions.

    +

    +

    The general shapes of the two distributions are similar. Some of the +lumpiness in figure on the right is due to age heaping in the adult ages +at decades and half-decades:

    -ah <- ageHeaping(svy$age, divisor = 10) 
-plot(ah, main = "Remainder of age / 10")
    +ah <- ageHeaping(svy$age, divisor = 10) +plot(ah, main = "Remainder of age / 10")

    -

    A more formal test of the age structure can be made by comparing observed and expected numbers. We can do this graphically:

    +

    A more formal test of the age structure can be made by comparing +observed and expected numbers. We can do this graphically:

    -ref <- ref[1:19, ]
-
-expectedProportions <- ref$All / sum(ref$All)
-expectedNumbers <- expectedProportions * sum(table(svy$ageGroup))
-
-mp <- barplot(table(svy$ageGroup), 
-              main = "Observed and expected numbers", 
-              ylim = c(0, max(expectedNumbers)), 
-              las = 2)
-
-lines(mp, expectedNumbers, lty = 2, lwd = 2)
    +ref <- ref[1:19, ] + +expectedProportions <- ref$All / sum(ref$All) +expectedNumbers <- expectedProportions * sum(table(svy$ageGroup)) + +mp <- barplot(table(svy$ageGroup), + main = "Observed and expected numbers", + ylim = c(0, max(expectedNumbers)), + las = 2) + +lines(mp, expectedNumbers, lty = 2, lwd = 2)

    -

    The observed and expected numbers are similar to each other. The lumpiness in the observed numbers is due to age heaping. See Figure ASA04.

    +

    The observed and expected numbers are similar to each other. The +lumpiness in the observed numbers is due to age heaping. See Figure +ASA04.

    Formal testing can be performed:

    -chisq.test(table(svy$ageGroup), 
-           p = expectedProportions)
    +chisq.test(table(svy$ageGroup), + p = expectedProportions)

    This gives:

    -
    #> Warning in chisq.test(table(svy$ageGroup), p = expectedProportions): Chi-squared
-#> approximation may be incorrect
-#> 
-#>  Chi-squared test for given probabilities
-#> 
-#> data:  table(svy$ageGroup)
-#> X-squared = 248.41, df = 18, p-value < 2.2e-16
    -

    The warning is due to small expected numbers (i.e. n < 5) in the older age-groups. R provides a more robust “Monte Carlo” test:

    +
    #> Warning in chisq.test(table(svy$ageGroup), p = expectedProportions): Chi-squared
+#> approximation may be incorrect
+#> 
+#>  Chi-squared test for given probabilities
+#> 
+#> data:  table(svy$ageGroup)
+#> X-squared = 248.41, df = 18, p-value < 2.2e-16
    +

    The warning is due to small expected numbers (i.e. n < 5) in the +older age-groups. R provides a more robust “Monte Carlo” test:

    -chisq.test(table(svy$ageGroup), 
-           p = expectedProportions, 
-           simulate.p.value = TRUE)
    +chisq.test(table(svy$ageGroup), + p = expectedProportions, + simulate.p.value = TRUE)

    This may take a few seconds to compute and yields:

    -
    #> 
-#>  Chi-squared test for given probabilities with simulated p-value (based
-#>  on 2000 replicates)
-#> 
-#> data:  table(svy$ageGroup)
-#> X-squared = 248.41, df = NA, p-value = 0.0004998
    -

    The test results need to be interpreted with caution. The sample size (\(n = 8736\)) is large in this example. This means that small differences, which may be due to age heaping, become statistically significant. This test cannot be considered to be good evidence that the age-structure of the sample differs from the expected age-structure of the population. We also need to examine the sex ratio of the sample. A sex ratio test can be performed using the sexRatioTest() function from the NiPN data quality toolkit and the sex ratio observed in the census data:

    +
    #> 
+#>  Chi-squared test for given probabilities with simulated p-value (based
+#>  on 2000 replicates)
+#> 
+#> data:  table(svy$ageGroup)
+#> X-squared = 248.41, df = NA, p-value = 0.0004998
    +

    The test results need to be interpreted with caution. The sample size +(\(n = 8736\)) is large in this +example. This means that small differences, which may be due to age +heaping, become statistically significant. This test cannot be +considered to be good evidence that the age-structure of the sample +differs from the expected age-structure of the population. We also need +to examine the sex ratio of the sample. A sex ratio test can be +performed using the sexRatioTest() function from the NiPN +data quality toolkit and the sex ratio observed in the census data:

    -censusM <- sum(ref$Males)
-censusF <- sum(ref$Females)
-
-sexRatioTest(svy$sex, 
-             codes = c(1, 2), 
-             pop = c(censusM, censusF))
    +censusM <- sum(ref$Males) +censusF <- sum(ref$Females) + +sexRatioTest(svy$sex, + codes = c(1, 2), + pop = c(censusM, censusF))

    This yields:

    -
    #> 
-#>  Sex Ratio Test
-#> 
-#> Expected proportion male = 0.4988
-#> Observed proportion male = 0.4914
-#> X-squared = 1.8770, p = 0.1707
    -

    There is no evidence that the sex ratio in the sample differs much from the expected sex ratio in the population.

    -

    The techniques outlined in this section are illustrative. This is because many surveys, other than nutritional anthropometry surveys in young children, are not standardised. A survey may sample only women of child-bearing age. The sample may be restricted to women aged between 15 and 45 years.

    -

    In this case the age-structure can be examined using the techniques outlined above but it would make no sense to examine the sex ratio. Care should be taken when examining data from surveys that may have deliberately oversampled specific age-groups.

    +
    #> 
+#>  Sex Ratio Test
+#> 
+#> Expected proportion male = 0.4988
+#> Observed proportion male = 0.4914
+#> X-squared = 1.8770, p = 0.1707
    +

    There is no evidence that the sex ratio in the sample differs much +from the expected sex ratio in the population.

    +

    The techniques outlined in this section are illustrative. This is +because many surveys, other than nutritional anthropometry surveys in +young children, are not standardised. A survey may sample only women of +child-bearing age. The sample may be restricted to women aged between 15 +and 45 years.

    +

    In this case the age-structure can be examined using the techniques +outlined above but it would make no sense to examine the sex ratio. Care +should be taken when examining data from surveys that may have +deliberately oversampled specific age-groups.

    - - - - + -
    -

    Developed by Mark Myatt, Ernest Guevarra.

    +
    +

    +

    Developed by Mark Myatt, Ernest Guevarra.

    -
    -

    Site built with pkgdown 1.6.1.

    +
    +

    +

    Site built with pkgdown 2.0.7.

    -
    +
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    -
    Age heaping
    -
    -
    Age and sex distributions
    -
    -
    Digit preference
    -
    -
    Identifying outliers using flags
    -
    -
    The NiPN data quality toolkit
    -
    -
    Checking ranges and legal values
    -
    -
    Using scatterplots to identify outliers
    -
    -
    Sex ratio
    -
    - - - +
    Distributions of variables and indices
    +
    +
    Age heaping
    +
    +
    Age and sex distributions
    +
    +
    Digit preference
    +
    +
    Identifying outliers using flags
    +
    +
    The NiPN data quality toolkit
    +
    +
    Checking ranges and legal values
    +
    +
    Using scatterplots to identify outliers
    +
    +
    Sex ratio
    +
    +
    + + + +
    +

    Developed by Mark Myatt, Ernest Guevarra.

    - -
    -
    -

    Developed by Mark Myatt, Ernest Guevarra.

    +
    +

    Site built with pkgdown 2.0.7.

    -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    - +
    + - - - + diff --git a/docs/articles/nipnTK.html b/docs/articles/nipnTK.html index 5e987c4..b3c68e5 100644 --- a/docs/articles/nipnTK.html +++ b/docs/articles/nipnTK.html @@ -4,7 +4,8 @@ - + + The NiPN data quality toolkit • nipnTK @@ -12,189 +13,177 @@ - - - + + + - - + + - -
    -
    -
    -
    -
    -

    The NiPN data quality toolkit

    + + +
    +
    +

    The NiPN data quality toolkit

    - Source: vignettes/nipnTK.Rmd - - + Source: vignettes/nipnTK.Rmd +
    nipnTK.Rmd
    -
    -

    -Introduction

    -

    This document presents a set of practical analytical methods that can be applied to variables in datasets to assess their quality. An index of data quality that both describes and scores the quality of the data is also presented.

    -

    The focus of this toolkit is on data required to assess anthropometric status such as measurements of weight, height or length, MUAC, sex and age.

    -

    The focus is on anthropometric status but many of presented methods could be applied to other variables. NiPN may commission additional toolkits to examine other variables or other types of variables.

    +
    +

    Introduction +

    +

    This document presents a set of practical analytical methods that can +be applied to variables in datasets to assess their quality. An index of +data quality that both describes and scores the quality of the data is +also presented.

    +

    The focus of this toolkit is on data required to assess +anthropometric status such as measurements of weight, height or length, +MUAC, sex and age.

    +

    The focus is on anthropometric status but many of presented methods +could be applied to other variables. NiPN may commission additional +toolkits to examine other variables or other types of variables.

    Data quality is assessed by:

      -

    • Range checks and value checks to identify univariate outliers.

      • -

    • Scatterplots and statistical methods to identify bivariate outliers.

      • -

    • Use of flags to identify outliers in anthropometric indices.

      • -

    • Examining the distribution and the statistics of the distribution of measurements and anthropometric indices.

      • -

    • Assessing the extent of digit preference in recorded measurements.

      • +

    • Range checks and value checks to identify univariate +outliers.

      • +

    • Scatterplots and statistical methods to identify bivariate +outliers.

      • +

    • Use of flags to identify outliers in anthropometric +indices.

      • +

    • Examining the distribution and the statistics of the distribution +of measurements and anthropometric indices.

      • +

    • Assessing the extent of digit preference in recorded +measurements.

    • Assessing the extent of age heaping in recorded ages.

    • Examining the sex ratio.

      • -

    • Examining age distributions and age by sex distributions.

      • +

    • Examining age distributions and age by sex +distributions.

    -

    These activities and a proposed order in which they should be performed are shown in the figure below.

    +

    These activities and a proposed order in which they should be +performed are shown in the figure below.

    -NiPN data quality workflow

    +NiPN data quality workflow

    NiPN data quality workflow

    -

    The material is intended to provide a practical or “hands on” introduction to assessing data quality and is presented as a series of computer-based exercises. Example datasets are provided.

    -

    Extensive use is made of the R language and environment for statistical computing. This is a free and powerful data analysis system. Methods have been described in sufficient detail to allow activities to be performed using other data analysis systems.

    -

    R provides a very extensive language for working with data. The material presented here has been written using only a small subset of the R language. Many of the data quality activities are supported by R functions that have been written specifically for this purpose. These simplify the assessment of the quality of data related to anthropometry and anthropometric indices. The basic R functions, the purpose written functions, and the filenames of example datasets are also shown in the figure above.

    +

    The material is intended to provide a practical or “hands on” +introduction to assessing data quality and is presented as a series of +computer-based exercises. Example datasets are provided.

    +

    Extensive use is made of the R language and environment for +statistical computing. This is a free and powerful data analysis system. +Methods have been described in sufficient detail to allow activities to +be performed using other data analysis systems.

    +

    R provides a very extensive language for working with data. The +material presented here has been written using only a small subset of +the R language. Many of the data quality activities are supported by R +functions that have been written specifically for this purpose. These +simplify the assessment of the quality of data related to anthropometry +and anthropometric indices. The basic R functions, the purpose written +functions, and the filenames of example datasets are also shown in the +figure above.

    The purpose written functions are described in detail here.

    -
    - - - +
    -
    -

    Developed by Mark Myatt, Ernest Guevarra.

    +
    +

    +

    Developed by Mark Myatt, Ernest Guevarra.

    -
    -

    Site built with pkgdown 1.6.1.

    +
    +

    +

    Site built with pkgdown 2.0.7.

    -
    +
    + diff --git a/docs/articles/rl.html b/docs/articles/rl.html index 41593b7..e3880f7 100644 --- a/docs/articles/rl.html +++ b/docs/articles/rl.html @@ -4,7 +4,8 @@ - + + Checking ranges and legal values • nipnTK @@ -12,396 +13,426 @@ - - - + + + - - + + - -
    -
    -
    -
    -
    -

    Checking ranges and legal values

    -

    Mark Myatt

    +
    +
    +

    Checking ranges and legal values

    +

    Mark Myatt

    - Source: vignettes/rl.Rmd - - + Source: vignettes/rl.Rmd +
    rl.Rmd
    -

    Checking that data are within an acceptable or plausible range is an important basic check to apply to quantitative data. Checking that data are recorded with appropriate legal values or codes is an important basic check to apply to categorical data.

    -
    -

    -Checking quantitative data

    -

    We will use the dataset rl.ex01 that is included in the nipnTK package.

    +

    Checking that data are within an acceptable or plausible range is an +important basic check to apply to quantitative data. Checking that data +are recorded with appropriate legal values or codes is an important +basic check to apply to categorical data.

    +
    +

    Checking quantitative data +

    +

    We will use the dataset rl.ex01 that is included in the +nipnTK package.

    -svy <- rl.ex01
-head(svy)
    -
    #>   age sex weight height muac oedema
-#> 1  12   2    6.7   68.5  148      2
-#> 2   6   1    6.4   65.0  125      2
-#> 3   6   2    6.5   65.6  125      2
-#> 4   8   1    7.2   68.4  144      2
-#> 5  12   M    6.1   65.4  114      2
-#> 6   8   1    7.7   66.5  146      2
    -

    The rl.ex01 dataset contains anthropometry data from a SMART survey from Angola.

    -

    We can use the summary() function to examine range (and other summary statistics) of a quantitative variable:

    +svy <- rl.ex01 +head(svy)
    +
    #>   age sex weight height muac oedema
+#> 1  12   2    6.7   68.5  148      2
+#> 2   6   1    6.4   65.0  125      2
+#> 3   6   2    6.5   65.6  125      2
+#> 4   8   1    7.2   68.4  144      2
+#> 5  12   M    6.1   65.4  114      2
+#> 6   8   1    7.7   66.5  146      2
    +

    The rl.ex01 dataset contains anthropometry data from a +SMART survey from Angola.

    +

    We can use the summary() function to examine range (and +other summary statistics) of a quantitative variable:

    -summary(svy$muac)
    +summary(svy$muac)

    This returns:

    -
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>    11.1   128.0   139.0   140.3   148.0   999.0
    +
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#>    11.1   128.0   139.0   140.3   148.0   999.0

    A graphical examination can also be made:

    -boxplot(svy$muac, horizontal = TRUE, xlab = "MUAC (mm)", frame.plot = FALSE)
    +boxplot(svy$muac, horizontal = TRUE, xlab = "MUAC (mm)", frame.plot = FALSE)

    -

    The “whiskers” on the boxplot extend to 1.5 times the interquartile range from the ends of the box (i.e., the lower and upper quartiles). This is known as the inner fence. Data points that are outside the inner fence are considered to be mild outliers. The NiPN data quality toolkit provides an R language function outliersUV() that uses the same method to identify outliers:

    +

    The “whiskers” on the boxplot extend to 1.5 times the interquartile +range from the ends of the box (i.e., the lower and upper quartiles). +This is known as the inner fence. Data points that are outside +the inner fence are considered to be mild outliers. The NiPN +data quality toolkit provides an R language function +outliersUV() that uses the same method to identify +outliers:

    -svy[outliersUV(svy$muac), ]
    +svy[outliersUV(svy$muac), ]

    This returns:

    -
    #> 
-#> Univariate outliers : Lower fence = 98, Upper fence = 178
-#>     age sex weight height  muac oedema
-#> 33   24   1    9.8   74.5 180.0      2
-#> 93   12   2    6.7   67.0  96.0      1
-#> 126  16   2    9.0   74.6 999.0      2
-#> 135  18   2    8.5   74.5 999.0      2
-#> 194  24   M    7.0   75.0  95.0      2
-#> 227   8   M    6.2   66.0  11.1      2
-#> 253  35   2    7.6   75.6  97.0      2
-#> 381  24   1   10.8   82.8  12.4      2
-#> 501  36   2   15.5   93.4 185.0      2
-#> 594  21   2    9.8   76.5  13.2      2
-#> 714  59   2   18.9   98.5 180.0      2
-#> 752  48   2   15.6  102.2 999.0      2
-#> 756  59   1   19.4  101.1 180.0      2
-#> 873  59   1   20.6  109.4 179.0      2
    +
    #> 
+#> Univariate outliers : Lower fence = 98, Upper fence = 178
+#>     age sex weight height  muac oedema
+#> 33   24   1    9.8   74.5 180.0      2
+#> 93   12   2    6.7   67.0  96.0      1
+#> 126  16   2    9.0   74.6 999.0      2
+#> 135  18   2    8.5   74.5 999.0      2
+#> 194  24   M    7.0   75.0  95.0      2
+#> 227   8   M    6.2   66.0  11.1      2
+#> 253  35   2    7.6   75.6  97.0      2
+#> 381  24   1   10.8   82.8  12.4      2
+#> 501  36   2   15.5   93.4 185.0      2
+#> 594  21   2    9.8   76.5  13.2      2
+#> 714  59   2   18.9   98.5 180.0      2
+#> 752  48   2   15.6  102.2 999.0      2
+#> 756  59   1   19.4  101.1 180.0      2
+#> 873  59   1   20.6  109.4 179.0      2

    We can count the number of outliers or use:

    -table(outliersUV(svy$muac))
    +table(outliersUV(svy$muac))

    This returns:

    -
    #> 
-#> Univariate outliers : Lower fence = 98, Upper fence = 178
-#> 
-#> FALSE  TRUE 
-#>   892    14
    +
    #> 
+#> Univariate outliers : Lower fence = 98, Upper fence = 178
+#> 
+#> FALSE  TRUE 
+#>   892    14

    We can express this as a proportion:

    +prop.table(table(outliersUV(svy$muac)))

    This returns:

    -
    #> 
-#> Univariate outliers : Lower fence = 98, Upper fence = 178
-#> 
-#>      FALSE       TRUE 
-#> 0.98454746 0.01545254
    +
    #> 
+#> Univariate outliers : Lower fence = 98, Upper fence = 178
+#> 
+#>      FALSE       TRUE 
+#> 0.98454746 0.01545254

    You may find it easier to use percentages:

    -prop.table(table(outliersUV(svy$muac))) * 100
    +prop.table(table(outliersUV(svy$muac))) * 100

    This returns:

    -
    #> 
-#> Univariate outliers : Lower fence = 98, Upper fence = 178
-#> 
-#>     FALSE      TRUE 
-#> 98.454746  1.545254
    -

    Some of the muac values identified as potential outliers are possible muac values:

    -
    #> 
-#> Univariate outliers : Lower fence = 98, Upper fence = 178
-#>     age sex weight height  muac oedema
-#> 33   24   1    9.8   74.5 180.0      2
-#> 93   12   2    6.7   67.0  96.0      1
-#> 126  16   2    9.0   74.6 999.0      2
-#> 135  18   2    8.5   74.5 999.0      2
-#> 194  24   M    7.0   75.0  95.0      2
-#> 227   8   M    6.2   66.0  11.1      2
-#> 253  35   2    7.6   75.6  97.0      2
-#> 381  24   1   10.8   82.8  12.4      2
-#> 501  36   2   15.5   93.4 185.0      2
-#> 594  21   2    9.8   76.5  13.2      2
-#> 714  59   2   18.9   98.5 180.0      2
-#> 752  48   2   15.6  102.2 999.0      2
-#> 756  59   1   19.4  101.1 180.0      2
-#> 873  59   1   20.6  109.4 179.0      2
    -

    The outliersUV() function provides a fence parameter which alters the threshold at which a data point is considered to be an outlier.

    -

    The default fence = 1.5 defines the inner fence (i.e 1.5 times the interquartile range below the lower quartile and above the upper quartile). This will identify mild and severe outliers.

    -

    The value fence = 3 defines the outer fence (i.e 3 times the interquartile range below the lower quartile and above the upper quartile). This will identify severe outliers only:

    +
    #> 
+#> Univariate outliers : Lower fence = 98, Upper fence = 178
+#> 
+#>     FALSE      TRUE 
+#> 98.454746  1.545254
    +

    Some of the muac values identified as potential +outliers are possible muac values:

    +
    #> 
+#> Univariate outliers : Lower fence = 98, Upper fence = 178
+#>     age sex weight height  muac oedema
+#> 33   24   1    9.8   74.5 180.0      2
+#> 93   12   2    6.7   67.0  96.0      1
+#> 126  16   2    9.0   74.6 999.0      2
+#> 135  18   2    8.5   74.5 999.0      2
+#> 194  24   M    7.0   75.0  95.0      2
+#> 227   8   M    6.2   66.0  11.1      2
+#> 253  35   2    7.6   75.6  97.0      2
+#> 381  24   1   10.8   82.8  12.4      2
+#> 501  36   2   15.5   93.4 185.0      2
+#> 594  21   2    9.8   76.5  13.2      2
+#> 714  59   2   18.9   98.5 180.0      2
+#> 752  48   2   15.6  102.2 999.0      2
+#> 756  59   1   19.4  101.1 180.0      2
+#> 873  59   1   20.6  109.4 179.0      2
    +

    The outliersUV() function provides a +fence parameter which alters the threshold at which a +data point is considered to be an outlier.

    +

    The default fence = 1.5 defines the inner +fence (i.e 1.5 times the interquartile range below +the lower quartile and above the upper quartile). This will identify +mild and severe outliers.

    +

    The value fence = 3 defines the outer fence +(i.e 3 times the interquartile range below the lower +quartile and above the upper quartile). This will identify +severe outliers only:

    -svy[outliersUV(svy$muac, fence = 3), ]
    +svy[outliersUV(svy$muac, fence = 3), ]

    This returns:

    -
    #> 
-#> Univariate outliers : Lower fence = 68, Upper fence = 208
-#>     age sex weight height  muac oedema
-#> 126  16   2    9.0   74.6 999.0      2
-#> 135  18   2    8.5   74.5 999.0      2
-#> 227   8   M    6.2   66.0  11.1      2
-#> 381  24   1   10.8   82.8  12.4      2
-#> 594  21   2    9.8   76.5  13.2      2
-#> 752  48   2   15.6  102.2 999.0      2
    -

    There is something wrong with all of these values of muac.

    -

    The intention was that the muac variable records mid-upper-arm-circumference (MUAC) in mm. There are some impossibly small (i.e. 11.1, 12.4, and 13.2) and impossibly large values (i.e. 999.0).

    -

    The three impossibly small values are probably due to data being recorded in cm rather than mm. It is probably safe to change these three values to 111, 124 and 132. It is easiest to do this each record separately:

    +
    #> 
+#> Univariate outliers : Lower fence = 68, Upper fence = 208
+#>     age sex weight height  muac oedema
+#> 126  16   2    9.0   74.6 999.0      2
+#> 135  18   2    8.5   74.5 999.0      2
+#> 227   8   M    6.2   66.0  11.1      2
+#> 381  24   1   10.8   82.8  12.4      2
+#> 594  21   2    9.8   76.5  13.2      2
+#> 752  48   2   15.6  102.2 999.0      2
    +

    There is something wrong with all of these values of +muac.

    +

    The intention was that the muac variable records +mid-upper-arm-circumference (MUAC) in mm. There are some impossibly +small (i.e. 11.1, 12.4, and +13.2) and impossibly large values +(i.e. 999.0).

    +

    The three impossibly small values are probably due to data being +recorded in cm rather than mm. It is probably safe to change these three +values to 111, 124 and 132. It is easiest to do this each record +separately:

    -svy$muac[svy$muac == 11.1] <- 111
    -

    An alternative approach is to specify row numbers instead of values:

    +svy$muac[svy$muac == 11.1] <- 111
    +

    An alternative approach is to specify row numbers instead of +values:

    -svy$muac[381] <- 124
-svy$muac[594] <- 132
    -

    The three 999.0 values are missing values coded as 999.0. It is safe to set these three values to missing using the special NA value:

    +svy$muac[381] <- 124 +svy$muac[594] <- 132
    +

    The three 999.0 values are missing values coded as +999.0. It is safe to set these three values to missing using the special +NA value:

    -svy$muac[svy$muac == 999.00] <- NA
    -

    Range checks should be repeated after editing the data to ensure that the problems have been fixed:

    +svy$muac[svy$muac == 999.00] <- NA +

    Range checks should be repeated after editing the data to ensure that +the problems have been fixed:

    -summary(svy$muac)
-svy[outliersUV(svy$muac), ]
-svy[outliersUV(svy$muac, fence = 3), ]
    -

    Following is a boxplot of the muac variable made using:

    +summary(svy$muac) +svy[outliersUV(svy$muac), ] +svy[outliersUV(svy$muac, fence = 3), ] +

    Following is a boxplot of the muac variable made +using:

    -boxplot(svy$muac, horizontal = TRUE, xlab = "MUAC (mm)", frame.plot = FALSE)
    -

    after the fixes for incorrectly entered data and missing values were made.

    +boxplot(svy$muac, horizontal = TRUE, xlab = "MUAC (mm)", frame.plot = FALSE) +

    after the fixes for incorrectly entered data and missing values were +made.

    There should now be no severe outliers:

    -prop.table(table(outliersUV(svy$muac, fence = 3))) * 100
    +prop.table(table(outliersUV(svy$muac, fence = 3))) * 100

    returns:

    -
    #> 
-#> Univariate outliers : Lower fence = 68, Upper fence = 208
-#> 
-#> FALSE 
-#>   100
    -

    It is usually better to identify and edit only the most extreme univariate outliers, as we have done here, and use the scatterplot and statistical distance methods described elsewhere in this toolkit to identify other potential outliers.

    +
    #> 
+#> Univariate outliers : Lower fence = 68, Upper fence = 208
+#> 
+#> FALSE 
+#>   100
    +

    It is usually better to identify and edit only the most extreme +univariate outliers, as we have done here, and use the +scatterplot and statistical distance methods described elsewhere in this +toolkit to identify other potential outliers.

    -
    -

    -Editing data

    -

    We have edited records with outliers at the R command line.

    -

    It is a good idea to edit data at the command line or using a script containing the required commands.

    +
    +

    Editing data +

    +

    We have edited records with outliers at the R command +line.

    +

    It is a good idea to edit data at the command line or using a script +containing the required commands.

    A script provides a record of changes made to the data.

    -

    R also keeps a record of whatever you do at the command line in a “history file”. The history file is a plain text file which is usually called .Rhistory and stored in your home directory.

    +

    R also keeps a record of whatever you do at the command line +in a “history file”. The history file is a plain text file which is +usually called .Rhistory and stored in your home directory.

    Some regulatory authorities require you to keep a history file.

    -

    Some publications may require you to provide a “reproducible data analysis”. This could be an edited and annotated copy of your history file.

    -

    The edit() function provides a basic tool for editing data interactively.

    -

    Editing data using the edit() function is typically a three stage process:

    +

    Some publications may require you to provide a “reproducible data +analysis”. This could be an edited and annotated copy of your history +file.

    +

    The edit() function provides a basic tool for editing +data interactively.

    +

    Editing data using the edit() function is typically a +three stage process:

      -

    1. Create a new object containing only the data that requires editing.

      2. -

    2. Use the edit() function to edit data in the new object closing the data editor window when you are finished.

      3. +

    3. Create a new object containing only the data that requires +editing.

      4. +

    4. Use the edit() function to edit data in the new +object closing the data editor window when you are finished.

    5. Replace the old records with the edited records.

    We will try this using a separate copy of the example data:

    -x <- rl.ex01
-records2update <- x[outliersUV(x$muac, fence = 3), ]
-records2update <- edit(records2update)
-x[row.names(records2update), ] <- records2update
    +x <- rl.ex01 +records2update <- x[outliersUV(x$muac, fence = 3), ] +records2update <- edit(records2update) +x[row.names(records2update), ] <- records2update

    We can check that the edits have been made using:

    -
    #> 
-#> Univariate outliers : Lower fence = 68, Upper fence = 208
    +
    #> 
+#> Univariate outliers : Lower fence = 68, Upper fence = 208
    -x[outliersUV(x$muac, fence = 3), ]
    +x[outliersUV(x$muac, fence = 3), ]

    If you have fixed the problems in the data this should return:

    -
    #> 
-#> Univariate outliers : Lower fence = 68, Upper fence = 208
-#>     age sex weight height  muac oedema
-#> 126  16   2    9.0   74.6 999.0      2
-#> 135  18   2    8.5   74.5 999.0      2
-#> 227   8   M    6.2   66.0  11.1      2
-#> 381  24   1   10.8   82.8  12.4      2
-#> 594  21   2    9.8   76.5  13.2      2
-#> 752  48   2   15.6  102.2 999.0      2
    -

    The edit() function works differently on different operating systems and with different graphical user interfaces. If you are using RStudio or RAnalyticFlow on OS X you will need to install XQuartz if you want to use the edit() function. XQuartz is available from:

    -

    https://www.xquartz.org/index.html

    +
    #> 
+#> Univariate outliers : Lower fence = 68, Upper fence = 208
+#>     age sex weight height  muac oedema
+#> 126  16   2    9.0   74.6 999.0      2
+#> 135  18   2    8.5   74.5 999.0      2
+#> 227   8   M    6.2   66.0  11.1      2
+#> 381  24   1   10.8   82.8  12.4      2
+#> 594  21   2    9.8   76.5  13.2      2
+#> 752  48   2   15.6  102.2 999.0      2
    +

    The edit() function works differently on different +operating systems and with different graphical user interfaces. If you +are using RStudio or RAnalyticFlow on OS X you will +need to install XQuartz if you want to use the +edit() function. XQuartz is available from:

    +

    https://www.xquartz.org/index.html

    -
    -

    -Checking categorical variables

    -

    We can use the table() function to examine the codes used in categorical variables. For example:

    +
    +

    Checking categorical variables +

    +

    We can use the table() function to examine the codes +used in categorical variables. For example:

    -table(svy$sex)
    +table(svy$sex)

    returns:

    -
    #> 
-#>   1   2   3   F   M 
-#> 404 458   1  24  19
    -

    The intention was that the sex variable was coded using 1 for male and 2 for female but in a small number of records the codes M for male and F for female have been used. A mixed coding scheme like this will complicate data-management and data-analysis. Data in the sex variable should be edited to ensure that consistent coding is used:

    +
    #> 
+#>   1   2   3   F   M 
+#> 404 458   1  24  19
    +

    The intention was that the sex variable was coded +using 1 for male and 2 for female but in a small number of records the +codes M for male and F for female have +been used. A mixed coding scheme like this will complicate +data-management and data-analysis. Data in the sex variable should be +edited to ensure that consistent coding is used:

    -svy$sex[svy$sex == "M"] <- 1
-svy$sex[svy$sex == "F"] <- 2
    -

    You may find that a few records contain meaningless codes. The code 3 in the example dataset has, very probably, no meaning and is likely to be a simple data entry error. This record should be checked and corrected, if possible. If the record cannot be corrected then the sex variable should be set to missing:

    +svy$sex[svy$sex == "M"] <- 1 +svy$sex[svy$sex == "F"] <- 2
    +

    You may find that a few records contain meaningless codes. The code +3 in the example dataset has, very probably, no meaning +and is likely to be a simple data entry error. This record should be +checked and corrected, if possible. If the record cannot be corrected +then the sex variable should be set to missing:

    -svy$sex[svy$sex == 3] <- NA
    -

    Legal value checks should be repeated after editing to ensure that problems have been fixed:

    +svy$sex[svy$sex == 3] <- NA +

    Legal value checks should be repeated after editing to ensure that +problems have been fixed:

    -table(svy$sex)
    +table(svy$sex)

    now returns:

    -
    #> 
-#>   1   2   3   F   M 
-#> 423 482   0   0   0
    -

    The table contains cells for the values M, F, and 3 because R imported the variable as a categorical or “factor” variable:

    +
    #> 
+#>   1   2   3   F   M 
+#> 423 482   0   0   0
    +

    The table contains cells for the values M, +F, and 3 because R imported +the variable as a categorical or “factor” variable:

    -str(svy)
    +str(svy)

    returns:

    -
    #> 'data.frame':    906 obs. of  6 variables:
-#>  $ age   : int  12 6 6 8 12 8 18 9 12 12 ...
-#>  $ sex   : Factor w/ 5 levels "1","2","3","F",..: 2 1 2 1 1 1 1 1 2 1 ...
-#>  $ weight: num  6.7 6.4 6.5 7.2 6.1 7.7 6.4 7.8 7.5 6.5 ...
-#>  $ height: num  68.5 65 65.6 68.4 65.4 66.5 66.7 65.3 69.1 70.3 ...
-#>  $ muac  : num  148 125 125 144 114 146 119 140 138 121 ...
-#>  $ oedema: int  2 2 2 2 2 2 2 2 2 2 ...
    +
    #> 'data.frame':    906 obs. of  6 variables:
+#>  $ age   : int  12 6 6 8 12 8 18 9 12 12 ...
+#>  $ sex   : Factor w/ 5 levels "1","2","3","F",..: 2 1 2 1 1 1 1 1 2 1 ...
+#>  $ weight: num  6.7 6.4 6.5 7.2 6.1 7.7 6.4 7.8 7.5 6.5 ...
+#>  $ height: num  68.5 65 65.6 68.4 65.4 66.5 66.7 65.3 69.1 70.3 ...
+#>  $ muac  : num  148 125 125 144 114 146 119 140 138 121 ...
+#>  $ oedema: int  2 2 2 2 2 2 2 2 2 2 ...

    We can fix this by redefining the levels of the sex variable:

    -levels(svy$sex) <- c("1", "2", NA, NA, NA)
-table(svy$sex)
-#> 
-#>   1   2 
-#> 423 482
    +levels(svy$sex) <- c("1", "2", NA, NA, NA) +table(svy$sex) +#> +#> 1 2 +#> 423 482 -
    -

    -Saving changes

    +
    +

    Saving changes +

    We have edited some data.

    We usually want to save changes.

    -

    It is simple to save a dataset in a comma-separated-value (CSV) text file using the write.table() function:

    +

    It is simple to save a dataset in a comma-separated-value (CSV) text +file using the write.table() function:

    -write.table(x = svy, file = "rl.ex01.clean.csv", sep = ",", quote = FALSE, 
-            row.names = FALSE, fileEncoding = "ASCII")
    -

    R can work with a variety of files format but it is usually simplest to work with simple text files.

    +write.table(x = svy, file = "rl.ex01.clean.csv", sep = ",", quote = FALSE, + row.names = FALSE, fileEncoding = "ASCII")
    +

    R can work with a variety of files format but it is usually +simplest to work with simple text files.

    - - - - + -
    -

    Developed by Mark Myatt, Ernest Guevarra.

    +
    +

    +

    Developed by Mark Myatt, Ernest Guevarra.

    -
    -

    Site built with pkgdown 1.6.1.

    +
    +

    +

    Site built with pkgdown 2.0.7.

    -
    +
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    -
    -
    -
    -
    -

    Using scatterplots to identify outliers

    -

    Mark Myatt

    +
    +
    +

    Using scatterplots to identify outliers

    +

    Mark Myatt

    - Source: vignettes/sp.Rmd - - + Source: vignettes/sp.Rmd +
    sp.Rmd
    -

    We can expect anthropometric variables in children to be strongly and positively associated with each other. This is because children tend to gain both weight and height as they grow. This allows us to use graphical and numeric methods to identify outliers (i.e. observations that are distant from most other observations) that may be due to errors.

    -

    It is important to note that anthropometric surveys often use a method of comparing observed values against reference values using a process known as “flagging” to identify and censor outliers. The methods outlined in this section are intended to complement rather than replace the “flagging”

    -
    -

    -Identifying outliers by observation

    +

    We can expect anthropometric variables in children to be strongly and +positively associated with each other. This is because children tend to +gain both weight and height as they grow. This allows us to use +graphical and numeric methods to identify outliers (i.e. observations +that are distant from most other observations) that may be due to +errors.

    +

    It is important to note that anthropometric surveys often use a +method of comparing observed values against reference values using a +process known as “flagging” to identify and censor outliers. The methods +outlined in this section are intended to complement rather than replace +the “flagging”

    +
    +

    Identifying outliers by observation +

    We will use the dataset sp.ex01:

    -svy <- sp.ex01
-head(svy)
-#>   age sex weight height muac oedema
-#> 1  54   1   20.5  111.5  180      2
-#> 2  53   1   19.3  108.0  167      2
-#> 3  51   2   19.3  106.0  163      2
-#> 4  44   1   18.9  111.0  163      2
-#> 5  47   1   18.8  103.0  173      2
-#> 6  48   2   18.6   95.3  171      1
    -

    The dataset sp.ex01 contains anthropometric data from a SMART survey from the Democratic Republic of Congo.

    -

    We will look at the relationship between height and weight in this dataset:

    +svy <- sp.ex01 +head(svy) +#> age sex weight height muac oedema +#> 1 54 1 20.5 111.5 180 2 +#> 2 53 1 19.3 108.0 167 2 +#> 3 51 2 19.3 106.0 163 2 +#> 4 44 1 18.9 111.0 163 2 +#> 5 47 1 18.8 103.0 173 2 +#> 6 48 2 18.6 95.3 171 1
    +

    The dataset sp.ex01 contains anthropometric data from a +SMART survey from the Democratic Republic of Congo.

    +

    We will look at the relationship between height and weight in this +dataset:

    -plot(svy$height, svy$weight)
    +plot(svy$height, svy$weight)

    The resulting plot is shown below.

    -

    There is a clear positive linear relationship between height and weight (i.e. weight increases with increasing height along a straight line). We can assess the strength of this relationship using the Pearson correlation coefficient:

    +

    There is a clear positive linear relationship between height and +weight (i.e. weight increases with increasing height along a straight +line). We can assess the strength of this relationship using the Pearson +correlation coefficient:

    -cor(svy$height, svy$weight, method = "pearson", use = "complete.obs")
    +cor(svy$height, svy$weight, method = "pearson", use = "complete.obs")

    which returns:

    -
    #> [1] 0.9204116
    -

    This is very close to one, which indicates a perfect positive association. There are, however a few points that lie outside of the bulk of the plotted points. These outliers may be due to errors in the data.

    -

    The presence of oedema can be associated with increased weight. This is a particular issue with severe oedema. An outlier with a high value of weight for a given height could be due to oedema. We can check this:

    +
    #> [1] 0.9204116
    +

    This is very close to one, which indicates a perfect positive +association. There are, however a few points that lie outside of the +bulk of the plotted points. These outliers may be due to errors in the +data.

    +

    The presence of oedema can be associated with increased weight. This +is a particular issue with severe oedema. An outlier with a high value +of weight for a given height could be due to oedema. We can check +this:

    -plot(svy$height, svy$weight, pch = ifelse(svy$oedema == 1, 19, 1))
    -

    The pch = ifelse(svy$oedema == 1, 19, 1) tells the plot() function to plot filled circles for oedema cases and open circles for children without oedema. The resulting plot is shown below.

    +plot(svy$height, svy$weight, pch = ifelse(svy$oedema == 1, 19, 1))
    +

    The pch = ifelse(svy$oedema == 1, 19, 1) tells the +plot() function to plot filled circles for oedema cases and +open circles for children without oedema. The resulting plot is shown +below.

    -

    A single high weight for height outlier appears to be due to the presence of oedema.

    -

    The other filled circles that are located in the main mass of plotted points show that children with oedema may have a body weight within the normal range for their height. These children may not be not wasted but they are suffering from a form of severe acute malnutrition (SAM) known as kwashiorkor.

    -

    Outliers can be identified by eye. The identify() function can help with this:

    +

    A single high weight for height outlier appears to be due to the +presence of oedema.

    +

    The other filled circles that are located in the main mass of plotted +points show that children with oedema may have a body weight within the +normal range for their height. These children may not be not wasted but +they are suffering from a form of severe acute malnutrition (SAM) known +as kwashiorkor.

    +

    Outliers can be identified by eye. The identify() +function can help with this:

    -plot(svy$height, svy$weight, pch = ifelse(svy$oedema == 1, 19, 1))
-identify(svy$height, svy$weight)
    -

    Clicking on any point will cause the record (row) number associated with each point to be displayed on the plot (as shown below).

    +plot(svy$height, svy$weight, pch = ifelse(svy$oedema == 1, 19, 1)) +identify(svy$height, svy$weight)
    +

    Clicking on any point will cause the record (row) number associated +with each point to be displayed on the plot (as shown below).

    -

    Right-clicking on the plot or pressing the “escape” key will stop identify().

    -

    The behaviour of the identify() function may be different when you use an alternative user interface for R such as RStudio or RAnalyticFlow.

    -

    The identify() function will, by default, display record (row) numbers for identified points. This is usually what is needed. Alternative labels can be displayed. For example:

    +

    Right-clicking on the plot or pressing the “escape” key will stop +identify().

    +

    The behaviour of the identify() function may be +different when you use an alternative user interface for R such as +RStudio or RAnalyticFlow.

    +

    The identify() function will, by default, display record +(row) numbers for identified points. This is usually what is needed. +Alternative labels can be displayed. For example:

    -plot(svy$height, svy$weight, pch = ifelse(svy$oedema == 1, 19, 1)) 
-identify(svy$height, svy$weight, 
-         labels = paste(svy$height, svy$weight, sep = ";"), 
-         cex = 0.75)
    +plot(svy$height, svy$weight, pch = ifelse(svy$oedema == 1, 19, 1)) +identify(svy$height, svy$weight, + labels = paste(svy$height, svy$weight, sep = ";"), + cex = 0.75)

    displays the height and weight values at selected points.

    -

    The ability to display custom labels is useful if there is a variable (column) in a dataset that contains unique record identifiers.

    -

    It is useful to be able to store the record (row) numbers of identified points:

    +

    The ability to display custom labels is useful if there is a variable +(column) in a dataset that contains unique record identifiers.

    +

    It is useful to be able to store the record (row) numbers of +identified points:

    -plot(svy$height, svy$weight, pch = ifelse(svy$oedema == 1, 19, 1)) 
-stored <- identify(svy$height, svy$weight)
    -

    If the same points shown in the previous figure are clicked to identify them then:

    +plot(svy$height, svy$weight, pch = ifelse(svy$oedema == 1, 19, 1)) +stored <- identify(svy$height, svy$weight)
    +

    If the same points shown in the previous figure are clicked to +identify them then:

    -stored
    +stored

    will return:

    -
    #> [1] "1"  "6"  "16" "62" "66"
    +
    #> [1] "1"  "6"  "16" "62" "66"

    We can examine the data for the identified points:

    -svy[stored, ]
    +svy[stored, ]

    This returns:

    -
    #>    age sex weight height muac oedema
-#> 1   54   1   20.5  111.5  180      2
-#> 6   48   2   18.6   95.3  171      1
-#> 16  30   1   16.9   92.5  188      2
-#> 62  55   1   15.1  118.0  156      2
-#> 66  56   1   15.0  115.0  148      2
    -

    The oedema data is coded 1 for present and 2 for absent.

    -

    Data can be checked and edited if needed. Note that record 6 is an oedema case and should probably be left alone.

    -

    If your dataset has many variables (columns) then you may specify only the variables (columns) of interest:

    +
    #>    age sex weight height muac oedema
+#> 1   54   1   20.5  111.5  180      2
+#> 6   48   2   18.6   95.3  171      1
+#> 16  30   1   16.9   92.5  188      2
+#> 62  55   1   15.1  118.0  156      2
+#> 66  56   1   15.0  115.0  148      2
    +

    The oedema data is coded 1 for +present and 2 for absent.

    +

    Data can be checked and edited if needed. Note that record +6 is an oedema case and should probably be left +alone.

    +

    If your dataset has many variables (columns) then you may specify +only the variables (columns) of interest:

    -svy[stored, c("weight", "height", "oedema")]
    +svy[stored, c("weight", "height", "oedema")]

    This returns:

    -
    #>    weight height oedema
-#> 1    20.5  111.5      2
-#> 6    18.6   95.3      1
-#> 16   16.9   92.5      2
-#> 62   15.1  118.0      2
-#> 66   15.0  115.0      2
    +
    #>    weight height oedema
+#> 1    20.5  111.5      2
+#> 6    18.6   95.3      1
+#> 16   16.9   92.5      2
+#> 62   15.1  118.0      2
+#> 66   15.0  115.0      2
    -
    -

    -Identifying outliers using statistical distance

    -

    A more formal method of identifying outliers is to use a measure of the statistical distance. A common measure of statistical distance that is applied to scatterplot data is the Mahalanobis distance. This treats the bivariate probability distribution as an ellipsoid. The Mahalanobis distance is the distance of a point from the centre of mass of the distribution divided by width of the ellipsoid in the direction of the point:

    -

    -

    In directions in which the ellipsoid has a short axis the test point must be close to the centre of mass of the distribution. In directions in which the ellipsoid has a long axis the test point may be more distant from the centre of mass of the distribution.

    -

    The NiPN data quality toolkit provides an R language function outliersMD() that uses the Mahalanobis distance to identify outliers in the same dataset:

    +
    +

    Identifying outliers using statistical distance +

    +

    A more formal method of identifying outliers is to use a measure of +the statistical distance. A common measure of statistical +distance that is applied to scatterplot data is the Mahalanobis +distance. This treats the bivariate probability distribution as an +ellipsoid. The Mahalanobis distance is the distance of a point from the +centre of mass of the distribution divided by width of the ellipsoid in +the direction of the point:

    +

    +

    In directions in which the ellipsoid has a short axis the test point +must be close to the centre of mass of the distribution. In directions +in which the ellipsoid has a long axis the test point may be more +distant from the centre of mass of the distribution.

    +

    The NiPN data quality toolkit provides an R language +function outliersMD() that uses the Mahalanobis distance to +identify outliers in the same dataset:

    -svy[outliersMD(svy$height, svy$weight), ]
    +svy[outliersMD(svy$height, svy$weight), ]

    This returns the same set of records that was identified by eye:

    -
    #>    age sex weight height muac oedema
-#> 1   54   1   20.5  111.5  180      2
-#> 6   48   2   18.6   95.3  171      1
-#> 16  30   1   16.9   92.5  188      2
-#> 62  55   1   15.1  118.0  156      2
-#> 66  56   1   15.0  115.0  148      2
    -

    Data can be checked and edited if needed. Note that record 6 is an oedema case and should probably be left alone.

    -

    We can use the outliersMD() to identify and display outliers on a scatterplot:

    +
    #>    age sex weight height muac oedema
+#> 1   54   1   20.5  111.5  180      2
+#> 6   48   2   18.6   95.3  171      1
+#> 16  30   1   16.9   92.5  188      2
+#> 62  55   1   15.1  118.0  156      2
+#> 66  56   1   15.0  115.0  148      2
    +

    Data can be checked and edited if needed. Note that record +6 is an oedema case and should probably be left +alone.

    +

    We can use the outliersMD() to identify and display +outliers on a scatterplot:

    -plot(svy$height, svy$weight,
-pch = ifelse(outliersMD(svy$height, svy$weight), 19, 1))
    +plot(svy$height, svy$weight, +pch = ifelse(outliersMD(svy$height, svy$weight), 19, 1))

    -

    The outliersMD() function has an alpha parameter. The default value for the alpha parameter is alpha = 0.001. This value is used automatically unless another value is specified.

    -

    When we use alpha = 0.001 we are looking for records with values so extreme that we would expect to find them with a probability of 0.001 when there are no problems with the data.

    -

    We can calculate the number of outliers that we expect to see by chance with alpha = 0.001 using:

    +

    The outliersMD() function has an alpha +parameter. The default value for the alpha parameter is +alpha = 0.001. This value is used automatically unless +another value is specified.

    +

    When we use alpha = 0.001 we are looking for records +with values so extreme that we would expect to find them with a +probability of 0.001 when there are no problems with the data.

    +

    We can calculate the number of outliers that we expect to see by +chance with alpha = 0.001 using:

    -round(nrow(svy) * 0.001)
    +round(nrow(svy) * 0.001)

    This returns:

    -
    #> [1] 1
    -

    We found five potential outliers. The difference between the number that we expected and the number that we observed (i.e. one expected vs. five observed) suggests that some of the identified outliers are true outliers or due to data errors.

    -

    Another way of looking at the alpha parameter is that it alters the sensitivity of the outlierMD() function for detecting outliers by altering the threshold distance that is used to define outliers. This can be useful when using the outlierMD() function with some, but not all, curvilinear relationships (see below).

    -

    Larger values of alpha will tend to detect more potential outliers. For example:

    +
    #> [1] 1
    +

    We found five potential outliers. The difference between the number +that we expected and the number that we observed (i.e. one expected +vs. five observed) suggests that some of the identified outliers are +true outliers or due to data errors.

    +

    Another way of looking at the alpha parameter is +that it alters the sensitivity of the outlierMD() function +for detecting outliers by altering the threshold distance that is used +to define outliers. This can be useful when using the +outlierMD() function with some, but not all, curvilinear +relationships (see below).

    +

    Larger values of alpha will tend to detect more +potential outliers. For example:

    -plot(svy$height, svy$weight,
-     pch = ifelse(outliersMD(svy$height, svy$weight, alpha = 0.01), 19, 1))
    +plot(svy$height, svy$weight, + pch = ifelse(outliersMD(svy$height, svy$weight, alpha = 0.01), 19, 1))

    and:

    -svy[outliersMD(svy$height,svy$weight, alpha = 0.01), ]
-#>     age sex weight height muac oedema
-#> 1    54   1   20.5  111.5  180      2
-#> 2    53   1   19.3  108.0  167      2
-#> 3    51   2   19.3  106.0  163      2
-#> 4    44   1   18.9  111.0  163      2
-#> 5    47   1   18.8  103.0  173      2
-#> 6    48   2   18.6   95.3  171      1
-#> 16   30   1   16.9   92.5  188      2
-#> 32   43   1   16.2   92.6  166      2
-#> 61   26   1   15.1   87.6  168      2
-#> 62   55   1   15.1  118.0  156      2
-#> 66   56   1   15.0  115.0  148      2
-#> 477  38   2   10.3   94.6  160      2
-#> 487  32   2   10.2   93.0  150      2
-#> 722  17   2    8.6   63.3  136      2
    -

    In almost all cases the default alpha = 0.001 will be appropriate.

    -

    The techniques outlined above can be used to examine the relationships between other pairs of anthropometric variables (e.g. weight and muac) and to identify outliers. All sensible pairings of variables should be examined.

    +svy[outliersMD(svy$height,svy$weight, alpha = 0.01), ] +#> age sex weight height muac oedema +#> 1 54 1 20.5 111.5 180 2 +#> 2 53 1 19.3 108.0 167 2 +#> 3 51 2 19.3 106.0 163 2 +#> 4 44 1 18.9 111.0 163 2 +#> 5 47 1 18.8 103.0 173 2 +#> 6 48 2 18.6 95.3 171 1 +#> 16 30 1 16.9 92.5 188 2 +#> 32 43 1 16.2 92.6 166 2 +#> 61 26 1 15.1 87.6 168 2 +#> 62 55 1 15.1 118.0 156 2 +#> 66 56 1 15.0 115.0 148 2 +#> 477 38 2 10.3 94.6 160 2 +#> 487 32 2 10.2 93.0 150 2 +#> 722 17 2 8.6 63.3 136 2 +

    In almost all cases the default alpha = 0.001 will +be appropriate.

    +

    The techniques outlined above can be used to examine the +relationships between other pairs of anthropometric variables +(e.g. weight and muac) and to identify +outliers. All sensible pairings of variables should be examined.

    -
    -

    -Anthropometric measurements and age

    -

    We also expect anthropometric variables to be associated with age. This relationship is particularly strong in children. It will be less strong in adults and may be weak or even reversed in older people.

    -

    We can explore the relationship between an anthropometric variable and age using the techniques described above. For example:

    +
    +

    Anthropometric measurements and age +

    +

    We also expect anthropometric variables to be associated with age. +This relationship is particularly strong in children. It will be less +strong in adults and may be weak or even reversed in older people.

    +

    We can explore the relationship between an anthropometric variable +and age using the techniques described above. For example:

    -plot(svy$age, svy$height, pch = ifelse(outliersMD(svy$age, svy$height), 19, 1)) 
-svy[outliersMD(svy$age, svy$height), ]
    +plot(svy$age, svy$height, pch = ifelse(outliersMD(svy$age, svy$height), 19, 1)) +svy[outliersMD(svy$age, svy$height), ]

    -
    #>    age sex weight height muac oedema
-#> 4   44   1   18.9    111  163      2
-#> 62  55   1   15.1    118  156      2
    -

    There are some problems with this approach. Age is often reported and recorded with considerable age heaping. Age is unlikely to be approximately normally distributed, which is an assumption of the Mahalanobis distance method. The relationship between anthropometric variables and age usually follows a “growth curve” rather than a straight line.

    -

    The combination of age heaping, non-normality, and a curvilinear relationship may reduce the effectiveness of the Mahalanobis distance method for detecting outliers. It may be useful, in such cases, to increase the value of the alpha parameter. For example:

    +
    #>    age sex weight height muac oedema
+#> 4   44   1   18.9    111  163      2
+#> 62  55   1   15.1    118  156      2
    +

    There are some problems with this approach. Age is often reported and +recorded with considerable age heaping. Age is unlikely to be +approximately normally distributed, which is an assumption of the +Mahalanobis distance method. The relationship between anthropometric +variables and age usually follows a “growth curve” rather than a +straight line.

    +

    The combination of age heaping, non-normality, and a curvilinear +relationship may reduce the effectiveness of the Mahalanobis distance +method for detecting outliers. It may be useful, in such cases, to +increase the value of the alpha parameter. For +example:

    -plot(svy$age, svy$height, pch = ifelse(outliersMD(svy$age, svy$height, alpha = 0.025), 19, 1))
    +plot(svy$age, svy$height, pch = ifelse(outliersMD(svy$age, svy$height, alpha = 0.025), 19, 1))

    -

    Outliers can be listed using the same value for alpha:

    +

    Outliers can be listed using the same value for +alpha:

    -svy[outliersMD(svy$age, svy$height, alpha = 0.025), ]
-#>     age sex weight height muac oedema
-#> 1    54   1   20.5  111.5  180      2
-#> 4    44   1   18.9  111.0  163      2
-#> 7    55   1   18.6  109.3  156      2
-#> 14   48   1   17.0  109.0  175      2
-#> 27   56   2   16.4  110.0  149      2
-#> 62   55   1   15.1  118.0  156      2
-#> 66   56   1   15.0  115.0  148      2
-#> 113  58   1   14.2   92.0  148      2
-#> 129  23   2   14.0   95.0  161      2
-#> 190  15   2   13.0   90.5  150      2
-#> 212  21   2   12.8   93.0  152      2
-#> 378  51   2   11.2   83.0  141      2
-#> 453  49   1   10.6   83.0  139      2
-#> 461  54   2   10.5   86.8  132      2
-#> 551  41   2    9.8   78.0  139      2
-#> 599  50   1    9.5   84.7  123      2
-#> 660  49   1    9.1   79.5  129      2
-#> 722  17   2    8.6   63.3  136      2
-#> 809  41   2    7.9   75.7  120      2
-#> 881  30   1    6.5   69.6  103      2
-#> 893  18   2    5.8   63.2  106      2
    -

    The Mahalanobis distance method is usually robust enough to deal with age data provided an appropriate value for alpha is used.

    +svy[outliersMD(svy$age, svy$height, alpha = 0.025), ] +#> age sex weight height muac oedema +#> 1 54 1 20.5 111.5 180 2 +#> 4 44 1 18.9 111.0 163 2 +#> 7 55 1 18.6 109.3 156 2 +#> 14 48 1 17.0 109.0 175 2 +#> 27 56 2 16.4 110.0 149 2 +#> 62 55 1 15.1 118.0 156 2 +#> 66 56 1 15.0 115.0 148 2 +#> 113 58 1 14.2 92.0 148 2 +#> 129 23 2 14.0 95.0 161 2 +#> 190 15 2 13.0 90.5 150 2 +#> 212 21 2 12.8 93.0 152 2 +#> 378 51 2 11.2 83.0 141 2 +#> 453 49 1 10.6 83.0 139 2 +#> 461 54 2 10.5 86.8 132 2 +#> 551 41 2 9.8 78.0 139 2 +#> 599 50 1 9.5 84.7 123 2 +#> 660 49 1 9.1 79.5 129 2 +#> 722 17 2 8.6 63.3 136 2 +#> 809 41 2 7.9 75.7 120 2 +#> 881 30 1 6.5 69.6 103 2 +#> 893 18 2 5.8 63.2 106 2 +

    The Mahalanobis distance method is usually robust enough to deal with +age data provided an appropriate value for alpha is +used.

    -
    -

    -Difficult relationships for the Mahalanobis distance method

    -

    The Mahalanobis distance method works well with pairs of variables as long as the relationship between the two variables is monotonic (i.e. one variables always increases or always decreases in value as the other variable increases in value). This is usually the case with anthropometric data.

    -

    We will explore the use of the Mahalanobis distance method with data that is not monotonic using generated data:

    +
    +

    Difficult relationships for the Mahalanobis distance method +

    +

    The Mahalanobis distance method works well with pairs of variables as +long as the relationship between the two variables is monotonic +(i.e. one variables always increases or always decreases in value as the +other variable increases in value). This is usually the case with +anthropometric data.

    +

    We will explore the use of the Mahalanobis distance method with data +that is not monotonic using generated data:

    -x <- c(4, 8, 16, 17, 22, 27, 38, 40, 47, 48, 53, 55, 63, 71, 76, 85, 92, 96) 
-y <- c(6, 22, 34, 42, 51, 59, 64, 69, 70, 20, 70, 63, 63, 55, 46, 33, 19, 6)
-plot(x, y)
    +x <- c(4, 8, 16, 17, 22, 27, 38, 40, 47, 48, 53, 55, 63, 71, 76, 85, 92, 96) +y <- c(6, 22, 34, 42, 51, 59, 64, 69, 70, 20, 70, 63, 63, 55, 46, 33, 19, 6) +plot(x, y)

    -

    There is a clear relationship between x and y but it is not a monotonic relationship (i.e. it is not always increasing or decreasing). There is a single obvious outlier. The Mahalanobis distance method will not work well with this data. This:

    +

    There is a clear relationship between x and +y but it is not a monotonic +relationship (i.e. it is not always increasing or decreasing). There is +a single obvious outlier. The Mahalanobis distance method will +not work well with this data. This:

    -plot(x, y, pch = ifelse(outliersMD(x, y), 19, 1))
    +plot(x, y, pch = ifelse(outliersMD(x, y), 19, 1))

    fails to detect the outlier. Relaxing the alpha parameter:

    -plot(x, y, pch = ifelse(outliersMD(x, y, alpha = 0.025), 19, 1))
    +plot(x, y, pch = ifelse(outliersMD(x, y, alpha = 0.025), 19, 1))

    does not help. Relaxing the alpha parameter further:

    -plot(x, y, pch = ifelse(outliersMD(x, y, alpha = 0.1), 19, 1))
    +plot(x, y, pch = ifelse(outliersMD(x, y, alpha = 0.1), 19, 1))

    -

    results in false positive results but fails to identify the clear outlier.

    -

    Although Mahalanobis distance cannot be used directly to identify outliers in non-monotonic relationships, it can be applied to residuals from fitted non-linear models. This technique is unlikely to be required with anthropometric data and is not covered in this toolkit.

    -

    It is very unlikely that you will see non-monotonic relationships with anthropometric data. You are likely to see “growth curves” that look like this:

    +

    results in false positive results but fails to identify the clear +outlier.

    +

    Although Mahalanobis distance cannot be used directly to identify +outliers in non-monotonic relationships, it can be applied to residuals +from fitted non-linear models. This technique is unlikely to be required +with anthropometric data and is not covered in this toolkit.

    +

    It is very unlikely that you will see non-monotonic relationships +with anthropometric data. You are likely to see “growth curves” that +look like this:

    -set.seed(0)
-x <- 0:100
-y <- 1 - exp(-x / 50) + rnorm(101, 0, 0.05) 
-plot(x, y)
-lines(x, 1 - exp(-x / 50), lty = 2)
    +set.seed(0) +x <- 0:100 +y <- 1 - exp(-x / 50) + rnorm(101, 0, 0.05) +plot(x, y) +lines(x, 1 - exp(-x / 50), lty = 2)

    -

    This is a monotonic relationship. The Mahalanobis distance method should work well with this data. If we add a clear outlier:

    +

    This is a monotonic relationship. The Mahalanobis distance method +should work well with this data. If we add a clear outlier:

    -y[50] <- 0.3
-plot(x, y)
    +y[50] <- 0.3 +plot(x, y)

    -

    this can be detected using the Mahalanobis distance method using a slightly relaxed alpha value:

    +

    this can be detected using the Mahalanobis distance method using a +slightly relaxed alpha value:

    -plot(x, y, pch = ifelse(outliersMD(x, y, alpha = 0.005), 19, 1))
    +plot(x, y, pch = ifelse(outliersMD(x, y, alpha = 0.005), 19, 1))

    -
    -

    -Working with data from older children

    -

    We will now look at using scatterplots and Mahalanobis distance methods with data from older children.

    +
    +

    Working with data from older children +

    +

    We will now look at using scatterplots and Mahalanobis distance +methods with data from older children.

    We will use the sp.ex02 dataset:

    -svy <- sp.ex02
-head(svy)
-#>   region school ageMonths sex weight height   haz   waz   baz
-#> 1      1      1        64   1   13.9   97.7 -3.12 -2.56 -0.56
-#> 2      1      1        72   1   21.1  118.7  0.56  0.21 -0.25
-#> 3      1      1        75   2   15.6  103.7 -2.47 -2.02 -0.53
-#> 4      1      1        75   2   16.0  102.7 -2.66 -1.82 -0.08
-#> 5      1      1        75   2   17.5  108.7 -1.51 -1.16 -0.31
-#> 6      1      1        79   1   15.0  101.0 -3.57 -2.99 -0.53
    -

    The dataset sp.ex02 contains anthropometric data from a survey of school-age (i.e. between 5 and 15 years) children from Pakistan.

    +svy <- sp.ex02 +head(svy) +#> region school ageMonths sex weight height haz waz baz +#> 1 1 1 64 1 13.9 97.7 -3.12 -2.56 -0.56 +#> 2 1 1 72 1 21.1 118.7 0.56 0.21 -0.25 +#> 3 1 1 75 2 15.6 103.7 -2.47 -2.02 -0.53 +#> 4 1 1 75 2 16.0 102.7 -2.66 -1.82 -0.08 +#> 5 1 1 75 2 17.5 108.7 -1.51 -1.16 -0.31 +#> 6 1 1 79 1 15.0 101.0 -3.57 -2.99 -0.53
    +

    The dataset sp.ex02 contains anthropometric data from a +survey of school-age (i.e. between 5 and 15 years) children from +Pakistan.

    We can summarise the dataset using:

    -summary(svy)
    +summary(svy)

    This returns:

    -
    #>      region          school        ageMonths          sex       
-#>  Min.   :1.000   Min.   : 1.00   Min.   : 60.0   Min.   :1.000  
-#>  1st Qu.:3.000   1st Qu.: 8.00   1st Qu.: 83.0   1st Qu.:1.000  
-#>  Median :4.000   Median :15.00   Median : 98.0   Median :1.000  
-#>  Mean   :4.491   Mean   :15.51   Mean   :104.8   Mean   :1.397  
-#>  3rd Qu.:7.000   3rd Qu.:23.00   3rd Qu.:124.0   3rd Qu.:2.000  
-#>  Max.   :8.000   Max.   :30.00   Max.   :178.0   Max.   :2.000  
-#>                                                                 
-#>      weight          height           haz              waz        
-#>  Min.   :10.30   Min.   : 86.2   Min.   :-5.730   Min.   :-5.350  
-#>  1st Qu.:17.20   1st Qu.:108.7   1st Qu.:-2.640   1st Qu.:-2.380  
-#>  Median :21.30   Median :120.9   Median :-1.790   Median :-1.615  
-#>  Mean   :22.62   Mean   :121.2   Mean   :-1.705   Mean   :-1.581  
-#>  3rd Qu.:27.00   3rd Qu.:132.6   3rd Qu.:-0.790   3rd Qu.:-0.805  
-#>  Max.   :51.90   Max.   :164.2   Max.   : 3.550   Max.   : 3.010  
-#>                                                   NA's   :267     
-#>       baz         
-#>  Min.   :-4.7000  
-#>  1st Qu.:-1.2900  
-#>  Median :-0.7600  
-#>  Mean   :-0.7758  
-#>  3rd Qu.:-0.2100  
-#>  Max.   : 1.9900  
-#>  NA's   :8
    -

    The baz variable contains the BMI-for-age z-score calculated from the ageMonths, sex, weight, and height variables using the WHO growth reference. A key thing to notice in the summary is the large number of missing values in the waz variable. This is because the weight-for-age z-score is not calculated for children aged older than 120 months. You can check this using:

    +
    #>      region          school        ageMonths          sex       
+#>  Min.   :1.000   Min.   : 1.00   Min.   : 60.0   Min.   :1.000  
+#>  1st Qu.:3.000   1st Qu.: 8.00   1st Qu.: 83.0   1st Qu.:1.000  
+#>  Median :4.000   Median :15.00   Median : 98.0   Median :1.000  
+#>  Mean   :4.491   Mean   :15.51   Mean   :104.8   Mean   :1.397  
+#>  3rd Qu.:7.000   3rd Qu.:23.00   3rd Qu.:124.0   3rd Qu.:2.000  
+#>  Max.   :8.000   Max.   :30.00   Max.   :178.0   Max.   :2.000  
+#>                                                                 
+#>      weight          height           haz              waz        
+#>  Min.   :10.30   Min.   : 86.2   Min.   :-5.730   Min.   :-5.350  
+#>  1st Qu.:17.20   1st Qu.:108.7   1st Qu.:-2.640   1st Qu.:-2.380  
+#>  Median :21.30   Median :120.9   Median :-1.790   Median :-1.615  
+#>  Mean   :22.62   Mean   :121.2   Mean   :-1.705   Mean   :-1.581  
+#>  3rd Qu.:27.00   3rd Qu.:132.6   3rd Qu.:-0.790   3rd Qu.:-0.805  
+#>  Max.   :51.90   Max.   :164.2   Max.   : 3.550   Max.   : 3.010  
+#>                                                   NA's   :267     
+#>       baz         
+#>  Min.   :-4.7000  
+#>  1st Qu.:-1.2900  
+#>  Median :-0.7600  
+#>  Mean   :-0.7758  
+#>  3rd Qu.:-0.2100  
+#>  Max.   : 1.9900  
+#>  NA's   :8
    +

    The baz variable contains the BMI-for-age z-score +calculated from the ageMonths, sex, +weight, and height variables using the +WHO growth reference. A key thing to notice in the summary is the large +number of missing values in the waz variable. This is +because the weight-for-age z-score is not calculated for children aged +older than 120 months. You can check this using:

    -by(svy$ageMonths, is.na(svy$waz), summary)
    +by(svy$ageMonths, is.na(svy$waz), summary)

    This gives:

    -
    #> is.na(svy$waz): FALSE
-#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>   60.00   76.00   88.00   88.24   99.00  120.00 
-#> ------------------------------------------------------------ 
-#> is.na(svy$waz): TRUE
-#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>   121.0   125.5   141.0   140.8   151.0   178.0
    -

    There appears to be nothing odd about the large number of missing values in the waz variable.

    -

    We should investigate the missing values in the baz variable:

    +
    #> is.na(svy$waz): FALSE
+#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#>   60.00   76.00   88.00   88.24   99.00  120.00 
+#> ------------------------------------------------------------ 
+#> is.na(svy$waz): TRUE
+#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+#>   121.0   125.5   141.0   140.8   151.0   178.0
    +

    There appears to be nothing odd about the large number of missing +values in the waz variable.

    +

    We should investigate the missing values in the baz +variable:

    -svy[is.na(svy$baz), ]
    +svy[is.na(svy$baz), ]

    This returns:

    -
    #>     region school ageMonths sex weight height   haz   waz baz
-#> 83       1      3       143   2   14.0  125.9 -3.64    NA  NA
-#> 158      2      6        96   1   12.3  118.4 -1.57 -5.26  NA
-#> 275      3     10        77   1   10.3  113.9 -0.88 -5.35  NA
-#> 415      4     15        75   1   33.0  108.3 -1.90  3.01  NA
-#> 508      5     19        85   2   11.1  111.5 -1.78 -4.84  NA
-#> 529      6     20        78   1   12.1  111.9 -1.37 -4.45  NA
-#> 761      8     28        62   1   13.3  115.4  0.99 -2.70  NA
-#> 806      8     29       100   1   13.2  121.2 -1.36 -5.01  NA
    -

    The data required to calculate the BMI-for-age z-score are present. Given the extreme values in the waz variable it is likely that the BMI-for-age z-scores in these records were calculated, found to be outside of the upper and lower flagging criteria, and the value for baz set to missing. We should check this and recalculate the BMI-for-age z-scores.

    -

    We can use scatterplots to examine the relationship between ageMonths, weight, and height:

    +
    #>     region school ageMonths sex weight height   haz   waz baz
+#> 83       1      3       143   2   14.0  125.9 -3.64    NA  NA
+#> 158      2      6        96   1   12.3  118.4 -1.57 -5.26  NA
+#> 275      3     10        77   1   10.3  113.9 -0.88 -5.35  NA
+#> 415      4     15        75   1   33.0  108.3 -1.90  3.01  NA
+#> 508      5     19        85   2   11.1  111.5 -1.78 -4.84  NA
+#> 529      6     20        78   1   12.1  111.9 -1.37 -4.45  NA
+#> 761      8     28        62   1   13.3  115.4  0.99 -2.70  NA
+#> 806      8     29       100   1   13.2  121.2 -1.36 -5.01  NA
    +

    The data required to calculate the BMI-for-age z-score are present. +Given the extreme values in the waz variable it is +likely that the BMI-for-age z-scores in these records were calculated, +found to be outside of the upper and lower flagging criteria, and the +value for baz set to missing. We should check this and +recalculate the BMI-for-age z-scores.

    +

    We can use scatterplots to examine the relationship between +ageMonths, weight, and +height:

    -plot(svy$ageMonths, svy$weight)
    +plot(svy$ageMonths, svy$weight) 

    -plot(svy$ageMonths, svy$height)
    +plot(svy$ageMonths, svy$height) 

    -plot(svy$height, svy$weight)
    +plot(svy$height, svy$weight)

    These relationships are not as simple as in younger children:

    -

    Variability in weight appears to increase with increasing ageMonths.

    -

    The relationship between height and ageMonths may not be entirely linear.

    -

    The relationship between weight and height is clearly non-linear.

    -

    All of these relationships are monotonic so we should still be able to use the Mahalanobis distance method to identify outliers:

    +

    Variability in weight appears to increase with +increasing ageMonths.

    +

    The relationship between height and +ageMonths may not be entirely linear.

    +

    The relationship between weight and +height is clearly non-linear.

    +

    All of these relationships are monotonic so we should still be able +to use the Mahalanobis distance method to identify outliers:

    -plot(svy$ageMonths, svy$weight,
-     pch = ifelse(outliersMD(svy$ageMonths, svy$weight), 19, 1))
    +plot(svy$ageMonths, svy$weight, + pch = ifelse(outliersMD(svy$ageMonths, svy$weight), 19, 1)) 

    -
-plot(svy$ageMonths, svy$height,
-     pch = ifelse(outliersMD(svy$ageMonths, svy$height), 19, 1))
    + +plot(svy$ageMonths, svy$height, + pch = ifelse(outliersMD(svy$ageMonths, svy$height), 19, 1)) 

    -
-plot(svy$height, svy$weight,
-     pch = ifelse(outliersMD(svy$height, svy$weight), 19, 1))
    + +plot(svy$height, svy$weight, + pch = ifelse(outliersMD(svy$height, svy$weight), 19, 1))

    -

    You may want to experiment with different values of the alpha parameter of the outliersMD() function as described above. Records containing values identified as outliers can be listed:

    +

    You may want to experiment with different values of the +alpha parameter of the outliersMD() +function as described above. Records containing values identified as +outliers can be listed:

    -svy[outliersMD(svy$ageMonths, svy$weight), ] 
-#>     region school ageMonths sex weight height   haz  waz   baz
-#> 57       1      2       161   1   47.0  158.7 -0.05   NA  0.05
-#> 83       1      3       143   2   14.0  125.9 -3.64   NA    NA
-#> 139      2      5       123   2   46.5  144.9  0.64   NA  1.82
-#> 319      3     11       143   1   45.2  156.0  1.06   NA  0.50
-#> 407      4     14       132   1   46.2  155.3  1.73   NA  0.97
-#> 415      4     15        75   1   33.0  108.3 -1.90 3.01    NA
-#> 672      7     24       175   1   50.5  163.5 -0.42   NA -0.25
-#> 727      7     26       147   1   46.1  162.7  1.67   NA -0.14
-#> 731      7     26       173   1   51.9  164.2 -0.21   NA -0.03
-svy[outliersMD(svy$ageMonths, svy$height), ] 
-#>     region school ageMonths sex weight height  haz  waz   baz
-#> 457      5     17       110   1   37.5    155 3.55 1.62 -0.32
-svy[outliersMD(svy$weight, svy$height), ]
-#>     region school ageMonths sex weight height   haz   waz   baz
-#> 57       1      2       161   1   47.0  158.7 -0.05    NA  0.05
-#> 83       1      3       143   2   14.0  125.9 -3.64    NA    NA
-#> 139      2      5       123   2   46.5  144.9  0.64    NA  1.82
-#> 275      3     10        77   1   10.3  113.9 -0.88 -5.35    NA
-#> 319      3     11       143   1   45.2  156.0  1.06    NA  0.50
-#> 322      3     11       155   2   39.0  135.5 -3.01    NA  0.84
-#> 369      4     13       118   2   35.5  129.7 -1.32  0.66  1.66
-#> 407      4     14       132   1   46.2  155.3  1.73    NA  0.97
-#> 415      4     15        75   1   33.0  108.3 -1.90  3.01    NA
-#> 438      4     15       163   2   39.0  138.3 -2.92    NA  0.41
-#> 611      6     22       148   1   41.9  146.5 -0.66    NA  0.75
-#> 672      7     24       175   1   50.5  163.5 -0.42    NA -0.25
-#> 731      7     26       173   1   51.9  164.2 -0.21    NA -0.03
-#> 806      8     29       100   1   13.2  121.2 -1.36 -5.01    NA
    -

    These records can be checked, edited (if required), and anthropometric indices recalculated.

    +svy[outliersMD(svy$ageMonths, svy$weight), ] +#> region school ageMonths sex weight height haz waz baz +#> 57 1 2 161 1 47.0 158.7 -0.05 NA 0.05 +#> 83 1 3 143 2 14.0 125.9 -3.64 NA NA +#> 139 2 5 123 2 46.5 144.9 0.64 NA 1.82 +#> 319 3 11 143 1 45.2 156.0 1.06 NA 0.50 +#> 407 4 14 132 1 46.2 155.3 1.73 NA 0.97 +#> 415 4 15 75 1 33.0 108.3 -1.90 3.01 NA +#> 672 7 24 175 1 50.5 163.5 -0.42 NA -0.25 +#> 727 7 26 147 1 46.1 162.7 1.67 NA -0.14 +#> 731 7 26 173 1 51.9 164.2 -0.21 NA -0.03 +svy[outliersMD(svy$ageMonths, svy$height), ] +#> region school ageMonths sex weight height haz waz baz +#> 457 5 17 110 1 37.5 155 3.55 1.62 -0.32 +svy[outliersMD(svy$weight, svy$height), ] +#> region school ageMonths sex weight height haz waz baz +#> 57 1 2 161 1 47.0 158.7 -0.05 NA 0.05 +#> 83 1 3 143 2 14.0 125.9 -3.64 NA NA +#> 139 2 5 123 2 46.5 144.9 0.64 NA 1.82 +#> 275 3 10 77 1 10.3 113.9 -0.88 -5.35 NA +#> 319 3 11 143 1 45.2 156.0 1.06 NA 0.50 +#> 322 3 11 155 2 39.0 135.5 -3.01 NA 0.84 +#> 369 4 13 118 2 35.5 129.7 -1.32 0.66 1.66 +#> 407 4 14 132 1 46.2 155.3 1.73 NA 0.97 +#> 415 4 15 75 1 33.0 108.3 -1.90 3.01 NA +#> 438 4 15 163 2 39.0 138.3 -2.92 NA 0.41 +#> 611 6 22 148 1 41.9 146.5 -0.66 NA 0.75 +#> 672 7 24 175 1 50.5 163.5 -0.42 NA -0.25 +#> 731 7 26 173 1 51.9 164.2 -0.21 NA -0.03 +#> 806 8 29 100 1 13.2 121.2 -1.36 -5.01 NA +

    These records can be checked, edited (if required), and +anthropometric indices recalculated.

    - - - - + -
    -

    Developed by Mark Myatt, Ernest Guevarra.

    +
    +

    +

    Developed by Mark Myatt, Ernest Guevarra.

    -
    -

    Site built with pkgdown 1.6.1.

    +
    +

    +

    Site built with pkgdown 2.0.7.

    -
    +
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