Last updated: 2023-01-28
Checks: 1 1
Knit directory: multiclass_AUC/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
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the R Markdown file created these results, you’ll want to first commit
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ignore this warning. When you’re finished, you can run
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Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version b13919c. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
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the analysis have been committed to Git prior to generating the results
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Ignored files:
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Ignored: renv/library/
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Untracked: analysis/05_score_calibration_1.Rmd
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Modified: analysis/index.Rmd
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in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b13919c | Ross Gayler | 2023-01-26 | Add notebook 04_score_dependencies |
| html | b13919c | Ross Gayler | 2023-01-26 | Add notebook 04_score_dependencies |
| Rmd | 0cdb34a | Ross Gayler | 2023-01-24 | Initial commit |
| html | 0cdb34a | Ross Gayler | 2023-01-24 | Initial commit |
This is the website for the research project “Multiclass AUC”. See the About page for a summary description of this project.
If you have cloned the project to a local computer this website is
rendered in the docs subdirectory of the project
directory.
If you are using workflowr to publish the research
website it will also be rendered online to GitHub Pages.
This page acts as a table of contents for the
website.
There are links below to the web pages generated from the analysis
notebooks.
Read the example datasets, calculate some check counts, reformat the data for analysis, and save to an R data file.
Check the ranges of the variables in the example data.
Investigate the distribution of each class score in response to cases of each class.
Investigate the dependencies between all the class scores calculated on the same case.
Investigate the the scaling and calibration of the class scores (first attempt).