plotor 0.7.0
Compatibility and testing fixes:
-
Test suite (#68)
Addressed issues where snapshot tests failed depending on the installed version of ggplot2. Snapshots produced by different ggplot2 versions were causing test failures, especially with
vdiffr::expect_doppelganger().The temporary solution was to suspend these visual comparison tests to avoid unnecessary failures for users not on the latest ggplot2.
-
Readiness for upcoming {ggplot2} release (#65)
Investigated and resolved failures with the upcoming major release of ggplot2, ensuring that the package’s examples, vignettes, and tests remain compatible.
Enhancements for model diagnostics:
-
New function
check_or()(#62)Added an exported function,
check_or(), to provide users with detailed feedback on whether their logistic regression models meet underlying assumptions. Previously, detailed diagnostics were only accessible via undocumented internal functions. -
Assumptions: check for sample size (#41)
Introduced a check for sufficient sample size, further improving diagnostics for logistic regression models.
These improvements make the package more robust to upstream changes in dependencies and offer users more transparent and accessible model validation tools.
plotor 0.6.0
CRAN release: 2025-05-28
User-focussed changes
-
Summary OR tables (#28)
Introduced summary tables for odds ratios, making it easier to view and interpret results from your model.
-
Faster estimates of confidence intervals (#53)
Optional argument,
confint_fast_estimate, for bothplot_or()andtable_or()that allows for faster approximation of confidence intervals usingstats::confint.default(). This can be helpful for large data sets where confidence intervals can take a long time to calculate for. -
Improved validation of the confidence level (#29)
Enhanced how the package checks user input for confidence levels, reducing the risk of invalid values being used.
This included enhanced checks in the internal function
validate_conf_level_inputs()with enhanced error handling and user feedback (#31). -
Assumption checks
Started a suite of checks that assumptions for logistic regression are upheld. Implemented in this release:
Assumptions: check outcome is binary (#42)
Added logic to confirm the outcome variable is binary, as required for odds ratio calculations.
Assumptions: check for multicollinearity (#43)
Implemented checks to detect multicollinearity among predictors, helping users identify and address issues that could affect model validity.
Assumptions: check for separation (#47)
Added checks for separation in the data, which can cause estimation problems in logistic regression.
-
Updated README
Improved the README documentation, making it easier for users to get started and understand the package.
Developer-focussed changes
-
Test suite (Developer focus) (#33, #37)
Added and developed a suite of tests for ensuring code reliability and maintaining quality as the package evolves.
-
Bug fixes
Addressed and resolved warnings related to the {tidyselect} package, leading to cleaner output and better compatibility with the tidyverse ecosystem. (#34)
Updated the way class descriptions are handled, consolidating them into single strings for consistency and clarity. (#50)
Fixed ordering of terms and levels in
table_or(), so results are presented in a logical and expected sequence. (#54, #56)
For the full details, see the changelog: https://github.com/craig-parylo/plotor/compare/v0.5.2…v0.6.0
plotor 0.5.2
CRAN release: 2025-02-09
plot_or()now respects the order of covariates in the formula when plotting (#15).plot_or()handles missing information to avoid {ggplot2} related warning messages (#11).plot_or()accepts customised confidence limits, e.g. 99%, used when calculating the confidence intervals (#19).plot_or()conducts checks on inputs - ensuring the {glm} model is a logistic regression (family = ‘binomial’ and link = ‘logit’) and validates the confidence limit to be within the range 0.001 to 0.999 (#22, #19).