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.
Investigated and resolved failures with the upcoming major release of ggplot2, ensuring that the package’s examples, vignettes, and tests remain compatible.
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.
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.
Introduced summary tables for odds ratios, making it easier to view and interpret results from your model.
Optional argument, confint_fast_estimate
, for both plot_or()
and
table_or()
that allows for faster approximation of confidence intervals
using stats::confint.default()
. This can be helpful for large data sets
where confidence intervals can take a long time to calculate for.
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).
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.
Improved the README documentation, making it easier for users to get started and understand the package.
Added and developed a suite of tests for ensuring code reliability and maintaining quality as the package evolves.
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
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).
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