Runtime benchmarking & prediction
Benchmarks measuring plot / table runtime across dataset sizes and
predictor mixes, plus a packaged regression model and lightweight
wrapper predict_process_time() that returns an estimated
runtime (ms) with uncertainty bounds. Benchmark outputs and the fitted
model are stored as .rds assets for package use (#94)
Confirm before long runs
double_check_confint_fast_estimate() prompts users before
running slower confidence-interval computations when the predictored
runtime exceeds the default threshold (60s). Reduces unexpected long
operations in interactive sessions (#94)
Background processing with spinner
Long-running work now runs in background R processes
(callr::r_bg()), while the main process shows a
{cli} spinner. Helpers include
get_summary_table_with_spinner(),
check_assumptions_with_spinner(), and
user_spinner(). Spinners are suppressed in tests / CI (#96) (#97)
Influential-observation diagnostics
New assumption_no_extreme_values() detects influential
observations using Cook’s distance, leverage and standardised residuals
with adaptive thresholds and conservative flagging. Test-data generators
get_df_influential() and get_lr_influential()
added (#98)
Reproducible README figures
Script tools/generate_readme_figures.R, refreshed figures
assets in man/figures and added Suggests dependencies for
their reproduction (here, webshot2) so README
builds are reproducible (#100)
Runtime prediction integrated into UI
plot_or() and table_or() call
predict_process_time() and prompt via the double-check
helper when long runs are predicted. Benchmark code moved to
data-raw/; serialised assets are shipped for app use (no
re-benchmarking at runtime) (#94)
Safe background wrappers
Wrapper functions run heavy work in background processes and return
identical results while surfacing errors / warnings. Spinner display is
controlled by use_spinner() (#96)
Namespace and tidy-eval hardening
Explicit qualifications (utils::stack(),
stats::predict()), use of .data$ pronoun and
improved Roxygen for use_spinner() reduce R CMD check notes
and improve compatibility with dplyr v1.2+.
callr added to Imports (#97)
Stable transformations
Replaced log1p() with asinh() for
continuous-predictor transforms to handle negative values and improve
numerical stability (#98)
README rewrite
README content simplified and focused; figures and generation workflow
updated for reproducibility (#100)
Separation detection
Fixed tidy-eval bugs, zero-count detection, empty-summary guards and
NA-to_TRUE/FALSE issues in assumption_no_separation_fast()
so separation is reliably detected (#96)
Spinner / test stability
Spinners are disabled in non-interactive / test environments; minor
test-data tweaks improve CI reliability (#97)
Assumption checks robustness
Improved numerical stability, error handling and reporting across
assumption checks to avoid spurious results (#96)
R CMD check issues
Resolved undefined global variables and unqualified generic calls to
clean up check output (#97)
callr is now required (imported) for background
processing (#97)
Interactive users will see a spinner during long operations; spinners are suppressed in CI / tests (#96)
No breaking API changes expected; function signatures remain compatible
Support for ordered-factor predictors
Formula parsing and model-summary utilities now recognise and correctly
handle ordered factors (#91)
New helper
make_ordered_factors_compatible_with_broom() aligns term
names and labels with broom::tidy() (#91)
Tests and examples
Tests, example data and models covering ordered-factor predictors,
including a truncated example that verifies the sample-size assumption
warning (#91)
Revert to gtExtras for CI mini-plots
Replaced hard-coded copies with gtExtras code for
confidence-interval plots produced by table_or() and
require gtExtras >= 0.6.1 to reduce duplication and use
upstream fixes (#90)
Bump ggplot2 requirement
Update dependency to ggplot2 >= 4.0.0 (#90)
Refined predictor-type detection
Refactored to return more detailed classes (e.g. “ordered factor”,
“factor”, “numeric”) for improved downstream handling and displays (#91)
Version bump
Bumped package version to 0.9.0 to support a GitHub release (#93)
Dependency updates required
Users must have gtExtras >= 0.6.1 and
ggplot2 >= 4.0.0 installed; older versions may cause
plot failures (#90)
No external API changes
Plotting and table functions should behave as before while preserving
ordered-factor level ordering. (#90)(#91)
Univariable Odds Ratios
Added functionality to produce univariable odds ratios alongside
multivariate model odds ratios in plot_or() and
table_or(). This allows for direct comparison between
predictors in isolation and as part of a full model.
(#59)
Assumption Checks Control
Both plot_or() and table_or() now include a
parameter to enable or disable assumption checks, giving experienced
analysts more control over feedback from these functions.
(#75)
Check for Linearity
Continuous predictors are now checked for linearity in relation to the
log-odds of the outcome, improving model diagnostics.
(#21)
Privacy Options
A new option in table_or() allows suppression of low counts
(below a user-defined threshold) and rounding of other counts, to help
protect sensitive data.
(#58)
Contextual Guidance for Model Assumptions
Now, when assumption checks fail in plot_or() or
table_or(), users are prompted to run
check_or() for detailed diagnostics (unless it has just
been used).
(#76)
Performance of Confidence Interval
Calculation
Improved processing speed of the confint_fast_estimate
parameter for large datasets by optimizing the assumption check
functions.
(#57)
Sample Size Check with Complete Separation
Resolved errors in assumption_sample_size() when the model
exhibits complete separation by better handling of NA
values in outcome counts.
(#73)
Documentation Correction
Documentation for anonymise_count_values() has been
corrected: this internal function is no longer exported in the help
files.
(#84)
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.
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.
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 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.
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.
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
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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