Type:	Package
Title:	Odds Ratio Tools for Logistic Regression
Version:	0.8.0
Maintainer:	Craig Parylo <craig.parylo2@nhs.net>
Description:	Produces odds ratio analyses with comprehensive reporting tools. Generates plots, summary tables, and diagnostic checks for logistic regression models fitted with 'glm()' using binomial family. Provides visualisation methods, formatted reporting tables via 'gt', and tools to assess logistic regression model assumptions.
License:	MIT + file LICENSE
Encoding:	UTF-8
Imports:	broom, car, cli, detectseparation, dplyr, forcats, ggplot2, glue, gt, janitor, purrr, rlang, scales, stats, stringr, tibble, tidyr, tidyselect
RoxygenNote:	7.3.3
Suggests:	datasets, knitr, labelled, magrittr, MASS, medicaldata (≥ 0.2.0.9001), NHANES, R4HCR, rmarkdown, svglite, testthat (≥ 3.0.0), vdiffr
VignetteBuilder:	knitr
URL:	https://github.com/craig-parylo/plotor, https://craig-parylo.github.io/plotor/
BugReports:	https://github.com/craig-parylo/plotor/issues
Config/testthat/edition:	3
Depends:	R (≥ 4.1.0)
Config/Remotes:	higgi13425/medicaldata
NeedsCompilation:	no
Packaged:	2025-12-08 08:00:24 UTC; craigparylo
Author:	Craig Parylo [aut, cre, cph]
Repository:	CRAN
Date/Publication:	2025-12-08 08:20:07 UTC

plotor: Odds Ratio Tools for Logistic Regression

Description

Produces odds ratio analyses with comprehensive reporting tools. Generates plots, summary tables, and diagnostic checks for logistic regression models fitted with 'glm()' using binomial family. Provides visualisation methods, formatted reporting tables via 'gt', and tools to assess logistic regression model assumptions.

Author(s)

Maintainer: Craig Parylo craig.parylo2@nhs.net (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/craig-parylo/plotor

• https://craig-parylo.github.io/plotor/

• Report bugs at https://github.com/craig-parylo/plotor/issues

Check OR

Description

Performs a series of tests to ensure that assumptions for logistic regression are met, with optional detailed feedback if any tests fail.

Usage

check_or(glm_model_results, confint_fast_estimate = FALSE, details = TRUE)

Arguments

Value

Logical, TRUE if all assumption tests pass, FALSE if one or more tests fail

Examples

# Load the Titanic dataset
df <- datasets::Titanic |>
  dplyr::as_tibble() |>
  # convert aggregated counts to individual observations
  dplyr::filter(n > 0) |>
  tidyr::uncount(weights = n) |>
  # convert character variables to factors
  dplyr::mutate(dplyr::across(dplyr::where(is.character), as.factor))

# Perform logistic regression using `glm`
lr <- stats::glm(
  data = df,
  family = binomial,
  formula = Survived ~ Class + Sex + Age
)

# Check the model for logistic regression assumption violations
check_or(lr)

Plot OR

Description

Produces an Odds Ratio plot to visualise the results of a logistic regression analysis.

Usage

plot_or(
  glm_model_results,
  conf_level = 0.95,
  confint_fast_estimate = FALSE,
  assumption_checks = TRUE
)

Arguments

Value

The function returns an object of class gg and ggplot, which can be customised and extended using various ggplot2 functions.

See Also

• See vignette('using_plotor', package = 'plotor') for more details on usage.

• More details and examples can be found on the website: https://craig-parylo.github.io/plotor/index.html

Examples

# Load required libraries
library(plotor)
library(datasets)
library(dplyr)
library(ggplot2)
library(stats)
library(forcats)
library(tidyr)

# Load the Titanic dataset
df <- datasets::Titanic |>
  as_tibble() |>
  # convert aggregated counts to individual observations
  filter(n > 0) |>
  uncount(weights = n) |>
  # convert character variables to factors
  mutate(across(where(is.character), as.factor))

# Perform logistic regression using `glm`
lr <- glm(
  data = df,
  family = 'binomial',
  formula = Survived ~ Class + Sex + Age
)

# Produce the Odds Ratio plot
plot_or(lr)

Table OR

Description

Produces a formatted table displaying the outputs from the Odds Ratio analysis, including details on covariate characteristics and model results.

Usage

table_or(
  glm_model_results,
  conf_level = 0.95,
  output = c("tibble", "gt"),
  output_type = c("multivariable", "uni_and_multi"),
  confint_fast_estimate = FALSE,
  assumption_checks = TRUE,
  anonymise_counts = FALSE,
  use_model_data_only = TRUE
)

Arguments

Details

The table includes the following information:

• Covariate Characteristics:

  • Number of observations for each characteristic

  • Number of observations resulting in the outcome of interest

  • Conversion rate of the outcome based on the number of observations

• Model Results:

  • Estimated Odds Ratio, standard error, and p-value

  • Calculated confidence interval for the specified confidence level

A visualisation of the Odds Ratio plot is also provided for an at-a-glance view of the model results.

If anonymise_counts is set to TRUE, counts below 10 are suppressed as ⁠<10⁠, and other counts are rounded to the nearest multiple of 5. This feature is helpful when working with sensitive data.

Value

The returned object depends on the output parameter:

• If output = 'tibble', the function returns an object of class "tbl_df", "tbl", and "data.frame".

• If output = 'gt', the function returns an object of class "gt_tbl" and "list".

Examples

# Load the Titanic dataset
df <- datasets::Titanic |>
  dplyr::as_tibble() |>
  # convert aggregated counts to individual observations
  dplyr::filter(n > 0) |>
  tidyr::uncount(weights = n) |>
  # convert character variables to factors
  dplyr::mutate(dplyr::across(dplyr::where(is.character), as.factor))

# Perform logistic regression using `glm`
lr <- stats::glm(
  data = df,
  family = 'binomial',
  formula = Survived ~ Class + Sex + Age
)

# Produce the Odds Ratio table as a tibble
table_or(lr)

# Produce the Odds Ratio table as a gt object
table_or(lr, output = 'gt')