Type:	Package
Title:	Produces an Odds Ratio Plot from a Logistic Regression Model
Version:	0.7.0
Maintainer:	Craig Parylo <craig.parylo2@nhs.net>
Description:	Produces an Odds Ratio (OR) Plot to visualise the result of a logistic regression analysis. Provide it with a binomial regression model produced by 'glm()' and it will convert the estimates to odds ratios with a 95% confidence interval and plot the results using 'ggplot2'.
License:	MIT + file LICENSE
Encoding:	UTF-8
Imports:	broom, car, cli, detectseparation, dplyr, forcats, ggplot2, glue, gt, janitor, purrr, rlang, scales, stats, stringr, tibble, tidyselect
RoxygenNote:	7.3.2
Suggests:	datasets, knitr, labelled, rmarkdown, svglite, testthat (≥ 3.0.0), tidyr, 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)
NeedsCompilation:	no
Packaged:	2025-07-01 13:26:20 UTC; Craig.Parylo
Author:	Craig Parylo [aut, cre, cph]
Repository:	CRAN
Date/Publication:	2025-07-01 13:40:01 UTC

plotor: Produces an Odds Ratio Plot from a Logistic Regression Model

Description

Produces an Odds Ratio (OR) Plot to visualise the result of a logistic regression analysis. Provide it with a binomial regression model produced by 'glm()' and it will convert the estimates to odds ratios with a 95

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)

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 showing 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 = "tibble",
  confint_fast_estimate = FALSE
)

Arguments

Details

The table includes the following information:

• Covariate characteristics:

  • Number of observations for each characteristic

  • Number of observiations resulting in the outcome of interest

  • Conversion rate of outcome by 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 OR plot is also provided for an at-a-glance view of the model results

Includes details on the characteristics of the covariates, such as:

• the number of observations for each characteristic,

• the number of observations resulting in the outcome of interest,

• the conversion rate of outcome by the number of observations,

Details are calculated showing the:

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

• calculated confidence interval for the confidence level,

Also included is a visualisation of the OR plot to provide an at-a-glance view of the model results.

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')