plotor (version 0.7.0)

plot_or: 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)

Value

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

Arguments

glm_model_results

Results from a binomial Generalised Linear Model (GLM), as produced by stats::glm().

conf_level

Numeric value between 0.001 and 0.999 (default = 0.95) specifying the confidence level for the confidence interval.

confint_fast_estimate

Boolean (default = FALSE) indicating whether to use a faster estimate of the confidence interval. Note: this assumes normally distributed data, which may not be suitable for your data.

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)