plot_reg: Visualize a Regression Model as a Forest Plot

In gtregression: Tools for Creating Publication-Ready Regression Tables

Visualize a Regression Model as a Forest Plot

Description

Creates a forest plot from a 'gtsummary' object. Supports both univariate and multivariable models with hierarchical labels for categorical variables. Designed to work seamlessly with outputs from functions like 'uni_reg()' and 'multi_reg()'.

Usage

plot_reg(
  tbl,
  title = NULL,
  ref_line = 1,
  order_y = NULL,
  log_x = FALSE,
  xlim = NULL,
  breaks = NULL,
  point_color = "#1F77B4",
  errorbar_color = "#4C4C4C",
  base_size = 14,
  show_ref = TRUE
)

Arguments

tbl	A 'gtsummary' object from regression functions
title	Optional plot title (character).
ref_line	Numeric value for the reference line (default = 1).
order_y	Optional character vector to the customise y-axis order
log_x	Logical. If 'TRUE', uses a logarithmic x-axis (default = FALSE).
xlim	Optional numeric vector specifying x-axis limits
breaks	Optional numeric vector for x-axis tick breaks.
point_color	Color of the points (default is automatic).
errorbar_color	Color of the error bars (default is automatic).
base_size	Base font size for text elements.
show_ref	Logical. If 'TRUE', includes reference in the plot.

Value

A 'ggplot2' object representing the forest plot.

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data("PimaIndiansDiabetes2", package = "mlbench")
  library(dplyr)
  library(gtregression)

  # Prepare data
  pima <- PimaIndiansDiabetes2 |>
    mutate(
      diabetes = ifelse(diabetes == "pos", 1, 0),
      bmi_cat = cut(
        mass,
        breaks = c(-Inf, 18.5, 24.9, 29.9, Inf),
        labels = c("Underweight", "Normal", "Overweight", "Obese"))
  )

  # Univariate logistic regression
  uni_rr <- uni_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "bmi_cat"),
    approach = "logit"
  )
plot_reg(uni_rr)
}

gtregression documentation built on Aug. 18, 2025, 5:23 p.m.