Skip to contents

Visualize a Regression Model as a Forest Plot

Source: R/plot_reg.R
plot_reg.Rd

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

# \donttest{
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)
}
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: collapsing to unique 'x' values
#> Warning: Removed 5 rows containing missing values or values outside the scale range
#> (`geom_errorbarh()`).

# }