lmforest: Create Forest Plot for Linear Models

In summata: Publication-Ready Summary Tables and Forest Plots

Create Forest Plot for Linear Models

Description

Generates a publication-ready forest plot that combines a formatted data table with a graphical representation of regression coefficients from a linear model. The plot integrates variable names, group levels, sample sizes, coefficients with confidence intervals, p-values, and model diagnostics (R^2, F-statistic, AIC) in a single comprehensive visualization designed for manuscripts and presentations.

Usage

lmforest(
  x,
  data = NULL,
  title = "Linear Model",
  effect_label = "Coefficient",
  digits = 2,
  p_digits = 3,
  conf_level = 0.95,
  font_size = 1,
  annot_size = 3.88,
  header_size = 5.82,
  title_size = 23.28,
  plot_width = NULL,
  plot_height = NULL,
  table_width = 0.6,
  show_n = TRUE,
  indent_groups = FALSE,
  condense_table = FALSE,
  bold_variables = FALSE,
  center_padding = 4,
  zebra_stripes = TRUE,
  ref_label = "reference",
  labels = NULL,
  units = "in",
  color = "#5A8F5A",
  qc_footer = TRUE,
  number_format = NULL
)

Arguments

x	Either a fitted linear model object (class lm or lmerMod), a fit_result object from fit(), or a fullfit_result object from fullfit(). When a fit_result or fullfit_result is provided, the model, data, and labels are automatically extracted.
data	Data frame or data.table containing the original data used to fit the model. If NULL (default) and x is a model, the function attempts to extract data from the model object. If x is a fit_result, data is extracted automatically. Providing data explicitly is recommended when passing a model directly.
title	Character string specifying the plot title displayed at the top. Default is "Linear Model".
effect_label	Character string for the effect measure label on the forest plot axis. Default is "Coefficient".
digits	Integer specifying the number of decimal places for coefficients and confidence intervals. Default is 2.
p_digits	Integer specifying the number of decimal places for p-values. Values smaller than 10^(-p_digits) are displayed as "< 0.001" (for p_digits = 3), "< 0.0001" (for p_digits = 4), etc. Default is 3.
conf_level	Numeric confidence level for confidence intervals. Must be between 0 and 1. Default is 0.95 (95% confidence intervals). The CI percentage is automatically displayed in column headers (e.g., "90% CI" when conf_level = 0.90).
font_size	Numeric multiplier controlling the base font size for all text elements. Default is 1.0.
annot_size	Numeric value controlling the relative font size for data annotations. Default is 3.88.
header_size	Numeric value controlling the relative font size for column headers. Default is 5.82.
title_size	Numeric value controlling the relative font size for the main plot title. Default is 23.28.
plot_width	Numeric value specifying the intended output width in specified units. Default is NULL (automatic).
plot_height	Numeric value specifying the intended output height in specified units. Default is NULL (automatic).
table_width	Numeric value between 0 and 1 specifying the proportion of total plot width allocated to the data table. Default is 0.6.
show_n	Logical. If TRUE, includes a column showing group-specific sample sizes. Default is TRUE.
indent_groups	Logical. If TRUE, indents factor levels under their parent variable name, creating hierarchical structure. The "Group" column is hidden when TRUE. Default is FALSE.
condense_table	Logical. If TRUE, condenses binary categorical variables into single rows. Automatically sets indent_groups = TRUE. Default is FALSE.
bold_variables	Logical. If TRUE, variable names are displayed in bold. If FALSE (default), variable names are displayed in plain text.
center_padding	Numeric value specifying horizontal spacing between table and forest plot. Default is 4.
zebra_stripes	Logical. If TRUE, applies alternating gray background shading to different variables. Default is TRUE.
ref_label	Character string to display for reference categories of factor variables. Default is "reference".
labels	Named character vector providing custom display labels for variables. Example: c(age = "Age (years)", height = "Height (cm)"). Default is NULL.
units	Character string specifying units for plot dimensions: "in" (inches), "cm", or "mm". Default is "in".
color	Character string specifying the color for coefficient point estimates in the forest plot. Default is "#5A8F5A" (green). Use hex codes or R color names.
qc_footer	Logical. If TRUE, displays model quality control statistics in the footer (observations analyzed, R^2, adjusted R^2, F-statistic, AIC). Default is TRUE.
number_format	Character string or two-element character vector controlling thousand and decimal separators in formatted output. Named presets: "us" - Comma thousands, period decimal: 1,234.56 [default] "eu" - Period thousands, comma decimal: 1.234,56 "space" - Thin-space thousands, period decimal: 1 234.56 (SI/ISO 31-0) "none" - No thousands separator: 1234.56 Or provide a custom two-element vector c(big.mark, decimal.mark), e.g., c("'", ".") for Swiss-style: ⁠1'234.56⁠. When NULL (default), uses getOption("summata.number_format", "us"). Set the global option once per session to avoid passing this argument repeatedly: options(summata.number_format = "eu")

Details

Linear Model-Specific Features:

The linear model forest plot differs from logistic and Cox plots in several ways:

• Coefficients: Raw regression coefficients shown (not exponentiated)

• Reference line: At coefficient = 0 (not at 1)

• Linear scale: Forest plot uses linear scale (not log scale)

• No events column: Only sample sizes shown (no event counts)

• R^2 statistics: Model fit assessed by R^2 and adjusted R^2

• F-test: Overall model significance from F-statistic

Plot Components:

1. Title: Centered at top

2. Data Table (left): Contains:

   • Variable: Predictor names

   • Group: Factor levels (if applicable)

   • n: Sample sizes by group

   • Coefficient (95% CI); p-value: Raw coefficients with CIs and p-values

3. Forest Plot (right):

   • Point estimates (squares sized by sample size)

   • 95% confidence intervals (error bars)

   • Reference line at coefficient = 0

   • Linear scale

4. Model Statistics (footer):

   • Observations analyzed (with percentage of total data)

   • R^2 and adjusted R^2

   • F-statistic with degrees of freedom and p-value

   • AIC

Interpreting Coefficients:

Linear regression coefficients represent the change in the outcome variable for a one-unit change in the predictor:

• Continuous predictors: Coefficient = change in Y per unit of X

• Binary predictors: Coefficient = difference in Y between groups

• Factor predictors: Coefficients = differences from reference category

• Sign matters: Positive = increase in Y, Negative = decrease in Y

• Zero crossing: CI crossing zero suggests no significant effect

Example: If the coefficient for "age" is 0.50 when predicting BMI, BMI increases by 0.50 kg/m^2 for each additional year of age.

Model Fit Statistics:

The footer displays key diagnostics:

• R^2: Proportion of variance explained (0 to 1)

  • 0.0-0.3: Weak explanatory power

  • 0.3-0.5: Moderate

  • 0.5-0.7: Good

  • > 0.7: Strong (rare in social/biological sciences)

• Adjusted R^2: R^2 penalized for number of predictors

  • Always \le R^2

  • Preferred for model comparison

  • Accounts for model complexity

• F-statistic: Tests null hypothesis that all coefficients = 0

  • Degrees of freedom: df1 = # predictors, df2 = # observations - # predictors - 1

  • Significant p-value indicates model explains variance better than intercept-only

• AIC: For model comparison (lower is better)

Assumptions:

Linear regression assumes:

1. Linearity of relationships

2. Independence of observations

3. Homoscedasticity (constant variance)

4. Normality of residuals

5. No multicollinearity

Check assumptions using:

• plot(model) for diagnostic plots

• car::vif(model) for multicollinearity

• lmtest::bptest(model) for heteroscedasticity

• shapiro.test(residuals(model)) for normality

Reference Categories:

For factor variables:

• First level is the reference (coefficient = 0)

• Other levels show difference from reference

• Reference displayed with ref_label

• Relevel factors before modeling if needed: factor(x, levels = c("desired_ref", ...))

Sample Size Reporting:

The "n" column shows:

• For continuous variables: Total observations with non-missing data

• For factor variables: Number of observations in each category

• Footer shows total observations analyzed and percentage of original data (accounting for missing values)

Value

A ggplot object containing the complete forest plot. The plot can be:

• Displayed directly: print(plot)

• Saved to file: ggsave("forest.pdf", plot, width = 12, height = 8)

• Further customized with ggplot2 functions

The returned object includes an attribute "rec_dims" accessible via attr(plot, "rec_dims"), which is a list containing:

width

Numeric. Recommended plot width in specified units

height

Numeric. Recommended plot height in specified units

These recommendations are automatically calculated based on the number of variables, text sizes, and layout parameters, and are printed to console if plot_width or plot_height are not specified.

See Also

autoforest for automatic model detection, glmforest for logistic/GLM forest plots, coxforest for Cox model forest plots, uniforest for univariable screening forest plots, multiforest for multi-outcome forest plots, lm for fitting linear models, fit for regression modeling

Other visualization functions: autoforest(), coxforest(), glmforest(), multiforest(), uniforest()

Examples

data(clintrial)
data(clintrial_labels)

# Create example model
model1 <- lm(bmi ~ age + sex + smoking, data = clintrial)

# Example 1: Basic linear model forest plot
p <- lmforest(model1, data = clintrial)



old_width <- options(width = 180)

# Example 2: With custom labels and title
plot2 <- lmforest(
    x = model1,
    data = clintrial,
    title = "Predictors of Body Mass Index",
    effect_label = "Change in BMI (kg/m^2)",
    labels = clintrial_labels
)

# Example 3: Comprehensive model with indented layout
model3 <- lm(
    bmi ~ age + sex + smoking + hypertension + diabetes + creatinine,
    data = clintrial
)

plot3 <- lmforest(
    x = model3,
    data = clintrial,
    labels = clintrial_labels,
    indent_groups = TRUE,
    zebra_stripes = TRUE
)

# Example 4: Condensed layout
plot4 <- lmforest(
    x = model3,
    data = clintrial,
    condense_table = TRUE,
    labels = clintrial_labels
)

# Example 5: Different outcome (hemoglobin)
model5 <- lm(
    hemoglobin ~ age + sex + bmi + smoking + creatinine,
    data = clintrial
)

plot5 <- lmforest(
    x = model5,
    data = clintrial,
    title = "Predictors of Baseline Hemoglobin",
    effect_label = "Change in Hemoglobin (g/dL)",
    labels = clintrial_labels
)

# Example 6: Save with recommended dimensions
dims <- attr(plot5, "rec_dims")
ggplot2::ggsave(file.path(tempdir(), "linear_forest.pdf"),
                plot5, width = dims$width, height = dims$height)

options(old_width)

summata documentation built on May 7, 2026, 5:07 p.m.