# Forest Plots

Forest plots provide a graphical representation of effect estimates and their precision. Each row displays a point estimate (typically an odds ratio, hazard ratio, or regression coefficient) with a confidence interval, enabling rapid visual assessment of effect magnitude, direction, and statistical significance. The format is standard in published research and regulatory submissions.

The `summata` package provides specialized forest plot functions for each model type, plus an automatic detection function:

Function | Model Type | Effect Measure
--- | --- | ---
`lmforest()` | Linear regression | Coefficient (β)
`glmforest()` | Logistic/Poisson | Odds ratio / Rate ratio
`coxforest()` | Cox regression | Hazard ratio
`uniforest()` | Univariable screening | Model-dependent
`multiforest()` | Multi-outcome analysis | Model-dependent
`autoforest()` | Auto-detect | Auto-detect

These functions follow a standard syntax when called:

    forest_plot <- autoforest(x, data, ...)

where `x` is either a model or a `summata` fitted output (e.g., from `uniscreen()`, `fit()`, `fullfit()`, or `multifit()`), and `data` is the name of the dataset used. The `data` argument is optional and is primarily used to derive n and Events counts for various groups/subgroups.

All forest plot functions produce `ggplot2` objects that can be further customized. This vignette demonstrates the various capabilities of these functions using the included sample dataset.

# Preliminaries

The examples in this vignette use the `clintrial` dataset included with `summata`:

    library(summata)
    library(survival)
    library(ggplot2)

    data(clintrial)
    data(clintrial_labels)

> n.b.: To ensure correct font rendering and figure sizing, the forest plots below are displayed using a helper function (`queue_plot()`) that applies each plot’s recommended dimensions (stored in the `"rec_dims"` attribute) via the `ragg` graphics device. In practice, replace `queue_plot()` with `ggplot2::ggsave()` using recommended plot dimensions for equivalent results:

>     p <- glmforest(model, data = mydata)
>     dims <- attr(p, "rec_dims")
>     ggplot2::ggsave("forest_plot.png", p,
>                     width = dims$width,
>                     height = dims$height)

> This ensures that the figure size is always large enough to accommodate the constituent plot text and graphics, and it is generally the preferred method for saving forest plot outputs in `summata`.

# Creating Forest Plots from Model Objects

Forest plots can be created from standard R model objects or from `summata` function output.

## Example 1: Logistic Regression

Fit a model using base R, then create a forest plot:

    logistic_model <- glm(
      surgery ~ age + sex + stage + treatment + ecog,
      data = clintrial,
      family = binomial
    )

    example1 <- glmforest(
      x = logistic_model,
      data = clintrial,
      title = "Logistic Regression: Predictors of Outcome",
      labels = clintrial_labels
    )
    queue_plot(example1)

## Example 2: Linear Regression

For continuous outcomes, use `lmforest()`:

    linear_model <- lm(
      los_days ~ age + sex + stage + surgery + ecog,
      data = clintrial
    )

    example2 <- lmforest(
      x = linear_model,
      data = clintrial,
      title = "Linear Regression: Length of Stay",
      labels = clintrial_labels
    )
    queue_plot(example2)

## Example 3: Cox Regression

For survival models, use `coxforest()`:

    cox_model <- coxph(
      Surv(os_months, os_status) ~ age + sex + stage + treatment + ecog,
      data = clintrial
    )

    example3 <- coxforest(
      x = cox_model,
      data = clintrial,
      title = "Cox Regression: Survival Analysis",
      labels = clintrial_labels
    )
    queue_plot(example3)

## Example 4: Automatic Model Detection

The `autoforest()` function detects the model type automatically:

    example4 <- autoforest(
      x = cox_model,
      data = clintrial,
      labels = clintrial_labels
    )
    queue_plot(example4)

# Creating Forest Plots from summata Output

Forest plots integrate seamlessly with `fit()` and `fullfit()` output by extracting the attached model object.

## Example 5: Direct Extraction from Fit Output

The same approach works for logistic models:

    table_logistic <- fit(
      data = clintrial,
      outcome = "surgery",
      predictors = c("age", "sex", "stage", "treatment", "ecog"),
      model_type = "glm",
      labels = clintrial_labels
    )

    example5 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Predictors of Surgical Intervention",
      labels = clintrial_labels,
      zebra_stripes = TRUE
    )
    queue_plot(example5)

## Example 6: Model Attribute from Fit Output

And for Cox models:

    table_cox <- fit(
      data = clintrial,
      outcome = "Surv(os_months, os_status)",
      predictors = c("age", "sex", "stage", "treatment", "ecog"),
      model_type = "coxph",
      labels = clintrial_labels
    )

    example6 <- coxforest(
      x = attr(table_cox, "model"),
      title = "Predictors of Overall Survival",
      labels = clintrial_labels,
      zebra_stripes = TRUE
    )
    queue_plot(example6)

# Display Options

## Example 7: Indenting Factor Levels

The `indent_groups` parameter creates hierarchical display for a more compact aesthetic:

    example7 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Indented Factor Levels",
      labels = clintrial_labels,
      indent_groups = TRUE
    )
    queue_plot(example7)

## Example 8: Condensing Binary Variables

The `condense_table` parameter displays binary variables on single rows. Group indenting is applied automatically:

    example8 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Condensed Display",
      labels = clintrial_labels,
      condense_table = TRUE
    )
    queue_plot(example8)

## Example 9: Toggle Zebra Striping

Zebra striping is enabled by default. It can be disabled via `zebra_stripes = FALSE`:

    example9 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Without Zebra Striping",
      labels = clintrial_labels,
      indent_groups = TRUE,
      zebra_stripes = FALSE
    )
    queue_plot(example9)

## Example 10: Sample Size and Event Columns

Control display of sample size (n) and event counts:

    # Show both n and events
    example10a <- coxforest(
      x = attr(table_cox, "model"),
      title = "With Sample Size and Events",
      labels = clintrial_labels,
      show_n = TRUE,
      show_events = TRUE,
      indent_groups = TRUE,
      zebra_stripes = TRUE
    )
    queue_plot(example10a)

    # Minimal display
    example10b <- coxforest(
      x = attr(table_cox, "model"),
      title = "Minimal Display",
      labels = clintrial_labels,
      show_n = FALSE,
      show_events = FALSE,
      indent_groups = TRUE
    )
    queue_plot(example10b)

# Formatting Options

## Example 11: Adjusting Numeric Precision

The `digits` parameter controls decimal places for effect estimates and confidence intervals:

    example11 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Custom Precision (3 decimal places)",
      labels = clintrial_labels,
      digits = 3,
      indent_groups = TRUE
    )
    queue_plot(example11)

## Example 12: Custom Reference Label

Customize the label shown for reference categories:

    example12 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Custom Reference Label",
      labels = clintrial_labels,
      ref_label = "ref",
      indent_groups = TRUE
    )
    queue_plot(example12)

## Example 13: Custom Effect Measure Label

Change the column header for the effect measure:

    example13 <- coxforest(
      x = attr(table_cox, "model"),
      title = "Custom Effect Label",
      labels = clintrial_labels,
      effect_label = "Effect (95% CI)",
      indent_groups = TRUE
    )
    queue_plot(example13)

## Example 14: Custom Colors

The `color` parameter changes the point and line color:

    example14 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Custom Color",
      labels = clintrial_labels,
      color = "#E41A1C",
      indent_groups = TRUE
    )
    queue_plot(example14)

## Example 15: Change Font Sizes

Adjust text size with the `font_size` multiplier:

    example15 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Larger Font (1.5×)",
      labels = clintrial_labels,
      font_size = 1.5,
      indent_groups = TRUE
    )
    queue_plot(example15)

## Example 16: Table Width

The `table_width` parameter adjusts the proportion of space allocated to the table vs. the forest plot:

    # Wide table (for long variable names)
    example16a <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Wide Table (75%)",
      labels = clintrial_labels,
      table_width = 0.75
    )
    queue_plot(example16a)

    # Narrow table (emphasizes forest plot)
    example16b <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Narrow Table (50%)",
      labels = clintrial_labels,
      table_width = 0.50
    )
    queue_plot(example16b)

# Saving Forest Plots

Forest plots include a `rec_dims` attribute for optimal sizing when saving to files.

## Example 17: Recommended Dimensions

Use the recommended dimensions for optimal output:

    p <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Publication-Ready Plot",
      labels = clintrial_labels,
      indent_groups = TRUE,
      zebra_stripes = TRUE
    )

    # Get recommended dimensions
    dims <- attr(p, "rec_dims")
    cat("Width:", dims$width, "inches\n")
    cat("Height:", dims$height, "inches\n")

    # Save with recommended dimensions
    ggsave(
      filename = file.path(tempdir(), "forest_plot.pdf"),
      plot = p,
      width = dims$width,
      height = dims$height,
      units = "in"
    )

## Example 18: Multiple Formats

Export to different formats as needed:

    p <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Forest Plot",
      labels = clintrial_labels
    )

    dims <- attr(p, "rec_dims")

    # PDF (vector, best for publications)
    ggsave("forest.pdf", p, width = dims$width, height = dims$height)

    # PNG (raster, good for presentations)
    ggsave("forest.png", p, width = dims$width, height = dims$height, dpi = 300)

    # TIFF (high-quality raster, often required by journals)
    ggsave("forest.tiff", p, width = dims$width, height = dims$height, dpi = 300)

    # SVG (vector, good for web)
    ggsave("forest.svg", p, width = dims$width, height = dims$height)

# Advanced Customization

Forest plots support extensive customization for publication requirements.

## Example 19: Combined Options

Combine multiple options for publication-ready output:

    example19 <- coxforest(
      x = attr(table_cox, "model"),
      title = "Comprehensive Survival Analysis",
      labels = clintrial_labels,
      effect_label = "Hazard Ratio",
      digits = 2,
      show_n = TRUE,
      show_events = TRUE,
      indent_groups = TRUE,
      condense_table = FALSE,
      zebra_stripes = TRUE,
      ref_label = "reference",
      font_size = 1.0,
      table_width = 0.62,
      color = "#8A61D8"
    )
    queue_plot(example19)

## Example 20: Extensions with ggplot2

Forest plots are `ggplot2` objects and can be modified further:

    example20 <- glmforest(
      x = attr(table_logistic, "model"),
      title = "Extended with ggplot2",
      labels = clintrial_labels,
      indent_groups = TRUE
    )

    example20_modified <- example20 +
      theme(
        plot.title = element_text(face = "italic", color = "#A72727"),
        plot.background = element_rect(fill = "white", color = NA)
      )
    queue_plot(example20_modified)

# Additional GLM Families

The `glmforest()` function supports all GLM families. These can be plotted in a similar fashion to standard logistic regression forest plots. See Regression Modeling for the full list of supported model types.

## Example 21: Poisson Regression

For equidispersed count outcomes (variance ≈ mean), use Poisson regression:

    poisson_model <- glm(
      fu_count ~ age + stage + treatment + surgery,
      data = clintrial,
      family = poisson
    )

    example21 <- glmforest(
      x = poisson_model,
      data = clintrial,
      title = "Poisson Regression: Follow-Up Visits",
      labels = clintrial_labels
    )
    queue_plot(example21)

## Example 22: Negative Binomial Regression

For overdispersed count outcomes (variance > mean), negative binomial regression is preferred. Using `fit()` ensures proper handling:

    nb_result <- fit(
      data = clintrial,
      outcome = "ae_count",
      predictors = c("age", "treatment", "diabetes", "surgery"),
      model_type = "negbin",
      labels = clintrial_labels
    )

    example22 <- glmforest(
      x = nb_result,
      title = "Negative Binomial: Adverse Events"
    )
    queue_plot(example22)

# Function Parameter Summary

Parameter | Description | Default
--- | --- | ---
`x` | Model object or model from `fit()` output | Required
`data` | Data frame (required for model objects) | `NULL`
`title` | Plot title | `NULL`
`labels` | Named vector for variable labels | `NULL`
`indent_groups` | Indent factor levels under variable names | `FALSE`
`condense_table` | Show binary variables on single row | `FALSE`
`zebra_stripes` | Alternating row shading | `TRUE`
`show_n` | Display sample size column | `TRUE`
`show_events` | Display events column (Cox models) | `TRUE`
`digits` | Decimal places for estimates | `2`
`ref_label` | Label for reference categories | `"reference"`
`effect_label` | Column header for effect measure | Model-dependent
`color` | Color for points and lines | Effect-type dependent
`font_size` | Text size multiplier | `1.0`
`table_width` | Proportion of width for table | `0.55`

# Best Practices

## Model Preparation

1. Ensure all factor levels are properly defined before fitting
2. Use meaningful reference categories
3. Consider centering continuous variables for interpretability
4. Check model convergence before plotting

## Visual Design

1. Use `indent_groups = TRUE` for cleaner presentation of categorical variables
2. Match `table_width` to variable name lengths
3. Consider `condense_table = TRUE` for binary predictors
4. Use consistent colors across related figures

## Publication Preparation

1. Use `rec_dims` attribute for optimal sizing
2. Save as PDF for vector graphics in print
3. Use 300+ DPI for raster formats
4. Check that reference lines and confidence intervals are clearly visible

# Common Issues

## Long Variable Names

If variable names are truncated, increase `table_width`:

    p <- glmforest(model, table_width = 0.75)

## Overlapping Text

Reduce font size or increase figure dimensions:

    p <- glmforest(model, font_size = 0.9)
    ggsave(file.path(tempdir(), "plot.pdf"), p, width = 14, height = 8)

## Missing Labels

Ensure labels vector includes all variable names:

    labels <- c(
      age = "Age (years)",
      sex = "Sex",
      stage = "Disease Stage"
    )
    p <- glmforest(model, labels = labels)

# Further Reading

- Descriptive Tables: `desctable()` for baseline characteristics
- Survival Tables: `survtable()` for time-to-event summaries
- Regression Modeling: `fit()`, `uniscreen()`, and `fullfit()`
- Model Comparison: `compfit()` for comparing models
- Table Export: Export to PDF, Word, and other formats
- Multivariate Regression: `multifit()` for multi-outcome analysis
- Advanced Workflows: Interactions and mixed-effects models