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Heatmap of PCL-5 symptom selection frequency across optimization scenarios

Source: R/scenario_plots.R
plot_symptom_frequency.Rd

Visualises how often each of the 20 PCL-5 symptoms is selected across the top combinations of each optimization scenario in a compare_optimizations result. Replicates the symptom-frequency heatmap (Figure 1) of the PTSDdiag preprint and helps identify "core" symptoms that recur across data-driven combinations.

Usage

plot_symptom_frequency(
  comparison,
  type = c("relative", "absolute"),
  show_overall = TRUE,
  overall_includes_fixed = FALSE,
  symptom_labels = NULL,
  low_colour = "#f7fbff",
  high_colour = "#084594"
)

Arguments

comparison

A ptsdiag_comparison object.

type

"relative" (default; fill = RelFreq, percentage labels) or "absolute" (fill = Count).

show_overall

Logical. Include the pooled OVERALL row. Default TRUE.

overall_includes_fixed

Logical. If TRUE, fixed criteria contribute to the OVERALL row. Default FALSE.

symptom_labels

Optional character vector of length 20 used to label the x-axis ticks. Default uses the numeric indices 1:20.

low_colour, high_colour

Gradient endpoints for the fill scale.

Value

A ggplot object. Users can extend it with additional layers, themes, or labels via the usual + operator.

Details

Each tile shows the frequency with which a symptom appears in the stored combinations of a scenario. Fixed criteria (e.g. ICD-11) appear as rows with cells at RelFreq = 1 on their included symptoms and RelFreq = 0 elsewhere. The optional OVERALL row pools across optimization scenarios by default (set overall_includes_fixed = TRUE to include fixed criteria in the pool). It is rendered in a separate facet so it is visually distinct from the per-scenario rows.

Requires the ggplot2 package.

See also

compare_optimizations, symptom_frequency, summarize_top_combinations.

Examples

# \donttest{
# Use a 250-row subset and a small 4-symptom search to keep the example
# fast; omit `scenarios` to run the three default rules
ptsd_data <- rename_ptsd_columns(simulated_ptsd[1:250, ],
                                 id_col = c("patient_id", "age", "sex"))
comp <- compare_optimizations(
  ptsd_data,
  scenarios = list(
    "3/4 Non-hierarchical" = list(n_symptoms = 4, n_required = 3,
                                  hierarchical = FALSE)
  ),
  include_icd11 = TRUE, n_top = 5, show_progress = FALSE
)
#> ℹ Evaluated 4845 combinations. Best: 6, 7, 12, 17
plot_symptom_frequency(comp)

# }