Batch Process and Plot QINI Curves for Multiple Models — margot_plot_qini

This function processes a subset of models (or all models by default), creates QINI (Qini coefficient) plots for each model using the margot package.

Usage

margot_plot_qini_batch(
  mc_result,
  model_names = NULL,
  label_mapping = NULL,
  spend_levels = c(0.1, 0.4),
  show_spend_lines = TRUE,
  spend_line_color = "red",
  spend_line_alpha = 0.5,
  theme = "classic",
  show_ci = FALSE,
  ci_alpha = 0.05,
  ci_n_points = 20,
  ci_ribbon_alpha = 0.3,
  ci_ribbon_color = NULL,
  horizontal_line = TRUE,
  grid_step = NULL,
  ylim = NULL,
  baseline_method = "auto",
  cate_color = "#d8a739",
  ate_color = "#4d4d4d",
  scale = "average",
  treatment_cost = NULL,
  seed = 12345,
  fixed_ylim = FALSE
)

Arguments

mc_result: A list containing the results from margot_causal_forest().
model_names: Optional character vector of model names to process. Can be specified with or without the "model_" prefix. Default NULL (all models).
label_mapping: Optional named list for custom label mappings. Keys should be original variable names (with or without "model_" prefix), and values should be the desired display labels. Default is NULL.
spend_levels: Numeric vector of spend levels to show with vertical lines. Default is 0.1.
show_spend_lines: Logical indicating whether to show vertical lines at spend levels. Default is TRUE.
spend_line_color: Color for spend level lines. Default is "red".
spend_line_alpha: Alpha transparency for spend lines. Default is 0.5.
theme: Character string specifying the ggplot2 theme. Default is "classic".
show_ci: Logical or character indicating which confidence intervals to show. Options: FALSE (none), TRUE or "both" (both curves), "cate" (CATE only), "ate" (ATE only). Default is FALSE.
ci_alpha: Significance level for confidence intervals. Default is 0.05.
ci_n_points: Number of points at which to compute confidence intervals. Default is 20.
ci_ribbon_alpha: Alpha transparency for confidence interval ribbons. Default is 0.3.
ci_ribbon_color: Color for confidence interval ribbons. If NULL (default), uses the curve color.
horizontal_line: Logical indicating whether to draw horizontal lines where Qini curves plateau when the path is complete. Default is TRUE.
grid_step: Integer specifying the step size for subsampling the curve data. If NULL (default), uses max(floor(nrow(qini_data) / 1000), 1). Set to 1 to plot all points.
ylim: Numeric vector of length 2 specifying the y-axis limits c(min, max). Default is NULL (automatic scaling).
baseline_method: Method for generating baseline: "auto" (default), "maq_no_covariates", "simple", "maq_only", or "none". See details in margot_generate_qini_data().
cate_color: Color for the CATE (targeted treatment) curve. Default is "#d8a739" (gold).
ate_color: Color for the ATE (no-priority/uniform assignment) curve. Default is "#4d4d4d" (dark gray).
scale: Character string specifying the scale for gains: "average" (default), "cumulative", or "population". "average" shows expected policy effect per unit (maq default), "cumulative" shows traditional cumulative gains, "population" shows total population impact.
treatment_cost: Numeric scalar; the treatment cost used in QINI calculations. Default is NULL, which attempts to extract the cost from model metadata. If not found, assumes cost = 1. When cost differs from stored cost, QINI curves are automatically regenerated. When cost differs from 1, it will be shown in the plot subtitle.
seed: Integer; seed for reproducible QINI generation when treatment_cost differs from stored cost. Default is 12345.
fixed_ylim: Logical; if TRUE and ylim is NULL, calculates y-axis limits that would accommodate a cost=1 scenario for consistent scaling across different cost comparisons. Default is FALSE.

Value

A list containing the generated ggplot objects for each processed model.

Examples

if (FALSE) { # \dontrun{
# Process all models
qini_plots <- margot_plot_qini_batch(mc_result)

# Process specific models with confidence intervals
qini_plots <- margot_plot_qini_batch(
  mc_result,
  model_names = c("t2_belong_z", "t2_meaning_z"),
  show_ci = TRUE, # shows CI for both curves
  ci_n_points = 50
)

# Show CI only for CATE curves
qini_plots <- margot_plot_qini_batch(
  mc_result,
  model_names = c("anxiety", "depression"),
  show_ci = "cate"
)

# Custom label mapping
label_mapping <- list(
  "t2_belong_z" = "Belonging",
  "t2_meaning_z" = "Meaning in Life"
)
qini_plots <- margot_plot_qini_batch(
  mc_result,
  label_mapping = label_mapping
)
} # }