Generate and Plot SHAP Explanation Figures

Creates SHAP (SHapley Additive exPlanations) plots to explain feature contributions by training a surrogate model on the original model's scores.

Usage

figure_shap(data, raw_data, target_type, file = NULL, model_type = "xgboost")

Arguments

data: A list containing sample_score, a data frame with sample IDs and score.
raw_data: A data frame with original features. The first column must be the sample ID.
target_type: String, the analysis type: "diagnosis" or "prognosis". This determines which columns in raw_data are treated as features.
file: Optional. A string specifying the path to save the plot. If NULL (default), the plot object is returned.
model_type: String, the surrogate model for SHAP calculation. "xgboost" (default) or "lasso".

Value

A patchwork object combining SHAP summary and importance plots. If file is provided, the plot is also saved.

Examples

# \donttest{
# --- Example for a Diagnosis Model ---
set.seed(123)
train_dia_data <- data.frame(
  SampleID = paste0("S", 1:100),
  Label = sample(c(0, 1), 100, replace = TRUE),
  FeatureA = rnorm(100, 10, 2),
  FeatureB = runif(100, 0, 5)
)
model_results <- list(
  sample_score = data.frame(ID = paste0("S", 1:100), score = runif(100, 0, 1))
)

# Generate SHAP plot object
shap_plot <- figure_shap(
  data = model_results,
  raw_data = train_dia_data,
  target_type = "diagnosis",
  model_type = "xgboost"
)
#> Training 'xgboost' surrogate model and calculating SHAP values...
# To display the plot:
# print(shap_plot)
# }