predictive_biomarker_imp {BioPred}R Documentation

Plot Predictive Biomarker Importance based on XGBoost-based Subgroup Model

Description

This function calculates and plots the importance of biomarkers in a trained XGBoostSub_con, XGBoostSub_bin or XGBoostSub_sur model.

Usage

predictive_biomarker_imp(model)

Arguments

model

The trained XGBoost-based model.

Value

A barplot showing the biomarker importance.

Examples

X_data <- matrix(rnorm(100 * 10), ncol = 10)  # 100 samples with 10 features
y_data <- rnorm(100)  # continuous outcome variable
trt <- sample(c(1, -1), 100, replace = TRUE)  # treatment indicator (1 or -1)
pi <- runif(100, min = 0.3, max = 0.7)  # propensity scores between 0 and 1

# Define XGBoost parameters
params <- list(
  max_depth = 3,
  eta = 0.1,
  subsample = 0.8,
  colsample_bytree = 0.8
)

# Train the model using A-learning loss
model_A <- XGBoostSub_con(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  Loss_type = "A_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)
biomarker_imp=predictive_biomarker_imp(model_A)

[Package BioPred version 1.0.2 Index]