eval_metric_sur: Evaluation Metrics for XGBoostSub_sur Model

In BioPred: An R Package for Biomarkers Analysis in Precision Medicine

Evaluation Metrics for XGBoostSub_sur Model

Description

Function for evaluating XGBoostSub_sur model performance.

Usage

eval_metric_sur(
  model,
  X_feature,
  y_label,
  pi,
  trt,
  censor,
  Loss_type = "A_learning"
)

Arguments

model	The trained XGBoostSub_sur model object.
X_feature	The input features matrix.
y_label	The input y matrix.
pi	The propensity scores vector, which should range from 0 to 1, representing the probability of assignment to treatment.
trt	The treatment indicator vector. Should take values of 1 or -1, where 1 represents the treatment group and -1 represents the control group.
censor	The censor status vector. Should take values of 1 or 0, where 1 represents censoring and 0 represents an observed event.
Loss_type	Type of loss function to use: "A_learning" or "Weight_learning".

Details

eval_metric: Function for Evaluating XGBoostSub_con Model Performance

This function evaluates the performance of an XGBoostSub_con model using a A-learning or weight-learning function.

Value

Evaluation result of the XGBoostSub_sur model.

BioPred documentation built on Nov. 4, 2024, 9:06 a.m.