eval_metric_sur {BioPred} | R Documentation |
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.