RF_optimal_test: RF_optimal_test

In DCAN-Labs/RFRF: Functional Random Forest in R

RF_optimal_test

Description

this function tests the optimal random forest model from RF_optimal_train with testing data folds

Usage

RF_optimal_test(
  object = optimal_random_forest,
  newdata = testing_dataset,
  proximity_flag = TRUE
)

Arguments

proximity_flag	boolean; whether or not proximity should be calculated
optimal_random_forest	the resulting object from RF_optimal_train function
testing_dataset	the heldout data from holdout function

Examples

number_of_training_participants <- 10
number_of_testing_participants <- 10
covariance_matrix <- diag(2)
outcome_column <- 1
means <- c(0,0)
training_dataset <- simulate_data(number_of_training_participants,covariance_matrix,outcome_column,means)
mtry <- 1
nodesize <- 1
formula <- y~x
model_type <- rfsrc()
random_forest <- RF_Train(training_dataset,formula,mtry,nodesize,model_type)
testing_dataset <- simulate_data(number_of_testing_participants,covariance_matrix,outcome_column,means)
proximity <- TRUE
Optimal_RF_Parameters <- RF_Test(object=random_forest,newdata=testing_dataset,proximity=proximity)
optimal_random_forest <- RF_optimal_train(data=training_dataset,formula=y~x,Optimal_RF_Parameters=NULL,model_type="rfsrc")
RF_prediction <- RF_optimal_test(object=optimal_random_forest,newdata=testing_dataset,proximity_flag=TRUE)

DCAN-Labs/RFRF documentation built on March 15, 2024, 2:33 p.m.