RF_model: RF_model

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

RF_model

Description

this function trains then tests a random forest model with training and testing data folds

Usage

RF_model(
  RF_optimal_train = RF_optimal_train,
  RF_optimal_test = RF_optimal_test
)

Arguments

training_dataset	the training data
formula	formula describing the model fit
Optimal_RF_Parameters	returned from RF_test function
model_type	which type of rfsrc to use
optimal_random_forest	the resulting object from RF_optimal_train function
testing_dataset	the heldout data from holdout function
proximity_flag	boolean; whether or not proximity should be calculated

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=TRUE)
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)
RF_prediction <- RF_model(RF_optimal_train=RF_optimal_train,RF_optimal_test=RF_optimal_test)

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