Assess_Folds: Assess_Folds

In AndrewjSage/RFLOWESS: A robust approach to random forest regression

Description

Function to take partitioned data, and make predictions across all folds

Usage

Assess_Folds(dataset, partitions, p, fold, ntrees, ndsize, ntreestune, parvec,
  cvreps, cvfolds, tol)

Arguments

dataset	List of partitioned dataframes for training, test sets, and indicator of outlying observations
partitions	array containing indices of training and test cases
p	percentage of training cases for which to add contamination (using N(0, 5*sd(Y)))
fold	fold on which to assess performance
ntrees	number of trees
ndsize	nodesize
ntreestune	number of trees to use for tuning alpha
parvec	vector of candidate values for tuning parameter alpha
cvreps	number of repetitions to perform in cross validation
cvfolds	number of folds to perform in cross validation
tol	maximal change in interation for LOWESSRF weights in cross validation

Value

returns a list of 4 items 1. Datasets (TRAIN, TEST, and Outlier Indicator) 2. Matrix of 16 columns giving different predictions. Last column is true Y. 3. Number of iterations 4. Output from TuneMultifoldCV (a list of 8 items itself)

AndrewjSage/RFLOWESS documentation built on May 26, 2019, 6:38 a.m.