Assess_Folds: Assess_Folds

Description Usage Arguments Value

Description

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

Usage

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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.