Assess_Real_Data: Assess_Real_Data

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

Description

Function to divide real datasets into test and training sets, possibly add contamination and assess predictions

Usage

Assess_Real_Data(dataset, nfolds, p, ntrees, ndsize, ntreestune, parvec, cvreps,
  cvfolds, tol)

Arguments

dataset	real dataset to work with
nfolds	number of folds to partition into to assess performance
p	percentage of training cases for which to add contamination (using N(0, 5*sd(Y)))
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.