Evaluate_Tuning_Candidates: Evaluate Tuning Candidates

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

Description

This function applies Calc_fold_CV_Error over all folds of a cross validation. Use replicate if running multiple repetitions of cross-validation

Usage

Evaluate_Tuning_Candidates(TRAIN, OutlierInd, cvfolds, parvec, ndsize,
  ntreestune = 100, tol = 10^-4)

Arguments

TRAIN	matrix of training cases with response in last column
OutlierInd	Vector of zeros and ones indicating whether training cases came from contaminating distribution
cvfolds	number of folds to perform in cross validation
parvec	vector of candidate values for tuning parameter alpha
ndsize	nodesize random forest tuning parameter for cross validation
ntreestune	number of trees to use for forests involved in parameter tuning
tol	maximal change in interation for LOWESSRF weights in cross validation

Value

Returns array of dimensions 6 by 3 by length(parvec) by number of folds, containing errors. Dimensions index (1) type of error calculated 1-MSE without downweighting outliers in CV error 2-MAPE without downweighting outliers in CV error 3-MSE downweighting outliers according to BisqwtRF 4-MAPE downweighting outliers according to BisqwtRF 5-MSE downweighting outliers according to BisqwtRFL 6-MAPE downweighting outliers according to BisqwtRFL (2) applied to all cases in TRAIN2, outliers only, nonoutliers only (3) index of alpha from parvec (4) index of fold in cross validation

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