bagging.wrapper: Bagging Wrapper for Ensemble Features Selection

Description Usage Arguments Value Author(s)

Description

Compiles results of ensemble feature selection

Usage

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bagging.wrapper(X, Y, method, bags, f, aggregation.metric, k.folds, repeats,
  res, tuning.grid, optimize, optimize.resample, metric, model.features,
  allowParallel, verbose, theDots)

Arguments

X

A matrix containing numeric values of each feature

Y

A factor vector containing group membership of samples

method

A vector listing models to be fit

bags

Number of bags to be run

f

Number of features desired

aggregation.metric

string indicating the type of ensemble aggregation. Avialable options are "CLA" (Complete Linear), "EM" (Ensemble Mean), "ES" (Ensemble Stability), and "EE" (Ensemble Exponential)

k.folds

Number of folds generated during cross-validation

repeats

Number of times cross-validation repeated

res

Optional - Resolution of model optimization grid

tuning.grid

Optional list of grids containing parameters to optimize for each algorithm. Default "tuning.grid = NULL" lets function create grid determined by "res"

optimize

Logical argument determining if each model should be optimized. Default "optimize = TRUE"

optimize.resample

Logical argument determining if each resample should be re-optimized. Default "optimize.resample = FALSE" - Only one optimization run, subsequent models use initially determined parameters

metric

Criteria for model optimization. Available options are "Accuracy" (Predication Accuracy), "Kappa" (Kappa Statistic), and "AUC-ROC" (Area Under the Curve - Receiver Operator Curve)

model.features

Logical argument if should have number of features selected to be determined by the individual model runs. Default "model.features = FALSE"

allowParallel

Logical argument dictating if parallel processing is allowed via foreach package. Default allowParallel = FALSE

verbose

Logical argument if should output progress

theDots

Optional arguments provided for specific models or user defined parameters if "optimize = FALSE".

Value

results

List with the following elements:

bestTunes

If "optimize.resample = TRUE" then returns list of best parameters for each iteration

Author(s)

Charles Determan Jr


cdeterman/OmicsMarkeR documentation built on May 13, 2019, 2:35 p.m.