Usage
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".