ensembleRf: Build ensemble random forest classification panel

In singha53/amritr: Accessory Functions Used by Amrit Singh

Description

Build ensemble random forest classification panel

Usage

ensembleRf(X.trainList, y.train, panelLength = 15, X.testList = NULL,
  y.test = NULL, filter = "none", topranked = 50,
  keepVarList = NULL)

Arguments

X.trainList	list of training datasets (nxpi); i number of elements
y.train	n-vector of class labels (must be a factor)
panelLength	number of variables to select based on the Importance score
X.testList	list of test datasets (nxpi); i number of elements
y.test	n-vector of class labels (must be a factor)
filter	pre-filtering of initial datasets - "none" or "p.value"
topranked	Number of topranked features based on differential expression to use to build classifer
keepVarList	which variables to keep and not omit (set to NULL if no variables are forced to be kept)

Value

model

testPerf

X.trainList

y.train

alphaList

lambdaList

family

X.testList

y.test

filter

topranked

keepVarList

singha53/amritr documentation built on July 21, 2019, 3:46 p.m.