ClassifierBuild: Building a classifier as a combination of preprocessing and...

In MCRestimate: Misclassification error estimation with cross-validation

Description

builds a classifier as a combination of preprocessing and classification methods

Usage

ClassifierBuild(eset,
                class.column,
                reference.class=NULL,
                classification.fun,
                variableSel.fun ="identity",
                cluster.fun ="identity",
                poss.parameters=list(),
                cross.inner=10,
                rand=123,
                information=TRUE,
                thePreprocessingMethods=c(variableSel.fun,cluster.fun))

Arguments

eset	an object of class exprSet or exprSetRG
class.column	a number or a character string which indicated the column of the expression set's phenodata containing the class label
reference.class	a character string with the name of one class - if specified the class will form the first class and all the other classes will form the second class
classification.fun	a character string which names the function that should be used for the classification
variableSel.fun	character string which names the function that should be used for variable selection
cluster.fun	character string which names the function that should be used for clustering the variables
thePreprocessingMethods	vector of character with the names of all preprocessing functions- can be used instead of 'variableSel.fun' and 'cluster.fun' - see details
poss.parameters	a list of possible values for the parameter of the classification method
cross.inner	integer - the number of nearly equal sized parts the train set should be divided into
rand	integer - the random number generator will be put in a reproducible state
information	information - should classifier specific data be given(depends on the wrapper for the classification method)

Value

a list with the following arguments:

classifier.for.matrix
classifier.for.exprSet
parameter	a list consisting of the estimated 'best' parameter for each cross-validation part
class.method	string which names the function used for the classification
thePreprocessingMethods	character string - name of the preprocessing functions that have been used
cross.inner	number of blocks for a the inner cross-validation
information	classifier specific data

Author(s)

Markus Ruschhaupt mailto:[e-mail]

Examples

library(golubEsets)
data(Golub_Train)

class.column <- "ALL.AML"
Preprocessingfunctions <- c("varSel.highest.var")
list.of.poss.parameter <- list(var.numbers = c(250,1000))
classification.funct <- "RF.wrap"
cross.inner <- 5

RF.classifier <- ClassifierBuild(Golub_Train,
     class.column,
			classification.fun = classification.funct,
     thePreprocessingMethods = Preprocessingfunctions,
     poss.parameters = list.of.poss.parameter,
     cross.inner = cross.inner)

MCRestimate documentation built on Oct. 31, 2019, 10:29 a.m.