ClassifierBuild: Building a classifier as a combination of preprocessing and classification method

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

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

Examples

Run this code


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)

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