ClassifierBuild {MCRestimate} | R Documentation |
builds a classifier as a combination of preprocessing and classification methods
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))
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 classificator specific data be given(depends on the wrapper for the classification method) |
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 |
classificator specific data |
Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de
library(MCRestimate) 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)