inst/scripts/Template.R

library(MCRestimate)
library(randomForest)
library(pamr)
library(e1071)
the.expression.set <- get(load(DataSet))

if(RF){
list.of.parameter <- c(list(mtry=mtry.range,ntree=ntree.range),parameter.for.preprocessing)
 r.forest <- MCRestimate(the.expression.set,
                         class.colum,
                         classification.fun="RF.wrap",
                         poss.parameters=list.of.parameter,
			 thePreprocessingMethods=thePreprocessingfunktionsRF,
                         cross.outer=cross.outer,
                         cross.inner=cross.inner,
                         cross.repeat=cross.repeat,
                         reference.class=ref.class,
                         plot.label=plot.label,
			 rand=SEED)
 save(r.forest, file=paste("backRF",SEED,".RData",sep=""))
    }


if(GPLS)
   {r.gpls <- MCRestimate(the.expression.set,
                          class.colum,
                          classification.fun="GPLS.wrap",
                          poss.parameter=parameter.for.preprocessing,
			  thePreprocessingMethods=thePreprocessingfunktionsGPLS,
                          cross.outer=cross.outer,
                          cross.repeat=cross.repeat,
                          cross.inner=cross.inner,
                          reference.class=ref.class,
                          plot.label=plot.label,
			 rand=SEED)
    save(r.gpls, file=paste("backGPLS",seed,".RData",sep=""))
  }



if(PAM){
list.of.parameter <- c(list(threshold=thresholds),parameter.for.preprocessing)

r.pam <- MCRestimate(the.expression.set,
                     class.colum,
                     classification.fun="PAM.wrap",
                     poss.parameter=list.of.parameter,
		     thePreprocessingMethods=thePreprocessingfunktionsPAM,
                     cross.outer=cross.outer,
                     cross.repeat=cross.repeat,
                     cross.inner=cross.inner,
                     reference.class=ref.class,
                     plot.label=plot.label,
                     rand=SEED)
save(r.pam,file=paste("backPAM",SEED,".RData",sep=""))
}


if(PLR){
list.of.parameter <- c(list(kappa=kappa.range),parameter.for.preprocessing)
r.logReg <- MCRestimate(the.expression.set,
                          class.colum,
                          classification.fun="PLR.wrap",
                          poss.parameter=list.of.parameter,
			  thePreprocessingMethods=thePreprocessingfunktionsPLR,
                          cross.outer=cross.outer,
                          cross.repeat=cross.repeat,
                          cross.inner=cross.inner,
                          reference.class=ref.class,
                          plot.label=plot.label,
			 rand=SEED)
save(r.logReg,file=paste("backlogReg",SEED,".RData",sep=""))
}

if(SVM){
 list.of.parameter <- c(list (gamma=gamma.range,cost=cost.range),parameter.for.preprocessing)
 r.svm <- MCRestimate(the.expression.set,
                      class.colum,
                      classification.fun="SVM.wrap",
                      poss.parameter=list.of.parameter,
		      thePreprocessingMethods=thePreprocessingfunktionsSVM,
                      cross.outer=cross.outer,
                      cross.repeat=cross.repeat,
                      cross.inner=cross.inner,
                      reference.class=ref.class,
                      plot.label=plot.label,
			 rand=SEED)
 save(r.svm, file=paste("backSVM",SEED,".RData",sep=""))
}

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MCRestimate documentation built on Nov. 1, 2018, 3:32 a.m.