model {BioSeqClass} | R Documentation

## Classification Models

### Description

These functions build various classification models.

### Usage

      classifyModelLIBSVM(train,svm.kernel="linear",svm.scale=FALSE)
      classifyModelSVMLIGHT(train,svm.path,svm.options="-t 0")
      classifyModelNB(train)
      classifyModelRF(train)
      classifyModelKNN(train, test, knn.k=1)
      classifyModelTree(train)
      classifyModelNNET(train, nnet.size=2, nnet.rang=0.7, nnet.decay=0, nnet.maxit=100)
      classifyModelRPART(train)
      classifyModelCTREE(train)
      classifyModelCTREELIBSVM(train, test, svm.kernel="linear",svm.scale=FALSE)
      classifyModelBAG(train)

### Arguments

`train` | a data frame including the feature matrix and class label. The last column is a vector of class label comprising of "-1" or "+1"; Other columns are features.
`svm.kernel` | a string for kernel function of SVM.
`svm.scale` | a logical vector indicating the variables to be scaled.
`svm.path` | a character for path to SVMlight binaries (required, if path is unknown by the OS).
`svm.options` | Optional parameters to SVMlight. For further details see: "How to use" on http://svmlight.joachims.org/. (e.g.: "-t 2 -g 0.1"))
`nnet.size` | number of units in the hidden layer. Can be zero if there are skip-layer units.
`nnet.rang` | Initial random weights on [-rang, rang]. Value about 0.5 unless the inputs are large, in which case it should be chosen so that rang * max(|x|) is about 1.
`nnet.decay` | parameter for weight decay.
`nnet.maxit` | maximum number of iterations.
`knn.k` | number of neighbours considered in function `classifyModelKNN`.
`test` | a data frame including the feature matrix and class label. The last column is a vector of class label comprising of "-1" or "+1"; Other columns are features.

### Details

`classifyModelLIBSVM` builds support vector machine model by LibSVM. R package "e1071" is needed.

`classifyModelSVMLIGHT` builds support vector machine model by SVMlight. R package "klaR" is needed.

`classifyModelNB` builds naive bayes model. R package "klaR" is needed.

`classifyModelRF` builds random forest model. R package "randomForest" is needed.

`classifyModelKNN` builds k-nearest neighbor model. R package "class" is needed.

`classifyModelTree` builds tree model. R package "class" is needed.

`classifyModelRPART` builds recursive partitioning trees model. R package "rpart" is needed.

`classifyModelCTREE` builds conditional inference trees model. R package "party" is needed.

`classifyModelCTREELIBSVM` combines conditional inference trees and support vecotr machine. R package "party" and "e1071" is needed.

`classifyModelBAG` uses bagging method. R package "ipred" is needed.

### Author(s)

Hong Li

### Examples

      ## read positive/negative sequence from files.
      tmpfile1 = file.path(path.package("BioSeqClass"), "example", "acetylation_K.pos40.pep")
      tmpfile2 = file.path(path.package("BioSeqClass"), "example", "acetylation_K.neg40.pep")
      posSeq = as.matrix(read.csv(tmpfile1,header=FALSE,sep="\t",row.names=1))[,1]
      negSeq = as.matrix(read.csv(tmpfile2,header=FALSE,sep="\t",row.names=1))[,1]
      data = data.frame(rbind(featureBinary(posSeq,elements("aminoacid")),
           featureBinary(negSeq,elements("aminoacid")) ),
           class=c(rep("+1",length(posSeq)),
           rep("-1",length(negSeq))) )

      ## sample train and test data
      tmp = c(sample(1:length(posSeq),length(posSeq)*0.8),
        sample(length(posSeq)+(1:length(negSeq)),length(negSeq)*0.8))
      train = data[tmp,]
      test = data[-tmp,]

      ## Build classification model using training data
      model1 = classifyModelLIBSVM(train,svm.kernel="linear",svm.scale=FALSE)
      ## Predict test data by classification model
      testClass = predict(model1, test[,-ncol(test)])

[Package BioSeqClass version 1.45.0 Index]