Description Usage Arguments Details Author(s) Examples

These functions build various classification models.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
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)
``` |

`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 |

`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. |

`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.

Hong Li

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## 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)])
``` |

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