model {BioSeqClass} | R Documentation |
These functions build various classification models.
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
## 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)])