SVMTrain: Using Golden Standard Data Sets to Train an SVM Classifier

In ppiPre: Predict Protein-Protein Interactions Based on Functional and Topological Similarities

Description

Golden standard data sets is given by user, the trained SVM classifier will be returned

Usage

SVMTrain(input, replaceNA = 0)

Arguments

input	Gold-standard data sets, including positive and negative samples
replaceNA	The value to replace NA in training set

Value

The trained SVM classifier

Author(s)

Yue Deng <[e-mail]>

See Also

SVMPredict

Examples

  #edges <- data.frame(node1=c("1132", "1133", "1134", "1134", "1145", "1147"),
  #                    node2=c("1134", "1134", "1145", "1147", "1147", "1149"),
  #                    label=c(1, 1, 1, 0, 0, 0))
  #graph<-graph.data.frame(edges,directed=FALSE)
  #samplefile <- "ppiPre-ComputeAllEvidences-sample.csv"
  #write.csv(edges,file=samplefile,row.names=FALSE) 
  #ComputeAllEvidences(input=samplefile,output="AllEvidences-svmtrainsample.csv",organism="human", 
  #                    drop ="IEA", header=TRUE, sep=",") 
  #SVMTrain("AllEvidences-svmtrainsample.csv", replaceNA=0)

ppiPre documentation built on May 30, 2017, 4:33 a.m.