assess.R

CvSampling<- function(Nobs=1000,K=5){
  #======================
  # return sample row number: used to sampling the subset to cross-validation predition.
  # rank<-CvSampling(Nobs=dim(data)[1],K=5)
  # train=x[[1]]$train
  # test=x[[1]]$test
  #====================
  rs <- runif(Nobs)
  id <- seq(Nobs)[order(rs)]
  k <- as.integer(Nobs*seq(1,K-1)/K)
  k <- matrix(c(0,rep(k,each=2),Nobs),ncol=2,byrow=TRUE)
  k[,1] <- k[,1]+1
  l <- lapply(seq.int(K),function(x,k,d) list(train=d[!(seq(d) %in% seq(k[x,1],k[x,2]))], test=d[seq(k[x,1],k[x,2])]),k=k,d=id)
  return(l)
}
chan<-function(x){
  x[x<0.5]<-0; x[x>=0.5]<-1
  x
}
assess<-function(model,train,test,trainlabel,testlabel){
  t1<-table(chan(predict(model, train)), trainlabel)
  t2<-table(chan(predict(model, test)), testlabel)
  if(nrow(t1)==2 && nrow(t2)==2){
  sen.train<-t1[2,2]/(t1[1,2]+t1[2,2])
  spe.train<-t1[1,1]/(t1[2,1]+t1[1,1])
  sen.test<-t2[2,2]/(t2[1,2]+t2[2,2])
  spe.test<-t2[1,1]/(t2[2,1]+t2[1,1])
  accu.train<-(t1[1,1]+t1[2,2])/sum(t1)
  accu.test<-(t2[1,1]+t2[2,2])/sum(t2)
  rlt<-c(sen.train,spe.train, accu.train,sen.test,spe.test,accu.test)
  rlt
  }
}
assess2<-function(model1,model2,trainlabel,testlabel){
  t1<-table(model1,trainlabel)
  t2<-table(model2,testlabel)
  sen.train<-t1[2,2]/(t1[1,2]+t1[2,2])
  spe.train<-t1[1,1]/(t1[2,1]+t1[1,1])
  sen.test<-t2[2,2]/(t2[1,2]+t2[2,2])
  spe.test<-t2[1,1]/(t2[2,1]+t2[1,1])
  accu.train<-(t1[1,1]+t1[2,2])/sum(t1)
  accu.test<-(t2[1,1]+t2[2,2])/sum(t2)
  rlt<-c(sen.train,spe.train, accu.train,sen.test,spe.test,accu.test)
  rlt
}
assess3<-function(model,train,test,trainlabel,testlabel){
  t1<-table((predict(model, train)), trainlabel)
  t2<-table((predict(model, test)), testlabel)
  sen.train<-t1[2,2]/(t1[1,2]+t1[2,2])
  spe.train<-t1[1,1]/(t1[2,1]+t1[1,1])
  sen.test<-t2[2,2]/(t2[1,2]+t2[2,2])
  spe.test<-t2[1,1]/(t2[2,1]+t2[1,1])
  accu.train<-(t1[1,1]+t1[2,2])/sum(t1)
  accu.test<-(t2[1,1]+t2[2,2])/sum(t2)
  rlt<-c(sen.train,spe.train, accu.train,sen.test,spe.test,accu.test)
  rlt
}
assess4<-function(model1,model2,trainlabel,testlabel){
  t1<-table(chan(model1), trainlabel)
  t2<-table(chan(model2), testlabel)
  sen.train<-t1[2,2]/(t1[1,2]+t1[2,2])
  spe.train<-t1[1,1]/(t1[2,1]+t1[1,1])
  sen.test<-t2[2,2]/(t2[1,2]+t2[2,2])
  spe.test<-t2[1,1]/(t2[2,1]+t2[1,1])
  accu.train<-(t1[1,1]+t1[2,2])/sum(t1)
  accu.test<-(t2[1,1]+t2[2,2])/sum(t2)
  rlt<-c(sen.train,spe.train, accu.train,sen.test,spe.test,accu.test)
  rlt
}