library(randomForest)
library(caret)
library(pROC)
library(ROCR)
#data
getwd()
#setwd("D:\\paper\\natal home\\data\\juvenile")
nh.syc<-read.csv("rf-i3j3.csv",header = T)
#presyc<-read.csv("adult.csv",header=T)
#trainlist
trainlist<-createDataPartition(nh.syc$Areas,p=0.75,list=F)
trainset<-nh.syc[trainlist,]
testset<-nh.syc[-trainlist,]
#build model
set.seed(6666)
rf.train<-randomForest(as.factor(Areas)~.,
                       data=trainset,importance=TRUE,na.action = na.pass)
plot(rf.train)
rf.train
#predicet
set.seed(666)
rf.test<-predict(rf.train,newdata=testset,type="class")
rf.cf<-caret::confusionMatrix(as.factor(rf.test),
                              as.factor(testset$Areas))
rf.cf
#ROC
rf.test2<-predict(rf.train,newdata=testset,type="prob")
rf.test2
coc.rf<-multiclass.roc(testset$Areas,rf.test2[,1])
coc.rf
auc(coc.rf)
#discrimination
presyc<-read.csv("adult.csv")
rf2.pre<-predict(rf.train,newdata=presyc,type="class")
rf2.pre