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随机森林回归分析(by R)

发表于 更新于 分类于 Valine：
通过R实现Random Forest对数据进行回归分析
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#导入包
library(randomForest)
library(caret)
library(hydroGOF)

#导入数据
origindata <- read.table('E:/data/R.txt',encoding="UTF-8",header = T)

#最后一行是标签，名字改为target
colnames(origindata)[length(origindata)]<-"target"

#设置随机数，随机划分训练集(8:2)
set.seed(1)
choose<-createDataPartition(y=origindata$target,p=0.8,list=F)
traindata<-origindata[choose,]
testdata<-origindata[-choose,]

#最后一列作为y，其余作为x
ORP<-as.formula(paste0("target~",paste(colnames(traindata)[1:length(traindata)-1],collapse="+")))
ORP_rf<-randomForest(formula = ORP, data = traindata, ntree = 500, mtry = 1,importance = T)

#预测训练集和测试集
trainpred<-predict(ORP_rf,newdata = traindata)
testpred<-predict(ORP_rf,newdata = testdata)

#打印误差
#训练集精度指标
trainR2<-1-sum((traindata$target-trainpred)^2)/sum((traindata$target-mean(traindata$target))^2)
trainRMSE<-sqrt(mean((traindata$target-trainpred)^2))
trainMAE<-mean(abs(traindata$target-trainpred))
trainRSE<-sqrt(sum((traindata$target-trainpred)^2)) / sqrt(sum((traindata$target-mean(traindata$target))^2))
trainMSE<-mean((traindata$target-trainpred)^2)
trainMAPE<-mean(abs((traindata$target-trainpred) / traindata$target))
trainMSPE<-mean(((traindata$target-trainpred) / traindata$target)^2)
cat("训练集R2:",trainR2,",RMSE:",trainRMSE,",MAE:",trainMAE,",RSE:",trainRSE,",MSE:",trainMSE,",MAPE:",trainMAPE,",MSPE:",trainMSPE)
#测试集集精度指标
testR2<-1-sum((testdata$target-testpred)^2)/sum((testdata$target-mean(testdata$target))^2)
testRMSE<-sqrt(mean((testdata$target-testpred)^2))
testMAE<-mean(abs(testdata$target-testpred))
testRSE<-sqrt(sum((testdata$target-testpred)^2)) / sqrt(sum((testdata$target-mean(testdata$target))^2))
testMSE<-mean((testdata$target-testpred)^2)
testMAPE<-mean(abs((testdata$target-testpred) / testdata$target))
testMSPE<-mean(((testdata$target-testpred) / testdata$target)^2)
cat("测试集R2:",testR2,",RMSE:",testRMSE,",MAE:",testMAE,",RSE:",testRSE,",MSE:",testMSE,",MAPE:",testMAPE,",MSPE:",testMSPE)
cat("target平均:",mean(testdata$target),"target平均误差范围:±",mean(abs(testdata$target-testpred)),"target最大误差:±",max((testdata$target-testpred)))

#画图
test_group <- rep('Test Samples',times=nrow(dtest))
testPlot <- data.frame(testdata$target,testpred,test_group)
colnames(testPlot)<-c('DO',
                      'Prediction_DO',
                      'Group')
train_group <- rep('Train Samples',times=nrow(dtrain))
trainPlot <- data.frame(traindata$target,trainpred,train_group)
colnames(trainPlot)<-c('DO',
                       'Prediction_DO',
                       'Group')
Plotset <- rbind(testPlot,trainPlot)
library(ggplot2)
library(ggThemeAssist)
Plot2 <- ggplot(data = Plotset)+
  geom_point(data = Plotset[1:nrow(testPlot),],
             aes(x=DO,y=Prediction_DO,color='Test Samples'),
             size=1.5,
             shape=8)+
  theme(axis.line = element_line(colour = "black"))+
  geom_point(data = Plotset[nrow(testPlot)+1:nrow(Plotset),],
             aes(x=DO,y=Prediction_DO,color='Train Samples'),
             shape=8) + theme(panel.grid.major = element_line(colour = NA),
                              panel.grid.minor = element_line(colour = NA),
                              legend.title = element_blank(),
                              panel.background = element_rect(fill = "white"),
                              legend.background = element_rect(colour = "black",linetype = "solid"), 
                              legend.position = c(0.25,0.72))+
  geom_smooth(data=Plotset,
              aes(x=DO,y=Prediction_DO),
              method = 'lm',formula = y~x,se = FALSE,
              color='black',size=0.5)+
  theme(plot.title = element_text(hjust = 0.5))+coord_equal()+
  scale_x_continuous(limits = c(min(c(testPlot$DO,testPlot$Prediction_DO)), max(c(testPlot$DO,testPlot$Prediction_DO))))+
  scale_y_continuous(limits = c(min(c(testPlot$DO,testPlot$Prediction_DO)), max(c(testPlot$DO,testPlot$Prediction_DO))))+
  theme(axis.text.x = element_text(size = 14,family="serif"))+
  theme(axis.text.y = element_text(size = 14,family="serif"))+
  theme(legend.text=element_text(size=14,family="serif"))+
  theme(axis.title.x = element_text(size = 14,family="serif"))+
  theme(axis.title.y = element_text(size = 14,family="serif"))
Plot2

#设置默认路径，即后续图片保存的位置
setwd("E:\\output")
jpeg(filename =paste0("target预测.jpg"),width=1000,height=1000,res=250)#res 是指 PPI（Pixel Per Inch） ，即图像的采样率（在图像中，每英寸所包含的像素数目）
Plot2
dev.off()