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library(caret)
library(hydroGOF)
origindata <- read.table('C:/data/test.txt',encoding="UTF-8",header = T)
colnames(origindata)[length(origindata)]<-"target"
set.seed(1)
choose<-createDataPartition(y=origindata$target,p=0.8,list=F)
traindata<-origindata[choose,]
testdata<-origindata[-choose,]
ORP<-lm(traindata$target~.,data = traindata)
summary(ORP)
trainpred<-predict(ORP,newdata = traindata)
testpred<-predict(ORP,newdata = testdata)
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$target)^2)
cat("训练集R2:",trainR2,",RMSE:",trainRMSE,",MAE:",trainMAE,",RSE:",trainRSE,",MSE:",trainMSE,",MAPE:",trainMAPE,",MSPE:",trainMSPE)
testR2<-1-sum((testdata$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$target-testpred)))
test_group <- rep('Test Samples',times=nrow(dtest))
testPlot <- data.frame(testdata$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
setwd("E:\\output")
jpeg(filename =paste0("target预测.jpg"),width=1000,height=1000,res=250)
Plot2
dev.off()
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