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regmodelSuit=function(df, ...){
total <- 20
pb = txtProgressBar(min = 0, max = total, style = 3)
dv=as.character(sapply(substitute(list(...))[-1], deparse))
fml <- as.formula(paste(dv[1],paste((dv[2:length(dv)]),collapse="+"),sep="~"))
names(fml)<-sub(".*\\$", "",names(fml))
dat=data.frame(df)
(na.cols=function(x){
y <- sapply(x, function(xx)any(is.na(xx)))
names(y[y])
})
if(any(is.na(dat))){stop(paste("Remove NA in columns: ", paste(na.cols(dat), collapse=", ")))}
for(i in 1:5){
Sys.sleep(0.5)
setTxtProgressBar(pb, i)}
LM= train(fml, data = dat, method = "lm", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
RF= train(fml, data = dat, method = "rf", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
SVM= train(fml, data = dat, method = "svmLinear", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
BGLM= train(fml, data = dat, method = "bayesglm", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
CARTB= train(fml, data = dat, method = "treebag", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
CUB= train(fml, data = dat, method = "cubist",trControl=trainControl( method = "cv",number=5,returnResamp = "all",savePredictions = TRUE, search = "random",verboseIter = FALSE))
CART= train(fml, data = dat, method = "rpart", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
RANGE= train(fml, data = dat, method = "ranger", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
QRF= train(fml, data = dat, method = "qrf", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
QRNN= train(fml, data = dat, method = "qrnn", trControl=trainControl( method = "cv",number=5,savePredictions = TRUE, verboseIter = FALSE))
for(i in 5:10){
Sys.sleep(0.5)
setTxtProgressBar(pb, i)}
{ LM_MAE=mean(LM$results$MAE)
RF_MAE=mean(RF$results$MAE)
SVM_MAE=mean(SVM$results$MAE)
BGLM_MAE=mean(BGLM$results$MAE)
CARTB_MAE=mean(CARTB$results$MAE)
CUB_MAE=mean(CUB$results$MAE)
CART_MAE=mean(CART$results$MAE)
RAN_MAE=mean(RANGE$results$MAE)
QRF_MAE=mean(QRF$results$MAE)
QRNN_MAE=mean(QRNN$results$MAE)
LM_RMSE=mean(LM$results$RMSE)
RF_RMSE=mean(RF$results$RMSE)
SVM_RMSE=mean(SVM$results$RMSE)
BGLM_RMSE=mean(BGLM$results$RMSE)
CARTB_RMSE=mean(CARTB$results$RMSE)
CUB_RMSE=mean(CUB$results$RMSE)
CART_RMSE=mean(CART$results$RMSE)
RAN_RMSE=mean(RANGE$results$RMSE)
QRF_RMSE=mean(QRF$results$RMSE)
QRNN_RMSE=mean(QRNN$results$RMSE)
LM_Rsquared=mean(LM$results$Rsquared)
RF_Rsquared=mean(RF$results$Rsquared)
SVM_Rsquared=mean(SVM$results$Rsquared)
BGLM_Rsquared=mean(BGLM$results$Rsquared)
CARTB_Rsquared=mean(CARTB$results$Rsquared)
CUB_Rsquared=mean(CUB$results$Rsquared)
CART_Rsquared=mean(CART$results$Rsquared)
RAN_Rsquared=mean(RANGE$results$Rsquared)
QRF_Rsquared=mean(QRF$results$Rsquared)
QRNN_Rsquared=mean(QRNN$results$Rsquared)}
for(i in 10:12){
Sys.sleep(0.5)
setTxtProgressBar(pb, i)}
drt=data.frame(dat[1],fitted(LM),fitted(RF),predict(SVM),fitted(BGLM),fitted(CARTB),fitted(CUB),fitted(CART),fitted(RANGE),fitted(QRF),fitted(QRNN))#
colnames(drt)=c("measured","LM","RF","SVM","BGLM","CARTB","CUB","CART","RANGE","QRF","QRNN")
SSE_LM=1-sum((drt$LM-drt$measured)^2,na.rm=TRUE)/sum((drt$LM-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_RF=1-sum((drt$RF-drt$measured)^2,na.rm=TRUE)/sum((drt$RF-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_SVM=1-sum((drt$SVM-drt$measured)^2,na.rm=TRUE)/sum((drt$SVM-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_BGLM=1-sum((drt$BGLM-drt$measured)^2,na.rm=TRUE)/sum((drt$BGLM-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_CARTB=1-sum((drt$CARTB-drt$measured)^2,na.rm=TRUE)/sum((drt$CARTB-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_CUB=1-sum((drt$CUB-drt$measured)^2,na.rm=TRUE)/sum((drt$CUB-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_CART=1-sum((drt$CART-drt$measured)^2,na.rm=TRUE)/sum((drt$CART-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_RAN=1-sum((drt$RANGE-drt$measured)^2,na.rm=TRUE)/sum((drt$RANGE-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_QRF=1-sum((drt$QRF-drt$measured)^2,na.rm=TRUE)/sum((drt$QRF-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
SSE_QRNN=1-sum((drt$QRNN-drt$measured)^2,na.rm=TRUE)/sum((drt$QRNN-mean(drt$measured,na.rm=TRUE))^2,na.rm=TRUE)
for(i in 12:15){
Sys.sleep(0.5)
setTxtProgressBar(pb, i)}
twy=matrix(c(LM_MAE,RF_MAE,SVM_MAE,BGLM_MAE,CARTB_MAE,CUB_MAE,CART_MAE,RAN_MAE,QRF_MAE,QRNN_MAE,
LM_RMSE,RF_RMSE,SVM_RMSE,BGLM_RMSE,CARTB_RMSE,CUB_RMSE,CART_RMSE,RAN_RMSE,QRF_RMSE,QRNN_RMSE,
LM_Rsquared,RF_Rsquared,SVM_Rsquared,BGLM_Rsquared,CARTB_Rsquared,CUB_Rsquared,CART_Rsquared,RAN_Rsquared,QRF_Rsquared,QRNN_Rsquared,
SSE_LM,SSE_RF,SSE_SVM,SSE_BGLM,SSE_CARTB,SSE_CUB,SSE_CART,SSE_RAN,SSE_QRF,SSE_QRNN),nrow=10,ncol = 4,byrow = FALSE)
colnames(twy)=c("ME","RMSE","R2","NSE")
rownames(twy)=c("Linear","RandomForest","SVM","BayesianGLM","BaggedCART","Cubist","CART","Ranger","QuantRandForest","QuantNeuralNT")
for(i in 15:total){
Sys.sleep(0.5)
setTxtProgressBar(pb, i)}
return(twy)
}
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