Nothing
pedoTransfer=function(method="linear", df, ...){
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))
df=data.frame(df)
(na.cols=function(x){
y <- sapply(x, function(xx)any(is.na(xx)))
names(y[y])
})
if(any(is.na(df))){stop(paste("Remove NA in columns: ", paste(na.cols(df), collapse=", ")))}
if(method=="linear"){
hk.lm=lm(fml,data=df)
W=(dv[1])
df$B=df[,grepl(W, colnames(df))]
df$dv1=fitted(hk.lm)
hk=hk.lm
}
if(method=="randomforest"){
hk.rf=randomForest(fml,data=df, importance = TRUE)
W=(dv[1])
df$B=df[,grepl(W, colnames(df))]
df$dv1=hk.rf$predicted
RF_R2=cor(df$dv1,df$B)^2
hk=hk.rf
}
if(method=="svm"){
hk.sv=svm(fml,data=df)
W=(dv[1])
df$B=df[,grepl(W, colnames(df))]
df$dv1=fitted(hk.sv)
SV_R2=cor(df$dv1,df$B)^2
hk=hk.sv
}
if(method=="neuralnetwork"){
hk.nn= nnet(fml,data=df,size=10, linout=TRUE, skip=TRUE, MaxNWts=10000, trace=FALSE, maxit=100)
W=(dv[1])
df$B=df[,grepl(W, colnames(df))]
df$dv1=fitted(hk.nn)
NN_R2=cor(df$dv1,df$B)^2
hk=hk.nn
}
return(hk)
}
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