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# apply regtools:::fineTuning() to choosing the expansion factors in
# kNN()
# arguments:
# data: data frame (or matrix with col names), including both "X" and "Y"
# yName: name of the data column containing "Y"
# expandVars: indices of the column numbers
knnFineTune <- function(data,yName,k,expandVars,ws,classif=FALSE,
seed=9999)
{
if (classif) stop('not ready for classification problems')
ycol <- which(names(data) == yName)
# may need to shift some of expandVars over, once "Y" is removed
if (ycol < ncol(data)) {
topvars <- which(expandVars > ycol)
if (length(topvars) > 0) {
expandVars[topvars] <- expandVars[topvars] - 1
}
}
expandNms <- colnames(data[,-ycol])[expandVars]
theCall <- function(dtrn,dtst,cmbi) {
x <- dtrn[,-ycol]
y <- dtrn[,ycol]
newx <- dtst[,-ycol]
newy <- dtst[,ycol]
cmbi <- as.numeric(cmbi)
knnout <- kNN(x,y,newx,k,expandVars=expandVars,expandVals=cmbi)
mean(abs(knnout$regests - newy))
}
# wcols <- paste0('w',1:length(expandVars),'=ws',collapse=',')
# wcols <- paste0('list(',wcols,')')
wcols <- paste0(expandNms,'=ws',collapse=',')
wcols <- paste0('list(',wcols,')')
fineTuning(dataset=data,pars=eval(parse(text=wcols)),regCall=theCall,
nXval=10)
}
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