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# explore use of the ExpandVars arg
# arguments:
# xtrn: vector or matrix for "X" portion of training data
# ytrn: vector or matrix for "Y" portion of training data; matrix
# case is for vector "Y", i.e. multiclass
# xtst,ytst: test data analogs of xtrn, ytrn
# k: number of nearest neighbors
# eVar: column number of the predictor to be expanded
# maxEVal: maximum expansion
# lossFtn: loss function; internal offerings are 'MAPE' and 'propMisclass'
# eValIncr: expansion value increment
# value:
# mean loss, evaluated from 0 to maxEVal, increments of eValIncr
exploreExpVars <-
function(xtrn,ytrn,xtst,ytst,k,eVar,maxEVal,loss,incr=0.05)
{
dfr <- data.frame(NULL,NULL)
for (w in seq(0.05,1.5,eValIncr)) {
preds <- kNN(xtrn,ytrn,xtst,k,expandVars=eVar,expandVals=w)
dfr <- rbind(dfr,c(w,mean(loss(preds$regests,ytst)
abs(preds$regests-ytst))))
}
names(dfr) <- c('w',loss)
frmla <- as.formula(paste0(loss, ' ~ w'))
lwout <- loess(frmla,data=dfr)
lwout$fitted
}
# plot accuracy of applying one or more instances of the ExpandVars arg
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