z.condldens.est <- function(z,x,algorithm,regFunc = regressionFunction.NW){
if(tolower(algorithm) == 'glm'){
df = as.data.frame(cbind(z,x))
out <- glm(z~., data = df, family = 'gaussian')
}
else if(tolower(algorithm) == 'flexcode'){
require(FlexCoDE)
x = as.matrix(x)
n = dim(x)[1]
nTrain=round(0.7*n)
nValidation=round(0.25*n)
nTest=n-nTrain-nValidation
# split data
randomIndex=sample(1:n)
xTrain=x[randomIndex[1:nTrain],]
xValidation=x[randomIndex[(nTrain+1):(nTrain+nValidation)],]
xTest=x[randomIndex[(nTrain+nValidation+1):n],]
zTrain=z[randomIndex[1:nTrain]]
zValidation=z[randomIndex[(nTrain+1):(nTrain+nValidation)]]
zTest=z[randomIndex[(nTrain+nValidation+1):n]]
# Fit FlexCoDE
out=fitFlexCoDE(xTrain,zTrain,xValidation,zValidation,xTest,zTest,
nIMax = 40,regressionFunction = regFunc,
regressionFunction.extra=list(nCores=3),verbose = T)
}
else{stop('Use flexcode or glm as algorithm')}
return(out)
}
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