Hi,

Does anyone know well about the parameter optimization for loess or smooth.spline?

In function loess or smooth.spline, the parameter span (or spar for smooth.spline) is very important for the fitting. I have tried to use the following method to optimize the parameter spar for smooth.spline: 

tuneSpline = function(x,y,span.vals=seq(0.1,1,by=0.05),fold=10){

    mae <- numeric(length(span.vals))

    fun.fit <- function(x,y,span) {smooth.spline(x = x,y = ,spar = span)}

    fun.predict <- function(fit,x0) {predict(fit,x0)$y}

    

    ii = 0

    for(span in span.vals){

        ii <- ii+1

        y.cv <- crossval(x,y,fun.fit,fun.predict,span=span,ngroup = fold)$cv.fit

        fltr <- !is.nay.cv)

        save(fltr,y.cv,y,file="tmp.rda")

        mae[ii] <- mean(abs(y[fltr]-y.cv[fltr]))

        

    }

    span <- span.vals[which.min(mae)]

    return(span)

}

require(graphics)

attach(cars)

tuneSpline(speed,dist,fold = length(dist))

## return 0.1

But the optimized spar by this method is always 0.1 (the minimum value in span.vals). 

It's weird and I think the result may not be right.

Can anyone help me about this issue?

Best regards!

Bo