Giovanni,
You can use the '...' for that, as in:
loocv <- function(data, fnc, ...) {
n <- length(data.x)
score <- 0
for (i in 1:n) {
x_i <- data.x[-i]
y_i <- data.y[-i]
yhat <- fnc(x=x_i,y=y_i, ...)
score <- score + (y_i - yhat)^2
}
score <- score/n
return(score)
}
scoreks <- loocv(data,gaussiankernel, h=0.5)
Regards,
Jan
On Mon, Apr 26, 2010 at 1:53 AM, Giovanni Azua <[email protected]> wrote:
> The beauty of trial and error ... if I leave the non x, y parameters i.e. h
> as global parameters rather than formal parameters for gaussiankernel it
> works fine basically I don't pass anymore h=0.5 to gaussiankernel but consume
> it from a global variable. Ugly but works ...
>
> Best regards,
> Giovanni
>
> On Apr 26, 2010, at 1:38 AM, Giovanni Azua wrote:
>
>> Hello,
>>
>> I have the following function that receives a "function pointer" formal
>> parameter name "fnc":
>>
>> loocv <- function(data, fnc) {
>> n <- length(data.x)
>> score <- 0
>> for (i in 1:n) {
>> x_i <- data.x[-i]
>> y_i <- data.y[-i]
>> yhat <- fnc(x=x_i,y=y_i)
>> score <- score + (y_i - yhat)^2
>> }
>> score <- score/n
>> return(score)
>> }
>>
>> I would like to use it like this:
>>
>> ##
>> ## Estimator function using Gaussian Kernel
>> ##
>> gaussiankernel <- function(x,y,h) {
>> modelks <- ksmooth(x,y,kernel="normal",bandwidth=h,x.points=x)
>> yhat <- modelks$y
>> return(yhat)
>> }
>>
>> scoreks <- loocv(data,gaussiankernel(h=0.5))
>>
>> I expected this to work but it doesn't :( basically I wanted to take
>> advantage of the named parameters so I could pass the partially specified
>> function parameter "gaussiankernel" to loocv specifying only the h parameter
>> and then let loocv specify the remaining parameters as needed ... can this
>> be tweaked to work? The idea is to have loocv generic so it can work for any
>> estimator implementation ...
>>
>> I have more than 6 books now in R and none explains this important concept.
>>
>> Thanks in advance,
>> Best regards,
>> Giovanni
>>
>
>
> [[alternative HTML version deleted]]
>
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