r - How to extrapolate beyond the x points passed to `ksmooth`? -

i have kernel function so:

x <- 1:100 y <- rnorm(100, mean=(x/2000)^2) plot(x,y) kernel <- ksmooth(x,y, kernel="normal", bandwidth=10) print(kernel$y) 

if try predict @ point outside of range of x values, give me nan, because attempting extrapolate beyond data:

x <- 1:100 y <- rnorm(100, mean=(x/2000)^2) plot(x,y) kernel <- ksmooth(x,y, kernel="normal", bandwidth=10, x.points=c(130)) print(kernel$y)  > print(kernel$y) [1] na 

even when change range.x doesn't budge:

x <- 1:100 y <- rnorm(100, mean=(x/2000)^2) plot(x,y) kernel <- ksmooth(x,y, kernel="normal", bandwidth=10, range.x=c(1,200) , x.points=c(130)) print(kernel$y)  > print(kernel$y) [1] na 

how ksmooth function extrapolate beyond data? know bad idea in theory, in practice issue comes time.

to answer side question, looking @ code of ksmooth, range.x used when x.points not provided explains why not see used. let's @ code in ksmooth:

function (x, y, kernel = c("box", "normal"), bandwidth = 0.5,      range.x = range(x), n.points = max(100l, length(x)), x.points)  {     if (missing(y) || is.null(y))          stop("numeric y must supplied.\nfor density estimation use density()")     kernel <- match.arg(kernel)     krn <- switch(kernel, box = 1l, normal = 2l)     x.points <- if (missing(x.points))          seq.int(range.x[1l], range.x[2l], length.out = n.points)     else {         n.points <- length(x.points)         sort(x.points)     }     ord <- order(x)     .call(c_ksmooth, x[ord], y[ord], x.points, krn, bandwidth) } 

from see need not provide x.points make sure range.x used. if run:

x <- 1:100 y <- rnorm(100, mean=(x/2000)^2) plot(x,y) kernel <- ksmooth(x,y, kernel="normal", bandwidth=10, range.x=c(1,200)) plot(kernel$x, kernel$y) 

now you'll see kernel evaluated beyond 100 (although not 200). increasing bandwidth parameter allows further away 100.