Is there an R implementation of a scheme for automatic smoothing
parameter selection with loess, e.g., by minimizing one of the AIC/GCV
statistics discussed by Hurvich, Simonoff & Tsai (1998)?
Below is a function that calculates the relevant values of AICC,
AICC1 and GCV--- I think, because I to guess from the names of the
components returned in a loess object.
I guess I could use optimize(), or do a simple line search on span=,
but I'm not sure how to use loess.aic to write a function
that would act as a wrapper for loess() and return the mimimizing
loess fit for a specified criterion.
loess.aic <- function (x) {
# extract values from loess object
if (!(inherits(x,"loess"))) stop("Error: argument must be a loess
object")
span <- x$pars$span
n <- x$n
traceL <- x$trace.hat
sigma2 <- sum( x$residuals^2 ) / (n-1)
delta1 <- x$one.delta
delta2 <- x$two.delta
enp <- x$enp
aicc <- log(sigma2) + 1 + 2* (2*(traceL+1)) / (n-traceL-2)
aicc1<- n*log(sigma2) + n* (
(delta1/(delta2*(n+enp)))/(delta1^2/delta2)-2 )
gcv <- n*sigma2 / (n-traceL)^2
result <- list(span=span, aicc=aicc, aicc1=aicc1, gcv=gcv)
return(result)
}
> cars.lo <- loess(dist ~ speed, cars)
>
> (values <- loess.aic(cars.lo))
$span
[1] 0.75
$aicc
[1] 6.93678
$aicc1
[1] 167.7267
$gcv
[1] 5.275487
>
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street http://www.math.yorku.ca/SCS/friendly.html
Toronto, ONT M3J 1P3 CANADA
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