Hi, Paul,
Thank you for the reply.
I have attempted to write some code to test out what you've suggested but ran
into some errors.
library(fields)
k <- Krig(ozone$x, ozone$y, theta=20)
# define objective function
kfunc <- function(para,k) {
xcoord=para[1]
ycoord=para[2]
minimum <- NULL
testpoint <- c(xcoord, ycoord)
minimum <- predict(k, testpoint)
return(minimum)
}
initialpar <- c(max(ozone$x), max(ozone$y))
initialpar
# optimization
best <- optim(initialpar, kfunc, NULL, method = "BFGS", hessian=TRUE)
# Error in predict(k, testpoint) : argument "k" is missing, with no default
regards,
Jeff
----------------------------------------
> Date: Fri, 11 Jun 2010 15:04:32 +0200
> From: p.hiems...@geo.uu.nl
> To: vivac...@hotmail.sg
> CC: r-sig-geo@stat.math.ethz.ch
> Subject: Re: [R-sig-Geo] How to locate local minima points on a Krige-fitted
> surface?
>
> Dear Jeff,
>
> I think you cannot escape discretizing your kriging surface as there is
> no mathematical expression that captures the kriging surface. To use
> optim you could make an objective function that was two inputs, x and y
> location. The functions calls the fields package to estimate the kriging
> prediction at this point and returns it. This ofcourse assumes that the
> variogram model is known. You have to play with the settings of optim to
> get it working and prevent a local minimum. You could take a look at
> SANN (simulated anealing) which is part of optim.
>
> cheers,
> Paul
>
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