Dear All,
I used the script below to create interpolation surface using ordinary
kriging and inverse distance weighting approach.Could anyone guide me on
how to cross validate the two interpolation methods to identify the
optimal? I intend to obtain the diagnostic statistics such mse, rmse and
Hi Fabricio,
Thanks, but the use case I was considering was that a tess object itself
includes marks of some kind that are lost in coercion. This is not the
case here, so it is very much better that users actually learn to use the
objects provided. The minimal incantation to go from a
Hi,
The krigeST function has a 'nmax' parameter that sets the maximum number of
neighbouring observations to be used in the prediction. However, the help
for this function states that it does not support kriging in a local
neighbourhood. So, is this just a mistake in the help?
Also, in case
On 15.05.2014 15:47, Edzer Pebesma wrote:
On 05/15/2014 10:58 AM, Roelof Coster wrote:
Also, in case kriging in a neighbourhood is possible, how does the function
determine this neighbourhood? How is distance in space compared to distance
in time?
It uses
Thank you for your kind reply
therefore as I have used the Osl method for regression, my result will never
match the universal kriging; However, in order to validate my method, I'm
trying to implement in the script a calculation loop witch runs n times (the
number of stations) regression + kriging
Dear all
I have a shape file of UK. I have used it serveral times before. But now when I
want to use this shapefile the following error will apear.
shp-readShapePoly(GreaterLondon_ward)
Error in read.shape(filen = fn, verbose = verbose, repair = repair) :
File size and implied file size
