On Monday 22 February 2010, Edzer Pebesma wrote:
> Dylan Beaudette wrote:
> > On Monday 22 February 2010, Edzer Pebesma wrote:
> >> Carlos, please try:
> >>
> >> library(gstat)
> >> data(meuse)
> >> meuse$rn = 1:nrow(meuse)
> >> coordinates(meuse)=~x+y
> >> spplot(meuse["rn"])
> >> data(meuse.grid)
Dylan Beaudette wrote:
On Monday 22 February 2010, Edzer Pebesma wrote:
Carlos, please try:
library(gstat)
data(meuse)
meuse$rn = 1:nrow(meuse)
coordinates(meuse)=~x+y
spplot(meuse["rn"])
data(meuse.grid)
gridded(meuse.grid)=~x+y
out = krige(rn~1, meuse, meuse.grid, nmax=1)
spplot(out[1])
On Monday 22 February 2010, Edzer Pebesma wrote:
> Carlos, please try:
>
> library(gstat)
> data(meuse)
> meuse$rn = 1:nrow(meuse)
> coordinates(meuse)=~x+y
> spplot(meuse["rn"])
> data(meuse.grid)
> gridded(meuse.grid)=~x+y
> out = krige(rn~1, meuse, meuse.grid, nmax=1)
> spplot(out[1])
> meuse$w
Carlos, please try:
library(gstat)
data(meuse)
meuse$rn = 1:nrow(meuse)
coordinates(meuse)=~x+y
spplot(meuse["rn"])
data(meuse.grid)
gridded(meuse.grid)=~x+y
out = krige(rn~1, meuse, meuse.grid, nmax=1)
spplot(out[1])
meuse$w = as.vector(table(out[[1]])/sum(table(out[[1]])))
spplot(out[1],col.reg
Hello everybody in the list.
My question have been treated previously by Edzer Pebesma in his
communications with Stefano Pegoretti in july 2007, however I would
find very useful some examples.
Basically, Edzer Pebesma pointed to two methods to perform cell declustering.
One of these metho
Stefano, sorry for my rudeness to forward your private reply to r-sig-geo.
A quick and dirty answer would be to work with grids, and e.g. retain a
single value in each grid cell. Does anyone more familiar with the
point pattern theory know of a more elegant way that does not need all
kind of a
Stefano,
I thought cell declustering meant finding the size of the region of
influence for an observation and using that as weight in further
analysis. An approach would be using voronoi diagrams (package deldir,
and read the list archives), another using the number of nearest cells
based on a
Dear List,
I have to perform a "cell spatial declustering" on my radon data,
i.e. divide the study domain into a defined number of cell and for each
of them randomly extracts a defined number of samples: does anybody know
if there are function or packages in R to quickly to this?
Thanks, an
