Thanks edzer, As you said , I hope it can choose the number of
nested structures, at least keep the eyefitted number and the corresponding
models automatically.
But, for my case , the fit seems do noting except change the second Nug to
1.
R:> uk.eye1 <- vgm(psill = 0.155, model = "Gau", range=
On 12/05/2014 05:08 AM, Bingwei Tian wrote:
> Dear list,
> It seems only gstat can make a 3D nested variogram but can not auto fit it
> until now, .
?fit.variogram gives me
Description:
Fit ranges and/or sills from a simple or nested variogram model to
a sample variogram
Or do you m
Dear list,
It seems only gstat can make a 3D nested variogram but can not auto fit it
until now, .
Recently I find georob is also a useful package to do 3D estimation with
trend data,
But I did not find the way to create a 3D nested variogram, Does anyone
kown about this,
or any other package for
It's a limitation of ncdf4 (that's why the error refers to ncvar_put). Just
write the vector out in parts using the 'start' and 'count' arguments.
There's plenty of examples of doing that in the help page for ncvar_put,
though it's a bit abstract if you've not encountered that kind of thing
before
Thank you for your prompt and kind replies! I think that maybe what
Jose Hidasi was suggesting is what I want. I will give it a go! Just
to clarify, what I would like to extract is the only the area (or
proportion) of a particular cell covered by the polygon and the
corresponding raster category. I
Hi All:
I am trying to write a 3-dimensional big netcdf file with the ncdf4 package.
I found that when the length of the variable is larger than 2^31 (or file
size larger than 16 GB), it will show an error "ncvar_put doesn't support
long vector yet". Is that the limitation of the ncdf4 package
Yes, there is a round issues when you use "extract or mask",
http://gis.stackexchange.com/questions/112274/is-this-a-known-issue-in-gaps-between-masked-raster-and-spatial-polygon-in-r-wit.
I use "disaggregate" to improve(?) raster resolution.
This is an example
yourraster.dis <- disaggregate("yo
It is difficult for us to know exactly what happens as you are not
providing a reproducible example. But my guess would be that this is
related to your use of a Gaussian variogram without nugget for the first
part of the variogram. This can cause rather large weights (positive and
negative) if
Hello Simone,
You can also intersect the raster with the polygon and then calculate the
area covered by each raster cell. If that's what you want, you might need
to:
1) use "rasterToPolygons()" function to transform the raster into a polygon.
2) apply an equal area projection (e.g. lambert azimu
