Hi all,
I want to do a spatio-temporal regression on a quite large dataset. I have
100 k records. These correspond to measurements taken at 3000 locations,
approximately every half year. The geographic area is all of the
Netherlands (240 x 300 km).
Is spatio-temporal kriging advisable for a
Hi all,
I just released the new glcm package to CRAN. The glcm package
enables calculating image textures derived from grey-level
co-occurrence matrices (GLCMs) in R. The underlying texture
calculation code is implemented in C++ using RcppArmadillo.
I have tested the calculated textures versus
On 02/13/2014 01:30 PM, Roelof Coster wrote:
Hi all,
I want to do a spatio-temporal regression on a quite large dataset. I have
100 k records. These correspond to measurements taken at 3000 locations,
approximately every half year. The geographic area is all of the
Netherlands (240 x 300
On 13.02.2014 14:00, Edzer Pebesma wrote:
On 02/13/2014 01:30 PM, Roelof Coster wrote:
When I make the sample space-time variogram (with variogramST), it
automatically chooses a time-lag difference of about 2 days. This is much
too small to be meaningful for my data; half-year periods would be
when confronted with a large dataset i usually begin by trying methods out on
subsets, e.g. start with 2^n for n = 8 and work your way up to 2^17... you
might find convergence to the answer to your questions before analyzing the
full dataset.
Lee De Cola
- Original Message -
From:
Thanks for the answers. I have cleaned up the data by rounding the dates to
half years, and now the variogram turns out beautiful. Now I can proceed to
kriging and see what I get.
Best regards,
Roelof Coster
2014-02-13 17:54 GMT+01:00 ldec...@comcast.net:
when confronted with a large
