Alternatively, you might also consider data binning (implemented in
several packages: KernSmooth, ks, sm, npsp ,...). This technique is
commonly used in nonparametric statistics to reduce the computational
time (see e.g. Wand, M. P. (1994), Fast Computation of Multivariate
Kernel Estimators,
Kriging the noiseless version of Y is not �the solution of the
(standard) kriging system with a nugget effect in the Covariance
structure� (the semivariances/variogram at lag 0 may not be zero).
See e.g. Cressie, 1993, p. 128 (for instance, eq. 3.2.27 shows the
correct expression of the kriging
As I am interested in the topic, I find those comments very useful and I
also want to share my thoughts…
From my point of view UK (Universal Kriging) is a particular case of RK
(Regression kriging), UK assumes a linear trend (where spatial
coordinates could be used as explanatory variables) .
Hi Adeela,
This is the the data set and want to make variogram for Cd15.
Start with an exploratory analysis of the data. For example, using the
geoR package:
lyallpur - read.csv('lyallpur.csv')
library(geoR)
datgeo - as.geodata(lyallpur, coords.col = 2:3, data.col = lyallpur$Cd15)
#
Hi Vasya,
You have to take into account, when doing bootstrap, that you are
dealing with dependent data. Standard bootstrap will destroy this
dependence, so you should use alternatives such as some kind of block
bootstrap (see e.g. Politis and Romano, 1994. The stationary bootstrap.
Hello,
I use the attached script to extract climate data from the OISST
sea surface temperature netCDF sst.wkmean.1990-present.nc. This script
is a slight modification of one written by Luke Miller (downloaded from
his web page http://lukemiller.org, some years ago... ) to convert the
Hello Michele,
Universal kriging is equivalent to Linear Regression (with the
generalized-least-squaresestimator) + Simple Kriging of residuals (e.g.
Cressie, 1993, section 3.4.5). The differences you observe are probably
due to the use of ordinary least squares. If you use
