Guess the parameters of the spatial covariance function of a random, regionalized variable. A guess of such parameters is required to start the fitting functions of many geostatistical packages such as gstat, geoR, and georob.
variogramGuess(
z,
coords,
lags,
cutoff = 0.5,
method = "a",
min.npairs = 30,
model = "matern",
nu = 0.5,
estimator = "qn",
plotit = FALSE
)vgmICP(
z,
coords,
lags,
cutoff = 0.5,
method = "a",
min.npairs = 30,
model = "matern",
nu = 0.5,
estimator = "qn",
plotit = FALSE
)
Numeric vector with the values of the regionalized variable for which the values for the spatial covariance parameters should be guessed.
Data frame or matrix with the projected x- and y-coordinates.
Numeric scalar defining the width of the variogram bins or a numeric vector with the
lower and upper bounds of the variogram bins. If missing, the variogram bins are computed using
variogramBins()
. See ‘Details’ for more information.
Numeric value defining the fraction of the diagonal of the rectangle that spans the
data (bounding box) that should be used to set the maximum distance up to which variogram bins
should be computed. Defaults to cutoff = 0.5
, i.e. half the diagonal of the bounding box.
Character keyword defining the method used for guessing the spatial covariance
parameters of the regionalized variable. Defaults to method = "a"
. See ‘Details’ for
more information.
Positive integer defining the minimum number of point-pairs required so that a
variogram bin is used to guessing the spatial covariance parameters of the of the regionalized
variable. Defaults to min.npairs = 30
.
Character keyword defining the spatial covariance function that will be fitted to
the data of the regionalized variable. Currently, most of the basic spatial covariance function
are accepted. See geoR::cov.spatial()
for more information. Defaults to model = "matern"
.
Numerical value for the additional smoothness parameter \(\nu\) of the spatial
covariance function of the regionalized variable. See RandomFields::RMmodel()
and argument
kappa
of geoR::cov.spatial()
for more information.
Character keyword defining the estimator for computing the sample (experimental)
variogram of the regionalized variable, with options "qn"
(default), "mad"
, "matheron"
, and
"ch"
. See georob::sample.variogram()
for more details.
Should the guessed spatial covariance parameters be plotted along with the sample
(experimental) variogram of the regionalized variable? Defaults to plotit = FALSE
.
A vector of numerical values, the guesses for the spatial covariance parameters of the regionalized variable:
nugget
partial sill
range
The georob package, provider of functions for the robust geostatistical analysis of spatial
data in R, is required for variogramGuess()
to work. The old versions of the
georob package are available on the CRAN archive at
https://cran.r-project.org/src/contrib/Archive/georob/.
There are five methods two guess the covariance parameters. Two of them, "a"
and "c"
, rely on
a sample variogram with exponentially growing variogram bins, while the other three, "b"
,
"d"
, and "e"
, use equal-width variogram bins (see variogramBins()
). All of
them are heuristic.
Method "a"
was developed in-house and is the most elaborated of them, specially for guessing
the nugget variance.
Method "b"
was proposed by 10.1016/0098-3004(95)00095-XJian et al. (1996) and
is implemented in SAS/STAT(R) 9.22.
Method "c"
is implemented in the automap-package and was developed by
10.1016/j.cageo.2008.10.011Hiemstra et al. (2009).
Method "d"
was developed by 10.1007/s11004-012-9434-1Desassis & Renard (2012).
Method "e"
was proposed by Larrondo et al. (2003)
http://www.ccgalberta.com/ccgresources/report05/2003-122-varfit.pdf and is implemented in the
VARFIT module of GSLIB http://www.gslib.com/.
Desassis, N. & Renard, D. Automatic variogram modelling by iterative least squares: univariate and multivariate cases. Mathematical Geosciences. Springer Science + Business Media, v. 45, p. 453-470, 2012.
Hiemstra, P. H.; Pebesma, E. J.; Twenh<U+00F6>fel, C. J. & Heuvelink, G. B. Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Computers & Geosciences. Elsevier BV, v. 35, p. 1711-1721, 2009.
Jian, X.; Olea, R. A. & Yu, Y.-S. Semivariogram modelling by weighted least squares. Computers & Geosciences. Elsevier BV, v. 22, p. 387-397, 1996.
Larrondo, P. F.; Neufeld, C. T. & Deutsch, C. V. VARFIT: a program for semi-automatic variogram modelling. Edmonton: Department of Civil and Environmental Engineering, University of Alberta, p. 17, 2003.
# NOT RUN {
if (all(c(require(sp), require(georob)))) {
data(meuse, package = "sp")
icp <- variogramGuess(z = log(meuse$copper), coords = meuse[, 1:2])
}
# }
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