vgmICP {pedometrics} | R Documentation |
Guess the initial values for the covariance parameters required to fit a variogram model.
vgmICP(z, coords, lags, cutoff = 0.5, method = "a", min.npairs = 30, model = "matern", nu = 0.5, estimator = "qn", plotit = FALSE)
z |
Numeric vector with the values of the response variable for which the initial values for the covariance parameters should be guessed. |
coords |
Data frame or matrix with the projected x- and y-coordinates. |
lags |
Numeric scalar defining the width of the lag-distance classes,
or a numeric vector with the lower and upper bounds of the lag-distance
classes. If missing, the lag-distance classes are computed using
|
cutoff |
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 lag-distance classes should be computed.
Defaults to |
method |
Character keyword defining the method used for guessing the
initial covariance parameters. Defauls to |
min.npairs |
Positive integer defining the minimum number of
point-pairs required so that a lag-distance class is used for guessing the
initial covariance parameters. Defaults to |
model |
Character keyword defining the variogram model that will be
fitted to the data. Currently, most basic variogram models are accepted.
See |
nu |
numerical value for the additional smoothness parameter ν
of the correlation function. See |
estimator |
Character keyword defining the estimator for computing the
sample variogram, with options |
plotit |
Should the guessed initial covariance parameters be plotted
along with the sample variogram? Defaults to |
There are five methods two guess the initial covariance parameters
(ICP). Two of them ("a"
and "b"
) rely a sample variogram with
exponentially spaced lag-distance classes, while the other three ("b"
,
"d"
, and "e"
) use equidistant lag-distance classes (see
vgmLags
). 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 "c"
is implemented
in the automap-package and was developed by
Hiemstra et al. (2009).
Method "b"
was proposed by
Jian et al. (1996) and
is implemented in SAS/STAT(R) 9.22.
Method "d"
was developed by
Desassis & Renard (2012).
Method "e"
was proposed by
Larrondo et al. (2003) and is implemented in the VARFIT module of
GSLIB.
A vector of numeric values: the guesses for the covariance parameters nugget, partial sill, and range.
Alessandro Samuel-Rosa <alessandrosamuelrosa@gmail.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.; Twenhöfel, 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.
vgmLags
,
sample.variogram
,
autofitVariogram
data(meuse, package = "sp") icp <- vgmICP(z = log(meuse$copper), coords = meuse[, 1:2])