| vgm {gstat} | R Documentation |
Generates a variogram model, or adds to an existing model.
print.variogramModel prints the essence of a variogram model.
vgm(psill, model, range, nugget, add.to, anis, kappa = 0.5, ..., covtable) ## S3 method for class 'variogramModel': print(x, ...) as.vgm.variomodel(m)
psill |
(partial) sill of the variogram model component |
model |
model type, e.g. "Exp", "Sph", "Gau", "Mat". Calling vgm() without a model argument returns a data.frame with available models. |
range |
range of the variogram model component; in case of anisotropy: major range |
kappa |
smoothness parameter for the Matern class of variogram models |
nugget |
nugget component of the variogram (this basically adds a nugget compontent to the model) |
add.to |
the variogram model to which we want to add a component (structure) |
anis |
anisotropy parameters: see notes below |
x |
a variogram model to print |
... |
arguments that will be passed to print, e.g. digits
(see examples) |
covtable |
if model is Tab, instead of model parameters a one-dimensional
covariance table can be passed here. See covtable.R in tests directory, and example below. |
m |
object of class variomodel, see geoR |
an object of class variogramModel, which extends data.frame.
When called without a model argument, a data.frame with available models
is returned, having two columns: short (abbreviated names, to be used
as model argument: "Exp", "Sph" etc) and long (with some description).
as.vgm.variomodel tries to convert an object of class variomodel (geoR)
to vgm.
Geometric anisotropy can be modelled for each individual simple model by giving two or five anisotropy parameters, two for two-dimensional and five for three-dimensional data. In any case, the range defined is the range in the direction of the strongest correlation, or the major range. Anisotropy parameters define which direction this is (the main axis), and how much shorter the range is in (the) direction(s) perpendicular to this main axis.
In two dimensions, two parameters define an anisotropy ellipse, say
anis = c(30, 0.5). The first parameter, 30, refers to
the main axis direction: it is the angle for the principal direction
of continuity (measured in degrees, clockwise from positive Y, i.e. North).
The second parameter, 0.5, is the anisotropy ratio, the ratio
of the minor range to the major range (a value between 0 and 1). So,
in our example, if the range in the major direction (North-East) is 100,
the range in the minor direction (South-East) is 0.5 x 100 = 50.
In three dimensions, five values should be given in the form anis
= c(p,q,r,s,t). Now, $p$ is the angle for the principal direction of
continuity (measured in degrees, clockwise from Y, in direction of X),
$q$ is the dip angle for the principal direction of continuity (measured
in positive degrees up from horizontal), $r$ is the third rotation angle
to rotate the two minor directions around the principal direction defined
by $p$ and $q$. A positive angle acts counter-clockwise while looking
in the principal direction. Anisotropy ratios $s$ and $t$ are the ratios
between the major range and each of the two minor ranges. The anisotropy code
was taken from GSLIB. Note that in
http://pangea.stanford.edu/ERE/research/scrf/software/gslib/bug/#ANGLE
(end of page) it is reported that this code has a bug. Quoting from this
site: ``The third angle in all GSLIB programs operates in the opposite
direction than specified in the GSLIB book. Explanation - The books
says (pp27) the angle is measured clockwise when looking toward
the origin (from the postive principal direction), but it should be
counter-clockwise. This is a documentation error. Although rarely used,
the correct specification of the third angle is critical if used.''
(Note that anis = c(p,s) is equivalent to anis = c(p,0,0,s,1).)
The implementation in gstat for 2D and 3D anisotropy was taken from the gslib (probably 1992) code. I have seen a paper where it is argued that the 3D anisotropy code implemented in gslib (and so in gstat) is in error, but I have not corrected anything afterwards.
Edzer J. Pebesma
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.
Deutsch, C.V. and Journel, A.G., 1998. GSLIB: Geostatistical software library and user's guide, second edition, Oxford University Press.
show.vgms to view the available models, fit.variogram, variogramLine, variogram for the sample variogram.
vgm()
vgm(10, "Exp", 300)
x <- vgm(10, "Exp", 300)
vgm(10, "Nug", 0)
vgm(10, "Exp", 300, 4.5)
vgm(10, "Mat", 300, 4.5, kappa = 0.7)
vgm( 5, "Exp", 300, add.to = vgm(5, "Exp", 60, nugget = 2.5))
vgm(10, "Exp", 300, anis = c(30, 0.5))
vgm(10, "Exp", 300, anis = c(30, 10, 0, 0.5, 0.3))
# Matern variogram model:
vgm(1, "Mat", 1, kappa=.3)
x <- vgm(0.39527463, "Sph", 953.8942, nugget = 0.06105141)
x
print(x, digits = 3);
# to see all components, do
print.data.frame(x)
vv=vgm(model = "Tab", covtable =
variogramLine(vgm(1, "Sph", 1), 1, n=1e4, min = 0, covariance = TRUE))