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Generates a variogram model, or adds to an existing model.
print.variogramModel
prints the essence of a variogram model.
vgm(psill = NA, model, range = NA, nugget, add.to, anis, kappa = 0.5, ..., covtable,
Err = 0)
# S3 method for variogramModel
print(x, ...)
# S3 method for variogramModel
plot(x, cutoff, ..., type = 'l')
as.vgm.variomodel(m)
If a single model is passed, an object of class variogramModel
extending data.frame
.
In case a vector ofmodels is passed, an object of class
variogramModelList
which is a list of variogramModel
objects.
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.
(partial) sill of the variogram model component, or model: see Details
model type, e.g. "Exp", "Sph", "Gau", or "Mat". Can be a character vector of model types combined with c(), e.g. c("Exp", "Sph"), in which case the best fitting is returned. Calling vgm() without a model argument returns a data.frame with available models.
range parameter of the variogram model component; in case of anisotropy: major range
smoothness parameter for the Matern class of variogram models
nugget component of the variogram (this basically adds a nugget compontent to the model); if missing, nugget component is omitted
the variogram model to which we want to add a component (structure)
anisotropy parameters: see notes below
a variogram model to print or plot
arguments that will be passed to print
, e.g. digits
(see examples), or to variogramLine
for the plot method
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.
numeric; if larger than zero, the measurement error variance component that will not be included to the kriging equations, i.e. kriging will now smooth the process Y instead of predict the measured Z, where Z=Y+e, and Err is the variance of e
object of class variomodel
, see geoR
maximum distance up to which variogram values are computed
plot type
Edzer Pebesma
If only the first argument (psill
) is given a
character
value/vector indicating one or more models, as in vgm("Sph")
,
then this taken as a shorthand form of vgm(NA,"Sph",NA,NA)
,
i.e. a spherical variogram with nugget and unknown parameter values;
see examples below. Read fit.variogram to find out how
NA
variogram parameters are given initial values for a fitting
a model, based on the sample variogram. Package automap
gives further options for automated variogram modelling.
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers and 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.
For the validity of variogram models on the sphere, see Huang, Chunfeng, Haimeng Zhang, and Scott M. Robeson. On the validity of commonly used covariance and variogram functions on the sphere. Mathematical Geosciences 43.6 (2011): 721-733.
show.vgms to view the available models, fit.variogram, variogramLine, variogram for the sample variogram.
vgm()
vgm("Sph")
vgm(NA, "Sph", NA, NA)
vgm(, "Sph") # "Sph" is second argument: NO nugget in this case
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))
vgm(c("Mat", "Sph"))
vgm(, c("Mat", "Sph")) # no nugget
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