vgm: Generate, or Add to Variogram Model

Description

Generates a variogram model, or adds to an existing model. print.variogramModel prints the essence of a variogram model.

Usage

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)

Value

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.

Arguments

psill: (partial) sill of the variogram model component, or model: see Details
model: 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: range parameter 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); if missing, nugget component is omitted
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 or plot
...: arguments that will be passed to print, e.g. digits (see examples), or to variogramLine for the plot method
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.
Err: 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
m: object of class variomodel, see geoR
cutoff: maximum distance up to which variogram values are computed
type: plot type

Author

Edzer Pebesma

Details

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.

References

http://www.gstat.org/

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.

Examples

Run this code

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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