| gstat {gstat} | R Documentation |
Function that creates gstat objects; objects that hold all the information necessary for univariate or multivariate geostatistical prediction (simple, ordinary or universal (co)kriging), or its conditional or unconditional Gaussian or indicator simulation equivalents. Multivariate gstat object can be subsetted.
gstat(g, id, formula, locations, data, model = NULL, beta, nmax = Inf,
nmin = 0, maxdist = Inf, dummy = FALSE, set, fill.all = FALSE,
fill.cross = TRUE, variance = "identity", weights = NULL, merge,
degree = 0, vdist = FALSE, lambda = 1.0)
## S3 method for class 'gstat':
print(x, ...)
g |
gstat object to append to; if missing, a new gstat object is created |
id |
identifier of new variable; if missing, varn is used with
n the number for this variable. If a cross variogram is entered,
id should be a vector with the two id values , e.g.
c("zn", "cd"), further only supplying arguments g
and model. It is advisable not to use expressions, such
as log(zinc), as identifiers, as this may lead to complications
later on. |
formula |
formula that defines the dependent variable as a linear
model of independent variables; suppose the dependent variable has name
z, for ordinary and simple kriging use the formula z~1;
for simple kriging also define beta (see below); for universal
kriging, suppose z is linearly dependent on x and y,
use the formula z~x+y |
locations |
formula with only independent variables that define the
spatial data locations (coordinates), e.g. ~x+y; if data
has a coordinates method to extract its coordinates this argument
can be ignored (see package sp for classes for point or grid data). |
data |
data frame; contains the dependent variable, independent variables, and locations. |
model |
variogram model for this id; defined by a call to
vgm; see argument id to see how cross variograms are entered |
beta |
only for simple kriging (and simulation based on simple kriging); vector with the trend coefficients (including intercept); if no independent variables are defined the model only contains an intercept and this should be the simple kriging mean |
nmax |
for local kriging: the number of nearest observations that should be used for a kriging prediction or simulation, where nearest is defined in terms of the space of the spatial locations |
nmin |
for local kriging: if the number of nearest observations
within distance maxdist is less than nmin, a missing
value will be generated; see maxdist |
maxdist |
for local kriging: only observations within a distance
of maxdist from the prediction location are used for prediction
or simulation; if combined with nmax, both criteria apply |
dummy |
logical; if TRUE, consider this data as a dummy variable (only necessary for unconditional simulation) |
set |
named list with optional parameters to be passed to
gstat (only set commands of gstat are allowed, and not all of
them may be relevant; see the manual for gstat stand-alone, URL below ) |
x |
gstat object to print |
fill.all |
logical; if TRUE, fill all of the direct variogram and,
depending on the value of fill.cross also all cross
variogram model slots in g with the given variogram model |
fill.cross |
logical; if TRUE, fill all of the cross variograms, if
FALSE fill only all direct variogram model slots in g with the
given variogram model (only if fill.all is used) |
variance |
character; variance function to transform to non-stationary covariances; "identity" does not transform, other options are "mu" (Poisson) and "mu(1-mu)" (binomial) |
weights |
numeric vector; if present, covariates are present, and variograms are missing weights are passed to OLS prediction routines resulting in WLS; if variograms are given, weights should be 1/variance, where variance specifies location-specific measurement error; see references section below |
merge |
either character vector of length 2, indicating two ids
that share a common mean; the more general gstat merging of any two
coefficients across variables is obtained when a list is passed, with
each element a character vector of length 4, in the form
c("id1", 1,"id2", 2). This merges the first parameter
for variable id1 to the second of variable id2. |
degree |
order of trend surface in the location, between 0 and 3 |
vdist |
logical; if TRUE, instead of Euclidian distance variogram distance is used for selecting the nmax nearest neighbours, after observations within distance maxdist (Euclidian/geographic) have been pre-selected |
lambda |
test feature; doesn't do anything (yet) |
... |
arguments that are passed to the printing of variogram models only |
to print the full contents of the object g returned,
use as.list(g) or print.default(g)
an object of class gstat, which inherits from list.
Its components are:
data |
list; each element is a list with the formula,
locations, data, nvars, beta, etc., for a
variable |
model |
list; each element contains a variogram model; names are
those of the elements of data; cross variograms have names of
the pairs of data elements, separated by a . (e.g.:
var1.var2 |
set |
list; named list, corresponding to set name=value;
gstat commands (look up the set command in the gstat manual for a full list) |
The function currently copies the data objects into the gstat object, so this may become a large object. I would like to copy only the name of the data frame, but could not get this to work. Any help is appreciated.
Subsetting (see examples) is done using the id's of the variables,
or using numeric subsets. Subsetted gstat objects only contain cross
variograms if (i) the original gstat object contained them and (ii) the
order of the subset indexes increases, numerically, or given the order
they have in the gstat object.
The merge item may seem obscure. Still, for colocated cokriging, it is
needed. See texts by Goovaerts, Wackernagel, Chiles and Delfiner, or
look for standardised ordinary kriging in the 1992 Deutsch and Journel
or Isaaks and Srivastava. In these cases, two variables share a common
mean parameter. Gstat generalises this case: any two variables may share
any of the regression coefficients; allowing for instance analysis of
covariance models, when variograms were left out (see e.g. R. Christensen's
``Plane answers'' book on linear models). The tests directory of the
package contains examples in file merge.R. There is also demo(pcb)
which merges slopes across years, but with year-dependent intercept.
Edzer J. Pebesma
http://www.gstat.org/ Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.
for kriging with known, varying measurement errors (weights), see e.g.
Delhomme, J.P. Kriging in the hydrosciences. Advances in Water
Resources, 1(5):251-266, 1978; see also the section Kriging with known
measurement errors in the gstat user's manual, http://www.gstat.org/
data(meuse)
# let's do some manual fitting of two direct variograms and a cross variogram
g <- gstat(id = "ln.zinc", formula = log(zinc)~1, locations = ~x+y,
data = meuse)
g <- gstat(g, id = "ln.lead", formula = log(lead)~1, locations = ~x+y,
data = meuse)
# examine variograms and cross variogram:
plot(variogram(g))
# enter direct variograms:
g <- gstat(g, id = "ln.zinc", model = vgm(.55, "Sph", 900, .05))
g <- gstat(g, id = "ln.lead", model = vgm(.55, "Sph", 900, .05))
# enter cross variogram:
g <- gstat(g, id = c("ln.zinc", "ln.lead"), model = vgm(.47, "Sph", 900, .03))
# examine fit:
plot(variogram(g), model = g$model, main = "models fitted by eye")
# see also demo(cokriging) for a more efficient approach
g["ln.zinc"]
g["ln.lead"]
g[c("ln.zinc", "ln.lead")]
g[1]
g[2]
# Inverse distance interpolation with inverse distance power set to .5:
# (kriging variants need a variogram model to be specified)
data(meuse)
data(meuse.grid)
meuse.gstat <- gstat(id = "zinc", formula = zinc ~ 1, locations = ~ x + y,
data = meuse, nmax = 7, set = list(idp = .5))
meuse.gstat
z <- predict(meuse.gstat, meuse.grid)
library(lattice) # for levelplot
levelplot(zinc.pred~x+y, z, aspect = "iso")
# see demo(cokriging) and demo(examples) for further examples,
# and the manuals for predict.gstat and image