- 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
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 expected value; for cross
variogram computations: mean parameters to be used instead of the
OLS estimates
- 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, unless force==TRUE
; see maxdist
- omax
maximum number of observations to select per octant (3D) or
quadrant (2D); only relevant if maxdist
has been defined as well
- 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
- force
for local kriging, force neighbourhood selection: in case
nmin
is given, search beyond maxdist
until nmin
neighbours are found. A missing value is returned if this is not possible.
- 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