make.gmMultivariateGaussianSpatialModel {gmGeostats}

Construct a Gaussian gmSpatialModel for regionalized multivariate data

Description

Construct a regionalized multivariate data container to be used for Gaussian-based geostatistics: variogram modelling, cokriging and simulation.

Usage

make.gmMultivariateGaussianSpatialModel(
  data,
  coords = attr(data, "coords"),
  model = NULL,
  beta = model$beta,
  formula = model$formula,
  ng = NULL,
  nmax = ng$nmax,
  nmin = ng$nmin,
  omax = ng$omax,
  maxdist = ng$maxdist,
  force = ng$force
)

Arguments

data

either a data set of any data.frame similar class, or else a sp::SpatialPointsDataFrame() containing it

coords

the coordinates of the sampling locations, if no SpatialPointsDataFrame was provided

model

a variogram model, of any relevant class

beta

(see formula) the coefficients of dependence of the mean of the random field, if these are known; e.g. if formula=~1 constant mean, and the mean is indeed known, beta would be a compositional mean; seldom used directly

formula

a formula without left-hand-side term, e.g. ~1 or ~Easting+Northing, specifying what do we know of the dependence of the mean of the random field; this follows the same ideas than in gstat::gstat()

ng

optional neighborhood information, typically created with KrigingNeighbourhood()

nmax

optional, neighborhood description: maximum number of data points per cokriging system

nmin

optional, neighborhood description: minimum number of data points per cokriging system

omax

optional, neighborhood description: maximum number of data points per cokriging system per quadrant/octant

maxdist

optional, neighborhood description: maximum radius of the search neighborhood

force

optional logical, neighborhood description: if not nmin points are found inside maxdist radius, keep searching. This and all preceding arguments for neighborhood definition are borrowed from gstat::gstat()

Value

A "gmSpatialModel" object with all information provided appropriately structured. See gmSpatialModel.

See Also

SequentialSimulation(), TurningBands() or CholeskyDecomposition() for specifying the exact simulation method and its parameters, predict_gmSpatialModel for running predictions or simulations

Other gmSpatialModel: Predict(), as.gmSpatialModel(), gmSpatialModel-class, make.gmCompositionalGaussianSpatialModel(), make.gmCompositionalMPSSpatialModel()

Examples

data("jura", package="gstat")
X = jura.pred[,1:2]
Zc = jura.pred[,7:13]
make.gmMultivariateGaussianSpatialModel(data=Zc, coords=X)