computeV {spatialCovariance}R Documentation

Compute Covariance Matrix

Description

Observations are averages over congruent rectangular plots that like in a lattice. For extensive observations one needs to multiply the matrix by the $area^2$ where $area$ is the common area of each plot.

Various different classes of covariance functions, generalised covariance functions and their derivatives wrt parameters are built into this library. These include the Cauchy and Mat'ern covariance functions as well as specific sub models such as the Bessel$_0$, Exponential, Bessel$_1$, spline and logarithmic covariance functions.

Usage

V <- computeV(info,class="matern",params=c(0.2,0.5), ...)  ## matern model with inverse range 0.2 and smoothness 0.5
V <- computeV(info,class="ldt",rel.tol=1e-10,abs.tol=rel.tol,cat.level=1)  ## logarithmic model
V <- computeV(info,class="misc",K=K) ## Pass another function K
 

Arguments

info Result of the precompute stage
class The class of covariance functions,"ldt", "bess0", "exp", "bess1", "power", "powerNI", "matern", "spline", "cauchy". Can also be used to compute the derivatives of the covariance matrices for specific models, for example "dbess0", "dexp", "dexp2", "dbess1", "dpowerNI". Can also be used for any isotropic function K, simply define a function K in the workspace that has two arguments, distance and a vector of parameters. Then call computeV with class="special".
params Parameters that go with a specific class of models, for the "matern" class it requires an inverse range parameter and a smoothness parameter, for example params=c(1,0.5), this corresponds to the case when class="exp", params=c(1).
rel.tol Relative Tolerance for one dimensional numerical integration
abs.tol Absolute Tolerance for one dimensional numerical integration
cat.level Controls level of time output, takes values 0, 0.5, 1
K If class="misc" pass the function K here

Author(s)

David Clifford

Examples

## Example for extensive variables - variances of combined plots
library(spatialCovariance)
nrows <- 1
ncols <- 2
rowwidth <- 1.1
colwidth <- 1.2
rowsep <- 0
colsep <- 0

info <- precompute(nrows,ncols,rowwidth,colwidth,rowsep,colsep)
V <- computeV(info,class="matern",params=c(1,1))

info2 <- precompute(nrows=1,ncols=1,rowwidth=rowwidth,colwidth=colwidth*2,0,0)
V2 <- computeV(info2,class="matern",params=c(1,1))

c(1,1) 

(rowwidth * (2*colwidth))^2 * V2 

[Package spatialCovariance version 0.6-4 Index]