sim.weed {WeedMap}R Documentation

Simulate weed exact count data and a covariate

Description

The data simulated are of two kinds: exact weed counts and image derived indexes. The counts w arise from a transformed Gaussian model with Gamma marginal. The image indexes i relate to w as i(s) = λ w(s) * ε(s), where ε is a spatially uncorrelated noise. The simulation is carried out on the unit square.

Usage

sim.weed(nx, ny, nxy, nz, param.cov, mu, sigma, lambda, tau, nbin, true.field = FALSE, npix = NULL, z.on.grid = TRUE)

Arguments

nx number of sites where only w is observed
ny number of sites where only i is observed
nxy number of sites where w and i are observed
nz number of sites where w will be predicted. If z.on.grid=TRUE, nz should be a square.
param.cov Vector of parameters of the underlying Gaussian randomFields. The simulation of the Gaussian random fields performed assuming an exponential covariance function. The vector of parameters should have four components, namely: mea, variance, nugget, scale and should be given as e.g.:c(mean=0,variance=1,nugget=0,scale=.1). See the documentation of GaussRF in package RandomFields for details.
mu Mean of w
sigma Variance of w
lambda Scaling factor relating w and i
tau Variance of ε
nbin Number of bins for the binned data
true.field Logical: shoud values on a dense grid be given
npix A vector giving the number of pixels horizontally and vertically of the grid if true.field=TRUE
z.on.grid Logical: set TRUE if sites of prediction z are required on a grid; thennz should be a square.

Value

x Coordinates of sites where weed counts only are given
xy Coordinates of sites where weeds and images are given
y Coordinates of sites where image indexes only are given
z Coordinates of sites where predcited values are sought
coord.grid Logical telling whether all the realisation of the weed random field is also given on a grid
wx Weed values at sites x
wy Weed values at sites y
wxy Weed values at sites xy
wz Weed values at sites z
vx Binned weed values at sites x
vxy Binned weed values at sites xy
i Image values at sites xy and y. Values are concatenated in the same vector in this order
wgrid Weed counts values at the node of a grid
igrid Image index values at the node of a grid
bin Binning of the weed counts
param.cov Parameters of the covariance function of the underlying Gaussian random field
mu Mean of w
sigma Variance of w
alpha Shape parameter of w
beta Scale parameter of w
lambda Scaling parameter relating w and i as i = λ w ε
tau Variance of noise epsilon
npix number of pixel in the horizontal direction (same value is assumed for the vertical direction)

Author(s)

Gilles Guillot

References

G. Guillot, N. Loren, M. Rudemo, Bayesian spatial prediction of weed intensities from exact count data and picture based indexes, 2006, submitted


[Package WeedMap version 0.1 Index]