pred.weed {WeedMap}R Documentation

Spatial prediction of weed counts

Description

Makes joint Bayesian inference and spatial prediction within a model for weed count data and a covariate

Usage

pred.weed(nit, thin = 1, x = NULL, xy = NULL, y = NULL, z, wx = NULL,
wxy = NULL,
i = NULL, alpha = NULL, beta = NULL, lambda = NULL, tau = NULL, kappa =
NULL,
sd.prop.h = 0.1, sd.prop.alpha = 0.5, sd.prop.beta = 0.5, sd.prop.lambda
= 0.1, sd.prop.tau = 0.1,
delta.prop.kappa = 2, mprior.alpha, vprior.alpha, mprior.beta, vprior.beta, mprior.kappa, vprior.kappa = 999, mprior.tau = 1, vprior.tau = 999, mprior.lambda = 1, vprior.lambda = 999, n.kappa = 1, kappa.max = 0)

Arguments

nit Number of iterations
thin Thinning of the Markov chain
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
wx Weed values at sites x
wxy Weed values at sites xy
i Image values at sites xy and y. Values have to be concatenated in the same vector in this order
alpha Init value for the shape parameter of w
beta Init value for the scale parameter of w
lambda Init value for the scaling parameter relating w and i as i = λ w ε
tau Init value for the variance of noise epsilon
kappa Init value for the spatial scale parameter of the Gausian random field
sd.prop.h Standard deviation of the Gaussian increment in the proposal of Gaussian components h
sd.prop.alpha Standard deviation of the Gaussian increment in the proposal for alpha
sd.prop.beta Standard deviation of the Gaussian increment in the proposal for beta
sd.prop.lambda Standard deviation of the Gaussian increment in the proposal for lambda
sd.prop.tau Standard deviation of the Gaussian increment in the proposal for tau
delta.prop.kappa Maximum number of increments allowed (the amplitude of an increment being kappa.max/n.kappa) for a move in the proposal of kappa
mprior.alpha A priori mean of alpha
vprior.alpha A priori variance of alpha
mprior.beta A priori mean of beta
vprior.beta A priori variance of beta
mprior.kappa A priori mean of kappa
vprior.kappa A priori variance of kappa
mprior.tau A priori mean of tau
vprior.tau A priori variance of tau
mprior.lambda A priori mean of lambda
vprior.lambda A priori variance of lambda
n.kappa Number of steps in the discretisation of the support of kappa
kappa.max Maximum value in the truncation of the support of kappa

Details

If standard deviation of the Gaussian increment in the update of alpha, beta, lambda or tau, or if the step in the increment of kappa is equal to 0, then this variable is not processed in the MCMC run and stays at its initial value. This is the way to specify that inference should not be made on a one or several variable. See examples in WeedMap where lambda is initialised at 1 and not updated (sd.prop.lambda=0).

If init values are not given, the corresponding parameters are initialised from the prior.

Value

A list whose elements are:

x Coordinates of sites where weed counts only are given
y Coordinates of sites where image indexes only are given
z Coordinates of sites where predcited values are sought
wx Weed values at sites x
i Image values at sites xy and y. Values are concatenated in the same vector in this order
nit Number of iterations
thin Thinning of the Markov chain
wy.MC A matrix with ny rows and nit/thin columns containing sampled values of wy
wz.MC A matrix with nz rows and nit/thin columns containing sampled values of wz
alpha.MC A vector of length nit/thin of simulated alpha values
beta.MC A vector of length nit/thin of simulated beta values
lambda.MC A vector of length nit/thin of simulated lambda values
tau.MC A vector of length nit/thin of simulated tau values
kappa.MC A vector of length nit/thin of simulated kappa values
n.kappa Number of steps in the discretisation of the support of kappa
kappa.max Maximum value in the truncation of the support of kappa

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]