| WeedMap-package {WeedMap} | R Documentation |
Simulation, inference and prediction for a Bayesian spatial statistical model for weed intensities and a covariate
| Package: | WeedMap |
| Type: | Package |
| Version: | 0.1 |
| Date: | 2006-09-28 |
| License: | GPL |
The function sim.weed makes simulation from the model. The
simulated dataset can be graphically displayed with show.sim.weed. The
function pred.weed makes inference and prediction whose results
can be graphically displayed with show.pred.weed (monitoring of Markov
chain simulation) and with check.model.weed (goodness of fit assessment).
Gilles Guillot www.inapg.inra.fr/ens_rech/mathinfo/personnel/guillot/welcome.html
G. Guillot, N. Loren, M. Rudemo, Bayesian spatial prediction of weed intensities from exact count data and picture based indexes, 2006, submitted
## Simulate a data set
sim <- sim.weed(nx=30, ny=20, nxy=20, nz=49,
param.cov=c(mean=0,variance=1,nugget=0,scale=.1),
mu=80, sigma=70, lambda=1, tau=0.2, nbin=10,
true.field = TRUE, npix = c(100,100), z.on.grid = TRUE)
## show the graphics
show.sim.weed(sim)
## Not run:
## make joint inference and prediction
res <- pred.weed(nit=10000,
thin=10,
## data
x=sim$x,
xy=sim$xy,
y=sim$y,
z=sim$z,
wx=sim$wx,
wxy=sim$wxy,
i=sim$i,
## init
#alpha=alpha,
#beta=beta,
lambda=1,
#tau=tau,
#kappa=kappa,
## proposals
sd.prop.h=0.1,
sd.prop.alpha=0.1,
sd.prop.beta=0.01,
sd.prop.lambda=0.,
sd.prop.tau=0.5,
delta.prop.kappa=2,
## priors
mprior.alpha=0.625,
vprior.alpha=1,
mprior.beta=0.0125,
vprior.beta=1,
mprior.kappa=.5,
vprior.kappa=999,
mprior.lambda=1,
vprior.lambda=1,
mprior.tau=0.1,
vprior.tau=10,
n.kappa=30,
kappa.max=5*sim$param.cov[4])
show.pred.weed(sim=sim,
res=res,
param=TRUE,
pairs=TRUE,
wy=FALSE,
wz=FALSE,
nit=res$nit,
thin=res$thin,
burnin=500)
check.model.weed(x=sim$x,
xy=sim$xy,
y=sim$y,
wx=sim$wx,
wxy=sim$wxy,
i=sim$i,
## output of MCMC run
res=res,
## options
nit=res$nit,
thin=res$thin,
burnin=500,
bin=seq(.1,.5,.05),
nqqplot=500,
nresamp=200)
## End(Not run)