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)