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####################################################################
##
## hSDM.ZIP.iCAR.R
##
####################################################################
##
## Original code by Ghislain Vieilledent, November 2013
## CIRAD UR B&SEF
## ghislain.vieilledent@cirad.fr / ghislainv@gmail.com
##
####################################################################
##
## This software is distributed under the terms of the GNU GENERAL
## PUBLIC LICENSE Version 2, June 1991. See the package LICENSE
## file for more information.
##
## Copyright (C) 2011 Ghislain Vieilledent
##
####################################################################
hSDM.ZIP.iCAR <- function (# Observations
counts,
suitability, abundance, spatial.entity, data,
# Spatial structure
n.neighbors, neighbors,
# Predictions
suitability.pred=NULL, spatial.entity.pred=NULL,
# Chains
burnin=5000, mcmc=10000, thin=10,
# Starting values
beta.start,
gamma.start,
Vrho.start,
# Priors
mubeta=0, Vbeta=1.0E6,
mugamma=0, Vgamma=1.0E6,
priorVrho="1/Gamma",
shape=0.5, rate=0.0005,
Vrho.max=1000,
# Various
seed=1234, verbose=1,
save.rho=0, save.p=0)
{
#========
# Basic checks
#========
check.mcmc.parameters(burnin, mcmc, thin)
check.verbose(verbose)
check.save.rho(save.rho)
check.save.p(save.p)
#========
# Form response, covariate matrices and model parameters
#========
#= Response
Y <- counts
nobs <- length(Y)
#= Suitability
mf.suit <- model.frame(formula=suitability,data=data)
X <- model.matrix(attr(mf.suit,"terms"),data=mf.suit)
#= Abundance
mf.obs <- model.frame(formula=abundance,data=data)
W <- model.matrix(attr(mf.obs,"terms"),data=mf.obs)
#= Spatial correlation
ncell <- length(n.neighbors)
cells <- spatial.entity
#= Predictions
if (is.null(suitability.pred) | is.null(spatial.entity.pred)) {
X.pred <- X
cells.pred <- cells
npred <- nobs
}
if (!is.null(suitability.pred) & !is.null(spatial.entity.pred)) {
mf.pred <- model.frame(formula=suitability,data=suitability.pred)
X.pred <- model.matrix(attr(mf.pred,"terms"),data=mf.pred)
cells.pred <- spatial.entity.pred
npred <- length(cells.pred)
}
#= Model parameters
np <- ncol(X)
nq <- ncol(W)
ngibbs <- mcmc+burnin
nthin <- thin
nburn <- burnin
nsamp <- mcmc/thin
#==========
# Check data
#==========
check.Y.poisson(Y)
check.X(X,nobs)
check.W(W,nobs)
check.cells(cells,nobs)
check.neighbors(n.neighbors,ncell,neighbors)
check.cells.pred(cells.pred,npred)
#========
# Initial starting values for M-H
#========
beta.start <- form.beta.start(beta.start,np)
gamma.start <- form.gamma.start(gamma.start,nq)
rho.start <- rep(0,ncell) # Starting values for spatial random effects set to zero.
Vrho.start <- check.Vrho.start(Vrho.start)
#========
# Form and check priors
#========
mubeta <- check.mubeta(mubeta,np)
Vbeta <- check.Vbeta(Vbeta,np)
mugamma <- check.mugamma(mugamma,nq)
Vgamma <- check.Vgamma(Vgamma,nq)
check.ig.prior(shape,rate)
Vrho.max <- check.Vrho.max(Vrho.max)
priorVrho <- form.priorVrho(priorVrho)
#========
# Parameters to save
#========
beta <- rep(beta.start,nsamp)
gamma <- rep(gamma.start,nsamp)
if (save.rho==0) {rho_pred <- rho.start}
if (save.rho==1) {rho_pred <- rep(rho.start,nsamp)}
Vrho <- rep(Vrho.start,nsamp)
prob_p_latent <- rep(0,nobs)
prob_q_latent <- rep(0,nobs)
if (save.p==0) {prob_p_pred <- rep(0,npred)}
if (save.p==1) {prob_p_pred <- rep(0,npred*nsamp)}
Deviance <- rep(0,nsamp)
#========
# call C++ code to draw sample
#========
Sample <- .C("hSDM_ZIP_iCAR",
#= Constants and data
ngibbs=as.integer(ngibbs), nthin=as.integer(nthin), nburn=as.integer(nburn), ## Number of iterations, burning and samples
nobs=as.integer(nobs),
ncell=as.integer(ncell),
np=as.integer(np),
nq=as.integer(nq),
Y_vect=as.integer(c(Y)),
X_vect=as.double(c(X)),
W_vect=as.double(c(W)),
#= Spatial correlation
C_vect=as.integer(c(cells)-1), # Cells range is 1,...,ncell in R. Must start at 0 for C. Don't forget the "-1" term.
nNeigh=as.integer(c(n.neighbors)),
Neigh_vect=as.integer(c(neighbors-1)), # Cells range is 1,...,ncell in R. Must start at 0 for C. Don't forget the "-1" term.
#= Predictions
npred=as.integer(npred),
X_pred_vect=as.double(c(X.pred)),
C_pred_vect=as.integer(c(cells.pred)-1),
#= Starting values for M-H
beta_start=as.double(c(beta.start)),
gamma_start=as.double(c(gamma.start)),
rho_start=as.double(c(rho.start)),
#= Parameters to save
beta.nonconst=as.double(beta), ## Fixed parameters of the regression
gamma.nonconst=as.double(gamma),
rho_pred.nonconst=as.double(rho_pred),
Vrho.nonconst=as.double(Vrho),
#= Defining priors
mubeta=as.double(c(mubeta)), Vbeta=as.double(c(Vbeta)),
mugamma=as.double(c(mugamma)), Vgamma=as.double(c(Vgamma)),
priorVrho=as.double(priorVrho),
shape=as.double(shape), rate=as.double(rate),
Vrho.max=as.double(Vrho.max),
#= Diagnostic
Deviance.nonconst=as.double(Deviance),
prob_p_latent.nonconst=as.double(prob_p_latent), ## Predictive posterior mean
prob_q_latent.nonconst=as.double(prob_q_latent), ## Predictive posterior mean
prob_p_pred.nonconst=as.double(prob_p_pred),
#= Seed
seed=as.integer(seed),
#= Verbose
verbose=as.integer(verbose),
#= Save rho and p
save_rho=as.integer(save.rho),
save_p=as.integer(save.p),
PACKAGE="hSDM")
#= Matrix of MCMC samples
Matrix <- matrix(NA,nrow=nsamp,ncol=np+nq+2)
names.fixed <- c(paste("beta.",colnames(X),sep=""),paste("gamma.",colnames(W),sep=""))
colnames(Matrix) <- c(names.fixed,"Vrho","Deviance")
#= Filling-in the matrix
Matrix[,c(1:np)] <- matrix(Sample[[20]],ncol=np)
Matrix[,c((np+1):(np+nq))] <- matrix(Sample[[21]],ncol=nq)
Matrix[,ncol(Matrix)-1] <- Sample[[23]]
Matrix[,ncol(Matrix)] <- Sample[[32]]
#= Transform Sample list in an MCMC object
MCMC <- mcmc(Matrix,start=nburn+1,end=ngibbs,thin=nthin)
#= Save rho
if (save.rho==0) {rho.pred <- Sample[[22]]}
if (save.rho==1) {
Matrix.rho.pred <- matrix(Sample[[22]],ncol=ncell)
colnames(Matrix.rho.pred) <- paste("rho.",c(1:ncell),sep="")
rho.pred <- mcmc(Matrix.rho.pred,start=nburn+1,end=ngibbs,thin=nthin)
}
#= Save pred
if (save.p==0) {prob.p.pred <- Sample[[35]]}
if (save.p==1) {
Matrix.p.pred <- matrix(Sample[[35]],ncol=npred)
colnames(Matrix.p.pred) <- paste("p.",c(1:npred),sep="")
prob.p.pred <- mcmc(Matrix.p.pred,start=nburn+1,end=ngibbs,thin=nthin)
}
#= Output
return (list(mcmc=MCMC,
rho.pred=rho.pred, prob.p.pred=prob.p.pred,
prob.p.latent=Sample[[33]], prob.q.latent=Sample[[34]]))
}
#===================================================================
# END
#===================================================================
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