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spGPP.r
1949 lines (1937 loc) · 69.1 KB
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spGPP.r
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##
## priors checking gpp
##
priors.checking.gpp<-function(x=NULL,r,p){
#
if(is.null(x)){
x$prior_a=NULL; x$prior_b=NULL;
x$mu_beta=NULL; x$delta2_beta=NULL;
x$mu_rho=NULL; x$delta2_rho=NULL;
x$alpha_l=NULL; x$delta2_l=NULL;
x$prior_a<-2; x$prior_b<-1;
x$mu_beta<-rep(0,p); x$delta2_beta<-matrix(0,p,p);
diag(x$delta2_beta)<-10^10;
x$mu_rho<-0; x$delta2_rho<-10^10;
x$alpha_l<-rep(0,r); x$delta2_l<-rep(10^10,r)
x
}
else{
if(is.null(x$prior_a)){
x$prior_a<-2
}
if(is.null(x$prior_b)){
x$prior_b<-1
}
if(is.null(x$mu_beta)){
x$mu_beta<-rep(0,p)
}
if(length(c(x$mu_beta)) != p){
x$mu_beta<-rep(c(x$mu_beta),p)
}
if(is.null(x$delta2_beta)){
x$delta2_beta<-matrix(0,p,p);
diag(x$delta2_beta)<-10^10;
}
if(length(c(x$delta2_beta)) != (p*p)){
delta2_beta<-matrix(0,p,p);
diag(delta2_beta)<-x$delta2_beta;
x$delta2_beta<-delta2_beta
}
if(is.null(x$mu_rho)){
x$mu_rho<-0;
}
if(is.null(x$delta2_rho)){
x$delta2_rho<-10^10;
}
if(is.null(x$alpha_l)){
x$alpha_l<-rep(0,r);
}
if(length(c(x$alpha_l)) != r){
x$alpha_l<-rep(c(x$alpha_l),r)
}
if(is.null(x$delta2_l)){
x$delta2_l<-rep(10^10,r);
}
if(length(c(x$delta2_l)) != r){
x$delta2_l<-rep(c(x$delta2_l),r)
}
x
}
}
##
## initials checking for the GPP
##
initials.checking.gpp<-function(x,z,X,n,r,T,d){
# checking unequal T
if(length(T)>1){
rT <- sum(T)
}
else{
rT <- r*T
}
#
# x = initial values
#
if(is.null(x)){
x$phi=NULL; x$sig2eps=NULL; x$sig2eta=NULL;
x$sig2l=NULL; x$rho=NULL; x$beta=NULL; x$mu_l=NULL;
x$phi<-(-log(0.05)/max(c(d)))#(3/max(c(d)))
x$sig2eps <- 0.01; x$sig2eta <- 0.1; x$rho<-0.5
x$sig2l <- rep(0.1, r);
x$mu_l<-rep(NA,r);
zm <- matrix(z,rT,n)
for(i in 1:r){
x$mu_l[i] <- 0
}
lm.coef<-lm(c(z) ~ X-1)$coef
lm.coef[is.na(lm.coef)]<-0
x$beta<-lm.coef[1:(dim(X)[[2]])]
x
}
else{
if(is.null(x$phi)){
x$phi<-(3/max(c(d)))#(-log(0.05)/max(c(d)))
}
if(is.null(x$sig2eps)){
x$sig2eps<-0.01
}
if(is.null(x$sig2eta)){
x$sig2eta<-0.1
}
if(is.null(x$rho)){
x$rho<-0.5
}
if(is.null(x$sig2l)){
x$sig2l <- rep(0.1, r);
}
if(is.null(x$mu_l)){
x$mu_l<-rep(NA,r); zm <- matrix(z,rT,n)
for(i in 1:r){
x$mu_l[i] <- 0
}
}
if(is.null(x$beta)){
lm.coef<-lm(c(z) ~ X-1)$coef
lm.coef[is.na(lm.coef)]<-0
x$beta<-lm.coef[1:(dim(X)[[2]])]
}
x
}
}
##
## MCMC sampling with only one phi parameter
##
spGPP.Gibbs<-function(formula, data=parent.frame(), time.data,
knots.coords, coords, priors=NULL, initials=NULL,
nItr, nBurn=0, report=1, tol.dist=2, distance.method="geodetic:km",
cov.fnc="exponential", scale.transform="NONE",
spatial.decay, fitted.values, X.out=TRUE, Y.out=FALSE)
{
start.time<-proc.time()[3]
#
#
if(nBurn >= nItr){
stop(paste("\n Error: iterations < nBurn\n Here, nBurn = ",nBurn," and iterations = ",nItr,"."))
}
#
#
if (missing(formula)) {
stop("\n Error: formula must be specified \n")
}
#
if (!inherits(formula, "formula")) {
stop("\n Error: equation must be in formula-class \n ...")
}
#
if (inherits(formula, "formula")) {
XY <- Formula.matrix(formula, data)
Y <- XY[[1]]
X <- as.matrix(XY[[2]])
x.names <- XY[[3]]
Xsp <- XY[[4]]
x.names.sp <- XY[[5]]
Xtp <- XY[[6]]
x.names.tp <- XY[[7]]
#if((!is.null(x.names.sp)) | (!is.null(x.names.tp))){
# stop("\n## \n# Error: spatially and/or temporally varying approach is not available for the GPP model\n ##\n")
#}
if((!is.null(x.names.tp))){
stop("\n## \n# Error: dynamic temporally varying approach is not available for the GPP model\n ##\n")
}
}
#
#
if (missing(coords)) {
stop("Error: need to specify the coords")
}
if ( !is.matrix(coords) ) {
stop("Error: coords must be a (n x 2) matrix of xy-coordinate locations")
}
if ( (!is.numeric(coords[,1])) | (!is.numeric(coords[,2]))) {
stop("\n Error: coords columns should be numeric \n")
}
#
if (missing(knots.coords)) {
stop("Error: need to specify the knots.coords")
}
if ( !is.matrix(knots.coords) ) {
stop("Error: knots.coords must be a (n x 2) matrix of xy-coordinate locations")
}
if ( (!is.numeric(knots.coords[,1])) | (!is.numeric(knots.coords[,2]))) {
stop("\n Error: knots.coords columns should be numeric \n")
}
#
# check time.data
if(is.null(time.data)){
time.data<-list(1,length(Y)/length(coords[,1]))
}
else{
time.data<-time.data
}
#
#
n <- length(coords[,1]) # number of sites
r <- time.data[[1]] # number of years
T <- time.data[[2]] # number of days
#
# checking unequal T
if(length(T) > 1){
rT <- sum(T)
}
else{
rT <- r*T
}
N <- n*rT
p <- length(x.names)
knots <- length(knots.coords[,1])
#
if(n <= knots){
stop("Error: n must not be smaller than or equal to knots")
}
#
method <- distance.method
spT.check.sites.inside(knots.coords, method, tol=tol.dist)
#
if(method=="geodetic:km"){
coords.D.knots <- as.matrix(spT.geodist(Lon=knots.coords[,1],Lat=knots.coords[,2], KM=TRUE))
}
else if(method=="geodetic:mile"){
coords.D.knots <- as.matrix(spT.geodist(Lon=knots.coords[,1],Lat=knots.coords[,2], KM=FALSE))
}
else {
coords.D.knots <- as.matrix(dist(knots.coords, method, diag=TRUE, upper=TRUE))
}
#
if(knots != length(coords.D.knots[,1])){
stop("Error: knots must be equal to knots.coords")
}
#
all.coords <- rbind(coords,knots.coords)
spT.check.sites.inside(all.coords, method, tol=tol.dist)
#
if(method=="geodetic:km"){
coords.D.all <- as.matrix(spT.geodist(Lon=all.coords[,1],Lat=all.coords[,2], KM=TRUE))
coords.D.obs.knots <- coords.D.all[1:length(coords[,1]),(length(coords[,1])+1):length(all.coords[,1])]
}
else if(method=="geodetic:mile"){
coords.D.all <- as.matrix(spT.geodist(Lon=all.coords[,1],Lat=all.coords[,2], KM=FALSE))
coords.D.obs.knots <- coords.D.all[1:length(coords[,1]),(length(coords[,1])+1):length(all.coords[,1])]
}
else {
coords.D.all <- as.matrix(dist(all.coords, method, diag=TRUE, upper=TRUE))
coords.D.obs.knots <- coords.D.all[1:length(coords[,1]),(length(coords[,1])+1):length(all.coords[,1])]
}
#
#
if (N != length(Y)) {
stop("#\n# Error: Years, Months, and Days are misspecified,\n i.e., total number of observations in the data set \n should be equal to N\n : N = n * r * T \n where, N = total number of observations in the data,\n n = total number of sites,\n r = total number of years,\n T = total number of days. \n# Check the function spT.time.\n#\n")
}
#
priors<-priors.checking.gpp(priors,r,p)
#
shape_e <- N/2+priors$prior_a
shape_eta <- (knots*rT)/2+priors$prior_a
shape_l <- (knots*r)/2+priors$prior_a
#
zm <- matrix(Y,rT,n)
zm <- apply(zm,1,median,na.rm=TRUE)
zm <- rep(zm,n)
zm <- cbind(Y,zm)
zm[is.na(zm[,1]),1] <- zm[is.na(zm[,1]),2]
zm[is.na(zm[,1]),1] <- median(zm[,2],na.rm=TRUE)
#
zm <- zm[,1]
#
flag <- matrix(NA,n*rT,2)
flag[,1] <- c(Y)
flag[!is.na(flag[,1]),2] <- 0
flag[is.na(flag[,1]),2] <- 1
flag <- flag[,2]
#
#
if(cov.fnc=="exponential"){
cov <- 1
}
else if(cov.fnc=="gaussian"){
cov <- 2
}
else if(cov.fnc=="spherical"){
cov <- 3
}
else if(cov.fnc=="matern"){
cov <- 4
}
else{
stop("\n Error: cov.fnc is not correctly specified \n")
}
#
if(scale.transform=="NONE"){
zm <- zm
trans <- 0
}
else if(scale.transform=="SQRT"){
zm <- sqrt(zm)
trans <- 1
}
else if(scale.transform=="LOG"){
zm <- log(zm)
trans <- 2
}
else{
stop("\n Error: scale.transform is not correctly specified \n")
}
#
#
initials<-initials.checking.gpp(initials,zm,X,n,r,T,coords.D.all)
#
#
if(fitted.values=="ORIGINAL"){
if(scale.transform=="NONE"){
ft <- 0
}
else if(scale.transform=="SQRT"){
ft <- 1
}
else if(scale.transform=="LOG"){
ft <- 2
}
else{
stop("\n Error: scale.transform is not correctly specified \n")
}
}
else if(fitted.values=="TRANSFORMED"){
ft <- 0
}
else{
stop("\n Error: fitted.values option is not correctly specified \n")
}
#
#
if(spatial.decay$type=="FIXED"){
spdecay <- 1
if(is.null(spatial.decay$value)){
spatial.decay$value <- (3/max(c(coords.D.all)))
}
init.phi <- spatial.decay$value
tuning <- 0; phis<-0; phik<-0;
phi_a <- 0; phi_b <- 0;
}
else if(spatial.decay$type=="DISCRETE"){
spdecay <- 2
init.phi <- initials$phi
tuning <-0;
phis<-spatial.decay$value;
phik<-spatial.decay$segments;
phi_a<-0; phi_b<-0
}
else if(spatial.decay$type=="MH"){
spdecay <- 3
init.phi <- initials$phi
tuning <- spatial.decay$tuning
phis<-0; phik<-0;
phi_a<-spatial.decay$val[1]
phi_b<-spatial.decay$val[2]
}
else{
stop("\n Error: spatial.decay is not correctly specified \n")
}
#
#
if (length(initials$mu_l) != r){
stop("Error: need to specify correct number of years (r) for mu_l initials.")
}
if (length(initials$sig2l) != r){
stop("Error: need to specify correct number of years (r) for sig2l initials.")
}
if (length(initials$beta) != p){
stop("Error: need to specify correct number (p) of initial parameters for beta.")
}
#
if (length(priors$mu_beta) != p){
stop("Error: need to specify correct number (p) of mu_beta priors.")
}
if (length(priors$delta2_beta) != p*p){
stop("Error: need to specify correct number (p) of delta2_beta priors.")
}
if (length(priors$alpha_l) != r){
stop("Error: need to specify correct number (r) of alpha_l priors.")
}
if (length(priors$delta2_l) != r){
stop("Error: need to specify correct number (r) of delta2_l priors.")
}
#
tmp <- zm-median(zm)
tmp<-matrix(tmp,rT,n)
tmp<-apply(tmp,1,mean)
w<-rep(tmp,knots)
rm(tmp)
w<-matrix(w,rT,knots)
w<-t(w); w<-c(w);
w0 <- rep(0,knots*r)
#
if((length(x.names.sp) == 0) & (length(x.names.tp) == 0)){
# non-spatial and non-temporal beta
# check for T
if(r > 1){
if(length(T) != r){
T<-rep(T,r)
}
}
#
out <- NULL
out <- .C("GIBBS_zfitsum_onephi_gpp", as.integer(cov),
as.integer(spdecay), as.double(flag), as.integer(nItr),
as.integer(nBurn), as.integer(n), as.integer(knots),
as.integer(T), as.integer(r), as.integer(rT),
as.integer(p), as.integer(N), as.integer(report), as.integer(ft),
as.double(shape_e), as.double(shape_eta),as.double(shape_l),
as.double(phi_a), as.double(phi_b),
as.double(priors$prior_a),as.double(priors$prior_b), as.double(priors$mu_beta),
as.double(priors$delta2_beta), as.double(priors$mu_rho),
as.double(priors$delta2_rho), as.double(priors$alpha_l),
as.double(priors$delta2_l), as.double(init.phi),
as.double(tuning), as.double(phis), as.integer(phik),
as.double(coords.D.knots), as.double(coords.D.obs.knots),
as.integer(1),
as.double(initials$sig2eps), as.double(initials$sig2eta),
as.double(initials$sig2l), as.double(initials$beta),
as.double(initials$rho), as.double(initials$mu_l),
as.double(X), as.double(zm), as.double(w0), as.double(w),
as.integer(trans),
phip = double(nItr), accept = double(1), nup = double(nItr),
sig2eps = double(nItr), sig2etap = double(nItr),
betap = matrix(double(p*nItr), p, nItr), rhop = double(nItr),
mu_lp = matrix(double(r*nItr), r, nItr),
sig2lp = matrix(double(r*nItr),r, nItr),
w0p = matrix(double(knots * r * nItr),knots * r, nItr),
wp = matrix(double(knots*rT*nItr), knots * rT, nItr),
gof = as.double(1), penalty = as.double(1),
fitted = matrix(double(2*n*rT),n*rT,2))[46:59]
} # end for fixed beta
#
else if((length(x.names.sp) > 0) & (length(x.names.tp) == 0)){
# for spatial beta
stop("\n#\n## Error: Dynamic spatially varying models are not available for the GPP based models. \n#")
} # end for spatial beta
else{
stop("\n#\n## Error: Dynamic temporally varying models are not available for the GPP based models. \n#")
} # end
#
###
#
if(X.out==TRUE){
if((length(x.names.sp) == 0) & (length(x.names.tp) == 0)){
# non-spatial and temporal beta
out$X <- X
}
else{
stop("\n#\n## Error: \n#")
}
}
if(Y.out==TRUE){
out$Y <- Y
}
#
out$accept <- round(out$accept/nItr*100,2)
out$call<-formula
#
cat("##","\n")
cat("# nBurn = ",nBurn,". Iterations = ",nItr,".", "\n")
cat("# Acceptance rate: (phi) = ",out$accept,"%", "\n")
cat("##","\n")
#
out$phip <- as.matrix(out$phip[(nBurn+1):nItr])
if(cov==4){
out$nup <- as.matrix(out$nup[(nBurn+1):nItr])
}
out$sig2eps <- as.matrix(out$sig2eps[(nBurn+1):nItr])
out$sig2etap <- as.matrix(out$sig2etap[(nBurn+1):nItr])
out$sig2lp <- matrix(out$sig2lp[1:r,(nBurn+1):nItr],r,length((nBurn+1):nItr))
out$rhop <- as.matrix(out$rhop[(nBurn+1):nItr])
out$betap <- matrix(out$betap[1:p,(nBurn+1):nItr],p,length((nBurn+1):nItr))
out$mu_lp <- matrix(out$mu_lp[1:r,(nBurn+1):nItr],r,length((nBurn+1):nItr))
out$w0p <- out$w0p[1:(knots*r),(nBurn+1):nItr]
out$wp <- out$wp[1:(knots*rT),(nBurn+1):nItr]
dimnames(out$fitted)[[2]] <- c("Mean","SD")
out$tol.dist<-tol.dist
out$distance.method<-method
out$cov.fnc<-cov.fnc
out$scale.transform<-scale.transform
out$sampling.sp.decay<-spatial.decay
out$covariate.names<-c(x.names)
out$Distance.matrix.knots<-coords.D.knots
out$knots.coords<-knots.coords
out$coords<-coords
if(method=="geodetic:km"){
out$KM <- TRUE
}
if(method=="geodetic:mile"){
out$KM <- FALSE
}
out$n <- n
out$r <- r
out$T <- T
out$p <- p
if(length(x.names.sp) != 0){
out$q<-q
}
out$knots <- knots
out$initials <- initials
out$priors <- priors
out$gof <- round(out$gof,2)
out$penalty <- round(out$penalty,2)
tmp <- matrix(c(out$gof,out$penalty,out$gof+out$penalty),1,3)
dimnames(tmp)[[2]]<-c("Goodness.of.fit","Penalty","PMCC")
dimnames(tmp)[[1]]<-c("values:")
out$PMCC <- tmp
tmp <- NULL
out$gof <- NULL
out$penalty <- NULL
#
out$iterations <- nItr
out$nBurn <- nBurn
#
#
end.time <- proc.time()[3]
comp.time<-end.time-start.time
comp.time<-fnc.time(comp.time)
out$computation.time<-comp.time
#
#
#class(out) <- "spGPP"
#
out
}
##
## Prediction of Z_lt for the GPP models using MCMC samples
##
spGPP.prediction<-function(nBurn, pred.data, pred.coords,
posteriors, tol.dist, Summary=TRUE, fitted.X=NULL)
{
start.time<-proc.time()[3]
#
if (missing(posteriors)) {
stop("Error: need to provide the posterior MCMC samples.")
}
#
if (is.null(posteriors$X)) {
stop("Error: need to provide the fitted covariate values.")
}
fitted.X <- posteriors$X
#
knots.coords<-posteriors$knots.coords
coords<-posteriors$coords
#
if (missing(knots.coords)) {
stop("Error: need to specify the knots coords.")
}
if (!is.matrix(knots.coords)) {
stop("Error: knots coords must be a (knots x 2) matrix of xy-coordinate locations.")
}
if ( (!is.numeric(knots.coords[,1])) | (!is.numeric(knots.coords[,2]))) {
stop("\n Error: knots coords columns should be numeric \n")
}
if (missing(coords)) {
stop("Error: need to specify the coords")
}
if ( !is.matrix(coords) ) {
stop("Error: coords must be a (n x 2) matrix of xy-coordinate locations")
}
if ( (!is.numeric(coords[,1])) | (!is.numeric(coords[,2]))) {
stop("\n Error: coords columns should be numeric \n")
}
if (missing(pred.coords)) {
stop("Error: need to specify the prediction coords.")
}
if (!is.matrix(pred.coords)) {
stop("Error: prediction coords must be a (n.pred.site x 2) matrix of xy-coordinate locations.")
}
if ( (!is.numeric(pred.coords[,1])) | (!is.numeric(pred.coords[,2]))) {
stop("\n Error: prediction coords columns should be numeric \n")
}
#
coords.all <- rbind(knots.coords,pred.coords)
tn.knotsites <- length(knots.coords[, 1])
nknot.sites <- 1:tn.knotsites
tn.predsites <- length(coords.all[, 1]) - tn.knotsites
npred.sites <- (tn.knotsites + 1):(length(coords.all[, 1]))
#
method <- posteriors$distance.method
spT.check.sites.inside(coords.all, method, tol=tol.dist)
#
if(method == "geodetic:km"){
coords.D <- as.matrix(spT.geodist(Lon=coords.all[,1],Lat=coords.all[,2], KM=TRUE))
}
else if(method == "geodetic:mile"){
coords.D <- as.matrix(spT.geodist(Lon=coords.all[,1],Lat=coords.all[,2], KM=FALSE))
}
else {
coords.D <- as.matrix(dist(coords.all, method, diag=TRUE, upper=TRUE))
}
#
#
dmns <- coords.D[nknot.sites, npred.sites] # m x ns
dnsm <- t(dmns) # ns x m
dm <- coords.D[1:tn.knotsites, 1:tn.knotsites] # m x m
#
all.coords <- rbind(coords,knots.coords)
spT.check.sites.inside(all.coords, method, tol=tol.dist)
#
if(method == "geodetic:km"){
coords.D.all <- as.matrix(spT.geodist(Lon=all.coords[,1],Lat=all.coords[,2], KM=TRUE))
}
else if(method == "geodetic:mile"){
coords.D.all <- as.matrix(spT.geodist(Lon=all.coords[,1],Lat=all.coords[,2], KM=FALSE))
}
else {
coords.D.all <- as.matrix(dist(all.coords, method, diag=TRUE, upper=TRUE))
}
#
dnm <- coords.D.all[1:length(coords[,1]),(length(coords[,1])+1):length(all.coords[,1])]
#
al.coords <- rbind(pred.coords,coords)
spT.check.sites.inside(al.coords, method, tol=tol.dist)
if(method == "geodetic:km"){
coords.D.al <- as.matrix(spT.geodist(Lon=al.coords[,1],Lat=al.coords[,2], KM=TRUE))
dnsn <- coords.D.al[1:length(pred.coords[,1]),(length(pred.coords[,1])+1):length(al.coords[,1])]
}
else if(method == "geodetic:mile"){
coords.D.al <- as.matrix(spT.geodist(Lon=al.coords[,1],Lat=al.coords[,2], KM=FALSE))
dnsn <- coords.D.al[1:length(pred.coords[,1]),(length(pred.coords[,1])+1):length(al.coords[,1])]
}
else {
coords.D.al <- as.matrix(dist(al.coords, method, diag=TRUE, upper=TRUE))
dnsn <- coords.D.al[1:length(pred.coords[,1]),(length(pred.coords[,1])+1):length(al.coords[,1])]
}
#
nsite <- tn.predsites
#
#
if(posteriors$cov.fnc=="exponential"){
cov <- 1
}
else if(posteriors$cov.fnc=="gaussian"){
cov <- 2
}
else if(posteriors$cov.fnc=="spherical"){
cov <- 3
}
else if(posteriors$cov.fnc=="matern"){
cov <- 4
}
else{
stop("\n Error: cov.fnc is not correctly specified \n")
}
#
#
nItr <- posteriors$iterations
if((nBurn+posteriors$nBurn) >= nItr){
stop(paste("\n Error: burn-in >= iterations\n Here, nBurn = ",nBurn+posteriors$nBurn," and iterations = ",nItr,"."))
}
nItr <-(nItr-posteriors$nBurn)
itt <-(nItr-nBurn)
#
m <- tn.knotsites
r <- posteriors$r
T <- posteriors$T
# checking unequal T
if(length(T) > 1){
rT <- sum(T)
}
else{
rT <- r*T
}
p <- length(c(posteriors$betap))/nItr
n <- posteriors$n
#
#
# adding the formula in to the prediction dataset
if(!is.data.frame(pred.data) & !is.null(pred.data)){
stop("#\n# Error: pred.data should be in data format\n#")
}
call.f<-posteriors$call
call.f<-as.formula(paste("tmp~",paste(call.f,sep="")[[3]]))
if(is.data.frame(pred.data)){
if((nsite*rT)!=dim(pred.data)[[1]]){
print("#\n # Check the pred.data \n#\n")
}
pred.data$tmp<-rep(1,nsite*rT)
}
if(is.null(pred.data)){
pred.data<-data.frame(tmp=rep(1,nsite*rT))
}
pred.x<-Formula.matrix(call.f,data=pred.data)[[2]]
pred.xsp<-Formula.matrix(call.f,data=pred.data)[[4]]
pred.xtp<-Formula.matrix(call.f,data=pred.data)[[6]]
#
#
phip<-posteriors$phip[(nBurn+1):nItr,]
if(cov==4){
nup<-posteriors$nup[(nBurn+1):nItr,]
}
else{
nup<-0
}
sig2ep<-posteriors$sig2ep[(nBurn+1):nItr,]
sig2etap<-posteriors$sig2etap[(nBurn+1):nItr,]
sig2lp<-matrix(posteriors$sig2lp,r,nItr)
sig2lp<-sig2lp[,(nBurn+1):nItr]
rhop<-posteriors$rhop[(nBurn+1):nItr,]
mu_lp<-matrix(posteriors$mu_lp,r,nItr)
mu_lp<-mu_lp[,(nBurn+1):nItr]
betap<-matrix(posteriors$betap,p,nItr)
betap<-betap[,(nBurn+1):nItr]
w0p<-matrix(posteriors$w0p,m*r,nItr)
w0p<-w0p[,(nBurn+1):nItr]
wp<-matrix(posteriors$wp,m*rT,nItr)
wp<-wp[,(nBurn+1):nItr]
#
#
if(posteriors$scale.transform=="NONE"){
scale.transform <- 1
}
else if(posteriors$scale.transform=="SQRT"){
scale.transform <- 2
}
else if(posteriors$scale.transform=="LOG"){
scale.transform <- 3
}
else {
stop("scale.transform is not correctly defined.")
}
#
if((is.null(pred.xsp)) & (is.null(pred.xtp))){
# fixed beta
out<-matrix(.C('z_pr_its_gpp1', as.integer(cov),
as.integer(scale.transform), as.integer(itt),
as.integer(nsite),as.integer(n),as.integer(m),
as.integer(r),as.integer(T),as.integer(rT),as.integer(p),
as.integer(nsite*rT),as.double(phip),as.double(nup),as.double(dm),
as.double(dnsm),as.double(wp),as.double(sig2ep),
as.double(betap),as.double(pred.x),as.integer(1),
zpred=double(itt*nsite*rT))$zpred,rT*nsite,itt)
#
}
else if((!is.null(pred.xsp)) & (is.null(pred.xtp))){
# spatial beta
stop("Error: spatially varying dynamic modellings is not currently available for the GPP based models")
#
}
else if((is.null(pred.xsp)) & (!is.null(pred.xtp))){
# temporal beta
stop("Error: temporal dynamic modellings is not currently available for the GPP based models")
}
else if((!is.null(pred.xsp)) & (!is.null(pred.xtp))){
# both spatial and temporal beta
stop("Error: spatio-dynamic modellings is not currently available for the GPP based models")
}
else{
stop("Error: correctly define X variables")
}
#
output<-NULL
output$pred.samples<-out
#
out<-NULL
##
output$knots.coords <- posteriors$knots.coords
output$pred.coords <- pred.coords
output$distance.method <- posteriors$distance.method
output$cov.fnc <- posteriors$cov.fnc
output$scale.transform <- posteriors$scale.transform
output$n <- n
output$r <- r
output$T <- T
output$knots <- m
#
if(Summary == TRUE){
if(itt < 40){
cat("##", "\n")
cat("# Summary statistics are not given, because the number of predicted samples (",nItr,") are too small.\n# nBurn = ",nBurn+posteriors$nBurn,". Iterations = ",posteriors$iterations,".", "\n")
cat("##", "\n")
#
end.time<-proc.time()[3]
comp.time<-end.time-start.time
comp.time<-fnc.time(comp.time)
output$computation.time<-comp.time
#
#class(output) <- "spGPP"
output
}
else {
cat("##", "\n")
cat("# Predicted samples and summary statistics are given.\n# nBurn = ",nBurn+posteriors$nBurn,". Iterations = ",posteriors$iterations,".", "\n")
cat("##", "\n")
#
if(posteriors$model == "truncatedGPP"){
output$pred.samples <- reverse.truncated.fnc(output$pred.samples,at=posteriors$truncation.para$at[1],lambda=posteriors$truncation.para$lambda,at2=posteriors$truncation.para$at[2])
output$prob.below.threshold <- matrix(prob.below.threshold(output$pred.samples, at=posteriors$truncation.para$at[1]),rT, nsite)
output$Mean <- matrix(apply(output$pred.samples,1,mean,na.rm=TRUE),rT, nsite)
output$Median <- matrix(apply(output$pred.samples,1,median,na.rm=TRUE),rT, nsite)
output$SD <- matrix(apply(output$pred.samples,1,sd,na.rm=TRUE),rT, nsite)
ck <- apply(output$pred.samples,1,quantile,c(0.025,0.975),na.rm=TRUE)
output$Low <- matrix(ck[1,],rT, nsite)
output$Up <- matrix(ck[2,],rT, nsite)
#output$prob.below.threshold <- prob.below.threshold(output$pred.samples, at=posteriors$truncation.para$at[1])
#szp<-spT.Summary.Stat(output$pred.samples[,])
#output$Mean <- matrix(szp$Mean,rT, nsite)
#output$Mean <- reverse.truncated.fnc(output$Mean,at=posteriors$truncation.para$at[1],lambda=posteriors$truncation.para$lambda,at2=posteriors$truncation.para$at[2])
#output$Median <- matrix(szp$Median,rT, nsite)
#output$Median <- reverse.truncated.fnc(output$Median,at=posteriors$truncation.para$at[1],lambda=posteriors$truncation.para$lambda,at2=posteriors$truncation.para$at[2])
#output$SD <- matrix(szp$SD,rT, nsite)
#output$Low <- matrix(szp[,4],rT, nsite)
#output$Up <- matrix(szp[,5],rT, nsite)
#szp <- NULL
#output$Low <- reverse.truncated.fnc(output$Low,at=posteriors$truncation.para$at[1],lambda=posteriors$truncation.para$lambda,at2=posteriors$truncation.para$at[2])
#output$Up <- reverse.truncated.fnc(output$Up,at=posteriors$truncation.para$at[1],lambda=posteriors$truncation.para$lambda,at2=posteriors$truncation.para$at[2])
#output$pred.samples <- reverse.truncated.fnc(output$pred.samples,at=posteriors$truncation.para$at[1],lambda=posteriors$truncation.para$lambda,at2=posteriors$truncation.para$at[2])
output$truncation.para <- posteriors$truncation.para
}
else{
#
szp<-spT.Summary.Stat(output$pred.samples[,])
output$Mean <- matrix(szp$Mean,rT, nsite)
output$Median <- matrix(szp$Median,rT, nsite)
output$SD <- matrix(szp$SD,rT, nsite)
output$Low <- matrix(szp[,4],rT, nsite)
output$Up <- matrix(szp[,5],rT, nsite)
szp <- NULL
}
#
end.time <- proc.time()[3]
comp.time<-end.time-start.time
comp.time<-fnc.time(comp.time)
output$computation.time<-comp.time
#
#class(output) <- "spGPP"
output
}
}
else {
cat("##", "\n")
cat("# Predicted samples are given.\n# nBurn = ",nBurn+posteriors$nBurn,", Iterations = ",posteriors$iterations,".", "\n")
cat("##", "\n")
#
end.time <- proc.time()[3]
comp.time<-end.time-start.time
comp.time<-fnc.time(comp.time)
output$computation.time<-comp.time
#
#class(output) <- "spGPP"
output
}
}
##
## K-step forecasts for the GPP models
##
spGPP.forecast<-function(nBurn, K, fore.data=NULL,
fore.coords=NULL, posteriors, tol.dist, Summary=TRUE)
{
start.time<-proc.time()[3]
#
if (missing(posteriors)) {
stop("Error: need to provide the posterior MCMC samples.")
}
#
nItr <- posteriors$iterations
if((nBurn+posteriors$nBurn) >= nItr){
stop(paste("\n Error: burn-in >= iterations\n Here, nBurn = ",nBurn+posteriors$nBurn," and iterations = ",nItr,"."))
}
nItr <-(nItr-posteriors$nBurn)
itt <-(nItr-nBurn)
#
scale.transform<-posteriors$scale.transform
coords <- posteriors$coords
knots.coords <- posteriors$knots.coords
#
if(is.null(fore.coords)){
stop("Error: need to provide fore.coords ")
}
if(missing(fore.coords)) {
stop("Error: need to specify the fore.coords")
}
if ( !is.matrix(fore.coords) ) {
stop("Error: fore.coords must be a matrix of xy-coordinate locations")
}
if ( (!is.numeric(fore.coords[,1])) | (!is.numeric(fore.coords[,2]))) {
stop("\n Error: fore.coords columns should be numeric \n")
}
#
nsite <- dim(fore.coords)[[1]]
n <- posteriors$n
r <- posteriors$r
T <- posteriors$T
m <- posteriors$knots
if(length(T)>1){
rT <- sum(T)
}
else{
rT <- r*T
}
N <- n*rT
p <- posteriors$p
#
#
if(posteriors$cov.fnc=="exponential"){
cov <- 1
}
else if(posteriors$cov.fnc=="gaussian"){
cov <- 2
}
else if(posteriors$cov.fnc=="spherical"){
cov <- 3
}
else if(posteriors$cov.fnc=="matern"){
cov <- 4
}
else{
stop("\n Error: cov.fnc is not correctly specified \n")
}
#
#
# adding the formula in to the forecast dataset
if(!is.data.frame(fore.data) & !is.null(fore.data)){
stop("#\n# Error: fore.data should be in data format\n#")
}
call.f<-posteriors$call
call.f<-as.formula(paste("tmp~",paste(call.f,sep="")[[3]]))
if(is.data.frame(fore.data)){
if((nsite*r*K)!=dim(fore.data)[[1]]){
stop("\n# Check the fore.data and/or fore.coords and/or spT.time function#\n")
#print("#\n # Check the fore.data \n#\n")
}
fore.data$tmp<-rep(1,nsite*r*K)
}
if(is.null(fore.data)){
fore.data<-data.frame(tmp=rep(1,nsite*r*K))
}
fore.x<-Formula.matrix(call.f,data=fore.data)[[2]]
#
#
#
dm <- posteriors$Distance.matrix.knots
#
coords.all <- rbind(knots.coords,fore.coords)
tn.knotsites <- length(knots.coords[, 1])
nknot.sites <- 1:tn.knotsites
tn.predsites <- length(coords.all[, 1]) - tn.knotsites
npred.sites <- (tn.knotsites + 1):(length(coords.all[, 1]))
#
method <- posteriors$distance.method
spT.check.sites.inside(coords.all, method, tol=tol.dist)
#
if(method == "geodetic:km"){
coords.D <- as.matrix(spT.geodist(Lon=coords.all[,1],Lat=coords.all[,2], KM=TRUE))
}
else if(method == "geodetic:mile"){
coords.D <- as.matrix(spT.geodist(Lon=coords.all[,1],Lat=coords.all[,2], KM=FALSE))
}
else {
coords.D <- as.matrix(dist(coords.all, method, diag=TRUE, upper=TRUE))
}
#
#
dmns <- coords.D[nknot.sites, npred.sites] # m x ns
dnsitem <- t(dmns) # ns x m
dmns <- NULL
#
#
phip<-posteriors$phip[(nBurn+1):nItr]
if(cov==4){
nup<-posteriors$nup[(nBurn+1):nItr,]
}
else{
nup<-0
}
sig_ep<-posteriors$sig2ep[(nBurn+1):nItr]
sig_etap<-posteriors$sig2etap[(nBurn+1):nItr]
rhop<-posteriors$rhop[(nBurn+1):nItr]
betap<-matrix(posteriors$betap,p,nItr)
betap<-betap[,(nBurn+1):nItr]
wp<-posteriors$wp[,(nBurn+1):nItr]
#
out<-NULL
out<-matrix(.C("zlt_fore_gpp_its",as.integer(cov),
as.integer(itt),as.integer(K),as.integer(nsite),
as.integer(m),as.integer(r),as.integer(p),as.integer(rT),
as.integer(T),as.integer(r*K),as.integer(nsite*r*K),
as.double(dnsitem),as.double(dm),as.double(phip),as.double(nup),
as.double(sig_ep),as.double(sig_etap),as.double(betap),
as.double(rhop),as.double(wp),as.double(fore.x),
as.integer(1),fz=double(nsite*r*K*itt))$fz,nsite*r*K,itt)
#
output <- NULL
#
if(posteriors$scale.transform=="NONE"){
output$fore.samples <- out
}
if(posteriors$scale.transform=="SQRT"){
output$fore.samples <- out^2
}
if(posteriors$scale.transform=="LOG"){
output$fore.samples <- exp(out)
}
#
output$knots.coords <- knots.coords
output$fore.coords <- fore.coords
output$distance.method<-posteriors$distance.method