stmv__twostep = function( p, dat, pa, nu=NULL, phi=NULL, varObs=varObs, varSpatial=varSpatial, ... ) {
#\\ twostep modelling time first as a simple ts and then spatial or spatio-temporal interpolation
#\\ nu is the bessel smooth param
# step 1 -- timeseries modelling
# use all available data in 'dat' to get a time trend .. and assume it applies to the prediction area of interest 'pa'
# some methods require a uniform (temporal with associated covariates) prediction grid based upon all dat locations
if (0) {
varObs = S[Si, i_sdObs]^2
varSpatial = S[Si, i_sdSpatial]^2
sloc = Sloc[Si,]
eps = 1e-9
}
vnt = c( p$stmv_variables$LOCS, p$stmv_variables$Y)
pa = data.table(pa)
px = dat # only the static parts .. time has to be a uniform grid so reconstruct below
ids = array_map( "xy->1", px[, c("plon", "plat")], gridparams=p$gridparams ) # 100X faster than paste / merge
todrop = which(duplicated( ids) )
if (length(todrop>0)) px = px[-todrop,]
ids = todrop=NULL
# static vars .. don't need to look up
tokeep = c(p$stmv_variables$LOCS )
if (exists("weights", dat) ) tokeep = c(tokeep, "weights")
if (p$nloccov > 0) {
for (ci in 1:p$nloccov) {
vn = p$stmv_variables$local_cov[ci]
pu = stmv_attach( p$storage_backend, p$ptr$Pcov[[vn]] )
nts = ncol(pu)
if ( nts==1 ) tokeep = c(tokeep, vn )
}
}
px = px[ , ..tokeep ]
px_n = nrow(px)
nts = vn = NULL
# add temporal grid
if ( exists("TIME", p$stmv_variables) ) {
px = cbind( px[ rep.int(1:px_n, p$nt), ],
rep.int(p$prediction_ts, rep(px_n, p$nt )) )
names(px)[ ncol(px) ] = p$stmv_variables$TIME
px = cbind( px, stmv_timecovars ( vars=p$stmv_variables$local_all, ti=px[[ p$stmv_variables$TIME ]] ) )
}
if (p$nloccov > 0) {
# add time-varying covars .. not necessary except when covars are modelled locally
for (ci in 1:p$nloccov) {
vn = p$stmv_variables$local_cov[ci]
pu = stmv_attach( p$storage_backend, p$ptr$Pcov[[vn]] )
nts = ncol(pu)
if ( nts== 1) {
# static vars are retained in the previous step
} else if ( nts == p$ny ) {
px$iy = px$yr - p$yrs[1] + 1 #yr index
px[,vn] = pu[ cbind(px$i, px$iy) ]
} else if ( nts == p$nt) {
px$it = p$nw*(px$tiyr - p$yrs[1] - p$tres/2) + 1 #ts index
px[,vn] = pu[ cbind(px$i, px$it) ]
}
} # end for loop
nts = vn = NULL
} # end if
rownames(px) = NULL
# print( "starting gam-timeseries mod/pred")
ts_preds = NULL
p$stmv_local_modelformula = p$stmv_local_modelformula_time
if (p$stmv_twostep_time == "inla" ) ts_preds = stmv__inla_ts( p, dat, px ) #TODO
if (p$stmv_twostep_time == "inla_ar1" ) ts_preds = stmv__inla_ar1( p, dat, px ) #TODO
if (p$stmv_twostep_time == "glm" ) ts_preds = stmv__glm( p, dat, px )
if (p$stmv_twostep_time == "gam" ) ts_preds = stmv__gam( p, dat, px )
if (p$stmv_twostep_time == "bayesx" ) ts_preds = stmv__bayesx( p, dat, px )
if (is.null( ts_preds)) return(NULL)
# if (ss$r.sq < p$stmv_rsquared_threshold ) return(NULL) # smooth/flat surfaces are ok ..
# temporal r-squared test
if (exists("stmv_rsquared_threshold", p)) {
if ( exists("stmv_stats", ts_preds)) {
if ( exists("rsquared", ts_preds$stmv_stats) ) {
# ts_preds_rsquared = ts_preds$stmv_stats$rsquared # store for now until return call
if (!is.finite(ts_preds$stmv_stats$rsquared) ) return(NULL)
if (ts_preds$stmv_stats$rsquared < p$stmv_rsquared_threshold ) return(NULL)
}
}
}
# range checks
rY = range( dat[[ p$stmv_variables$Y ]], na.rm=TRUE)
toosmall = which( ts_preds$predictions$mean < rY[1] )
toolarge = which( ts_preds$predictions$mean > rY[2] )
if (length(toosmall) > 0) ts_preds$predictions$mean[toosmall] = rY[1]
if (length(toolarge) > 0) ts_preds$predictions$mean[toolarge] = rY[2]
pxts = ts_preds$predictions
rownames(pxts) = NULL
ts_preds = NULL
names(pxts)[which(names(pxts)=="mean")] = p$stmv_variables$Y
names(pxts)[which(names(pxts)=="sd")] = paste(p$stmv_variables$Y, "sd", sep=".")
if(0){
# debugging plots
for (ti in 1:p$nt){
xi = which( pxts[ , p$stmv_variables$TIME ] == p$prediction_ts[ti] )
mbas = MBA::mba.surf( pxts[xi, ..vnt ], 300, 300, extend=TRUE)$xyz.est
image(mbas)
}
}
# step 2 :: spatial modelling .. essentially a time-space separable solution
if (!exists( "stmv_twostep_space", p)) p$stmv_twostep_space="krige" # default
out = NULL
if ( p$stmv_twostep_space == "krige" ) {
out = stmv__krige( p, dat=pxts, pa=pa, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial )
}
if ( p$stmv_twostep_space == "gstat" ) {
out = stmv__gstat( p, dat=pxts, pa=pa, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial )
}
if ( p$stmv_twostep_space == "inla_spde" ) {
out = stmv__inla_space_spde( p, dat=pxts, pa=pa, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial ) #TODO
}
if ( p$stmv_twostep_space == "inla_car" ) {
out = stmv__inla_space_car( p, dat=pxts, pa=pa, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial ) #TODO
}
if (p$stmv_twostep_space %in% c("tps") ) {
out = stmv__tps( p, dat=pxts, pa=pa, lambda=varObs/varSpatial, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial )
}
if (p$stmv_twostep_space %in% c("fft") ) {
out = stmv__fft( p=p, dat=pxts, pa=pa, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial )
}
if (p$stmv_twostep_space %in% c("gam") ) {
p$stmv_local_modelformula = p$stmv_local_modelformula_space
out = stmv__gam( p, dat=pxts, pa=pa, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial )
}
if (p$stmv_twostep_space %in% c("glm") ) {
p$stmv_local_modelformula = p$stmv_local_modelformula_space
out = stmv__glm( p, dat=pxts, pa=pa, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial )
}
if (p$stmv_twostep_space %in% c("bayesx") ) {
p$stmv_local_modelformula = p$stmv_local_modelformula_space
out = stmv__bayesx( p, dat=pxts, pa=pa, nu=nu, phi=phi, varObs=varObs, varSpatial=varSpatial )
}
# TODO
# evaluate goodness of fit of data (nonboosted):
# out$stmv_stats$rsquared =
return( out )
if (0) {
lattice::levelplot( mean ~ plon + plat, data=out$predictions[out$predictions[,p$stmv_variables$TIME]==2012.05,], col.regions=heat.colors(100), scale=list(draw=FALSE) , aspect="iso" )
lattice::levelplot( mean ~ plon + plat, data=out$predictions, col.regions=heat.colors(100), scale=list(draw=FALSE) , aspect="iso" )
for( i in sort(unique(out$predictions[,p$stmv_variables$TIME]))) print(lattice::levelplot( mean ~ plon + plat, data=out$predictions[out$predictions[,p$stmv_variables$TIME]==i,], col.regions=heat.colors(100), scale=list(draw=FALSE) , aspect="iso" ) )
}
}
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