lbm__gstat = function( p, dat, pa, nu, phi, varObs, varSpatial ) {
#\\ this is the core engine of lbm .. localised space (no-time) modelling interpolation
#\\ note: time is not being modelled and treated independently
#\\ .. you had better have enough data in each time slice .. essentially this is kriging
if (!exists( "lbm_gstat_formula", p)) p$lbm_gstat_formula = formula( paste( p$variables$Y, "~ 1 "))
sdTotal = sd(dat[,p$variable$Y], na.rm=T)
dat[, p$variables$Y] = p$lbm_local_family$linkfun ( dat[, p$variables$Y] )
approx_range = phi*sqrt( 8*nu)
dat$mean = NA
pa$mean = NA
pa$sd = sdTotal # leave as this as sd estimation is too expensive
for ( ti in 1:p$nt ) {
if ( exists("TIME", p$variables) ) {
xi = which( dat[ , p$variables$TIME ] == p$prediction.ts[ti] )
pa_i = which( pa[, p$variables$TIME]==p$prediction.ts[ti])
} else {
xi = 1:nrow(dat) # all data as p$nt==1
pa_i = 1:nrow(pa)
}
xy = dat[xi, c( p$variables$LOCS, p$variables$Y) ]
vMod0 = vgm(psill=varSpatial, model="Mat", range=phi, nugget=varObs, kappa=nu ) # starting model parameters
gs = gstat(id = "hmk", formula=p$lbm_gstat_formula, locations=~plon+plat, data=xy[xi,], maxdist=approx_range, nmin=p$n.min, nmax=p$n.max, force=TRUE, model=vMod0 )
# this step adds a lot of time ..
preds = predict(gs, newdata=xy[xi,] )
dat$mean[xi] = as.vector( preds[,1] )
ss = lm( dat$mean[xi] ~ dat[xi,p$variables$Y], na.action=na.omit)
if ( "try-error" %in% class( ss ) ) next()
rsquared = summary(ss)$r.squared
if (rsquared < p$lbm_rsquared_threshold ) next()
gsp = predict(gs, newdata=pa[pa_i,] ) # slow for large n
pa$mean[pa_i] = as.vector(gsp[,1] )
pa$sd[pa_i] = as.vector(gsp[,2] )
}
# plot(pred ~ z , dat)
# lattice::levelplot( mean ~ plon + plat, data=pa, col.regions=heat.colors(100), scale=list(draw=FALSE) , aspect="iso" )
ss = lm( dat$mean ~ dat[,p$variables$Y], na.action=na.omit)
if ( "try-error" %in% class( ss ) ) return( NULL )
rsquared = summary(ss)$r.squared
if (rsquared < p$lbm_rsquared_threshold ) return(NULL)
lbm_stats = list( sdTotal=sdTotal, rsquared=rsquared, ndata=nrow(dat) ) # must be same order as p$statsvars
return( list( predictions=pa, lbm_stats=lbm_stats ) )
}
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