lbm__tps = function( p, dat, pa, lambda ) {
#\\ this is the core engine of lbm .. localised space (no-time) modelling interpolation
# \ as a 2D gaussian process (basically, simple krigimg or TPS -- time is treated as being independent)
#\\ note: time is not being modelled and treated independently
#\\ .. you had better have enough data in each time slice .. essentially this is kriging
sdTotal = sd(dat[,p$variable$Y], na.rm=T)
dat[, p$variables$Y] = p$lbm_local_family$linkfun ( dat[, p$variables$Y] )
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)
}
ftpsmodel = try( Tps(x=dat[xi, p$variables$LOCS], Y=dat[xi, p$variables$Y], lambda=lambda ) )
if (inherits(ftpsmodel, "try-error") ) next()
dat$mean[xi] = ftpsmodel$fitted.values
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$lbm_rsquared_threshold ) next()
pa$mean[pa_i] = predict(ftpsmodel, x=pa[pa_i, p$variables$LOCS] )
pa$sd[pa_i] = predictSE(ftpsmodel, x=pa[pa_i, p$variables$LOCS] ) # SE estimates are slooow
if ( 0 ){
# debugging plots
surface(ftpsmodel)
}
}
# plot(pred ~ z , dat)
# lattice::levelplot( mean ~ plon + plat, data=pa, col.regions=heat.colors(100), scale=list(draw=FALSE) , aspect="iso" )
ss = lm( dat$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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