stmv__constant = function( p=NULL, dat=NULL, pa=NULL, ... ) {
#\\ this is the core engine of stmv .. 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 cubic b-splines interpolation
sdTotal = sd(dat[,p$stmv_variables$Y], na.rm=T)
vns = p$stmv_variables$LOCS
pa = data.table(pa)
x_r = range(pa[[vns[1] ]])
x_c = range(pa[[vns[2] ]])
nr = trunc( diff(x_r)/p$pres +1 )
nc = trunc( diff(x_c)/p$pres +1 )
xo = seq(x_r[1], x_r[2], length.out = nr )
yo = seq(x_c[1], x_c[2], length.out = nc )
dat$mean = NA
pa$mean = NA
pa$sd = NA
for ( ti in 1:p$nt ) {
if ( exists("TIME", p$stmv_variables) ) {
xi = which( dat[[ p$stmv_variables$TIME ]] == p$prediction_ts[ti] )
pa_i = which( pa[[ p$stmv_variables$TIME ]] == p$prediction_ts[ti] )
if (length(xi) < 5 ) {
# print( ti)
next()
}
} else {
xi = 1:nrow(dat) # all data as p$nt==1
pa_i = 1:nrow(pa)
}
pa$mean[pa_i] = mean( dat[xi][[ p$stmv_variables$Y ]], na.rm=TRUE )
pa$sd[pa_i] = sd ( dat[xi][[ p$stmv_variables$Y ]], na.rm=TRUE ) # just a crude guess for each timeslice
iYP = match(
array_map( "xy->1", dat[ xi, ..vns ], gridparams=p$gridparams ),
array_map( "xy->1", pa[ pa_i , ..vns ], gridparams=p$gridparams )
)
dat$mean[xi] = pa$mean[pa_i][iYP]
}
# 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$mean ~ dat[[ p$stmv_variables$Y ]], na.action=na.omit)
if ( inherits(ss, "try-error") ) return( NULL )
rsquared = summary(ss)$r.squared
if (rsquared < p$stmv_rsquared_threshold ) return(NULL)
stmv_stats = list( sdTotal=sdTotal, rsquared=rsquared, ndata=nrow(dat) )
return( list( predictions=pa, stmv_stats=stmv_stats ) )
}
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