R/stmv__mba.R

In jae0/stm: Spatiotemporal models of variability

stmv__kernel = 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

  library(fields)


  sdTotal = sd(dat[[ p$stmv_stmv_variables$Y ]] , na.rm=T)

  vns = p$stmv_variables$LOCS
  vnt = c( vns, p$stmv_variables$Y)

  x_r = range(dat[[ vns[1] ]])
  x_c = range(dat[[ vns[2] ]])

  nr = trunc( diff(x_r)/p$pres + 1L )
  nc = trunc( diff(x_c)/p$pres + 1L )

  dat$mean = NA

  pa = data.table(pa)

  pa$mean = NA
  pa$sd = NA  # this is ignored with fft

  origin=c(x_r[1], x_c[1])
  res=c(p$pres, p$pres)

  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)
    }


    X_i = array_map( "xy->2", coords=pa[pa_i, ..vns], origin=origin, res=res )
    tokeep = which( X_i[,1] >= 1 & X_i[,2] >= 1  & X_i[,1] <= nr & X_i[,2] <= nc )
    if (length(tokeep) < 1) next()
    X_i = X_i[tokeep,]

    pa$mean[pa_i[tokeep]] = mba.surf(dat[xi, ..vnt], no.X=nr, no.Y=nc, extend=TRUE)$xyz.est$z[X_i]
    pa$sd[pa_i[tokeep]] = sd( dat[xi] [[ p$stmv_variables$Y ]] , na.rm=T)   ## fix as NA

    X_i = NULL

    # dat[ xi, ..vns ] = trunc( dat[ xi, ..vns ] / p$pres  ) * p$pres + 1L
    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 = try( 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 ) )
}