R/lbm__krige.R

In AtlanticR/lbm: Lattice-based models

lbm__krige = function( p, dat, pa, nu, phi, varObs, varSpatial ) {
  #\\ 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)
    }

    fspmodel <- try( Krig( dat[xi, p$variables$LOCS], dat[xi, p$variables$Y], 
      sigma2=varObs, rho=varSpatial , cov.function="stationary.cov", 
      Covariance="Matern", range=phi, smoothness=nu) )
    if (inherits(fspmodel, "try-error") )  next()
    dat$mean[xi] = fspmodel$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(fspmodel, x=pa[pa_i, p$variables$LOCS] )
    # pa$sd[pa_i]   = predictSE(fspmodel, x=pa[pa_i, p$variables$LOCS] ) # SE estimates are slooow
 
    if ( 0 ){
      # debugging plots
      ti = 1
      xi = which( dat[ , p$variables$TIME ] == p$prediction.ts[ti] )
      mbas = MBA::mba.surf( dat[xi, c( p$variables$LOCS, p$variables$Y) ], 300, 300, extend=TRUE)$xyz.est
      image(mbas)
 
      surface(fspmodel)
      fsp.p<- predictSurface(fspmodel, nx=500, ny=500 ) # finer grid
      surface(fsp.p, type="I")
 
      fsp.p2<- predictSurfaceSE(fspmodel)
      surface(fsp.p2, type="C")
    }

  }

  # 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 ) )  
}