R/stmv__linear.R

In jae0/stm: Spatiotemporal models of variability

stmv__linear = 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

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


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

  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 = data.table(pa)

  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)
    }

    u = as.image(
      dat[xi] [[ p$stmv_variables$Y ]] ,
      ind=as.matrix(array_map( "xy->2", coords=dat[xi, ..vns], origin=origin, res=res )),
      na.rm=TRUE,
      nx=nr,
      ny=nc
    )

    X = as.vector( fields::interp.surface( u, loc=pa[pa_i, ..vns] ) ) # linear interpolation

    rY = range( dat[ xi] [[ p$stmv_variables$Y ]], na.rm=TRUE )
    lb = which( X < rY[1] )
    if (length(lb) > 0) X[lb] = rY[1]
    lb = NULL
    ub = which( X > rY[2] )
    if (length(ub) > 0) X[ub] = rY[2]
    ub = NULL

    pa$mean[pa_i] = X
    X = NULL

    pa$sd[pa_i] = sd(dat[ xi] [[ p$stmv_variables$Y ]], na.rm=TRUE)  # just a crude guess for each timeslice

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