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augmentPredictionGrid <- function(gridList=NULL, s, nx=NULL, ny=NULL, NNSize,
verbose=FALSE){
#
# NNSize used to add additional points so offGridWeights
# has enough neighbors.
#
M<- ncol( s)
if( M != 2){
stop("augment grid only works for 2D")
}
# check if data is outside range of the grid and if augmentation is even needed !
#
gridListOld<- gridList
if( !is.null(gridList)){
xr<- range( gridList$x)
dx<- (gridList$x[2] - gridList$x[1] )
yr<- range( gridList$y)
dy<- (gridList$y[2] - gridList$y[1] )
# data outside grid ???
if( (min(s[,1])< xr[1])|(max(s[,1])>xr[2])|
(min(s[,2])< yr[1])|(max(s[,2])>yr[2])) {
cat("range s[,1]", range(s[,1]),fill=TRUE )
cat("range grid$x", xr,fill=TRUE )
cat("range s[,2]", range(s[,1]),fill=TRUE )
cat("range grid$y", yr,fill=TRUE )
stop(" some observations outside grid")
}
# gridList OK as is ?
indLx<- (min(s[,1]) - xr[1])/dx
indRx<- (xr[2]- max(s[,1]))/dx
indLy<- (min(s[,2]) - yr[1])/dy
indRy<- (yr[2] - max(s[,2]))/dy
allMargins<- c(indLx,indRx,indLy,indRy )
# take into account round off.
OKGrid<- all( abs(allMargins -NNSize)<=1e-10)
if(OKGrid)
{
# grid is OK just use it!
return(
list(predictionGrid= gridList,
indexSubset= list( x = 1:length(gridList$x),
y = 1:length(gridList$y)),
expandGrid=FALSE)
)
}
}
# creating a new grid or expanding the one passed
if(is.null(gridList) ){
if(is.null(nx)|is.null(ny)){
stop("need to specify nx and ny")
}
sDimension <- ncol(s)
xr <- range(s[, 1])
dx <- (xr[2] - xr[1]) / (nx-1)
yr <- range(s[, 2])
dy <- (yr[2] - yr[1]) / (ny-1)
gridListOld<- list( x= seq( xr[1], xr[2],dx),
y= seq( yr[1], yr[2],dy)
)
}
else{
nx<- length(gridList$x)
xr<- range( gridList$x)
dx<- (gridList$x[2] - gridList$x[1] )
ny<- length(gridList$y)
yr<- range( gridList$y)
dy<- (gridList$y[2] - gridList$y[1] )
}
#
# now create expanded grid.
# right side has an extra row/column of grid boxes to avoid
# how largest locations are referenced to a grid box
# there may be other routes to deal with this but this seems the most straight
# forward
# in approximateCovariance2D here is how the lower left index is found:
#
# cbind(
# trunc( (sObs[,1]- predictionGrid$x[1] )/dx) + 1 ,
# trunc( (sObs[,2]- predictionGrid$y[1] )/dy) + 1
# )
#
xg <- seq( xr[1] - (NNSize-1)*dx, xr[2] + (NNSize)*dx, dx)
yg <- seq( yr[1] - (NNSize-1)*dy, yr[2] + (NNSize)*dy, dy)
predictionGrid <- list(x=xg, y = yg)
indexSubset<- list( x= (1:nx)+(NNSize-1), y= (1:ny)+(NNSize-1))
return(
list(predictionGrid= predictionGrid,
indexSubset=indexSubset,
expandGrid=TRUE,
gridList=gridListOld)
)
}
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