Hi all,
I've got a question regarding kriging outputs. I have an interpolated
dataset which due to the nugget effect contains some negative values as
the predictions. I would like to truncate these @ "0", rather than
having them as a negative prediction.
I've tried something similar with the meuse data set...
data(meuse.grid)
coordinates(meuse.grid) = c("x", "y")
gridded(meuse.grid) <- TRUE
meuse.grid[["idist"]] = 1 - meuse.grid[["dist"]]
Somewhat arbitrary, but essentially, I'd like to set all "dist" data less than 0.5 to
"0", and have the rest remain as they are...
meuse.grid[["class"]] = for(i in 1:length(meuse.grid[["dist"]])){
if (meuse.grid[["dist"]]<0.5) {meuse.grid[["class"]]==0} else
{meuse.grid[["class"]]=meuse.grid[["dist"]]}
}
All I've managed to get is the new attribute "class" all filled with 0.
Any insight would be appreciated.
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