I may be doing something wrong here, but I find that if I simplify my data frame by removing irrelevant columns the autoKrige function in the automap library gives different results. I have reproduced this problem with the meuse data in the automap library.
library(automap)
data(meuse)
colnames(meuse)
[1] "x" "y" "cadmium" "copper" "lead" "zinc" "elev"
[8] "dist" "om" "ffreq" "soil" "lime" "landuse" "dist.m"
coordinates(meuse) =~ x+y
data(meuse.grid)
gridded(meuse.grid) =~ x+y
kriging_result_01 = autoKrige(zinc~1, meuse)
plot(kriging_result_01)
meuse <- NULL
data(meuse)
meuse <- meuse[, c(1,2,6)]
coordinates(meuse) =~ x+y
data(meuse.grid)
gridded(meuse.grid) =~ x+y
kriging_result_02 = autoKrige(zinc~1, meuse)
plot(kriging_result_02)
identical(kriging_result_01, kriging_result_02)
[1] FALSE
The plots are also different in their detail.
Is this the expected behaviour?
Thanks, Bill
The problem is not the removed columns, but the grid to apply the fitted model on. Using your example, you can see:
library(automap)
set.seed(42)
data(meuse)
coordinates(meuse) =~ x+y
data(meuse.grid)
gridded(meuse.grid) =~ x+y
kriging_result_01 = autoKrige(zinc~1, meuse)
meuse <- NULL
data(meuse)
meuse <- meuse[, c(1,2,6)]
coordinates(meuse) =~ x+y
kriging_result_02 = autoKrige(zinc~1, meuse)
kriging_result_01$krige_output@grid
x1 x2
cellcentre.offset 178635.12844 329744.86186
cellsize 32.93423 32.93423
cells.dim 84.00000 118.00000
and
kriging_result_02$krige_output@grid
x1 x2
cellcentre.offset 178614.42379 329741.35016
cellsize 32.93423 32.93423
cells.dim 85.00000 118.00000
As you can see, the grids are slightly different, with 4999 and 4996 grid points respectively.
If you use
kriging_result_01 = autoKrige(zinc~1, meuse, meuse.grid)
kriging_result_02 = autoKrige(zinc~1, meuse, meuse.grid)
kriging_result_01$krige_output@grid
x y
cellcentre.offset 178460 329620
cellsize 40 40
cells.dim 78 104
kriging_result_02$krige_output@grid
x y
cellcentre.offset 178460 329620
cellsize 40 40
cells.dim 78 104
The grids are similar.