В Fri, 16 Aug 2024 11:32:58 +0200
<sibylle.stoec...@gmx.ch> пишет:
> # values and mask r1
> r1 <- getValues(r1)
> mask1 <- is.na(r1)
> # Do the same for r2
> r2 <- getValues(r2_resampled)
> mask2 <- is.na(r2)
>
> # Combine the masks
> all.equal(r1[!(mask1 & mask2)], r2[!(mask1 & mask2)])
Let's consider a more tangible example:
# The vectors `x` and `y` start out equal
x <- y <- 1:10
# But then their different elements are made missing
x[c(1,3,4)] <- NA
y[c(3,8)] <- NA
Now, `is.na(x) & is.na(y)` gives the third element as the only element
missing in both x and y:
mask1 <- is.na(x)
mask2 <- is.na(y)
all.equal( # not the comparison you are looking for
x[!(mask1 & mask2)], # still two more elements missing
y[!(mask1 & mask2)] # still one more element missing
)
If you want to ignore all missing elements, you should combine the
masks using the element-wise "or" operation ("missing in x and/or y"),
not the element-wise "and" operation ("missing in both x and y at the
same time"):
mask1 & mask2 # drops element 3
# [1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
mask1 | mask2 # drops elements 1, 3, 4, 8
# [1] TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
--
Best regards,
Ivan
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