The OP's original problem is that the all.equal method for raster
objects (raster:::all.equal.raster), which is a wrapper around the
compareRaster() function, compares a bunch of different properties of
rasters (extent, resolution, values, etc.) and only returns a single
overall logical (TRUE/FALSE) value. OP wanted to see the magnitude of
the difference (as you could get for more typical all.equal methods by
using tolerance=0), but in order to do that one has to dig into the code
of compareRaster() and pull out code to make the particular comparisons
one wants by applying all.equal to specific components of the raster (it
would be nice if there were a built-in way to get this information, but
I don't know of one)
On 8/18/24 14:40, Bert Gunter wrote:
"Is it true that all.equal just compares y values?"
The following may be a bit more than you may have wanted, but I hope
it is nevertheless useful.
The first place you should go to for questions like this is the Help
system, not here, i.e.
?all.equal
When you do this, you will find that all.equal() is a so-called S3
generic function, which, among other things, means that it works
differently (i.e. "dispatches") depending on its (usually) first
argument. So, for example, if the first argument is of (S3) class
"numeric", it will call the default method, all.equal.default(); if
it's a function, it will call all.equal.function(). Help for
all.equal's basic methods is found in the single all.equal (base) Help
page. However, for non-base R packages, there may be other different
methods provided for classed objects, e.g. perhaps of class "raster"
that would be found by ?all.equal.raster . Or maybe not, if the class
"inherits" from another class, such as "matrix" (Warning: I am
completely unfamiliar with the raster package, so these specifics are
very likely wrong).
To sort this sort of thing out, It would probably be useful for you to
find a tutorial on R's S3 class system (which is really a form of
multiple dispatch) and spend some time with it. There are many good
ones out there. This S3 system is widely used within R and many
packages, so doing this sort of homework now should serve you well in
your future R journey.
All IMO obviously.
Cheers,
Bert
On Sun, Aug 18, 2024 at 11:00 AM SIBYLLE STÖCKLI via R-help
<r-help@r-project.org> wrote:
Dear Ivan
Thanks a lot for this very nice example.
Is it true that all.equal just compares y values?
Based on this help here I think so and the value I got is the difference for
the y-values.
https://www.statology.org/all-equal-function-r/
However, here I see x and y testing?
https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/all.equal
I am actually interested in the x values (x-y coordinates). Test if x-y
coordinates of both 25-m-pixel rasters are the same. Ther may be a small shift
or differences in the number of decimal places.
Kind regards
Sibylle
-----Original Message-----
From: Ivan Krylov <ikry...@disroot.org>
Sent: Friday, August 16, 2024 11:45 AM
To: sibylle.stoec...@gmx.ch
Cc: 'SIBYLLE STÖCKLI via R-help' <r-help@r-project.org>
Subject: Re: [R] allequal diff
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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______________________________________________
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PLEASE do read the posting guide https://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
