Wrong result while using weighting in the util_classify function about landscapetools

[bitbacchus]

Sounds reasonable ;-) This is also trivial to change: 1775175

I guess I'll open a pull request, now.

[bitbacchus]

This surely is a bug. It turns out, the values in the raster columns are actually slightly different, and the classification has problems with more than 16 significant digits.

library(raster)
#> Loading required package: sp
#> Loading required package: sp
library(NLMR)
library(landscapetools)

set.seed(2020-01-25)
r = nlm_planargradient(100, 100, direction = 90)
r2 = util_classify(r, weighting = c(0.5, 0.5))

# See where the wrong classification occurs.
# Each for col the rows should contain the same values
mat <- as.matrix(r)
mat2 <- as.matrix(r2)
mat2[58:61,50] # col 50 between rows 59 and 60.
#> [1] 2 2 1 1
mat[58:61,50] # however, the raster values are the same!
#> [1] 0.4949495 0.4949495 0.4949495 0.4949495

sprintf("%.20f", mat[58:61,50]) # OR ARE THEY?!
#> [1] "0.49494949494949502800" "0.49494949494949502800" "0.49494949494949497248"
#> [4] "0.49494949494949497248"

^{Created on 2020-01-25 by the reprex package (v0.3.0)}

nlm_planargradient and util_classify both consider stupidly too many decimal digits and rounding errors do happen. Maybe the easiest fix would be to round to something between 6-16 digits in both, nlm_planargradient and util_classify.

[bitbacchus]

Can you have a look at cf700c1 ? I'm not 100% sure if that's the way it is intended to work, but it works and throws no errors.

[Nowosad]

Great fix @bitbacchus! Thanks a lot.
I've tried the new code on several 2-classes rasters and it worked well in every case.

library(raster)
#> Loading required package: sp
library(NLMR)
library(landscapetools)

set.seed(2020-01-25)
r = nlm_planargradient(100, 100, direction = 90)
r2 = util_classify(r, weighting = c(0.5, 0.5))

freq(r2)
#>      value count
#> [1,]     1  5000
#> [2,]     2  5000

plot(r)

plot(r2)

^{Created on 2020-01-26 by the reprex package (v0.3.0)}

Session info

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[Nowosad]

There is one difference though - the new raster has factor values (not numeric).

[bitbacchus]

There is one difference though - the new raster has factor values (not numeric).

Isn't that what we want? The values are classes, after all.

[Nowosad]

Hmm... I can think of two reasons against it:

1. It was numeric previously, therefore it is a breaking change
2. (My opinion) Factor rasters (from the raster package) are not greatly supported, and users can have issues operating on them