Convert dissimilarity metrics to similarity metrics
Source:R/similarity_dissimilarity_conversion.R
dissimilarity_to_similarity.RdThis function converts a data.frame of dissimilarity metrics
(beta diversity) between sites into similarity metrics.
Arguments
- dissimilarity
the output object from
dissimilarity()orsimilarity_to_dissimilarity().- include_formula
a
booleanindicating whether metrics based on custom formula(s) should also be converted (see Details). The default isTRUE.
Value
A data.frame with the additional class
bioregion.pairwise, providing similarity metrics for each pair of
sites based on a dissimilarity object.
Note
The behavior of this function changes depending on column names. Columns
Site1 and Site2 are copied identically. If there are columns called
a, b, c, A, B, C they will also be copied identically. If there
are columns based on your own formula (argument formula in
dissimilarity()) or not in the original list of dissimilarity metrics
(argument metrics in dissimilarity()) and if the argument
include_formula is set to FALSE, they will also be copied identically.
Otherwise there are going to be converted like they other columns (default
behavior).
If a column is called Euclidean, the similarity will be calculated based
on the following formula:
Euclidean similarity = 1 / (1 - Euclidean distance)
Otherwise, all other columns will be transformed into dissimilarity with the following formula:
similarity = 1 - dissimilarity
See also
For more details illustrated with a practical example, see the vignette: https://biorgeo.github.io/bioregion/articles/a3_pairwise_metrics.html.
Associated functions: similarity dissimilarity_to_similarity
Author
Maxime Lenormand (maxime.lenormand@inrae.fr)
Boris Leroy (leroy.boris@gmail.com)
Pierre Denelle (pierre.denelle@gmail.com)
Examples
comat <- matrix(sample(0:1000, size = 50, replace = TRUE,
prob = 1 / 1:1001), 5, 10)
rownames(comat) <- paste0("s", 1:5)
colnames(comat) <- paste0("sp", 1:10)
dissimil <- dissimilarity(comat, metric = "all")
dissimil
#> Data.frame of dissimilarity between sites
#> - Total number of sites: 5
#> - Total number of species: 10
#> - Number of rows: 10
#> - Number of dissimilarity metrics: 7
#>
#>
#> Site1 Site2 Jaccard Jaccardturn Sorensen Simpson Bray Brayturn
#> 2 s1 s2 0.3000000 0.0000000 0.1764706 0.0000000 0.7519084 0.5379147
#> 3 s1 s3 0.3333333 0.2500000 0.2000000 0.1428571 0.6119266 0.5893204
#> 4 s1 s4 0.3333333 0.2500000 0.2000000 0.1428571 0.8736196 0.7854167
#> 5 s1 s5 0.3333333 0.2500000 0.2000000 0.1428571 0.5719801 0.4147813
#> 8 s2 s3 0.2000000 0.0000000 0.1111111 0.0000000 0.5068871 0.1516588
#> 9 s2 s4 0.2000000 0.0000000 0.1111111 0.0000000 0.8447894 0.8341232
#> 10 s2 s5 0.2000000 0.0000000 0.1111111 0.0000000 0.6442396 0.5426540
#> 14 s3 s4 0.4000000 0.4000000 0.2500000 0.2500000 0.9099338 0.8583333
#> 15 s3 s5 0.2222222 0.2222222 0.1250000 0.1250000 0.5735381 0.4555053
#> 20 s4 s5 0.2222222 0.2222222 0.1250000 0.1250000 0.8285214 0.7958333
#> Euclidean a b c A B C
#> 2 573.3550 7 0 3 195 955 227
#> 3 601.5862 6 1 2 423 727 607
#> 4 689.8246 6 1 2 103 1047 377
#> 5 526.3411 6 1 2 388 762 275
#> 8 333.8053 8 2 0 358 64 672
#> 9 399.5973 8 2 0 70 352 410
#> 10 310.5978 8 2 0 193 229 470
#> 14 611.8660 6 2 2 68 962 412
#> 15 485.2061 7 1 1 361 669 302
#> 20 456.2510 7 1 1 98 382 565
similarity <- dissimilarity_to_similarity(dissimil)
similarity
#> Data.frame of similarity between sites
#> - Total number of sites: 5
#> - Total number of species: 10
#> - Number of rows: 10
#> - Number of similarity metrics: 7
#>
#>
#> Site1 Site2 Jaccard Jaccardturn Sorensen Simpson Bray Brayturn
#> 2 s1 s2 0.7000000 1.0000000 0.8235294 1.0000000 0.24809160 0.4620853
#> 3 s1 s3 0.6666667 0.7500000 0.8000000 0.8571429 0.38807339 0.4106796
#> 4 s1 s4 0.6666667 0.7500000 0.8000000 0.8571429 0.12638037 0.2145833
#> 5 s1 s5 0.6666667 0.7500000 0.8000000 0.8571429 0.42801986 0.5852187
#> 8 s2 s3 0.8000000 1.0000000 0.8888889 1.0000000 0.49311295 0.8483412
#> 9 s2 s4 0.8000000 1.0000000 0.8888889 1.0000000 0.15521064 0.1658768
#> 10 s2 s5 0.8000000 1.0000000 0.8888889 1.0000000 0.35576037 0.4573460
#> 14 s3 s4 0.6000000 0.6000000 0.7500000 0.7500000 0.09006623 0.1416667
#> 15 s3 s5 0.7777778 0.7777778 0.8750000 0.8750000 0.42646190 0.5444947
#> 20 s4 s5 0.7777778 0.7777778 0.8750000 0.8750000 0.17147857 0.2041667
#> Euclidean a b c A B C
#> 2 0.001741083 7 0 3 195 955 227
#> 3 0.001659514 6 1 2 423 727 607
#> 4 0.001447545 6 1 2 103 1047 377
#> 5 0.001896306 6 1 2 388 762 275
#> 8 0.002986810 8 2 0 358 64 672
#> 9 0.002496272 8 2 0 70 352 410
#> 10 0.003209265 8 2 0 193 229 470
#> 14 0.001631678 6 2 2 68 962 412
#> 15 0.002056741 7 1 1 361 669 302
#> 20 0.002186983 7 1 1 98 382 565