Convert similarity metrics to dissimilarity metrics
Source:R/similarity_dissimilarity_conversion.R
similarity_to_dissimilarity.RdThis function converts a data.frame of similarity metrics between sites
into dissimilarity metrics (beta diversity).
Arguments
- similarity
The output object from
similarity()ordissimilarity_to_similarity().- 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 additional class
bioregion.pairwise.metric, providing dissimilarity
metric(s) between each pair of sites based on a similarity 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 similarity())
or not in the original list of similarity metrics (argument metrics in
similarity()) 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, its distance will be calculated based
on the following formula:
Euclidean distance = (1 - Euclidean similarity) / Euclidean similarity
Otherwise, all other columns will be transformed into dissimilarity with the following formula:
dissimilarity = 1 - similarity
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: dissimilarity similarity_to_dissimilarity
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("Site", 1:5)
colnames(comat) <- paste0("Species", 1:10)
simil <- similarity(comat, metric = "all")
simil
#> 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 Site1 Site2 0.7000000 0.7777778 0.8235294 0.8750000 0.22441993 0.25213675
#> 3 Site1 Site3 0.8000000 0.8000000 0.8888889 0.8888889 0.31751996 0.35245047
#> 4 Site1 Site4 0.8000000 0.8000000 0.8888889 0.8888889 0.18467583 0.28119658
#> 5 Site1 Site5 0.8000000 0.8000000 0.8888889 0.8888889 0.25753705 0.32020330
#> 8 Site2 Site3 0.7000000 0.7777778 0.8235294 0.8750000 0.03556658 0.04483837
#> 9 Site2 Site4 0.8888889 1.0000000 0.9411765 1.0000000 0.46780893 0.61754626
#> 10 Site2 Site5 0.7000000 0.7777778 0.8235294 0.8750000 0.09973286 0.14231258
#> 14 Site3 Site4 0.8000000 0.8000000 0.8888889 0.8888889 0.06264916 0.10948905
#> 15 Site3 Site5 0.8000000 0.8000000 0.8888889 0.8888889 0.13631157 0.15120712
#> 20 Site4 Site5 0.8000000 0.8000000 0.8888889 0.8888889 0.23270440 0.47013977
#> Euclidean a b c A B C
#> 2 0.0011451633 7 2 1 295 875 1164
#> 3 0.0011979205 8 1 1 338 832 621
#> 4 0.0008944035 8 1 1 329 841 2064
#> 5 0.0012432343 8 1 1 252 918 535
#> 8 0.0008641731 7 1 2 43 1416 916
#> 9 0.0011208034 8 0 1 901 558 1492
#> 10 0.0010267693 7 1 2 112 1347 675
#> 14 0.0007422345 8 1 1 105 854 2288
#> 15 0.0010031767 8 1 1 119 840 668
#> 20 0.0009457680 8 1 1 370 2023 417
dissimilarity <- similarity_to_dissimilarity(simil)
dissimilarity
#> 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 Site1 Site2 0.3000000 0.2222222 0.17647059 0.1250000 0.7755801 0.7478632
#> 3 Site1 Site3 0.2000000 0.2000000 0.11111111 0.1111111 0.6824800 0.6475495
#> 4 Site1 Site4 0.2000000 0.2000000 0.11111111 0.1111111 0.8153242 0.7188034
#> 5 Site1 Site5 0.2000000 0.2000000 0.11111111 0.1111111 0.7424630 0.6797967
#> 8 Site2 Site3 0.3000000 0.2222222 0.17647059 0.1250000 0.9644334 0.9551616
#> 9 Site2 Site4 0.1111111 0.0000000 0.05882353 0.0000000 0.5321911 0.3824537
#> 10 Site2 Site5 0.3000000 0.2222222 0.17647059 0.1250000 0.9002671 0.8576874
#> 14 Site3 Site4 0.2000000 0.2000000 0.11111111 0.1111111 0.9373508 0.8905109
#> 15 Site3 Site5 0.2000000 0.2000000 0.11111111 0.1111111 0.8636884 0.8487929
#> 20 Site4 Site5 0.2000000 0.2000000 0.11111111 0.1111111 0.7672956 0.5298602
#> Euclidean a b c A B C
#> 2 872.2379 7 2 1 295 875 1164
#> 3 833.7799 8 1 1 338 832 621
#> 4 1117.0636 8 1 1 329 841 2064
#> 5 803.3536 8 1 1 252 918 535
#> 8 1156.1756 7 1 2 43 1416 916
#> 9 891.2171 8 0 1 901 558 1492
#> 10 972.9286 7 1 2 112 1347 675
#> 14 1346.2830 8 1 1 105 854 2288
#> 15 995.8333 8 1 1 119 840 668
#> 20 1056.3418 8 1 1 370 2023 417