Cz metrics
Usage
contribution(cluster_object, comat, indices = c("contribution", "Cz"))Arguments
- cluster_object
a
bioregion.clustersobject or adata.frameor a list ofdata.framecontaining multiple partitions. At least two partitions are required. If a list ofdata.frameis provided, they should all have the same number of rows (i.e., same items in the clustering for all partitions).- comat
a co-occurrence
matrixwith sites as rows and species as columns.- indices
a
characterspecifying the contribution metric to compute. Available options arecontribution.
Value
A list of data.frames if multiples indices are selected or a single
data.frame with three columns if one index is selected. Each data.frame
has three columns: the species, the bioregion, and the contribution
statistics.
Details
The contribution metric is derived from Lenormand2019bioregion. Its formula is the following: \((n_ij - ((n_i n_j)/n))/(sqrt(((n - n_j)/(n-1)) (1-(n_j/n)) ((n_i n_j)/n)))\)
with n the number of sites, n_i the number of sites in which species i is present, n_j the number of sites belonging to the bioregion j, n_ij the number of occurrences of species i in sites belonging to the bioregion j.
Author
Pierre Denelle (pierre.denelle@gmail.com), Boris Leroy (leroy.boris@gmail.com) and Maxime Lenormand (maxime.lenormand@inrae.fr)
Examples
comat <- matrix(sample(1000, 50), 5, 10)
rownames(comat) <- paste0("Site", 1:5)
colnames(comat) <- paste0("Species", 1:10)
comat <- matrix(sample(0:1000, size = 500, replace = TRUE, prob = 1/1:1001),
20, 25)
rownames(comat) <- paste0("Site",1:20)
colnames(comat) <- paste0("Species",1:25)
dissim <- dissimilarity(comat, metric = "Simpson")
clust1 <- nhclu_kmeans(dissim, n_clust = 3, index = "Simpson")
net <- similarity(comat, metric = "Simpson")
com <- netclu_greedy(net)
contribution(cluster_object = clust1, comat = comat,
indices = "contribution")
#> Bioregion Species Contribution
#> 1 3 Species1 284.576918
#> 2 3 Species2 575.060866
#> 3 3 Species3 142.939866
#> 4 3 Species4 316.926207
#> 5 3 Species5 387.651413
#> 6 3 Species6 29.890169
#> 7 3 Species7 301.104887
#> 8 3 Species8 725.161499
#> 9 3 Species9 273.157667
#> 10 3 Species10 447.522116
#> 11 3 Species11 297.695183
#> 12 3 Species12 957.225608
#> 13 3 Species13 22.085088
#> 14 3 Species14 306.209657
#> 15 3 Species15 39.520684
#> 16 3 Species16 685.280300
#> 17 3 Species17 430.368633
#> 18 3 Species18 610.480204
#> 19 3 Species19 232.490956
#> 20 3 Species20 823.755985
#> 21 3 Species21 146.723541
#> 22 3 Species22 473.961828
#> 23 3 Species23 24.596748
#> 24 3 Species24 682.672807
#> 25 3 Species25 478.765960
#> 26 1 Species1 258.797258
#> 27 1 Species2 248.043519
#> 28 1 Species3 871.910599
#> 29 1 Species4 1022.976664
#> 30 1 Species5 758.280607
#> 31 1 Species6 1321.535603
#> 32 1 Species7 1509.768523
#> 33 1 Species8 426.565646
#> 34 1 Species9 943.981980
#> 35 1 Species10 1158.314698
#> 36 1 Species11 1701.451243
#> 37 1 Species12 198.860582
#> 38 1 Species13 910.218094
#> 39 1 Species14 1238.131362
#> 40 1 Species15 2651.871882
#> 41 1 Species16 2605.726102
#> 42 1 Species17 993.709741
#> 43 1 Species18 1524.597799
#> 44 1 Species19 1728.500335
#> 45 1 Species20 855.888441
#> 46 1 Species21 1503.836813
#> 47 1 Species22 1053.466022
#> 48 1 Species23 1441.643622
#> 49 1 Species24 473.531759
#> 50 1 Species25 1234.685466
#> 51 2 Species1 61.912326
#> 52 2 Species2 761.348990
#> 53 2 Species3 97.982006
#> 54 2 Species4 418.245653
#> 55 2 Species5 311.157381
#> 56 2 Species6 727.300717
#> 57 2 Species7 13.468471
#> 58 2 Species8 -1.776432
#> 59 2 Species9 379.347190
#> 60 2 Species10 30.914678
#> 61 2 Species11 643.338009
#> 62 2 Species12 238.175608
#> 63 2 Species13 48.328700
#> 64 2 Species14 1118.373582
#> 65 2 Species15 418.407372
#> 66 2 Species16 269.079187
#> 67 2 Species17 61.620001
#> 68 2 Species18 589.193279
#> 69 2 Species19 17.655561
#> 70 2 Species20 576.941317
#> 71 2 Species21 215.146616
#> 72 2 Species22 65.551970
#> 73 2 Species23 19.799718
#> 74 2 Species24 197.802591
#> 75 2 Species25 25.331892
contribution(cluster_object = com, comat = comat,
indices = "contribution")
#> Bioregion Species Contribution
#> 1 1 Species1 4257.7266049
#> 2 1 Species2 11089.0000000
#> 3 1 Species3 6892.1348567
#> 4 1 Species4 11757.3994949
#> 5 1 Species5 9565.8988077
#> 6 1 Species6 13549.0000000
#> 7 1 Species7 11954.3472427
#> 8 1 Species8 8274.0000000
#> 9 1 Species9 10621.3228931
#> 10 1 Species10 10818.2623476
#> 11 1 Species11 12957.1712333
#> 12 1 Species12 10243.1488323
#> 13 1 Species13 6184.9640258
#> 14 1 Species14 17786.8351333
#> 15 1 Species15 19878.6443200
#> 16 1 Species16 23638.7678023
#> 17 1 Species17 8993.9268522
#> 18 1 Species18 13882.3917328
#> 19 1 Species19 12817.0175158
#> 20 1 Species20 14585.3648828
#> 21 1 Species21 7989.8352229
#> 22 1 Species22 10791.4225970
#> 23 1 Species23 8905.6893731
#> 24 1 Species24 9459.1163259
#> 25 1 Species25 8159.5408504
#> 26 2 Species1 -1.0000000
#> 27 2 Species2 12.8472811
#> 28 2 Species3 94.7574548
#> 29 2 Species4 2.3925840
#> 30 2 Species5 77.9743548
#> 31 2 Species6 -0.9176629
#> 32 2 Species7 12.0043852
#> 33 2 Species8 1.3764944
#> 34 2 Species9 10.2597835
#> 35 2 Species10 45.5301455
#> 36 2 Species11 1031.6315789
#> 37 2 Species12 -0.9733285
#> 38 2 Species13 22.5715237
#> 39 2 Species14 -0.9459053
#> 40 2 Species15 25.6494588
#> 41 2 Species16 4.2631579
#> 42 2 Species17 241.2773270
#> 43 2 Species18 1029.6734337
#> 44 2 Species19 18.4932420
#> 45 2 Species20 250.5536223
#> 46 2 Species21 952.8886277
#> 47 2 Species22 -0.8885233
#> 48 2 Species23 133.7368421
#> 49 2 Species24 32.2830138
#> 50 2 Species25 813.3782055