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 1 Species1 14.784446
#> 2 1 Species2 895.703803
#> 3 1 Species3 483.131789
#> 4 1 Species4 110.343071
#> 5 1 Species5 139.024408
#> 6 1 Species6 188.140523
#> 7 1 Species7 1250.813685
#> 8 1 Species8 452.066063
#> 9 1 Species9 94.212790
#> 10 1 Species10 846.959241
#> 11 1 Species11 81.361595
#> 12 1 Species12 680.112367
#> 13 1 Species13 577.523805
#> 14 1 Species14 785.206656
#> 15 1 Species15 624.122394
#> 16 1 Species16 142.603633
#> 17 1 Species17 1334.127367
#> 18 1 Species18 140.556556
#> 19 1 Species19 1325.490913
#> 20 1 Species20 234.860601
#> 21 1 Species21 958.958438
#> 22 1 Species22 892.442921
#> 23 1 Species23 517.184605
#> 24 1 Species24 879.105881
#> 25 1 Species25 577.269590
#> 26 2 Species1 1463.629119
#> 27 2 Species2 388.500675
#> 28 2 Species3 453.862764
#> 29 2 Species4 123.848681
#> 30 2 Species5 1142.427787
#> 31 2 Species6 243.057196
#> 32 2 Species7 390.350620
#> 33 2 Species8 325.593140
#> 34 2 Species9 1569.061298
#> 35 2 Species10 319.366459
#> 36 2 Species11 63.930518
#> 37 2 Species12 1063.286656
#> 38 2 Species13 358.594548
#> 39 2 Species14 1094.818182
#> 40 2 Species15 253.986310
#> 41 2 Species16 598.944427
#> 42 2 Species17 1776.030303
#> 43 2 Species18 2159.062626
#> 44 2 Species19 678.023274
#> 45 2 Species20 223.835591
#> 46 2 Species21 1176.113184
#> 47 2 Species22 311.595231
#> 48 2 Species23 605.793776
#> 49 2 Species24 787.028620
#> 50 2 Species25 623.004257
#> 51 3 Species1 12.289814
#> 52 3 Species2 7.664615
#> 53 3 Species3 220.506226
#> 54 3 Species4 567.254514
#> 55 3 Species5 97.939011
#> 56 3 Species6 375.037542
#> 57 3 Species7 217.295594
#> 58 3 Species8 225.643238
#> 59 3 Species9 306.551171
#> 60 3 Species10 713.659346
#> 61 3 Species11 142.209078
#> 62 3 Species12 160.822107
#> 63 3 Species13 198.031800
#> 64 3 Species14 618.250000
#> 65 3 Species15 386.816979
#> 66 3 Species16 194.821168
#> 67 3 Species17 40.750000
#> 68 3 Species18 400.227743
#> 69 3 Species19 378.469335
#> 70 3 Species20 279.890597
#> 71 3 Species21 616.484742
#> 72 3 Species22 264.122033
#> 73 3 Species23 624.403768
#> 74 3 Species24 377.680513
#> 75 3 Species25 79.024824
contribution(cluster_object = com, comat = comat,
indices = "contribution")
#> Bioregion Species Contribution
#> 1 1 Species1 7435.4146567
#> 2 1 Species2 10054.7994071
#> 3 1 Species3 8871.3616768
#> 4 1 Species4 5972.2238739
#> 5 1 Species5 10469.0000000
#> 6 1 Species6 6072.3644440
#> 7 1 Species7 14424.5069317
#> 8 1 Species8 6165.4997274
#> 9 1 Species9 14226.5170330
#> 10 1 Species10 14325.5119823
#> 11 1 Species11 2174.0000000
#> 12 1 Species12 14597.0517571
#> 13 1 Species13 8729.9403205
#> 14 1 Species14 18247.4980563
#> 15 1 Species15 9625.6089100
#> 16 1 Species16 6754.7553784
#> 17 1 Species17 22441.2176715
#> 18 1 Species18 20231.9430604
#> 19 1 Species19 16644.8222246
#> 20 1 Species20 5606.6960440
#> 21 1 Species21 20987.7340368
#> 22 1 Species22 11309.0000000
#> 23 1 Species23 13255.4237201
#> 24 1 Species24 15492.4481204
#> 25 1 Species25 9831.6666321
#> 26 2 Species1 883.7537604
#> 27 2 Species2 2.3925840
#> 28 2 Species3 -0.9733285
#> 29 2 Species4 0.1081476
#> 30 2 Species5 -0.9176629
#> 31 2 Species6 1.2797542
#> 32 2 Species7 -0.9733285
#> 33 2 Species8 349.4249411
#> 34 2 Species9 148.2703789
#> 35 2 Species10 3.3525760
#> 36 2 Species11 -0.9176629
#> 37 2 Species12 0.0000000
#> 38 2 Species13 -0.9733285
#> 39 2 Species14 177.9473684
#> 40 2 Species15 14.1673374
#> 41 2 Species16 76.8929536
#> 42 2 Species17 420.0526316
#> 43 2 Species18 36.9352207
#> 44 2 Species19 395.9284152
#> 45 2 Species20 -0.9459053
#> 46 2 Species21 2.0519567
#> 47 2 Species22 7.1118877
#> 48 2 Species23 -0.9733285
#> 49 2 Species24 40.2287961
#> 50 2 Species25 4.6182435