Description Usage Arguments Details Value References See Also Examples
conductance
compute win-loss probabilities for all possible pairs
based upon the combined information from directed wins/losses and
indirect win/loss pathways from the network.
1 | conductance(conf, maxLength, alpha = NULL, beta = 1, strict = FALSE)
|
conf |
a matrix of conf.mat class. An N-by-N conflict matrix whose |
maxLength |
an integer greater than 1 and less than 7, indicating the maximum length of paths to identify. |
alpha |
a positive integer that
reflects the influence of an observed win/loss interaction
on an underlying win-loss probability.
It is used in the calculation of the posterior distribution
for the win-loss probability of |
beta |
a positive numeric value that, like alpha, reflects the influence of an observed win/loss interaction on an underlying win-loss probability. Both α and β are chosen such that ((α + β)/(α + 2β))^2 is equal to the order-1 transitivity of the observed network. Therefore, β is commonly set to 1. |
strict |
a logical vector of length 1. It is used in transitivity definition for alpha estimation. It should be set to TRUE when a transitive triangle is defined as all pathways in the triangle go to the same direction; it should be set to FALSE when a transitive triangle is defined as PRIMARY pathways in the triangle go to the same direction. Strict = FALSE by default. |
This function performs two major steps.
First, repeated random walks through the empirical network
identify all possible directed win-loss pathways
between each pair of nodes in the network.
Second, the information from both direct wins/losses and
pathways of win/loss interactions are combined into an estimate of
the underlying probability of i
over j
, for all ij
pairs.
a list of two elements.
imputed.conf |
An N-by-N conflict matrix whose |
p.hat |
An N-by-N numeric matrix whose |
Fushing H, McAssey M, Beisner BA, McCowan B. 2011. Ranking network of a captive rhesus macaque society: a sophisticated corporative kingdom. PLoS ONE 6(3):e17817.
as.conflictmat
, findIDpaths
, transitivity
, simRankOrder
1 2 3 4 5 6 | # convert an edgelist to conflict matrix
confmatrix <- as.conflictmat(sampleEdgelist)
# find win-loss probability matrix
perm2 <- conductance(confmatrix, 2, strict = FALSE)
perm2$imputed.conf
perm2$p.hat
|
Kalani Kale Kalleigh Keira Kibitz Kimora
Kalani 0.00000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000
Kale 0.00000000 0.00000000 0.0000000 0.00000000 4.0557534 0.00000000
Kalleigh 0.05575338 1.00000000 0.0000000 0.11150676 0.2230135 1.11150676
Keira 0.00000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000
Kibitz 0.00000000 6.05575338 0.0000000 0.05575338 0.0000000 0.05575338
Kimora 0.05575338 0.05575338 0.1115068 0.05575338 1.0000000 0.00000000
Kioga 0.05575338 0.05575338 3.1115068 1.05575338 0.1672601 7.11150676
Kolyma 0.00000000 17.05575338 0.0000000 0.00000000 6.0557534 0.00000000
Koppy 1.00000000 0.05575338 11.1115068 0.11150676 0.1115068 0.16726014
Kuai 0.05575338 0.11150676 12.0557534 0.05575338 3.0557534 0.11150676
Kyushu 0.00000000 0.05575338 0.1115068 1.05575338 1.1115068 8.05575338
Kioga Kolyma Koppy Kuai Kyushu
Kalani 0.00000000 0.00000000 0.0000000 0.00000000 0.00000000
Kale 0.00000000 7.05575338 0.0000000 0.00000000 0.05575338
Kalleigh 1.16726014 0.05575338 5.1672601 6.16726014 1.16726014
Keira 0.00000000 0.00000000 0.0000000 0.00000000 0.00000000
Kibitz 0.05575338 18.05575338 0.0000000 0.05575338 1.00000000
Kimora 2.05575338 0.05575338 1.0557534 0.11150676 0.16726014
Kioga 0.00000000 0.00000000 1.1672601 2.16726014 10.16726014
Kolyma 0.00000000 0.00000000 0.0000000 0.00000000 0.05575338
Koppy 3.11150676 0.00000000 0.0000000 10.16726014 1.16726014
Kuai 0.16726014 0.05575338 5.0557534 0.00000000 3.16726014
Kyushu 9.05575338 0.05575338 0.1672601 4.05575338 0.00000000
attr(,"class")
[1] "conf.mat" "matrix"
Kalani Kale Kalleigh Keira Kibitz Kimora Kioga
Kalani 0.0000000 0.5000000 0.4236125 0.5000000 0.5000000 0.4236125 0.4236125
Kale 0.5000000 0.0000000 0.1180829 0.5000000 0.4040371 0.4236125 0.4236125
Kalleigh 0.5763875 0.8819171 0.0000000 0.6325280 0.7095211 0.8263286 0.2881139
Keira 0.5000000 0.5000000 0.3674720 0.0000000 0.4236125 0.4236125 0.1132596
Kibitz 0.5000000 0.5959629 0.2904789 0.5763875 0.0000000 0.1541064 0.3952394
Kimora 0.5763875 0.5763875 0.1736714 0.5763875 0.8458936 0.0000000 0.2332463
Kioga 0.5763875 0.5763875 0.7118861 0.8867404 0.6047606 0.7667537 0.0000000
Kolyma 0.5000000 0.7047444 0.4236125 0.5000000 0.2543067 0.4236125 0.5000000
Koppy 0.8819171 0.5763875 0.6791736 0.6325280 0.6325280 0.2100593 0.7118861
Kuai 0.5763875 0.6325280 0.6588720 0.5763875 0.9385078 0.5000000 0.1217430
Kyushu 0.5000000 0.5000000 0.1675738 0.8867404 0.5230320 0.9622779 0.4715468
Kolyma Koppy Kuai Kyushu
Kalani 0.5000000 0.1180829 0.42361253 0.5000000
Kale 0.2952556 0.4236125 0.36747202 0.5000000
Kalleigh 0.5763875 0.3208264 0.34112804 0.8324262
Keira 0.5000000 0.3674720 0.42361253 0.1132596
Kibitz 0.7456933 0.3674720 0.06149216 0.4769680
Kimora 0.5763875 0.7899407 0.50000000 0.0377221
Kioga 0.5000000 0.2881139 0.87825704 0.5284532
Kolyma 0.0000000 0.5000000 0.42361253 0.5000000
Koppy 0.5000000 0.0000000 0.66454550 0.8041909
Kuai 0.5763875 0.3354545 0.00000000 0.4410203
Kyushu 0.5000000 0.1958091 0.55897968 0.0000000
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