random_values {tnet} | R Documentation |
Finds the randomly expected values by simulations
random_values(net, NR=1000, step=c(1,2,3))
net |
A weighted edgelist |
NR |
Number of random networks |
step |
Which steps to perform: 1) calculating values on observed network, 2) calculating values on weight reshuffled networks, and 3) calculating values on link reshuffled networks. |
Summary information is written to the screen, and detailed information is returned as follows:
[[1]][[1]]
This is variable 1, which is the weighted clustering coefficient: clustering_w(net, measure=c("am", "gm", "ma", "mi","bi"))
[[2]]
This is variable 2, which is binary distance matrix: distance_w(net.b)
[[3]]
This is variable 3, which is weighted distance matrix: distance_w(net)
[[4]]
This is variable 4, which is matrix with the results from the weight reshuffled random networks (rows) and different measures (columns), which are
1 to 5: clustering_w(net.r, measure=c("am", "gm", "ma", "mi","bi"))
6: average binary distance
7: average weighted distance
8: normalised weighted distance
9: size of giant component
[[5]]
This is variable 5, which is matrix with the results from the link reshuffled random networks (rows) and different measures (columns), which are
1 to 5: clustering_w(net.r, measure=c("am", "gm", "ma", "mi","bi"))
6: average binary distance
7: average weighted distance
8: normalised weighted distance
9: size of giant component
version 1.0.0
Tore Opsahl; http://toreopsahl.com
## Load sample data sample <- rbind( c(1,2,4), c(1,3,2), c(2,1,4), c(2,3,4), c(2,4,1), c(2,5,2), c(3,1,2), c(3,2,4), c(4,2,1), c(5,2,2), c(5,6,1), c(6,5,1)) ## Run the programme random_values(sample, NR=2)