random_values {tnet}R Documentation

Finds the randomly expected values by simulations

Description

Finds the randomly expected values by simulations

Usage

random_values(net, NR=1000, step=c(1,2,3))

Arguments

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.

Value

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

Note

version 1.0.0

Author(s)

Tore Opsahl; http://toreopsahl.com

Examples

## 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)


[Package tnet version 0.1.2 Index]