randnet.test: Test Against Random Networks

Description

Performs significance tests for global measures of semantic networks against the global measures of equivalent size (and density) random networks

Usage

randnet.test(..., iter, cores)

Value

Returns a matrix containing p-values for the network measures of the input networks against the distribution of equivalent random networks. The last two columns contain the mean ("M.rand") and standard deviation ("SD.rand") of the network measures for the random network distribution

Arguments

...: Matrices or data frames. Semantic networks to be compared against random networks
iter: Numeric. Number of iterations in bootstrap. Defaults to 1000
cores: Number of computer processing cores to use for bootstrapping samples. Defaults to n - 1 total number of cores. Set to any number between 1 and maximum amount of cores on your computer

Author

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Viger, F., & Latapy, M. (2016). Efficient and simple generation of random simple connected graphs with prescribed degree sequence. Journal of Complex Networks, 4, 15-37.

Examples

Run this code

# Get openness data
one <- open.clean[which(open.group == "Low"),]
two <- open.clean[which(open.group == "High"),]

# Compute networks
net.one <- CN(one)
net.two <- CN(two)

# Perform random networks test
randnet.test(net.one, net.two, iter = 100, cores = 2)

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