analysis_random_graphs: Perform an analysis with random graphs for brain MRI data

Description

This function is not quite a "proper" function. It performs the steps needed for doing typical graph theory analyses with brain MRI data if you need to generate equivalent random graphs. This includes calculating small world parameters and normalized rich club coefficients.

Usage

analysis_random_graphs(g, N, covars, savedir = ".", ...)

Arguments

A list object containing all graphs (may be nested)

Integer specifying number of random graphs to generate per individual graph

covars

Data table of covariates (used for Group and subject names)

savedir

Character string specifying the directory in which to save the generated graphs (default: current working directory)

...

Other arguments passed to sim.rand.graph.par (e.g. clustering=F)

Value

A list containing:

rich

A list object containing normalized rich-club coefficients and p-values

small

A data table with small-world parameters

rand

A data table with some global graph measures for all random graphs generated

Details

First, a number of random graphs are generated for each group and density/threshold (and subject if you have subject-specific graphs). These graphs are all written to disk in a location you specify. All of these are read back into R and combined into large lists; these large lists are also written to disk (in a sub-directory named ALL), so you can delete the individual .rds files afterwards. Once all of the random graphs have been generated, the small world parameters are calculated, along with values for a few global graph measures that may be of interest. Additionally, the normalized rich club coefficients and associated p-values will be calculated.

Examples

Run this code

## Not run: ------------------------------------
# rand_all <- random_graph_analysis(g.norm, 1e2, covars.dti,
#   savedir='~/dti/rand', clustering=F)
## ---------------------------------------------

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