Description Usage Arguments Details Value Author(s) See Also
View source: R/set_brainGraph_attributes.R
This function sets a number of graph, vertex, and edge attributes for a
given igraph
graph object. These are all measures that are common in
MRI analyses of brain networks.
1 2 
g 
An 
atlas 
Character vector indicating which atlas was used (default:

rand 
Logical indicating if the graph is random or not (default:

use.parallel 
Logical indicating whether or not to use foreach
(default: 
A 
Numeric matrix; the (weighted) adjacency matrix, which can be used
for faster calculation of local efficiency (default: 
xfm.type 
Character string indicating how to transform edge weights
(default: 
... 
Other arguments passed to 
xfm.type
allows you to choose from 3 options for transforming edge
weights when calculating distancebased metrics (e.g., shortest paths). There
is no "bestpractice" for choosing one over the other, but the reciprocal is
probably most common.
1/w
: reciprocal (default)
log(w)
: the negative (natural) logarithm
1w
: subtract weights from 1
g An igraph
graph object with the following attributes:
Graphlevel 
Density, connected component sizes, diameter, \# of triangles, transitivity, average path length, assortativity, global & local efficiency, modularity, vulnerability, hub score, richclub coefficient, \# of hubs, edge asymmetry, and modality 
Vertexlevel 
Degree, strength; betweenness, eigenvector, and leverage centralities; hubs; transitivity (local); kcore, score; local & nodal efficiency; color (community, lobe, component); membership (community, lobe, component); gateway and participation coefficients, withinmodule degree zscore; vulnerability; and coordinates (x, y, and z) 
Edgelevel 
Color (community, lobe, component), edge betweenness, Euclidean distance (in mm), weight (if weighted) 
Christopher G. Watson, [email protected]
components
, diameter
,
clique_num
, centr_betw
,
part_coeff
, edge.betweenness
,
centr_eigen
, gateway_coeff
,
transitivity
, mean_distance
,
assortativity_degree
, efficiency
,
assortativity_nominal
, coreness
,
cluster_louvain
, set_edge_color
,
rich_club_coeff
, s_core
, centr_lev
,
within_module_deg_z_score
, edge_spatial_dist
,
vulnerability
, edge_asymmetry
,
graph.knn
, vertex_spatial_dist
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