brainGraph (version 2.0.2)

brainGraph_permute: Permutation test for group difference of graph measures

Description

brainGraph_permute draws permutations from linear model residuals to determine the significance of between-group differences of a global or vertex-wise graph measure. It is intended for structural covariance networks (in which there is only one graph per group), but can be extended to other types of data.

Usage

brainGraph_permute(densities, resids, N = 5000, perms = NULL, auc = FALSE,
  level = c("graph", "vertex", "other"), measure = c("btwn.cent", "degree",
  "E.nodal", "ev.cent", "knn", "transitivity", "vulnerability"), atlas = NULL,
  .function = NULL)
permute.group(densities, resids, N = 5000, perms = NULL,
  level = c("graph", "vertex", "lobe", "other"), measure = c("btwn.cent",
  "degree", "E.nodal", "ev.cent", "knn", "transitivity", "vulnerability"),
  atlas = NULL, .function = NULL)

Arguments

densities

Numeric vector of graph densities

resids

A data table of the residuals (from get.resid)

N

Integer; the number of permutations (default: 5e3)

perms

Numeric matrix of permutations, if you would like to provide your own (default: NULL)

auc

Logical indicating whether or not to calculate differences in the area-under-the-curve of metrics (default: FALSE)

level

A character string for the attribute "level" to calculate differences (default: graph)

measure

A character string specifying the vertex-level metric to calculate, only used if level='vertex' (default: btwn.cent)

atlas

Character string of the atlas name; required if level='graph' (default: NULL)

.function

A custom function you can pass if level='other'

Value

An object of class brainGraph_permute with input arguments in addition to:

DT

A data table with permutation statistics

obs.diff

A data table of the observed group differences

Details

If you would like to calculate differences in the area-under-the-curve (AUC) across densities, then specify auc=TRUE.

There are three possible "levels":

1. graph Calculate modularity (Louvain algorithm), clustering coefficient, characteristic path length, degree assortativity, global efficiency, lobe assortativity, and edge asymmetry.

2. vertex Choose one of: betweenness centrality, degree, nodal efficiency, k-nearest neighbor degree, transitivity, or vulnerability.

3. other Supply your own function. This is useful if you want to calculate something that I haven't hard-coded. It must take as its own arguments: g (a list of lists of igraph graph objects); and densities (numeric vector).

See Also

Other Group analysis functions: IndividualContributions, NBS, brainGraph_GLM, brainGraph_boot, brainGraph_mediate, mtpc

Other Structural covariance network functions: IndividualContributions, brainGraph_boot, brainGraph_init, corr.matrix, get.resid, plot.brainGraph_resids, plot_volumetric

Examples

# NOT RUN {
m <- get.resid(all.thick, covars)
myPerms <- shuffleSet(n=nrow(m$resids.all), nset=1e3)
out <- brainGraph_permute(densities, m$resids.all, perms=myPerms, atlas='dk')
out <- brainGraph_permute(densities, m$resids.all, perms=myPerms,
  level='vertex')
out <- brainGraph_permute(densities, m$resids.all, perms=myPerms,
  level='other', .function=myFun)
# }