brainGraph (version 3.1.1)

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", "coreness", "degree", "eccentricity", "clo.cent",
    "communicability", "ev.cent", "lev.cent", "pagerank", "subg.cent", "E.local",
    "E.nodal", "knn", "Lp", "transitivity", "vulnerability"),
  .function = NULL
)
# S3 method for brainGraph_permute
summary(
  object,
  measure = object$measure,
  alternative = c("two.sided", "less", "greater"),
  alpha = 0.05,
  p.sig = c("p", "p.fdr"),
  ...
)
# S3 method for brainGraph_permute
plot(
  x,
  measure = x$measure,
  alternative = c("two.sided", "less", "greater"),
  alpha = 0.05,
  p.sig = c("p", "p.fdr"),
  ptitle = NULL,
  ...
)

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

Group

Group names

The plot method returns a list of ggplot objects

Arguments

densities

Numeric vector of graph densities

resids

An object of class brainGraph_resids (the output 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). For the summary method, this is to focus on a single graph-level measure (since multiple are calculated at once).

.function

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

object, x

A brainGraph_permute object (output by brainGraph_permute).

alternative

Character string, whether to do a two- or one-sided test. Default: 'two.sided'

alpha

Numeric; the significance level. Default: 0.05

p.sig

Character string specifying which p-value to use for displaying significant results (default: p)

...

Unused

ptitle

Character string specifying a title for the plot (default: NULL)

Author

Christopher G. Watson,

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, and global efficiency.

2. vertex Choose one of: centrality metrics (betweenness, closeness, communicability, eigenvector, leverage, pagerank, subgraph); k-core; degree; eccentricity; nodal or local efficiency; k-nearest neighbor degree; shortest path length; 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: Bootstrapping, GLM, Mediation, NBS(), mtpc()

Other Structural covariance network functions: Bootstrapping, IndividualContributions, Residuals, corr.matrix(), import_scn(), plot_volumetric()

Examples

if (FALSE) {
myResids <- get.resid(lhrh, covars)
myPerms <- shuffleSet(n=nrow(myResids$resids.all), nset=1e3)
out <- brainGraph_permute(densities, m, perms=myPerms)
out <- brainGraph_permute(densities, m, perms=myPerms, level='vertex')
out <- brainGraph_permute(densities, m, perms=myPerms,
  level='other', .function=myFun)
}