brainGraph_permute {brainGraph}

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 class '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 class 'brainGraph_permute'
plot(x, measure = x$measure,
  alternative = c("two.sided", "less", "greater"), alpha = 0.05,
  p.sig = c("p", "p.fdr"), ptitle = NULL, ...)

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)

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).

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

Author(s)

Christopher G. Watson, [e-mail]

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

## Not run: 
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)

## End(Not run)