brainGraph (version 3.1.0)

Bootstrapping: Bootstrapping for global graph measures

Description

Perform bootstrapping to obtain groupwise standard error estimates of a global graph measure.

The plot method returns two ggplot objects: one with shaded regions based on the standard error, and the other based on confidence intervals (calculated using the normal approximation).

Usage

brainGraph_boot(densities, resids, R = 1000, measure = c("mod",
  "E.global", "Cp", "Lp", "assortativity", "strength", "mod.wt",
  "E.global.wt"), conf = 0.95, .progress = getOption("bg.progress"),
  xfm.type = c("1/w", "-log(w)", "1-w", "-log10(w/max(w))",
  "-log10(w/max(w)+1)"))
# S3 method for brainGraph_boot
summary(object, ...)
# S3 method for brainGraph_boot
plot(x, ..., alpha = 0.4)

Value

brainGraph_boot -- an object of class brainGraph_boot

containing some input variables, in addition to a list of

boot objects (one for each group).

plot -- list with the following elements:

se

A ggplot object with ribbon representing standard error

ci

A ggplot object with ribbon representing confidence intervals

Arguments

densities

Numeric vector of graph densities to loop through

resids

An object of class brainGraph_resids (the output from get.resid)

R

Integer; the number of bootstrap replicates. Default: 1e3

measure

Character string of the measure to test. Default: mod

conf

Numeric; the level for calculating confidence intervals. Default: 0.95

.progress

Logical indicating whether or not to show a progress bar. Default: getOption('bg.progress')

xfm.type

Character string specifying how to transform the weights. Default: 1/w

object, x

A brainGraph_boot object

...

Unused

alpha

A numeric indicating the opacity for the confidence bands

Author

Christopher G. Watson,

Details

The confidence intervals are calculated using the normal approximation at the \(100 \times conf\)% level (by default, 95%).

For getting estimates of weighted global efficiency, a method for transforming edge weights must be provided. The default is to invert them. See xfm.weights.

See Also

boot, boot.ci

Other Group analysis functions: GLM, Mediation, NBS, brainGraph_permute, mtpc

Other Structural covariance network functions: IndividualContributions, Residuals, brainGraph_permute, corr.matrix, import_scn, plot_volumetric

Examples

if (FALSE) {
boot.E.global <- brainGraph_boot(densities, resids.all, 1e3, 'E.global')
}