Bootstrapping {brainGraph} | R Documentation |
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 class 'brainGraph_boot'
summary(object, ...)
## S3 method for class 'brainGraph_boot'
plot(x, ..., alpha = 0.4)
Arguments
densities |
Numeric vector of graph densities to loop through |
resids |
An object of class |
R |
Integer; the number of bootstrap replicates. Default: |
measure |
Character string of the measure to test. Default: |
conf |
Numeric; the level for calculating confidence intervals. Default:
|
.progress |
Logical indicating whether or not to show a progress bar.
Default: |
xfm.type |
Character string specifying how to transform the weights.
Default: |
object , x |
A |
... |
Unused |
alpha |
A numeric indicating the opacity for the confidence bands |
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
.
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 |
Author(s)
Christopher G. Watson, cgwatson@bu.edu
See Also
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
## Not run:
boot.E.global <- brainGraph_boot(densities, resids.all, 1e3, 'E.global')
## End(Not run)