Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates the networkbased statistic (NBS), which allows for
familywise error (FWE) control over network data, introduced for brain MRI
data by Zalesky et al. Accepts a threedimensional array of all subjects'
connectivity matrices and a data.table
of covariates, and creates a
null distribution of the largest connected component size by permuting
subjects across groups. The covariates data.table
must have (at least)
a Group column.
1 2 
A 
Threedimensional array of all subjects' connectivity matrices 
covars 
A 
con.vec 
A numeric vector specifying the contrast of interest 
X 
A numeric matrix (optional), if you would like to supply your own
design matrix (default: 
p.init 
Numeric; the initial pvalue threshold (default: 
N 
Integer; the number of permutations (default: 
symmetric 
Logical indicating if input matrices are symmetric (default:

alternative 
Character string, whether to do a two or onesided test
(default: 
... 
Other arguments passed to 
The graph that is returned by this function will have a t.stat
edge
attribute which is the tstatistic for that particular connection, along with
a p
edge attribute, which is the pvalue for that connection.
Additionally, each vertex will have a p.nbs
attribute representing
1  the pvalue associated with that vertex's component.
A list containing:
g.nbs 
The 
obs 
Integer vector of the observed connected component sizes 
perm 
Integer vector of the permutation distribution of largest connected component sizes 
p.perm 
Numeric vector of the permutation pvalues for each component 
p.init 
Numeric; the initial pvalue threshold used 
Christopher G. Watson, cgwatson@bu.edu
Zalesky A., Fornito A., Bullmore E.T. (2010) Networkbased statistic: identifying differences in brain networks. NeuroImage, 53(4):11971207.
brainGraph_GLM_design, brainGraph_GLM_fit
1 2 3 4  ## Not run:
max.comp.nbs < NBS(A.norm.sub[[1]], covars.dti, N=5e3)
## End(Not run)

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