Description Usage Arguments Details Value Author(s) References See Also Examples

Calculates the *network-based statistic (NBS)*, which allows for
family-wise error (FWE) control over network data, introduced for brain MRI
data by Zalesky et al. Accepts a three-dimensional 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.

Print a summary of NBS analysis

1 2 3 4 5 6 7 8 | ```
NBS(A, covars, con.mat, con.type = c("t", "f"), X = NULL, con.name = NULL,
p.init = 0.001, N = 1000, perms = NULL, symm.by = c("max", "min",
"avg"), alternative = c("two.sided", "less", "greater"), long = FALSE,
...)
## S3 method for class 'NBS'
summary(object, contrast = NULL, digits = max(3L,
getOption("digits") - 2L), ...)
``` |

`A` |
Three-dimensional array of all subjects' connectivity matrices |

`covars` |
A |

`con.mat` |
Numeric matrix specifying the contrast(s) of interest; if only one contrast is desired, you can supply a vector |

`con.type` |
Character string; either |

`X` |
Numeric matrix, if you wish to supply your own design matrix
(default: |

`con.name` |
Character vector of the contrast name(s); if |

`p.init` |
Numeric; the initial p-value threshold (default: |

`N` |
Integer; number of permutations to create (default: 5e3) |

`perms` |
Matrix of permutations, if you would like to provide your own
(default: |

`symm.by` |
Character string; how to create symmetric off-diagonal
elements (default: |

`alternative` |
Character string, whether to do a two- or one-sided test
(default: |

`long` |
Logical indicating whether or not to return all permutation
results (default: |

`...` |
Other arguments passed to |

`object` |
A |

`contrast` |
Integer specifying the contrast to summarize; defaults to showing results for all contrasts |

`digits` |
Integer specifying the number of digits to display for p-values |

The graph that is returned by this function will have a `t.stat`

edge
attribute which is the t-statistic for that particular connection, along with
a `p`

edge attribute, which is the p-value for that connection.
Additionally, each vertex will have a `p.nbs`

attribute representing
*1 - * the p-value associated with that vertex's component.

An object of class `NBS`

with some input arguments in addition
to:

`X` |
The design matrix |

`removed` |
Character vector of subject ID's removed due to incomplete data (if any) |

`T.mat` |
List of numeric matrices (symmetric) containing the statistics for each edge |

`p.mat` |
List of numeric matrices (symmetric) containing the P-values |

`components` |
List containing data tables of the observed and permuted connected component sizes and P-values |

Christopher G. Watson, [email protected]

Zalesky A., Fornito A., Bullmore E.T. (2010) *Network-based
statistic: identifying differences in brain networks*. NeuroImage,
53(4):1197-1207.

`brainGraph_GLM_design, brainGraph_GLM_fit_t`

Other Group analysis functions: `IndividualContributions`

,
`brainGraph_GLM`

,
`brainGraph_boot`

,
`brainGraph_mediate`

,
`brainGraph_permute`

, `mtpc`

1 2 3 4 | ```
## Not run:
max.comp.nbs <- NBS(A.norm.sub[[1]], covars.dti, N=5e3)
## End(Not run)
``` |

brainGraph documentation built on May 29, 2018, 9:03 a.m.

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