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

Applies the *multi-threshold permutation correction (MTPC)* method to
perform inference in graph theory analyses of brain MRI data.

Print a summary of MTPC results

1 2 3 4 5 6 7 8 | ```
mtpc(g.list, thresholds, covars, measure, con.mat, con.type = c("t", "f"),
con.name = NULL, level = c("vertex", "graph"), clust.size = 3L,
N = 500L, perms = NULL, alpha = 0.05, res.glm = NULL, long = TRUE,
...)
## S3 method for class 'mtpc'
summary(object, contrast = NULL, digits = max(3L,
getOption("digits") - 2L), print.head = TRUE, ...)
``` |

`g.list` |
A list of lists of |

`thresholds` |
Numeric vector of the thresholds applied to the raw connectivity matrices. |

`covars` |
A |

`measure` |
Character string of the graph measure of interest |

`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 |

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

`level` |
Character string; either |

`clust.size` |
Integer indicating the size of "clusters" (i.e.,
consecutive thresholds for which the observed statistic exceeds the null)
(default: |

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

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

`alpha` |
Numeric; the significance level (default: 0.05) |

`res.glm` |
A list of |

`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 |

`print.head` |
Logical indicating whether or not to print only the first
and last 5 rows of the statistics tables (default: |

This is a multi-step procedure: (steps 3-4 are the time-consuming steps)

Apply thresholds

*τ*to the networks, and compute network metrics for all networks and thresholds. (already done beforehand)Compute test statistics

*S_{obs}*for each threshold. (done by`brainGraph_GLM`

)Permute group assignments and compute test statistics for each permutation and threshold. (done by

`brainGraph_GLM`

)Build a null distribution of the maximum statistic across thresholds (and across brain regions) for each permutation. (done by

`brainGraph_GLM`

)Determine the critical value,

*S_{crit}*from the null distribution of maximum statistics.Identify clusters where

*S_{obs} > S_{crit}*and compute the AUC for these clusters (denoted*A_{MTPC}*).Compute a critical AUC (

*A_{crit}*) from the mean of the supra-critical AUC's for the permuted tests.Reject

*H_0*if*A_{MTPC} > A_{crit}*.

An object of class `mtpc`

with some input arguments plus the
following elements:

`res.glm` |
List with length equal to the number of thresholds; each
list element is the output from |

`DT` |
A |

`stats` |
A data.table containing |

`null.dist` |
Numeric matrix with |

`perm.order` |
Numeric matrix; the permutation set applied for all thresholds (each row is a separate permutation) |

Christopher G. Watson, [email protected]

Drakesmith M, Caeyenberghs K, Dutt A, Lewis G, David AS, Jones
DK (2015). *Overcoming the effects of false positives and threshold
bias in graph theoretical analyses of neuroimaging data.* NeuroImage,
118:313-333.

Other Group analysis functions: `IndividualContributions`

,
`NBS`

, `brainGraph_GLM`

,
`brainGraph_boot`

,
`brainGraph_mediate`

,
`brainGraph_permute`

Other GLM functions: `GLMfit`

,
`brainGraph_GLM_design`

,
`brainGraph_GLM`

1 2 3 4 5 6 7 8 |

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