Computes the threshold for the correlation matrix in order to obtain an adjacency matrix with a given number of edges.

1 2 3 4 | ```
choose.thresh.nbedges(cor.mat, var.ind.mat = 0, n.ind = 0, thresh = 0.05,
nb.edges = 405, test.method = "gaussian",
proc.length = 518, num.levels, use.tanh = FALSE,
max.iter = 10)
``` |

`cor.mat` |
matrix containing the correlation values. (must be diagonal with 1 on the diagonal) |

`var.ind.mat` |
matrix containing the variance inter individuals of the correlation. Only used with |

`n.ind` |
number of individuals to take into account in the test. Only used with |

`thresh` |
indicates the rate at which the FDR procedure is controlled. (default 0.05) |

`nb.edges` |
indicates the exact number of edges that the final graph should contain. |

`test.method` |
name of the method to be applied. |

`proc.length` |
specifies the length of the original processes using to construct the |

`num.levels` |
specifies the number of the wavelet scale to take into account in the hypothesis test. Only used with |

`use.tanh` |
logical. If FALSE take the |

`max.iter` |
indicates the number of maximum iteration to compute before stopping the loop |

In order to compare graphs, the best way to do it is to make sure that all the graphs have the same number of edges!

Real number corresponding to the threshold value.

only in version 2 and higher

S. Achard

S. Achard, R. Salvador, B. Whitcher, J. Suckling, Ed Bullmore (2006)
A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs. *Journal of Neuroscience*, Vol. 26, N. 1, pages 63-72.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
data(brain)
brain<-as.matrix(brain)
# WARNING : To process only the first five regions
brain<-brain[,1:5]
#Construction of the correlation matrices for each level of the wavelet decomposition
wave.cor.list<-const.cor.list(brain, method = "modwt" ,wf = "la8", n.levels = 6,
boundary = "periodic", p.corr = 0.975)
#Construction of the adjacency matrice for scale 4
adj.mat.4<-const.adj.mat(wave.cor.list[[4]], sup = 0.44,proc.length=dim(brain)[1],
num.levels=4)
nb.edges<-sum(adj.mat.4)/2
sup.thresh<-choose.thresh.nbedges(wave.cor.list[[4]],nb.edges=nb.edges,
proc.length=dim(brain)[1],num.levels=4)
``` |

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