# Threshold associated to a given number of edges.

### Description

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

### Usage

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)
``` |

### Arguments

`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 |

### Details

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!

### Value

Real number corresponding to the threshold value.

### Note

only in version 2 and higher

### Author(s)

S. Achard

### References

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.

### Examples

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)
``` |