Ac3net.cutoff: Calculates a quick cutoff value from an adjacency matrix
In Ac3net: Inferring Directional Conservative Causal Core Gene Networks

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

Ac3net.cutoff A quick way to get a very rough estimate of the cutoff value for the significants of the input adjacency matrix

1	Ac3net.cutoff(mim, ratio_ = 0.002, PCmincutoff=0.6, PCmaxcutoff=0.96)

`mim`	An adjacency matrix, where the element at row i and column j corresponds to the correlation or mutual information between variables row i and column j. Row and columns of the matrix must have the variable names.
`ratio_`	The ratio of all the elements in the input adjacency matrix (even if the matrix is symmetric), which the user considers as significant portion.
`PCmincutoff`	The minimum absolute Pearson correlation value ( by default but can be different) where the below of is not considered as significant.
`PCmaxcutoff`	The maximum absolute Pearson correlation value ( by default but can be different) where all the above of is considered as significant.

Ac3net.cutoff takes an adjacency matrix, ratio and minimum absolute Pearson correlation information. Then return a cutoff value that either correspond to the input ratio or the minimum.

Ac3net.cutoff returns a scaler as a very rough estimate of the cutoff value for the significants of the input adjacency matrix.

Gokmen Altay

G. Altay,"Directed Conservative Causal Core Gene Networks", bioRxiv, 2018. G. Altay, F. Emmert-Streib, "Inferring the conservative causal core of gene regulatory networks", BMC Systems Biology (2010) 4:132.

Ac3net.maxmim, Ac3net.commonlinks,

 data(expdata)
 mim <- cor(t(expdata), method = 'pearson')
 cutoff <- Ac3net.cutoff(mim=mim, ratio_ = 0.002,
              PCmincutoff=0.6, PCmaxcutoff=0.96)