NetworkToolbox (version 1.2.3)

depna: Dependency Neural Networks

Description

Applies the dependency network approach to neural network array

Usage

depna(neuralarray, pB = TRUE, ...)

Arguments

neuralarray

Array from convertConnBrainMat function

pB

Should progress bar be displayed? Defaults to TRUE. Set FALSE for no progress bar

...

Additional arguments from depend function

Value

Returns an array of n x n x m dependency matrices

References

Jacob, Y., Winetraub, Y., Raz, G., Ben-Simon, E., Okon-Singer, H., Rosenberg-Katz, K., ... & Ben-Jacob, E. (2016). Dependency Network Analysis (DEPNA) reveals context related influence of brain network nodes. Scientific Reports, 6, 27444. doi: 10.1038/srep27444

Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E. (2010). Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market. PloS one, 5, e15032. doi: 10.1371/journal.pone.0015032

Examples

# NOT RUN {
neuralarray <- convertConnBrainMat()

dependencyneuralarray <- depna(neuralarray)
# }
# NOT RUN {
# }