NetworkToolbox (version 1.2.2)

depend: Dependency Network Approach

Description

Generates a dependency matrix of the data (index argument is still in testing phase)

Usage

depend(data, normal = FALSE, na.data = c("pairwise", "listwise",
  "fiml", "none"), index = FALSE, fisher = FALSE, progBar = TRUE)

Arguments

data

A set of data

normal

Should data be transformed to a normal distribution? Defaults to FALSE. Data is not transformed to be normal. Set to TRUE if data should be transformed to be normal (computes correlations using the cor_auto function)

na.data

How should missing data be handled? For "listwise" deletion the na.omit function is applied. Set to "fiml" for Full Information Maxmimum Likelihood (corFiml). Full Information Maxmimum Likelihood is recommended but time consuming

index

Should correlation with the latent variable (i.e., weighted average of all variables) be removed? Defaults to FALSE. Set to TRUE to remove common latent factor

fisher

Should Fisher's Z-test be used to keep significantly higher influences (index only)? Defaults to FALSE. Set to TRUE to remove non-significant influences

progBar

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

Value

Returns an adjacency matrix of dependencies

References

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

Kenett, D. Y., Huang, X., Vodenska, I., Havlin, S., & Stanley, H. E. (2015). Partial correlation analysis: Applications for financial markets. Quantitative Finance, 15, 569-578. doi: 10.1080/14697688.2014.946660

Examples

# NOT RUN {
D <- depend(neoOpen)

Dindex <- depend(neoOpen, index = TRUE)

# }