depend: Dependency Network Approach

In NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

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 Maximum Likelihood (corFiml). Full Information Maximum 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

Author(s)

Alexander Christensen <[e-mail]>

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.

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.

Examples

## Not run: 
D <- depend(neoOpen)

Dindex <- depend(neoOpen, index = TRUE)

## End(Not run)

NetworkToolbox documentation built on May 28, 2021, 5:11 p.m.