NetworkToolbox (version 1.1.1)

PMFG: Planar Maximally Filtered Graph

Description

Applies the Planar Maximally Filtered Graph (PMFG) filtering method (see and cite Tumminello et al., 2005). Currently very slow! (efficiency is being worked on)

Usage

PMFG(data, sparseList = FALSE, normal = FALSE, weighted = TRUE,
  na.data = c("pairwise", "listwise", "fiml", "none"), progBar = TRUE)

Arguments

data

Can be a dataset or a correlation matrix

sparseList

Should a sparse matrix be returned? Defaults to FALSE. Set to TRUE to return a sparse matrix list

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 from the qgraph package)

weighted

Should network be weighted? Defaults to TRUE. Set to FALSE to produce an unweighted (binary) network

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 (psych package). Full Information Maxmimum Likelihood is recommended but time consuming

progBar

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

Value

Returns a list of PMFG-filtered associaton matrix (pmfg). If sparseList = TRUE, then the list will contain a sparse list (List)

References

Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421-10426.

Examples

# NOT RUN {
weighted_PMFGnetwork<-PMFG(neoOpen)
# }