NetworkToolbox (version 1.1.2)

TMFG: Triangulated Maximally Filtered Graph

Description

Applies the Triangulated Maximally Filtered Graph (TMFG) filtering method (Please see and cite Massara et al., 2016)

Usage

TMFG(data, normal = FALSE, weighted = TRUE, depend = FALSE,
  na.data = c("pairwise", "listwise", "fiml", "none"))

Arguments

data

Can be a dataset or a correlation matrix

normal

Should data be transformed to a normal distribution? Input must be a dataset. 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)

weighted

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

depend

Is network a dependency (or directed) network? Defaults to FALSE. Set to TRUE to generate a TMFG-filtered dependency network (output obtained from the depend 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

Value

Returns a list of the adjacency matrix (A), separators (separators), and cliques (cliques)

References

Christensen, A. P., Kenett, Y. N., Aste, T., Silvia, P. J., & Kwapil, T. R. (2018). Network structure of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filtering approaches. Behavior Research Methods, 1-20, doi:10.3758/s13428-018-1032-9

Massara, G. P., Di Matteo, T., & Aste, T. (2016). Network filtering for big data: Triangulated maximally filtered graph. Journal of Complex Networks, 5(2), 161-178.

Examples

# NOT RUN {
weighted_TMFGnetwork<-TMFG(neoOpen)


unweighted_TMFGnetwork<-TMFG(neoOpen,weighted=FALSE)

# }