Applies the Triangulated Maximally Filtered Graph (TMFG) filtering method
(Please see and cite Massara et al., 2016). The TMFG method uses a structural
constraint that limits the number of zero-order correlations included in the network
(3n - 6; where n is the number of variables). The TMFG algorithm begins by
identifying four variables which have the largest sum of correlations to all other
variables. Then, it iteratively adds each variable with the largest sum of three
correlations to nodes already in the network until all variables have been added to
the network. This structure can be associated with the inverse correlation matrix
(i.e., precision matrix) to be turned into a GGM (i.e., partial correlation network)
by using LoGo.
TMFG(data, normal = FALSE, na.data = c("pairwise", "listwise", "fiml",
"none"), depend = FALSE)Can be a dataset or a correlation matrix
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)
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)
Returns a list containing:
The filtered adjacency matrix
The separators (3-cliques) in the network
(wrapper output for LoGo)
The cliques (4-cliques) in the network
(wrapper output for LoGo)
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, 161-178. doi: 10.1093/comnet/cnw015
# NOT RUN {
TMFG.net <- TMFG(neoOpen)
# }
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