MFCF: Maximally Filtered Clique Forest

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

Description

Applies the Maximally Filtered Clique Forest (MFCF) filtering method (Please see and cite Massara & Aste).

Usage

MFCF(
  data,
  cases = NULL,
  na.data = c("pairwise", "listwise", "fiml", "none"),
  time.series = FALSE,
  gain.fxn = c("logLik", "logLik.val", "rSquared.val"),
  min_size = 0,
  max_size = 8,
  pval = 0.05,
  pen = 0,
  drop_sep = FALSE,
  use_returns = FALSE
)

Arguments

data	Matrix (n x n or p x n) or data frame. Can be a dataset or a correlation matrix
cases	Numeric. If data is a (partial) correlation matrix, then number of cases must be input. Defaults to NULL
na.data	Character. How should missing data be handled? "listwise" Removes case if any missing data exists. Applies na.omit "pairwise" Estimates correlations using the available data for each variable "fiml" Estimates correlations using the Full Information Maximum Likelihood. Recommended and most robust but time consuming "none" Default. No missing data or missing data has been handled by the user
time.series	Boolean. Is data a time-series dataset? Defaults to FALSE. Set to TRUE to handle time-series data (n x p)
gain.fxn	Character. Gain function to be used for inclusion of nodes in cliques. There are several options available (see gain.functions for more details): "logLik", "logLik.val", "rSquared.val". Defaults to "rSquared.val"
min_size	Numeric. Minimum number of nodes allowed per clique. Defaults to 0
max_size	Numeric. Maximum number of nodes allowed per clique. Defaults to 8
pval	Numeric. p-value used to determine cut-offs for nodes to include in a clique
pen	Numeric. Multiplies the number of edges added to penalise complex models. Similar to the penalty term in AIC
drop_sep	Boolean. This parameter influences the MFCF only. Defaults to FALSE. If TRUE, then any separator can be used only once (similar to the TMFG)
use_returns	Boolean. Only used in "gain.fxn = rSquared.val". If set to TRUE the regression is performed on log-returns. Defaults to FALSE

Value

Returns a list containing:

A	MFCF filtered partial correlation network (adjacency matrix)
J	MFCF filtered inverse covariance matrix (precision matrix)
cliques	Cliques in the network (output for LoGo)
separators	Separators in the network (output for LoGo)

Author(s)

Guido Previde Massara <[e-mail]> and Alexander Christensen <[e-mail]>

References

Massara, G. P. & Aste, T. (2019). Learning clique forests. ArXiv.

Examples

# Load data
data <- neoOpen

## Not run:  
# Use polychoric correlations and R-squared method
MFCF.net <- MFCF(qgraph::cor_auto(data), cases = nrow(neoOpen))$A


## End(Not run)

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