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NetworkToolbox

MFCF

Maximally Filtered Clique Forest

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 <gprevide@gmail.com> and Alexander Christensen <alexpaulchristensen@gmail.com>

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

Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

v1.4.1

GPL (>= 3.0)

Authors

Alexander Christensen [aut, cre] (<https://orcid.org/0000-0002-9798-7037>), Guido Previde Massara [ctb] (<https://orcid.org/0000-0003-0502-2789>)

Initial release

2020-12-07

MFCF

Description

Usage

Arguments

Value

Author(s)

References

Examples

NetworkToolbox

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