NetworkToolbox (version 1.1.2)

LoGo: Local/Global Sparse Inverse Covariance Matrix

Description

Applies the Local/Global method to estimate the sparse inverse covariance matrix using a TMFG-filtered network (see and cite Barfuss et al., 2016)

Usage

LoGo(data, cliques, separators, partial = FALSE, normal = FALSE,
  standardize = FALSE, na.data = c("pairwise", "listwise", "fiml", "none"))

Arguments

data

Must be a dataset

cliques

Cliques defined in the network. Input can be a list or matrix

separators

Separators defined in the network. Input can be a list or matrix

partial

Should the output network's connections be the partial correlation between two nodes given all other nodes? Defaults to FALSE, which returns a sparse inverse covariance matrix. Set to TRUE for a partial correlation matrix

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)

standardize

Should inverse covariance matrix be standardized? Defaults to FALSE. Set to TRUE for inverse correlation matrix

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 the sparse LoGo-filtered inverse covariance matrix (partial = FALSE) or LoGo-filtered partial correlation matrix (partial = TRUE)

References

Barfuss, W., Massara, G. P., Di Matteo, T., & Aste, T. (2016). Parsimonious modeling with information filtering networks. Physical Review E, 94(6), 062306.

Examples

# NOT RUN {
LoGonet<-LoGo(neoOpen, partial = TRUE)
# }