MaST: Maximum Spanning Tree

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

Description

Applies the Maximum Spanning Tree (MaST) filtering method

Usage

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

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 TRUE. Computes correlations using the cor_auto function. Set to FALSE for Pearson's correlation
na.data	How should missing data be handled? For "listwise" deletion the na.omit function is applied. Set to "fiml" for Full Information Maximum Likelihood (corFiml). Full Information Maximum Likelihood is recommended but time consuming
depend	Is network a dependency (or directed) network? Defaults to FALSE. Set TRUE to generate a MaST-filtered dependency network (output obtained from the depend function)

Value

A sparse association matrix

Author(s)

Alexander Christensen <[e-mail]>

Examples

# Pearson's correlation only for CRAN checks
MaST.net <- MaST(neoOpen, normal = FALSE)

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