NetworkToolbox: MaST – R documentation

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MaST

Maximum Spanning Tree

Applies the Maximum Spanning Tree (MaST) filtering method

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

`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)

A sparse association matrix

Alexander Christensen <alexpaulchristensen@gmail.com>

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

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