NetworkToolbox (version 1.3.2)

MaST: Maximum Spanning Tree

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

References

Adapted from: https://www.mathworks.com/matlabcentral/fileexchange/23276-maximum-weight-spanning-tree--undirected

Examples

# NOT RUN {
# Pearson's correlation only for CRAN checks
MaST.net <- MaST(neoOpen, normal = FALSE)

# }