NetworkToolbox (version 1.4.2)

threshold: Threshold Network Estimation Methods

Description

Filters the network based on an r-value, alpha, adaptive alpha, bonferroni, false-discovery rate (FDR), or proportional density (fixed number of edges) value

Usage

threshold(
  data,
  a,
  thresh = c("alpha", "adaptive", "bonferroni", "FDR", "proportional"),
  normal = FALSE,
  na.data = c("pairwise", "listwise", "fiml", "none"),
  ...
)

Arguments

data

Can be a dataset or a correlation matrix

a

When thresh = "alpha", "adaptive", and "bonferroni" an \(\alpha\) threshold is applied (defaults to .05). For "adaptive", beta (Type II error) is set to \(\alpha*5\) for a medium effect size (r = .3). When thresh = "FDR", a q-value threshold is applied (defaults to .10). When thresh = "proportional", a density threshold is applied (defaults to .15)

thresh

Sets threshold. Defaults to "alpha". Set to any value 0> r >1 to retain values greater than set value, "adaptive" for an adapt.a based on sample size (Perez & Pericchi, 2014), "bonferroni" for the bonferroni correction on alpha, "FDR" for local false discovery rate, and "proportional" for a fixed density of edges (keeps strongest correlations within density)

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)

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

...

Additional arguments for fdrtool and adapt.a

Value

Returns a list containing:

A

The filtered adjacency matrix

r.cv

The critical correlation value used to filter the network

References

Strimmer, K. (2008). fdrtool: A versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics, 24, 1461-1462.

Examples

# NOT RUN {
threshnet<-threshold(neoOpen)

alphanet<-threshold(neoOpen, thresh = "alpha", a = .05)

bonnet<-threshold(neoOpen, thresh = "bonferroni", a = .05)

FDRnet<-threshold(neoOpen, thresh = "FDR", a = .10)

propnet<-threshold(neoOpen, thresh = "proportional", a = .15)
# }