EGAnet (version 0.9.6)

node.redundant: Detects Redundant Nodes in a Network

Description

Identifies redundant nodes in the network based on several measures. Computes the weighted topological overlap between each node and every other node in the network. The weighted topological overlap is implemented using the method from Nowick et al. (2009; see references) and the function wTO from the wTO package.

Usage

node.redundant(
  data,
  n = NULL,
  sig,
  method = c("wTO", "pcor", "thresh"),
  type = c("alpha", "bonferroni", "FDR", "adapt")
)

Arguments

data

Matrix or data frame. Input can either be data or a correlation matrix

n

Numeric. If input in data is a correlation matrix and method = "wTO", then sample size is required. Defaults to NULL

sig

Numeric. p-value for significance of overlap (defaults to .05). If more than 200 connections, then fdrtool is used to correct for false positives. In these instances, sig sets the q-value for significance of overlap (defaults to .10)

method

Character. Computes weighted topological overlap ("wTO" using EBICglasso), partial correlations ("pcor"), or thresholding based on a certain level of partial correlations ("thresh"). method = "thresh" will use the argument "sig" to input the desired threshold (defaults to sig = .20).

type

Character. Computes significance using the standard p-value ("alpha"), bonferroni corrected p-value ("bonferroni"), false-discovery rate corrected p-value ("FDR"), or adaptive alpha p-value (adapt.a). Defaults to "adapt"

Value

Returns a list:

redundant

Vectors nested within the list corresponding to redundant nodes with the name of object in the list

data

Returns original data

weights

Returns weights determine by weighted topological overlap or partial correlations

network

The network compute by EBICglasso

References

# simulation using node.redundant Christensen, A. P. (2020). Towards a network psychometrics approach to assessment: Simulations for redundancy, dimensionality, and loadings (Unpublished doctoral dissertation). University of North Carolina at Greensboro, Greensboro, NC, USA. https://doi.org/10.31234/osf.io/84kgd

# wTO Nowick, K., Gernat, T., Almaas, E., & Stubbs, L. (2009). Differences in human and chimpanzee gene expression patterns define an evolving network of transcription factors in brain. Proceedings of the National Academy of Sciences, 106, 22358-22363. https://doi.org/10.1073/pnas.0911376106

Examples

# NOT RUN {
# obtain SAPA items
items <- psychTools::spi[,c(11:20)]

# weighted topological overlap
redund <- node.redundant(items, method = "wTO", type = "adapt")

# partial correlation
redund <- node.redundant(items, method = "pcor", type = "adapt")

# threshold
redund <- node.redundant(items, method = "thresh", sig = .20)

# }