assumptionCheck: Assumption Checking Function

In networktools: Tools for Identifying Important Nodes in Networks

Assumption Checking Function

Description

Checks some basic assumptions about the suitability of network analysis on your data

Usage

assumptionCheck(
  data,
  type = c("network", "impact"),
  percent = 20,
  split = c("median", "mean", "forceEqual", "cutEqual", "quartiles"),
  plot = FALSE,
  binary.data = FALSE,
  na.rm = TRUE
)

Arguments

data	dataframe or matrix of observational data (rows: observations, columns: nodes)
type	which assumptions to check? "network" tests the suitability for network analysis in general. "impact" tests the suitability for analyzing impact
percent	percent difference from grand mean that is acceptable when comparing variances.
split	if type="impact", specifies the type of split to utilize
plot	logical. Should histograms each variable be plotted?
binary.data	logical. Defaults to FALSE
na.rm	logical. Should missing values be removed?

Details

Network analysis rests on several assumptions. Among these: - Variance of each node is (roughly) equal - Distributions are (roughly) normal

Comparing networks in impact rests on additional assumptions including: - Overall variances are (roughly) equal in each half

This function checks these assumptions and notifies any violations. This function is not intended as a substitute for careful data visualization and independent assumption checks.

See citations in the references section for further details.

References

Terluin, B., de Boer, M. R., & de Vet, H. C. W. (2016). Differences in Connection Strength between Mental Symptoms Might Be Explained by Differences in Variance: Reanalysis of Network Data Did Not Confirm Staging. PLOS ONE, 11(11), e0155205. Retrieved from https://doi.org/10.1371/journal.pone.0155205

networktools documentation built on Aug. 23, 2023, 1:06 a.m.