assumptionCheck {networktools}

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