EGAnet (version 2.0.7)

network.compare: Compares Network Structures Using Permutation

Description

A permutation implementation to determine statistical significance of whether the network structures are different from one another

Usage

network.compare(
  base,
  comparison,
  corr = c("auto", "cor_auto", "pearson", "spearman"),
  na.data = c("pairwise", "listwise"),
  model = c("BGGM", "glasso", "TMFG"),
  iter = 1000,
  ncores,
  verbose = TRUE,
  seed = NULL,
  ...
)

Value

Returns a list:

network

Data frame with row names of each measure, empirical value (statistic), and p-value based on the permutation test (p.value)

edges

List containing matrices of values for empirical values (statistic), p-values (p.value), and Benjamini-Hochberg corrected p-values (p.adjusted)

Arguments

base

Matrix or data frame. Should consist only of variables to be used in the analysis. First dataset

comparison

Matrix or data frame. Should consist only of variables to be used in the analysis. Second dataset

corr

Character (length = 1). Method to compute correlations. Defaults to "auto". Available options:

• "auto" --- Automatically computes appropriate correlations for the data using Pearson's for continuous, polychoric for ordinal, tetrachoric for binary, and polyserial/biserial for ordinal/binary with continuous. To change the number of categories that are considered ordinal, use ordinal.categories (see polychoric.matrix for more details)

• "cor_auto" --- Uses cor_auto to compute correlations. Arguments can be passed along to the function

• "pearson" --- Pearson's correlation is computed for all variables regardless of categories

• "spearman" --- Spearman's rank-order correlation is computed for all variables regardless of categories

For other similarity measures, compute them first and input them into data with the sample size (n)

na.data

Character (length = 1). How should missing data be handled? Defaults to "pairwise". Available options:

• "pairwise" --- Computes correlation for all available cases between two variables

• "listwise" --- Computes correlation for all complete cases in the dataset

model

Character (length = 1). Defaults to "glasso". Available options:

• "BGGM" --- Computes the Bayesian Gaussian Graphical Model. Set argument ordinal.categories to determine levels allowed for a variable to be considered ordinal. See ?BGGM::estimate for more details

• "glasso" --- Computes the GLASSO with EBIC model selection. See EBICglasso.qgraph for more details

• "TMFG" --- Computes the TMFG method. See TMFG for more details

iter

Numeric (length = 1). Number of permutations to perform. Defaults to 1000 (recommended)

ncores

Numeric (length = 1). Number of cores to use in computing results. Defaults to ceiling(parallel::detectCores() / 2) or half of your computer's processing power. Set to 1 to not use parallel computing

verbose

Boolean (length = 1). Should progress be displayed? Defaults to TRUE. Set to FALSE to not display progress

seed

Numeric (length = 1). Defaults to NULL or random results. Set for reproducible results. See Reproducibility and PRNG for more details on random number generation in EGAnet

...

Additional arguments that can be passed on to auto.correlate, network.estimation, community.detection, community.consensus, EGA, and jsd

Author

Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <[e-mail]>

References

Frobenius Norm
Ulitzsch, E., Khanna, S., Rhemtulla, M., & Domingue, B. W. (2023). A graph theory based similarity metric enables comparison of subpopulation psychometric networks. Psychological Methods.

Jensen-Shannon Similarity (1 - Distance)
De Domenico, M., Nicosia, V., Arenas, A., & Latora, V. (2015). Structural reducibility of multilayer networks. Nature Communications, 6(1), 1–9.

Total Network Strength
van Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., Borsboom, D., & Waldorp, L. J. (2023). Comparing network structures on three aspects: A permutation test. Psychological Methods, 28(6), 1273–1285.

Examples

# Load data
wmt <- wmt2[,7:24]

# Set groups (if necessary)
groups <- rep(1:2, each = nrow(wmt) / 2)

# Groups
group1 <- wmt[groups == 1,]
group2 <- wmt[groups == 2,]

if (FALSE) # Perform comparison
results <- network.compare(group1, group2)

# Print results
print(results)

# Plot edge differences
plot(results)