A permutation implementation to determine statistical significance of whether the network structures are different from one another
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, useordinal.categories
(seepolychoric.matrix
for more details)"cor_auto"
— Usescor_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 argumentordinal.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. SeeEBICglasso.qgraph
for more details"TMFG"
— Computes the TMFG method. SeeTMFG
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 to1
to not use parallel computing- verbose
Boolean (length = 1). Should progress be displayed? Defaults to
TRUE
. Set toFALSE
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 inEGAnet
- ...
Additional arguments that can be passed on to
auto.correlate
,network.estimation
,community.detection
,community.consensus
,EGA
, andjsd
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
)
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.
Author
Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
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)
#> Error in eval(expr, envir, enclos): object 'results' not found
# Plot edge differences
plot(results) # \dontrun{}
#> Error in eval(expr, envir, enclos): object 'results' not found