community.unidimensional: Approaches to Detect Unidimensional Communities

In EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Approaches to Detect Unidimensional Communities

Description

A function to apply several approaches to detect a unidimensional community in networks. There have many different approaches recently such as expanding the correlation matrix to have orthogonal correlations ("expand"), applying the Leading Eigenvalue community detection algorithm cluster_leading_eigen to the correlation matrix ("LE"), and applying the Louvain community detection algorithm cluster_louvain to the correlation matrix ("louvain"). Not necessarily intended for individual use – it's better to use EGA

Usage

community.unidimensional(
  data,
  n = NULL,
  corr = c("auto", "cor_auto", "pearson", "spearman"),
  na.data = c("pairwise", "listwise"),
  model = c("BGGM", "glasso", "TMFG"),
  uni.method = c("expand", "LE", "louvain"),
  verbose = FALSE,
  ...
)

Arguments

data	Matrix or data frame. Should consist only of variables that are desired to be in analysis
n	Numeric (length = 1). Sample size if data provided is a correlation matrix
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
uni.method	Character (length = 1). What unidimensionality method should be used? Defaults to "louvain". Available options: "expand" — Expands the correlation matrix with four variables correlated 0.50. If number of dimension returns 2 or less in check, then the data are unidimensional; otherwise, regular EGA with no matrix expansion is used. This method was used in the Golino et al.'s (2020) Psychological Methods simulation "LE" — Applies the Leading Eigenvector algorithm (cluster_leading_eigen) on the empirical correlation matrix. If the number of dimensions is 1, then the Leading Eigenvector solution is used; otherwise, regular EGA is used. This method was used in the Christensen et al.'s (2023) Behavior Research Methods simulation "louvain" — Applies the Louvain algorithm (cluster_louvain) on the empirical correlation matrix. If the number of dimensions is 1, then the Louvain solution is used; otherwise, regular EGA is used. This method was validated Christensen's (2022) PsyArXiv simulation. Consensus clustering can be used by specifying either "consensus.method" or "consensus.iter"
verbose	Boolean. Whether messages and (insignificant) warnings should be output. Defaults to FALSE (silent calls). Set to TRUE to see all messages and warnings for every function call
...	Additional arguments to be passed on to auto.correlate, network.estimation, community.consensus, and community.detection

Value

Returns the memberships of the community detection algorithm. The memberships will output regardless of whether the network is unidimensional

Author(s)

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

References

Expand approach
Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., Thiyagarajan, J. A., & Martinez-Molina, A. (2020). Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25, 292-320.

Leading Eigenvector approach
Christensen, A. P., Garrido, L. E., Guerra-Pena, K., & Golino, H. (2023). Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation. Behavior Research Methods.

Louvain approach
Christensen, A. P. (2023). Unidimensional community detection: A Monte Carlo simulation, grid search, and comparison. PsyArXiv.

Examples

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

# Louvain with Consensus Clustering (default)
community.unidimensional(wmt)

# Leading Eigenvector
community.unidimensional(wmt, uni.method = "LE")

# Expand
community.unidimensional(wmt, uni.method = "expand")

EGAnet documentation built on Sept. 28, 2024, 9:06 a.m.