riEGA()

Matrix or data frame. Should consist only of variables to be used in the analysis. Can be raw data or a correlation matrix.

If a correlation matrix is input, then the correlations are assumed to be computed using the original keyed data (where reverse-keyed items remain reverse-keyed). Using correlations matrices where reverse-keyed items have been recoded will result in invalid results.

Numeric (length = 1). Sample size if data provided is a correlation matrix

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

• "cosine" — Uses cosine to compute cosine similarity

• "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)

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

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

Character or igraph cluster_* function (length = 1). Defaults to "walktrap". Three options are listed below but all are available (see community.detection for other options):

• "leiden" — See cluster_leiden for more details

• "louvain" — By default, "louvain" will implement the Louvain algorithm using the consensus clustering method (see community.consensus for more information). This function will implement consensus.method = "most_common" and consensus.iter = 1000 unless specified otherwise

• "walktrap" — See cluster_walktrap for more details

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"

Boolean (length = 1). If TRUE, returns a plot of the network and its estimated dimensions. Defaults to TRUE

Boolean (length = 1). 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

Numeric (length = 1). Sets seed for reproducible results. Defaults to NULL or random results. Relevant when algorithm = "louvain": passed on to the internal EGA call, making the community detection reproducible

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

Results from EGA

A list containing information about the random-intercept model (if the model converged):

• fit — The fit object for the random-intercept model using cfa

• lavaan.args — The arguments used in cfa

• loadings — Standardized loadings from the random-intercept model

• correlation — Residual correlations after accounting for the random-intercept model

link[EGAnet]{tefi} for the estimated structure

Plot output if plot.EGA = TRUE