EGAnet (version 1.1.0)

riEGA: Random-Intercept EGA

Description

Estimates the number of substantive dimensions after controlling for wording effects. EGA is applied to a residual correlation matrix after subtracting and random intercept factor with equal unstandardized loadings from all the regular and unrecoded reversed items in the database

Usage

riEGA(
  data,
  n = NULL,
  uni.method = c("expand", "LE"),
  corr = c("cor_auto", "pearson", "spearman"),
  model = c("glasso", "TMFG"),
  model.args = list(),
  algorithm = c("walktrap", "louvain"),
  algorithm.args = list(),
  plot.EGA = TRUE,
  plot.type = c("GGally", "qgraph"),
  plot.args = list(),
  estimator = c("auto", "WLSMV", "MLR"),
  lavaan.args = list(),
  verbose = TRUE
)

Value

Returns a list containing:

EGA

Results from EGA

RI

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

Arguments

data

Matrix or data frame. Variables (down columns) or correlation matrix. If the input is a correlation matrix, then argument n (number of cases) is required. Variables MUST be unrecoded -- reversed items should remain reversed

n

Integer. Sample size if data provided is a correlation matrix

uni.method

Character. What unidimensionality method should be used? Defaults to "LE". Current options are:

• expand Expands the correlation matrix with four variables correlated .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 is the method used in the Golino et al. (2020) Psychological Methods simulation.

• LE Applies the leading eigenvalue algorithm (cluster_leading_eigen) on the empirical correlation matrix. If the number of dimensions is 1, then the leading eigenvalue solution is used; otherwise, regular EGA is used. This is the final method used in the Christensen, Garrido, and Golino (2021) simulation.

corr

Type of correlation matrix to compute. The default uses cor_auto. Current options are:

• cor_auto Computes the correlation matrix using the cor_auto function from qgraph.

• pearson Computes Pearson's correlation coefficient using the pairwise complete observations via the cor function.

• spearman Computes Spearman's correlation coefficient using the pairwise complete observations via the cor function.

model

Character. A string indicating the method to use. Defaults to "glasso". Current options are:

• glasso Estimates the Gaussian graphical model using graphical LASSO with extended Bayesian information criterion to select optimal regularization parameter

• TMFG Estimates a Triangulated Maximally Filtered Graph

model.args

List. A list of additional arguments for EBICglasso.qgraph or TMFG

algorithm

A string indicating the algorithm to use or a function from igraph Defaults to "walktrap". Current options are:

• walktrap Computes the Walktrap algorithm using cluster_walktrap

• louvain Computes the Louvain algorithm using cluster_louvain

algorithm.args

List. A list of additional arguments for cluster_walktrap, cluster_louvain, or some other community detection algorithm function (see examples)

plot.EGA

Boolean. If TRUE, returns a plot of the network and its estimated dimensions. Defaults to TRUE

plot.type

Character. Plot system to use. Current options are qgraph and GGally. Defaults to "GGally"

plot.args

List. A list of additional arguments for the network plot. For plot.type = "qgraph":

• vsize Size of the nodes. Defaults to 6.

For plot.type = "GGally" (see ggnet2 for full list of arguments):

• vsize Size of the nodes. Defaults to 6.

• label.size Size of the labels. Defaults to 5.

• alpha The level of transparency of the nodes, which might be a single value or a vector of values. Defaults to 0.7.

• edge.alpha The level of transparency of the edges, which might be a single value or a vector of values. Defaults to 0.4.

• legend.names A vector with names for each dimension

• color.palette The color palette for the nodes. For custom colors, enter HEX codes for each dimension in a vector. See color_palette_EGA for more details and examples

estimator

Character. Estimator to use for random-intercept model (see Estimators for more details). Defaults to "auto", which selects "MLR" for continuous data and "WLSMV" for mixed and categorical data. Data are considered continuous data if they have 6 or more categories (see Rhemtulla, Brosseau-Liard, & Savalei, 2012)

lavaan.args

List. If reduce.method = "latent", then lavaan's cfa function will be used to create latent variables to reduce variables. Arguments should be input as a list. Some example arguments (see lavOptions for full details)

verbose

Boolean. Should network estimation parameters be printed? Defaults to TRUE. Set to FALSE for no print out

Author

Alejandro Garcia-Pardina <[e-mail]>, Francisco J. Abad <[e-mail]>, Alexander P. Christensen <[e-mail]>, Hudson Golino <hfg9s at virginia.edu>, Luis Eduardo Garrido <[e-mail]>, and Robert Moulder <[e-mail]>

References

# Selection of CFA Estimator
Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17, 354-373.

Examples

# Obtain example data
data <- optimism

if (FALSE) {
# riEGA example
opt.res <- riEGA(data = optimism)
}