bootEGA {EGAnet}  R Documentation 
EGA
bootEGA
Estimates the number of dimensions of n bootstraps
using the empirical (partial) correlation matrix (parametric) or resampling from
the empirical dataset (nonparametric). It also estimates a typical
median network structure, which is formed by the median or mean pairwise (partial)
correlations over the n bootstraps.
bootEGA(
data,
n = NULL,
uni.method = c("expand", "LE"),
iter,
type = c("parametric", "resampling"),
seed = 1234,
corr = c("cor_auto", "pearson", "spearman"),
model = c("glasso", "TMFG"),
model.args = list(),
algorithm = c("walktrap", "louvain"),
algorithm.args = list(),
typicalStructure = TRUE,
plot.typicalStructure = TRUE,
plot.type = c("GGally", "qgraph"),
plot.args = list(),
ncores,
...
)
data 
Matrix or data frame.
Includes the variables to be used in the 
n 
Integer.
Sample size if 
uni.method 
Character.
What unidimensionality method should be used?
Defaults to

iter 
Numeric integer.
Number of replica samples to generate from the bootstrap analysis.
At least 
type 
Character. A string indicating the type of bootstrap to use. Current options are:

seed 
Numeric.
Seed to reproduce results. Defaults to 
corr 
Type of correlation matrix to compute. The default uses

model 
Character. A string indicating the method to use. Current options are:

model.args 
List.
A list of additional arguments for 
algorithm 
A string indicating the algorithm to use or a function from

algorithm.args 
List.
A list of additional arguments for 
typicalStructure 
Boolean.
If 
plot.typicalStructure 
Boolean.
If 
plot.type 
Character.
Plot system to use.
Current options are 
plot.args 
List.
A list of additional arguments for the network plot.
For
For

ncores 
Numeric.
Number of cores to use in computing results.
Defaults to If you're unsure how many cores your computer has,
then use the following code: 
... 
Additional arguments.
Used for deprecated arguments from previous versions of 
Returns a list containing:
iter 
Number of replica samples in bootstrap 
boot.ndim 
Number of dimensions identified in each replica sample 
boot.wc 
Item allocation for each replica sample 
bootGraphs 
Networks of each replica sample 
summary.table 
Summary table containing number of replica samples, median, standard deviation, standard error, 95% confidence intervals, and quantiles (lower = 2.5% and upper = 97.5%) 
frequency 
Proportion of times the number of dimensions was identified (e.g., .85 of 1,000 = 850 times that specific number of dimensions was found) 
EGA 
Output of the original 
typicalGraph 
A list containing:

Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
# Original implementation of bootEGA
Christensen, A. P., & Golino, H. (2021).
Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial.
Psych, 3(3), 479500.
# Structural consistency (see dimensionStability
)
Christensen, A. P., Golino, H., & Silvia, P. J. (2020).
A psychometric network perspective on the validity and validation of personality trait questionnaires.
European Journal of Personality, 34(6), 10951108.
EGA
to estimate the number of dimensions of an instrument using EGA
and CFA
to verify the fit of the structure suggested by EGA using confirmatory factor analysis.
# Load data
wmt < wmt2[,7:24]
# bootEGA glasso example
## plot.type = "qqraph" used for CRAN checks
## plot.type = "GGally" is the default
boot.wmt < bootEGA(data = wmt, iter = 500, plot.type = "qgraph",
type = "parametric", ncores = 2)
# bootEGA TMFG example
boot.wmt < bootEGA(data = wmt, iter = 500, model = "TMFG",
plot.type = "qgraph", type = "parametric", ncores = 2, seed = 1234)
# bootEGA Louvain example
boot.wmt < bootEGA(data = wmt, iter = 500, algorithm = "louvain",
plot.type = "qgraph", type = "parametric", ncores = 2, seed = 1234)
# bootEGA Spinglass example
boot.wmt < bootEGA(data = wmt, iter = 500, model = "TMFG", plot.type = "qgraph",
algorithm = igraph::cluster_spinglass, type = "parametric", ncores = 2)