bootEGA Estimates the number of dimensions of iter bootstraps
using the empirical zero-order correlation matrix ("parametric") or
"resampling" from the empirical dataset (non-parametric). bootEGA
estimates a typical median network structure, which is formed by the median or
mean pairwise (partial) correlations over the iter bootstraps (see
Details for information about the typical median network structure).
bootEGA(
data,
n = NULL,
corr = c("auto", "cor_auto", "cosine", "pearson", "spearman"),
na.data = c("pairwise", "listwise"),
model = c("BGGM", "glasso", "TMFG"),
algorithm = c("leiden", "louvain", "walktrap"),
uni.method = c("expand", "LE", "louvain"),
iter = 500,
type = c("parametric", "resampling"),
ncores,
EGA.type = c("EGA", "EGA.fit", "hierEGA", "riEGA"),
plot.itemStability = TRUE,
typicalStructure = FALSE,
plot.typicalStructure = FALSE,
seed = NULL,
verbose = TRUE,
...
)Returns a list containing:
Number of replica samples in bootstrap
A list containing the networks of each replica sample
A matrix of membership assignments for each replica network with variables down the columns and replicas across the rows
Number of dimensions identified in each replica sample
A data frame containing number of replica samples, median, standard deviation, standard error, 95% confidence intervals, and quantiles (lower = 2.5% and upper = 97.5%)
A data frame containing the proportion of times the number of dimensions was identified (e.g., .85 of 1,000 = 850 times that specific number of dimensions was found)
tefi value for each replica sample
Type of bootstrap used
Output of the empirical EGA results
(output will vary based on EGA.type)
Type of *EGA function used
A list containing:
graph --- Network matrix of the median network structure
typical.dim.variables --- An ordered matrix of item allocation
wc --- Membership assignments of the median network
Plot output if plot.typicalStructure = TRUE
Matrix or data frame. Should consist only of variables to be used in the analysis
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"
Numeric (length = 1).
Number of replica samples to generate from the bootstrap analysis.
Defaults to 500 (recommended)
Character (length = 1).
What type of bootstrap should be performed?
Defaults to "parametric".
Available options:
"parametric" --- Generates iter new datasets from
(multivariate normal random distributions) based on the
original dataset using mvrnorm
"resampling" --- Generates iter new datasets from random subsamples
of the original data
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 to 1 to not use parallel computing
If you're unsure how many cores your computer has,
then type: parallel::detectCores()
Character (length = 1).
Type of EGA model to use.
Defaults to "EGA"
Available options:
"EGA" --- Uses standard exploratory graph analysis
"EGA.fit" --- Uses tefi to determine best fit of
EGA
"hierEGA" --- Uses hierarchical exploratory graph analysis
"riEGA" --- Uses random-intercept exploratory graph analysis
Arguments for EGA.type can be added (see links
for details on specific function arguments)
Boolean (length = 1).
Should the plot be produced for item.replication?
Defaults to TRUE
Boolean (length = 1).
If TRUE, returns the median ("glasso" or "BGGM") or
mean ("TMFG") network structure and estimates its dimensions
(see Details for more information).
Defaults to FALSE
Boolean (length = 1).
If TRUE, returns a plot of the typical network structure.
Defaults to FALSE
Numeric (length = 1).
Defaults to NULL or random results.
Set for reproducible results.
See Reproducibility and PRNG
for more details on random number generation in EGAnet
Boolean (length = 1).
Should progress be displayed?
Defaults to TRUE.
Set to FALSE to not display progress
Additional arguments that can be passed on to
auto.correlate,
network.estimation,
community.detection,
community.consensus,
EGA,
EGA.fit,
hierEGA, and
riEGA
Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
The typical network structure is derived from the median (or mean) value of each pairwise relationship. These values tend to reflect the "typical" value taken by an edge across the bootstrap networks. Afterward, the same community detection algorithm is applied to the typical network as the bootstrap networks.
Because the community detection algorithm is applied to the typical network structure,
there is a possibility that the community algorithm determines
a different number of dimensions than the median number derived from the bootstraps.
The typical network structure (and number of dimensions) may not
match the empirical EGA number of dimensions or
the median number of dimensions from the bootstrap. This result is known
and not a bug.
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), 479-500.
itemStability to estimate the stability of
the variables in the empirical dimensions and
dimensionStability to estimate the stability of
the dimensions (structural consistency)
# Load data
wmt <- wmt2[,7:24]
if (FALSE) {
# Standard EGA parametric example
boot.wmt <- bootEGA(
data = wmt, iter = 500,
type = "parametric", ncores = 2
)
# Standard resampling example
boot.wmt <- bootEGA(
data = wmt, iter = 500,
type = "resampling", ncores = 2
)
# Example using {igraph} `cluster_*` function
boot.wmt.spinglass <- bootEGA(
data = wmt, iter = 500,
algorithm = igraph::cluster_spinglass,
# use any function from {igraph}
type = "parametric", ncores = 2
)
# EGA fit example
boot.wmt.fit <- bootEGA(
data = wmt, iter = 500,
EGA.type = "EGA.fit",
type = "parametric", ncores = 2
)
# Hierarchical EGA example
boot.wmt.hier <- bootEGA(
data = wmt, iter = 500,
EGA.type = "hierEGA",
type = "parametric", ncores = 2
)
# Random-intercept EGA example
boot.wmt.ri <- bootEGA(
data = wmt, iter = 500,
EGA.type = "riEGA",
type = "parametric", ncores = 2
)}
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