riEGA {EGAnet} | R Documentation |
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,
corr = c("auto", "cor_auto", "pearson", "spearman"),
na.data = c("pairwise", "listwise"),
model = c("glasso", "TMFG"),
algorithm = c("leiden", "louvain", "walktrap"),
uni.method = c("expand", "LE", "louvain"),
estimator = c("auto", "WLSMV", "MLR"),
plot.EGA = TRUE,
verbose = FALSE,
...
)
Arguments
data |
Matrix or data frame.
Should consist only of variables to be used in the analysis.
Must be raw data and not a correlation matrix
|
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:
|
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
|
algorithm |
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
|
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"
|
estimator |
Character (length = 1).
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 8 or
more categories (see Rhemtulla, Brosseau-Liard, & Savalei, 2012).
To change this behavior, set oridinal.categories as an argument
|
plot.EGA |
Boolean (length = 1).
If TRUE , returns a plot of the network and its estimated dimensions.
Defaults to TRUE
|
verbose |
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
|
... |
Additional arguments to be passed on to
auto.correlate ,
network.estimation ,
community.detection ,
community.consensus , and
EGA
|
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
|
TEFI |
link[EGAnet]{tefi} for the estimated structure
|
plot.EGA |
Plot output if plot.EGA = TRUE
|
Author(s)
Alejandro Garcia-Pardina <alejandrogp97@gmail.com>,
Francisco J. Abad <fjose.abad@uam.es>,
Alexander P. Christensen <alexpaulchristensen@gmail.com>,
Hudson Golino <hfg9s at virginia.edu>,
Luis Eduardo Garrido <luisgarrido@pucmm.edu.do>, and
Robert Moulder <rgm4fd@virginia.edu>
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.
See Also
plot.EGAnet
for plot usage in EGAnet
Examples
# Obtain example data
wmt <- wmt2[,7:24]
# riEGA example
riEGA(data = wmt, plot.EGA = FALSE)
# no plot for CRAN checks
[Package
EGAnet version 2.0.6
Index]