EGAEstimates 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
riEGA(
data,
n = NULL,
corr = c("auto", "cor_auto", "cosine", "pearson", "spearman"),
na.data = c("pairwise", "listwise"),
model = c("glasso", "TMFG"),
algorithm = c("leiden", "louvain", "walktrap"),
uni.method = c("expand", "LE", "louvain"),
plot.EGA = TRUE,
verbose = FALSE,
...
)Returns a list containing:
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
Matrix or data frame. Should consist only of variables to be used in the analysis. Must be raw data and not a correlation matrix
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
Additional arguments to be passed on to
auto.correlate,
network.estimation,
community.detection,
community.consensus, and
EGA
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>
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.
plot.EGAnet for plot usage in EGAnet
# Obtain example data
wmt <- wmt2[,7:24]
# riEGA example
riEGA(data = wmt, plot.EGA = FALSE)
# no plot for CRAN checks
Run the code above in your browser using DataLab