Estimates an EGM
based on EGA
and
uses the number of communities as the number of dimensions in exploratory factor analysis
(EFA) using fa
Usage
EGM.compare(
data,
constrain.structure = FALSE,
constrain.zeros = FALSE,
fm = "ml",
rotation = "geominQ",
...
)
Arguments
- data
Matrix or data frame. Should consist only of variables to be used in the analysis. Can be raw data or a correlation matrix
- constrain.structure
Boolean (length = 1). Whether memberships of the communities should be added as a constraint when optimizing the network loadings. Defaults to
TRUE
which ensures assigned loadings are guaranteed to never be smaller than any cross-loadings. Set toFALSE
to freely estimate each loading similar to exploratory factor analysisNote: This default differs from
EGM
. Constraining loadings puts EGM at a deficit relative to EFA and therefore biases the comparability between the methods. It's best to leave the default of unconstrained when using this function.- constrain.zeros
Boolean (length = 1). Whether zeros in the estimated network loading matrix should be retained when optimizing the network loadings. Defaults to
TRUE
which ensures that zero networks loadings are retained. Set toFALSE
to freely estimate each loading similar to exploratory factor analysisNote: This default differs from
EGM
. Constraining zeros puts EGM at a deficit relative to EFA and therefore biases the comparability between the methods. It's best to leave the default of unconstrained when using this function.- fm
Character (length = 1). Estimation method for the EFA. See argument in
fa
for more details. Defaults to"ml"
or maximum likelihood- rotation
Character (length = 1). A rotation to use to obtain a simpler structure for EFA. For a list of rotations, see
rotations
for options. Defaults to"geominQ"
- ...
Additional arguments to be passed on to
auto.correlate
,network.estimation
,community.detection
,community.consensus
,community.unidimensional
,EGA
,EGM
,net.loads
, andfa
Author
Hudson F. Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
Examples
# Get depression data
data <- depression[,24:44]
# Compare EGM (using EGA) with EFA
if (FALSE) { # \dontrun{
results <- EGM.compare(data)
# Print summary
summary(results)} # }