Computes the between- and within-community
strength of each item
for each community. This function uses the
comcat and
stable functions to calculate
the between- and within-community strength of each item, respectively.
net.loads(A, wc, pos.manifold = FALSE, min.load = 0, plot.NL = FALSE)Returns a list containing:
A matrix of the unstandardized within- and between-community strength values for each node
A matrix of the standardized within- and between-community strength values for each node
The minimum loading to appear in summary of network loadings.
Use print() or summary() to view
A qgraph plot of the network loadings.
Use plot to view
Matrix, data frame, or EGA object.
A network adjacency matrix
Numeric or character vector.
A vector of community assignments.
If input into A is an EGA object,
then wc is automatically detected
Boolean.
Should a positive manifold be applied (i.e., should
all dimensions be positively correlated)?
Defaults to FALSE.
Set to TRUE for a positive manifold
Numeric.
Sets the minimum loading allowed in the standardized
network loading matrix. Values equal or greater than
the minimum loading are kept in the output. Values
less than the minimum loading are removed. This matrix can
be viewed using print() or summary()
Defaults to 0
Boolean.
Should proportional loadings be plotted?
Defaults to FALSE.
Set to TRUE for plot with pie charts
visualizing the proportion of loading associated with
each dimension
Alexander P. Christensen <alexpaulchristensen@gmail.com> and Hudson Golino <hfg9s at virginia.edu>
Simulation studies have demonstrated that a node's strength centrality is roughly equivalent to factor loadings (Christensen, Golino, & Silvia, 2019; Hallquist, Wright, & Molenaar, in press). Hallquist and colleagues (in press) found that node strength represented a combination of dominant and cross-factor loadings. This function computes each node's strength within each specified dimension, providing a rough equivalent to factor loadings (including cross-loadings).
For more details, type vignette("Network_Scores")
Christensen, A. P., & Golino, H. (2021). On the equivalency of factor and network loadings. Behavior Research Methods, 53, 1563-1580.
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, 1095-1108.
Hallquist, M., Wright, A. C. G., & Molenaar, P. C. M. (2019). Problems with centrality measures in psychopathology symptom networks: Why network psychometrics cannot escape psychometric theory. Multivariate Behavioral Research, 1-25.
# Load data
wmt <- wmt2[,7:24]
if (FALSE) {
# Estimate EGA
ega.wmt <- EGA(wmt)
}
# Network loadings
net.loads(ega.wmt)
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