net.scores {EGAnet}  R Documentation 
This function computes network scores computed based on
each node's strength
within each
community (i.e., factor) in the network (see net.loads
).
These values are used as network "factor loadings" for the weights of each item.
Notably, network analysis allows nodes to contribution to more than one community.
These loadings are considered in the network scores. In addition,
if the construct is a hierarchy (e.g., personality questionnaire;
items in facet scales in a trait domain), then an overall
score can be computed (see argument global
). An important difference
is that the network scores account for crossloadings in their
estimation of scores
net.scores(data, A, wc, global = FALSE, impute, ...)
data 
Matrix or data frame. Must be a dataset 
A 
Matrix, data frame, or 
wc 
Numeric.
A vector of community assignments.
Not necessary if an 
global 
Boolean.
Should general network loadings be computed in scores?
Defaults to 
impute 
Character. In the presence of missing data, imputation can be implemented. Currently, three options are available:

... 
Additional arguments for 
For more details, type vignette("Network_Scores")
Returns a list containing:
unstd.scores 
The unstandardized network scores for each participant and community (including the overall score) 
std.scores 
The standardized network scores for each participant and community (including the overall score) 
commCor 
Partial correlations between the specified or identified communities 
loads 
Standardized network loadings for each item in each dimension
(computed using 
Alexander P. Christensen <alexpaulchristensen@gmail.com> and Hudson F. Golino <hfg9s at virginia.edu>
Christensen, A. P., & Golino, H. (2021). On the equivalency of factor and network loadings. Behavior Research Methods, 53, 15631580.
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, 10951108.
Golino, H., Christensen, A. P., Moulder, R., Kim, S., & Boker, S. M. (2021). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the rightwing and leftwing trolls in the 2016 US elections. Psychometrika.
# Load data
wmt < wmt2[,7:24]
## Not run:
# Estimate EGA
ega.wmt < EGA(wmt)
## End(Not run)
# Network scores
net.scores(data = wmt, A = ega.wmt)