### Description

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.

### Usage

net.loads(A, wc, pos.manifold = FALSE, min.load = 0, plot.NL = FALSE)


### Arguments

 A Matrix, data frame, or EGA object. A network adjacency matrix wc Numeric or character vector. A vector of community assignments. If input into A is an EGA object, then wc is automatically detected pos.manifold 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 min.load 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 plot.NL 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

### Details

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")

### Value

Returns a list containing:

 unstd A matrix of the unstandardized within- and between-community strength values for each node std A matrix of the standardized within- and between-community strength values for each node minLoad The minimum loading to appear in summary of network loadings. Use print() or summary() to view plot A qgraph plot of the network loadings. Use plot to view

### Author(s)

Alexander P. Christensen <alexpaulchristensen@gmail.com> and Hudson Golino <hfg9s at virginia.edu>

### References

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.

### Examples


wmt <- wmt2[,7:24]

## Not run:
# Estimate EGA
ega.wmt <- EGA(wmt)

## End(Not run)



[Package EGAnet version 1.1.0 Index]